Model Intelligence Sheet
richarderkhov/alibaba-nlp_-_gte-qwen2-7b-instruct-gguf overview
model.maxseqlength = 8192 queries = [ "how much protein should a female eat", "summit define", ] documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.", ] queryembeddings = model.encode(queries, promptname="query") documentembeddings = model.encode(documents) scores = (queryembeddings @ documentembeddings.T) * 100 print(scores.tolist()) python import torch import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def lasttokenpool(lasthiddenstates: Tensor, attentionmask: Tensor) -> Tensor: leftpadding = (attentionmask[:, -1].sum() == attentionmask.shape[0]) if leftpadding: return lasthiddenstates[:, -1] else: sequencelengths = attentionmask.sum(dim=1) - 1 batchsize = lasthiddenstates.shape[0] return lasthiddenstates[torch.arange(batchsize, device=lasthiddenstates.device), sequencelengths] def getdetailedinstruct(taskdescription: str, query: str) -> str: return f'Instruct: {taskdescription}\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ getdetailedinstruct(task, 'how much protein should a female eat'), getdetailedinstruct(task, 'summit define') ] # No need to add instruction for retrieval documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] inputtexts = queries + documents tokenizer = AutoTokenizer.frompretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trustremotecode=True) model = AutoModel.frompretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trustremotecode=True) maxlength = 8192 # Tokenize the input texts batchdict = tokenizer(inputtexts, maxlength=maxlength, padding=True, truncation=True, returntensors='pt') outputs = model(batchdict) embeddings = lasttokenpool(outputs.lasthiddenstate, batchdict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist())
Downloads
2,000
Likes
6
Pipeline
—
Library
—
Visibility
Public
Access
Open
Repository Files & Downloads
22 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| gte-Qwen2-7B-instruct.IQ3_M.gguf | GGUF | IQ3_M | 3.33 GB | Download |
| gte-Qwen2-7B-instruct.IQ3_S.gguf | GGUF | IQ3_S | 3.26 GB | Download |
| gte-Qwen2-7B-instruct.IQ3_XS.gguf | GGUF | IQ3_XS | 3.11 GB | Download |
| gte-Qwen2-7B-instruct.IQ4_NL.gguf | GGUF | IQ4_NL | 4.15 GB | Download |
| gte-Qwen2-7B-instruct.IQ4_XS.gguf | GGUF | IQ4_XS | 3.96 GB | Download |
| gte-Qwen2-7B-instruct.Q2_K.gguf | GGUF | Q2_K | 2.81 GB | Download |
| gte-Qwen2-7B-instruct.Q3_K.gguf | GGUF | Q3_K | 3.55 GB | Download |
| gte-Qwen2-7B-instruct.Q3_K_L.gguf | GGUF | Q3_K_L | 3.81 GB | Download |
| gte-Qwen2-7B-instruct.Q3_K_M.gguf | GGUF | Q3_K_M | 3.55 GB | Download |
| gte-Qwen2-7B-instruct.Q3_K_S.gguf | GGUF | Q3_K_S | 3.25 GB | Download |
| gte-Qwen2-7B-instruct.Q4_0.gguf | GGUF | — | 4.13 GB | Download |
| gte-Qwen2-7B-instruct.Q4_1.gguf | GGUF | — | 4.54 GB | Download |
| gte-Qwen2-7B-instruct.Q4_K.gguf | GGUF | Q4_K | 4.36 GB | Download |
| gte-Qwen2-7B-instruct.Q4_K_M.gguf | GGUF | Q4_K_M | 4.36 GB | Download |
| gte-Qwen2-7B-instruct.Q4_K_S.gguf | GGUF | Q4_K_S | 4.15 GB | Download |
| gte-Qwen2-7B-instruct.Q5_0.gguf | GGUF | — | 4.95 GB | Download |
| gte-Qwen2-7B-instruct.Q5_1.gguf | GGUF | — | 5.36 GB | Download |
| gte-Qwen2-7B-instruct.Q5_K.gguf | GGUF | Q5_K | 5.07 GB | Download |
| gte-Qwen2-7B-instruct.Q5_K_M.gguf | GGUF | Q5_K_M | 5.07 GB | Download |
| gte-Qwen2-7B-instruct.Q5_K_S.gguf | GGUF | Q5_K_S | 4.95 GB | Download |
| gte-Qwen2-7B-instruct.Q6_K.gguf | GGUF | Q6_K | 5.82 GB | Download |
| gte-Qwen2-7B-instruct.Q8_0.gguf | GGUF | — | 7.54 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
{
"metadata": {},
"card_data": {
"frontmatter": {},
"hero_image_url": "",
"summary": "model.max_seq_length = 8192 queries = [ \"how much protein should a female eat\", \"summit define\", ] documents = [ \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\", \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\", ] query_embeddings = model.encode(queries, prompt_name=\"query\") document_embeddings = model.encode(documents) scores = (query_embeddings @ document_embeddings.T) * 100 print(scores.tolist()) `` Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use model.encode(queries, prompt=\"Instruct: ...\\nQuery: \" to use a custom prompt of your choice. ### Transformers `python import torch import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) if left_padding: return last_hidden_states[:, -1] else: sequence_lengths = attention_mask.sum(dim=1) - 1 batch_size = last_hidden_states.shape[0] return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'how much protein should a female eat'), get_detailed_instruct(task, 'summit define') ] # No need to add instruction for retrieval documents documents = [ \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\", \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\" ] input_texts = queries + documents tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) max_length = 8192 # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\ngte-Qwen2-7B-instruct - GGUF\n- Model creator: https://huggingface.co/Alibaba-NLP/\n- Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [gte-Qwen2-7B-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q2_K.gguf) | Q2_K | 2.81GB |\n| [gte-Qwen2-7B-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_XS.gguf) | IQ3_XS | 3.11GB |\n| [gte-Qwen2-7B-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_S.gguf) | IQ3_S | 3.26GB |\n| [gte-Qwen2-7B-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_S.gguf) | Q3_K_S | 3.25GB |\n| [gte-Qwen2-7B-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_M.gguf) | IQ3_M | 3.33GB |\n| [gte-Qwen2-7B-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K.gguf) | Q3_K | 3.55GB |\n| [gte-Qwen2-7B-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_M.gguf) | Q3_K_M | 3.55GB |\n| [gte-Qwen2-7B-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_L.gguf) | Q3_K_L | 3.81GB |\n| [gte-Qwen2-7B-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ4_XS.gguf) | IQ4_XS | 3.96GB |\n| [gte-Qwen2-7B-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_0.gguf) | Q4_0 | 4.13GB |\n| [gte-Qwen2-7B-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ4_NL.gguf) | IQ4_NL | 4.15GB |\n| [gte-Qwen2-7B-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K_S.gguf) | Q4_K_S | 4.15GB |\n| [gte-Qwen2-7B-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K.gguf) | Q4_K | 4.36GB |\n| [gte-Qwen2-7B-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K_M.gguf) | Q4_K_M | 4.36GB |\n| [gte-Qwen2-7B-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_1.gguf) | Q4_1 | 4.54GB |\n| [gte-Qwen2-7B-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_0.gguf) | Q5_0 | 4.95GB |\n| [gte-Qwen2-7B-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K_S.gguf) | Q5_K_S | 4.95GB |\n| [gte-Qwen2-7B-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K.gguf) | Q5_K | 5.07GB |\n| [gte-Qwen2-7B-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K_M.gguf) | Q5_K_M | 5.07GB |\n| [gte-Qwen2-7B-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_1.gguf) | Q5_1 | 5.36GB |\n| [gte-Qwen2-7B-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q6_K.gguf) | Q6_K | 5.82GB |\n| [gte-Qwen2-7B-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q8_0.gguf) | Q8_0 | 7.54GB |\n\n\n\n\nOriginal model description:\n---\ntags:\n- mteb\n- sentence-transformers\n- transformers\n- Qwen2\n- sentence-similarity\nlicense: apache-2.0\nmodel-index:\n- name: gte-qwen2-7B-instruct\n results:\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_counterfactual\n name: MTEB AmazonCounterfactualClassification (en)\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 91.31343283582089\n - type: ap\n value: 67.64251402604096\n - type: f1\n value: 87.53372530755692\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_polarity\n name: MTEB AmazonPolarityClassification\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 97.497825\n - type: ap\n value: 96.30329547047529\n - type: f1\n value: 97.49769793778039\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_reviews_multi\n name: MTEB AmazonReviewsClassification (en)\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 62.564\n - type: f1\n value: 60.975777935041066\n - task:\n type: Retrieval\n dataset:\n type: mteb/arguana\n name: MTEB ArguAna\n config: default\n split: test\n revision: c22ab2a51041ffd869aaddef7af8d8215647e41a\n metrics:\n - type: map_at_1\n value: 36.486000000000004\n - type: map_at_10\n value: 54.842\n - type: map_at_100\n value: 55.206999999999994\n - type: map_at_1000\n value: 55.206999999999994\n - type: map_at_3\n value: 49.893\n - type: map_at_5\n value: 53.105000000000004\n - type: mrr_at_1\n value: 37.34\n - type: mrr_at_10\n value: 55.143\n - type: mrr_at_100\n value: 55.509\n - type: mrr_at_1000\n value: 55.509\n - type: mrr_at_3\n value: 50.212999999999994\n - type: mrr_at_5\n value: 53.432\n - type: ndcg_at_1\n value: 36.486000000000004\n - type: ndcg_at_10\n value: 64.273\n - type: ndcg_at_100\n value: 65.66199999999999\n - type: ndcg_at_1000\n value: 65.66199999999999\n - type: ndcg_at_3\n value: 54.352999999999994\n - type: ndcg_at_5\n value: 60.131\n - type: precision_at_1\n value: 36.486000000000004\n - type: precision_at_10\n value: 9.395000000000001\n - type: precision_at_100\n value: 0.996\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 22.428\n - type: precision_at_5\n value: 16.259\n - type: recall_at_1\n value: 36.486000000000004\n - type: recall_at_10\n value: 93.95400000000001\n - type: recall_at_100\n value: 99.644\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 67.283\n - type: recall_at_5\n value: 81.294\n - task:\n type: Clustering\n dataset:\n type: mteb/arxiv-clustering-p2p\n name: MTEB ArxivClusteringP2P\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 56.461169803700564\n - task:\n type: Clustering\n dataset:\n type: mteb/arxiv-clustering-s2s\n name: MTEB ArxivClusteringS2S\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 51.73600434466286\n - task:\n type: Reranking\n dataset:\n type: mteb/askubuntudupquestions-reranking\n name: MTEB AskUbuntuDupQuestions\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 67.57827065898053\n - type: mrr\n value: 79.08136569493911\n - task:\n type: STS\n dataset:\n type: mteb/biosses-sts\n name: MTEB BIOSSES\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 83.53324575999243\n - type: cos_sim_spearman\n value: 81.37173362822374\n - type: euclidean_pearson\n value: 82.19243335103444\n - type: euclidean_spearman\n value: 81.33679307304334\n - type: manhattan_pearson\n value: 82.38752665975699\n - type: manhattan_spearman\n value: 81.31510583189689\n - task:\n type: Classification\n dataset:\n type: mteb/banking77\n name: MTEB Banking77Classification\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 87.56818181818181\n - type: f1\n value: 87.25826722019875\n - task:\n type: Clustering\n dataset:\n type: mteb/biorxiv-clustering-p2p\n name: MTEB BiorxivClusteringP2P\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 50.09239610327673\n - task:\n type: Clustering\n dataset:\n type: mteb/biorxiv-clustering-s2s\n name: MTEB BiorxivClusteringS2S\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 46.64733054606282\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackAndroidRetrieval\n config: default\n split: test\n revision: f46a197baaae43b4f621051089b82a364682dfeb\n metrics:\n - type: map_at_1\n value: 33.997\n - type: map_at_10\n value: 48.176\n - type: map_at_100\n value: 49.82\n - type: map_at_1000\n value: 49.924\n - type: map_at_3\n value: 43.626\n - type: map_at_5\n value: 46.275\n - type: mrr_at_1\n value: 42.059999999999995\n - type: mrr_at_10\n value: 53.726\n - type: mrr_at_100\n value: 54.398\n - type: mrr_at_1000\n value: 54.416\n - type: mrr_at_3\n value: 50.714999999999996\n - type: mrr_at_5\n value: 52.639\n - type: ndcg_at_1\n value: 42.059999999999995\n - type: ndcg_at_10\n value: 55.574999999999996\n - type: ndcg_at_100\n value: 60.744\n - type: ndcg_at_1000\n value: 61.85699999999999\n - type: ndcg_at_3\n value: 49.363\n - type: ndcg_at_5\n value: 52.44\n - type: precision_at_1\n value: 42.059999999999995\n - type: precision_at_10\n value: 11.101999999999999\n - type: precision_at_100\n value: 1.73\n - type: precision_at_1000\n value: 0.218\n - type: precision_at_3\n value: 24.464\n - type: precision_at_5\n value: 18.026\n - type: recall_at_1\n value: 33.997\n - type: recall_at_10\n value: 70.35900000000001\n - type: recall_at_100\n value: 91.642\n - type: recall_at_1000\n value: 97.977\n - type: recall_at_3\n value: 52.76\n - type: recall_at_5\n value: 61.148\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackEnglishRetrieval\n config: default\n split: test\n revision: ad9991cb51e31e31e430383c75ffb2885547b5f0\n metrics:\n - type: map_at_1\n value: 35.884\n - type: map_at_10\n value: 48.14\n - type: map_at_100\n value: 49.5\n - type: map_at_1000\n value: 49.63\n - type: map_at_3\n value: 44.646\n - type: map_at_5\n value: 46.617999999999995\n - type: mrr_at_1\n value: 44.458999999999996\n - type: mrr_at_10\n value: 53.751000000000005\n - type: mrr_at_100\n value: 54.37800000000001\n - type: mrr_at_1000\n value: 54.415\n - type: mrr_at_3\n value: 51.815\n - type: mrr_at_5\n value: 52.882\n - type: ndcg_at_1\n value: 44.458999999999996\n - type: ndcg_at_10\n value: 54.157\n - type: ndcg_at_100\n value: 58.362\n - type: ndcg_at_1000\n value: 60.178\n - type: ndcg_at_3\n value: 49.661\n - type: ndcg_at_5\n value: 51.74999999999999\n - type: precision_at_1\n value: 44.458999999999996\n - type: precision_at_10\n value: 10.248\n - type: precision_at_100\n value: 1.5890000000000002\n - type: precision_at_1000\n value: 0.207\n - type: precision_at_3\n value: 23.928\n - type: precision_at_5\n value: 16.878999999999998\n - type: recall_at_1\n value: 35.884\n - type: recall_at_10\n value: 64.798\n - type: recall_at_100\n value: 82.345\n - type: recall_at_1000\n value: 93.267\n - type: recall_at_3\n value: 51.847\n - type: recall_at_5\n value: 57.601\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackGamingRetrieval\n config: default\n split: test\n revision: 4885aa143210c98657558c04aaf3dc47cfb54340\n metrics:\n - type: map_at_1\n value: 39.383\n - type: map_at_10\n value: 53.714\n - type: map_at_100\n value: 54.838\n - type: map_at_1000\n value: 54.87800000000001\n - type: map_at_3\n value: 50.114999999999995\n - type: map_at_5\n value: 52.153000000000006\n - type: mrr_at_1\n value: 45.016\n - type: mrr_at_10\n value: 56.732000000000006\n - type: mrr_at_100\n value: 57.411\n - type: mrr_at_1000\n value: 57.431\n - type: mrr_at_3\n value: 54.044000000000004\n - type: mrr_at_5\n value: 55.639\n - type: ndcg_at_1\n value: 45.016\n - type: ndcg_at_10\n value: 60.228\n - type: ndcg_at_100\n value: 64.277\n - type: ndcg_at_1000\n value: 65.07\n - type: ndcg_at_3\n value: 54.124\n - type: ndcg_at_5\n value: 57.147000000000006\n - type: precision_at_1\n value: 45.016\n - type: precision_at_10\n value: 9.937\n - type: precision_at_100\n value: 1.288\n - type: precision_at_1000\n value: 0.13899999999999998\n - type: precision_at_3\n value: 24.471999999999998\n - type: precision_at_5\n value: 16.991\n - type: recall_at_1\n value: 39.383\n - type: recall_at_10\n value: 76.175\n - type: recall_at_100\n value: 93.02\n - type: recall_at_1000\n value: 98.60900000000001\n - type: recall_at_3\n value: 60.265\n - type: recall_at_5\n value: 67.46600000000001\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackGisRetrieval\n config: default\n split: test\n revision: 5003b3064772da1887988e05400cf3806fe491f2\n metrics:\n - type: map_at_1\n value: 27.426000000000002\n - type: map_at_10\n value: 37.397000000000006\n - type: map_at_100\n value: 38.61\n - type: map_at_1000\n value: 38.678000000000004\n - type: map_at_3\n value: 34.150999999999996\n - type: map_at_5\n value: 36.137\n - type: mrr_at_1\n value: 29.944\n - type: mrr_at_10\n value: 39.654\n - type: mrr_at_100\n value: 40.638000000000005\n - type: mrr_at_1000\n value: 40.691\n - type: mrr_at_3\n value: 36.817\n - type: mrr_at_5\n value: 38.524\n - type: ndcg_at_1\n value: 29.944\n - type: ndcg_at_10\n value: 43.094\n - type: ndcg_at_100\n value: 48.789\n - type: ndcg_at_1000\n value: 50.339999999999996\n - type: ndcg_at_3\n value: 36.984\n - type: ndcg_at_5\n value: 40.248\n - type: precision_at_1\n value: 29.944\n - type: precision_at_10\n value: 6.78\n - type: precision_at_100\n value: 1.024\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 15.895000000000001\n - type: precision_at_5\n value: 11.39\n - type: recall_at_1\n value: 27.426000000000002\n - type: recall_at_10\n value: 58.464000000000006\n - type: recall_at_100\n value: 84.193\n - type: recall_at_1000\n value: 95.52000000000001\n - type: recall_at_3\n value: 42.172\n - type: recall_at_5\n value: 50.101\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackMathematicaRetrieval\n config: default\n split: test\n revision: 90fceea13679c63fe563ded68f3b6f06e50061de\n metrics:\n - type: map_at_1\n value: 19.721\n - type: map_at_10\n value: 31.604\n - type: map_at_100\n value: 32.972\n - type: map_at_1000\n value: 33.077\n - type: map_at_3\n value: 27.218999999999998\n - type: map_at_5\n value: 29.53\n - type: mrr_at_1\n value: 25.0\n - type: mrr_at_10\n value: 35.843\n - type: mrr_at_100\n value: 36.785000000000004\n - type: mrr_at_1000\n value: 36.842000000000006\n - type: mrr_at_3\n value: 32.193\n - type: mrr_at_5\n value: 34.264\n - type: ndcg_at_1\n value: 25.0\n - type: ndcg_at_10\n value: 38.606\n - type: ndcg_at_100\n value: 44.272\n - type: ndcg_at_1000\n value: 46.527\n - type: ndcg_at_3\n value: 30.985000000000003\n - type: ndcg_at_5\n value: 34.43\n - type: precision_at_1\n value: 25.0\n - type: precision_at_10\n value: 7.811\n - type: precision_at_100\n value: 1.203\n - type: precision_at_1000\n value: 0.15\n - type: precision_at_3\n value: 15.423\n - type: precision_at_5\n value: 11.791\n - type: recall_at_1\n value: 19.721\n - type: recall_at_10\n value: 55.625\n - type: recall_at_100\n value: 79.34400000000001\n - type: recall_at_1000\n value: 95.208\n - type: recall_at_3\n value: 35.19\n - type: recall_at_5\n value: 43.626\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackPhysicsRetrieval\n config: default\n split: test\n revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4\n metrics:\n - type: map_at_1\n value: 33.784\n - type: map_at_10\n value: 47.522\n - type: map_at_100\n value: 48.949999999999996\n - type: map_at_1000\n value: 49.038\n - type: map_at_3\n value: 43.284\n - type: map_at_5\n value: 45.629\n - type: mrr_at_1\n value: 41.482\n - type: mrr_at_10\n value: 52.830999999999996\n - type: mrr_at_100\n value: 53.559999999999995\n - type: mrr_at_1000\n value: 53.588\n - type: mrr_at_3\n value: 50.016000000000005\n - type: mrr_at_5\n value: 51.614000000000004\n - type: ndcg_at_1\n value: 41.482\n - type: ndcg_at_10\n value: 54.569\n - type: ndcg_at_100\n value: 59.675999999999995\n - type: ndcg_at_1000\n value: 60.989000000000004\n - type: ndcg_at_3\n value: 48.187000000000005\n - type: ndcg_at_5\n value: 51.183\n - type: precision_at_1\n value: 41.482\n - type: precision_at_10\n value: 10.221\n - type: precision_at_100\n value: 1.486\n - type: precision_at_1000\n value: 0.17500000000000002\n - type: precision_at_3\n value: 23.548\n - type: precision_at_5\n value: 16.805\n - type: recall_at_1\n value: 33.784\n - type: recall_at_10\n value: 69.798\n - type: recall_at_100\n value: 90.098\n - type: recall_at_1000\n value: 98.176\n - type: recall_at_3\n value: 52.127\n - type: recall_at_5\n value: 59.861\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackProgrammersRetrieval\n config: default\n split: test\n revision: 6184bc1440d2dbc7612be22b50686b8826d22b32\n metrics:\n - type: map_at_1\n value: 28.038999999999998\n - type: map_at_10\n value: 41.904\n - type: map_at_100\n value: 43.36\n - type: map_at_1000\n value: 43.453\n - type: map_at_3\n value: 37.785999999999994\n - type: map_at_5\n value: 40.105000000000004\n - type: mrr_at_1\n value: 35.046\n - type: mrr_at_10\n value: 46.926\n - type: mrr_at_100\n value: 47.815000000000005\n - type: mrr_at_1000\n value: 47.849000000000004\n - type: mrr_at_3\n value: 44.273\n - type: mrr_at_5\n value: 45.774\n - type: ndcg_at_1\n value: 35.046\n - type: ndcg_at_10\n value: 48.937000000000005\n - type: ndcg_at_100\n value: 54.544000000000004\n - type: ndcg_at_1000\n value: 56.069\n - type: ndcg_at_3\n value: 42.858000000000004\n - type: ndcg_at_5\n value: 45.644\n - type: precision_at_1\n value: 35.046\n - type: precision_at_10\n value: 9.452\n - type: precision_at_100\n value: 1.429\n - type: precision_at_1000\n value: 0.173\n - type: precision_at_3\n value: 21.346999999999998\n - type: precision_at_5\n value: 15.342\n - type: recall_at_1\n value: 28.038999999999998\n - type: recall_at_10\n value: 64.59700000000001\n - type: recall_at_100\n value: 87.735\n - type: recall_at_1000\n value: 97.41300000000001\n - type: recall_at_3\n value: 47.368\n - type: recall_at_5\n value: 54.93900000000001\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackRetrieval\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 28.17291666666667\n - type: map_at_10\n value: 40.025749999999995\n - type: map_at_100\n value: 41.39208333333333\n - type: map_at_1000\n value: 41.499249999999996\n - type: map_at_3\n value: 36.347\n - type: map_at_5\n value: 38.41391666666667\n - type: mrr_at_1\n value: 33.65925\n - type: mrr_at_10\n value: 44.085499999999996\n - type: mrr_at_100\n value: 44.94116666666667\n - type: mrr_at_1000\n value: 44.9855\n - type: mrr_at_3\n value: 41.2815\n - type: mrr_at_5\n value: 42.91491666666666\n - type: ndcg_at_1\n value: 33.65925\n - type: ndcg_at_10\n value: 46.430833333333325\n - type: ndcg_at_100\n value: 51.761\n - type: ndcg_at_1000\n value: 53.50899999999999\n - type: ndcg_at_3\n value: 40.45133333333333\n - type: ndcg_at_5\n value: 43.31483333333334\n - type: precision_at_1\n value: 33.65925\n - type: precision_at_10\n value: 8.4995\n - type: precision_at_100\n value: 1.3210000000000004\n - type: precision_at_1000\n value: 0.16591666666666666\n - type: precision_at_3\n value: 19.165083333333335\n - type: precision_at_5\n value: 13.81816666666667\n - type: recall_at_1\n value: 28.17291666666667\n - type: recall_at_10\n value: 61.12624999999999\n - type: recall_at_100\n value: 83.97266666666667\n - type: recall_at_1000\n value: 95.66550000000001\n - type: recall_at_3\n value: 44.661249999999995\n - type: recall_at_5\n value: 51.983333333333334\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackStatsRetrieval\n config: default\n split: test\n revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a\n metrics:\n - type: map_at_1\n value: 24.681\n - type: map_at_10\n value: 34.892\n - type: map_at_100\n value: 35.996\n - type: map_at_1000\n value: 36.083\n - type: map_at_3\n value: 31.491999999999997\n - type: map_at_5\n value: 33.632\n - type: mrr_at_1\n value: 28.528\n - type: mrr_at_10\n value: 37.694\n - type: mrr_at_100\n value: 38.613\n - type: mrr_at_1000\n value: 38.668\n - type: mrr_at_3\n value: 34.714\n - type: mrr_at_5\n value: 36.616\n - type: ndcg_at_1\n value: 28.528\n - type: ndcg_at_10\n value: 40.703\n - type: ndcg_at_100\n value: 45.993\n - type: ndcg_at_1000\n value: 47.847\n - type: ndcg_at_3\n value: 34.622\n - type: ndcg_at_5\n value: 38.035999999999994\n - type: precision_at_1\n value: 28.528\n - type: precision_at_10\n value: 6.902\n - type: precision_at_100\n value: 1.0370000000000001\n - type: precision_at_1000\n value: 0.126\n - type: precision_at_3\n value: 15.798000000000002\n - type: precision_at_5\n value: 11.655999999999999\n - type: recall_at_1\n value: 24.681\n - type: recall_at_10\n value: 55.81\n - type: recall_at_100\n value: 79.785\n - type: recall_at_1000\n value: 92.959\n - type: recall_at_3\n value: 39.074\n - type: recall_at_5\n value: 47.568\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackTexRetrieval\n config: default\n split: test\n revision: 46989137a86843e03a6195de44b09deda022eec7\n metrics:\n - type: map_at_1\n value: 18.627\n - type: map_at_10\n value: 27.872000000000003\n - type: map_at_100\n value: 29.237999999999996\n - type: map_at_1000\n value: 29.363\n - type: map_at_3\n value: 24.751\n - type: map_at_5\n value: 26.521\n - type: mrr_at_1\n value: 23.021\n - type: mrr_at_10\n value: 31.924000000000003\n - type: mrr_at_100\n value: 32.922000000000004\n - type: mrr_at_1000\n value: 32.988\n - type: mrr_at_3\n value: 29.192\n - type: mrr_at_5\n value: 30.798\n - type: ndcg_at_1\n value: 23.021\n - type: ndcg_at_10\n value: 33.535\n - type: ndcg_at_100\n value: 39.732\n - type: ndcg_at_1000\n value: 42.201\n - type: ndcg_at_3\n value: 28.153\n - type: ndcg_at_5\n value: 30.746000000000002\n - type: precision_at_1\n value: 23.021\n - type: precision_at_10\n value: 6.459\n - type: precision_at_100\n value: 1.1320000000000001\n - type: precision_at_1000\n value: 0.153\n - type: precision_at_3\n value: 13.719000000000001\n - type: precision_at_5\n value: 10.193000000000001\n - type: recall_at_1\n value: 18.627\n - type: recall_at_10\n value: 46.463\n - type: recall_at_100\n value: 74.226\n - type: recall_at_1000\n value: 91.28500000000001\n - type: recall_at_3\n value: 31.357000000000003\n - type: recall_at_5\n value: 38.067\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackUnixRetrieval\n config: default\n split: test\n revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53\n metrics:\n - type: map_at_1\n value: 31.457\n - type: map_at_10\n value: 42.888\n - type: map_at_100\n value: 44.24\n - type: map_at_1000\n value: 44.327\n - type: map_at_3\n value: 39.588\n - type: map_at_5\n value: 41.423\n - type: mrr_at_1\n value: 37.126999999999995\n - type: mrr_at_10\n value: 47.083000000000006\n - type: mrr_at_100\n value: 47.997\n - type: mrr_at_1000\n value: 48.044\n - type: mrr_at_3\n value: 44.574000000000005\n - type: mrr_at_5\n value: 46.202\n - type: ndcg_at_1\n value: 37.126999999999995\n - type: ndcg_at_10\n value: 48.833\n - type: ndcg_at_100\n value: 54.327000000000005\n - type: ndcg_at_1000\n value: 56.011\n - type: ndcg_at_3\n value: 43.541999999999994\n - type: ndcg_at_5\n value: 46.127\n - type: precision_at_1\n value: 37.126999999999995\n - type: precision_at_10\n value: 8.376999999999999\n - type: precision_at_100\n value: 1.2309999999999999\n - type: precision_at_1000\n value: 0.146\n - type: precision_at_3\n value: 20.211000000000002\n - type: precision_at_5\n value: 14.16\n - type: recall_at_1\n value: 31.457\n - type: recall_at_10\n value: 62.369\n - type: recall_at_100\n value: 85.444\n - type: recall_at_1000\n value: 96.65599999999999\n - type: recall_at_3\n value: 47.961\n - type: recall_at_5\n value: 54.676\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackWebmastersRetrieval\n config: default\n split: test\n revision: 160c094312a0e1facb97e55eeddb698c0abe3571\n metrics:\n - type: map_at_1\n value: 27.139999999999997\n - type: map_at_10\n value: 38.801\n - type: map_at_100\n value: 40.549\n - type: map_at_1000\n value: 40.802\n - type: map_at_3\n value: 35.05\n - type: map_at_5\n value: 36.884\n - type: mrr_at_1\n value: 33.004\n - type: mrr_at_10\n value: 43.864\n - type: mrr_at_100\n value: 44.667\n - type: mrr_at_1000\n value: 44.717\n - type: mrr_at_3\n value: 40.777\n - type: mrr_at_5\n value: 42.319\n - type: ndcg_at_1\n value: 33.004\n - type: ndcg_at_10\n value: 46.022\n - type: ndcg_at_100\n value: 51.542\n - type: ndcg_at_1000\n value: 53.742000000000004\n - type: ndcg_at_3\n value: 39.795\n - type: ndcg_at_5\n value: 42.272\n - type: precision_at_1\n value: 33.004\n - type: precision_at_10\n value: 9.012\n - type: precision_at_100\n value: 1.7770000000000001\n - type: precision_at_1000\n value: 0.26\n - type: precision_at_3\n value: 19.038\n - type: precision_at_5\n value: 13.675999999999998\n - type: recall_at_1\n value: 27.139999999999997\n - type: recall_at_10\n value: 60.961\n - type: recall_at_100\n value: 84.451\n - type: recall_at_1000\n value: 98.113\n - type: recall_at_3\n value: 43.001\n - type: recall_at_5\n value: 49.896\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackWordpressRetrieval\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 17.936\n - type: map_at_10\n value: 27.399\n - type: map_at_100\n value: 28.632\n - type: map_at_1000\n value: 28.738000000000003\n - type: map_at_3\n value: 24.456\n - type: map_at_5\n value: 26.06\n - type: mrr_at_1\n value: 19.224\n - type: mrr_at_10\n value: 28.998\n - type: mrr_at_100\n value: 30.11\n - type: mrr_at_1000\n value: 30.177\n - type: mrr_at_3\n value: 26.247999999999998\n - type: mrr_at_5\n value: 27.708\n - type: ndcg_at_1\n value: 19.224\n - type: ndcg_at_10\n value: 32.911\n - type: ndcg_at_100\n value: 38.873999999999995\n - type: ndcg_at_1000\n value: 41.277\n - type: ndcg_at_3\n value: 27.142\n - type: ndcg_at_5\n value: 29.755\n - type: precision_at_1\n value: 19.224\n - type: precision_at_10\n value: 5.6930000000000005\n - type: precision_at_100\n value: 0.9259999999999999\n - type: precision_at_1000\n value: 0.126\n - type: precision_at_3\n value: 12.138\n - type: precision_at_5\n value: 8.909\n - type: recall_at_1\n value: 17.936\n - type: recall_at_10\n value: 48.096\n - type: recall_at_100\n value: 75.389\n - type: recall_at_1000\n value: 92.803\n - type: recall_at_3\n value: 32.812999999999995\n - type: recall_at_5\n value: 38.851\n - task:\n type: Retrieval\n dataset:\n type: mteb/climate-fever\n name: MTEB ClimateFEVER\n config: default\n split: test\n revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380\n metrics:\n - type: map_at_1\n value: 22.076999999999998\n - type: map_at_10\n value: 35.44\n - type: map_at_100\n value: 37.651\n - type: map_at_1000\n value: 37.824999999999996\n - type: map_at_3\n value: 30.764999999999997\n - type: map_at_5\n value: 33.26\n - type: mrr_at_1\n value: 50.163000000000004\n - type: mrr_at_10\n value: 61.207\n - type: mrr_at_100\n value: 61.675000000000004\n - type: mrr_at_1000\n value: 61.692\n - type: mrr_at_3\n value: 58.60999999999999\n - type: mrr_at_5\n value: 60.307\n - type: ndcg_at_1\n value: 50.163000000000004\n - type: ndcg_at_10\n value: 45.882\n - type: ndcg_at_100\n value: 53.239999999999995\n - type: ndcg_at_1000\n value: 55.852000000000004\n - type: ndcg_at_3\n value: 40.514\n - type: ndcg_at_5\n value: 42.038\n - type: precision_at_1\n value: 50.163000000000004\n - type: precision_at_10\n value: 13.466000000000001\n - type: precision_at_100\n value: 2.164\n - type: precision_at_1000\n value: 0.266\n - type: precision_at_3\n value: 29.707\n - type: precision_at_5\n value: 21.694\n - type: recall_at_1\n value: 22.076999999999998\n - type: recall_at_10\n value: 50.193\n - type: recall_at_100\n value: 74.993\n - type: recall_at_1000\n value: 89.131\n - type: recall_at_3\n value: 35.472\n - type: recall_at_5\n value: 41.814\n - task:\n type: Retrieval\n dataset:\n type: mteb/dbpedia\n name: MTEB DBPedia\n config: default\n split: test\n revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659\n metrics:\n - type: map_at_1\n value: 9.953\n - type: map_at_10\n value: 24.515\n - type: map_at_100\n value: 36.173\n - type: map_at_1000\n value: 38.351\n - type: map_at_3\n value: 16.592000000000002\n - type: map_at_5\n value: 20.036\n - type: mrr_at_1\n value: 74.25\n - type: mrr_at_10\n value: 81.813\n - type: mrr_at_100\n value: 82.006\n - type: mrr_at_1000\n value: 82.011\n - type: mrr_at_3\n value: 80.875\n - type: mrr_at_5\n value: 81.362\n - type: ndcg_at_1\n value: 62.5\n - type: ndcg_at_10\n value: 52.42\n - type: ndcg_at_100\n value: 56.808\n - type: ndcg_at_1000\n value: 63.532999999999994\n - type: ndcg_at_3\n value: 56.654\n - type: ndcg_at_5\n value: 54.18300000000001\n - type: precision_at_1\n value: 74.25\n - type: precision_at_10\n value: 42.699999999999996\n - type: precision_at_100\n value: 13.675\n - type: precision_at_1000\n value: 2.664\n - type: precision_at_3\n value: 60.5\n - type: precision_at_5\n value: 52.800000000000004\n - type: recall_at_1\n value: 9.953\n - type: recall_at_10\n value: 30.253999999999998\n - type: recall_at_100\n value: 62.516000000000005\n - type: recall_at_1000\n value: 84.163\n - type: recall_at_3\n value: 18.13\n - type: recall_at_5\n value: 22.771\n - task:\n type: Classification\n dataset:\n type: mteb/emotion\n name: MTEB EmotionClassification\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 79.455\n - type: f1\n value: 74.16798697647569\n - task:\n type: Retrieval\n dataset:\n type: mteb/fever\n name: MTEB FEVER\n config: default\n split: test\n revision: bea83ef9e8fb933d90a2f1d5515737465d613e12\n metrics:\n - type: map_at_1\n value: 87.531\n - type: map_at_10\n value: 93.16799999999999\n - type: map_at_100\n value: 93.341\n - type: map_at_1000\n value: 93.349\n - type: map_at_3\n value: 92.444\n - type: map_at_5\n value: 92.865\n - type: mrr_at_1\n value: 94.014\n - type: mrr_at_10\n value: 96.761\n - type: mrr_at_100\n value: 96.762\n - type: mrr_at_1000\n value: 96.762\n - type: mrr_at_3\n value: 96.672\n - type: mrr_at_5\n value: 96.736\n - type: ndcg_at_1\n value: 94.014\n - type: ndcg_at_10\n value: 95.112\n - type: ndcg_at_100\n value: 95.578\n - type: ndcg_at_1000\n value: 95.68900000000001\n - type: ndcg_at_3\n value: 94.392\n - type: ndcg_at_5\n value: 94.72500000000001\n - type: precision_at_1\n value: 94.014\n - type: precision_at_10\n value: 11.065\n - type: precision_at_100\n value: 1.157\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 35.259\n - type: precision_at_5\n value: 21.599\n - type: recall_at_1\n value: 87.531\n - type: recall_at_10\n value: 97.356\n - type: recall_at_100\n value: 98.965\n - type: recall_at_1000\n value: 99.607\n - type: recall_at_3\n value: 95.312\n - type: recall_at_5\n value: 96.295\n - task:\n type: Retrieval\n dataset:\n type: mteb/fiqa\n name: MTEB FiQA2018\n config: default\n split: test\n revision: 27a168819829fe9bcd655c2df245fb19452e8e06\n metrics:\n - type: map_at_1\n value: 32.055\n - type: map_at_10\n value: 53.114\n - type: map_at_100\n value: 55.235\n - type: map_at_1000\n value: 55.345\n - type: map_at_3\n value: 45.854\n - type: map_at_5\n value: 50.025\n - type: mrr_at_1\n value: 60.34\n - type: mrr_at_10\n value: 68.804\n - type: mrr_at_100\n value: 69.309\n - type: mrr_at_1000\n value: 69.32199999999999\n - type: mrr_at_3\n value: 66.40899999999999\n - type: mrr_at_5\n value: 67.976\n - type: ndcg_at_1\n value: 60.34\n - type: ndcg_at_10\n value: 62.031000000000006\n - type: ndcg_at_100\n value: 68.00500000000001\n - type: ndcg_at_1000\n value: 69.286\n - type: ndcg_at_3\n value: 56.355999999999995\n - type: ndcg_at_5\n value: 58.687\n - type: precision_at_1\n value: 60.34\n - type: precision_at_10\n value: 17.176\n - type: precision_at_100\n value: 2.36\n - type: precision_at_1000\n value: 0.259\n - type: precision_at_3\n value: 37.14\n - type: precision_at_5\n value: 27.809\n - type: recall_at_1\n value: 32.055\n - type: recall_at_10\n value: 70.91\n - type: recall_at_100\n value: 91.83\n - type: recall_at_1000\n value: 98.871\n - type: recall_at_3\n value: 51.202999999999996\n - type: recall_at_5\n value: 60.563\n - task:\n type: Retrieval\n dataset:\n type: mteb/hotpotqa\n name: MTEB HotpotQA\n config: default\n split: test\n revision: ab518f4d6fcca38d87c25209f94beba119d02014\n metrics:\n - type: map_at_1\n value: 43.68\n - type: map_at_10\n value: 64.389\n - type: map_at_100\n value: 65.24\n - type: map_at_1000\n value: 65.303\n - type: map_at_3\n value: 61.309000000000005\n - type: map_at_5\n value: 63.275999999999996\n - type: mrr_at_1\n value: 87.36\n - type: mrr_at_10\n value: 91.12\n - type: mrr_at_100\n value: 91.227\n - type: mrr_at_1000\n value: 91.229\n - type: mrr_at_3\n value: 90.57600000000001\n - type: mrr_at_5\n value: 90.912\n - type: ndcg_at_1\n value: 87.36\n - type: ndcg_at_10\n value: 73.076\n - type: ndcg_at_100\n value: 75.895\n - type: ndcg_at_1000\n value: 77.049\n - type: ndcg_at_3\n value: 68.929\n - type: ndcg_at_5\n value: 71.28\n - type: precision_at_1\n value: 87.36\n - type: precision_at_10\n value: 14.741000000000001\n - type: precision_at_100\n value: 1.694\n - type: precision_at_1000\n value: 0.185\n - type: precision_at_3\n value: 43.043\n - type: precision_at_5\n value: 27.681\n - type: recall_at_1\n value: 43.68\n - type: recall_at_10\n value: 73.707\n - type: recall_at_100\n value: 84.7\n - type: recall_at_1000\n value: 92.309\n - type: recall_at_3\n value: 64.564\n - type: recall_at_5\n value: 69.203\n - task:\n type: Classification\n dataset:\n type: mteb/imdb\n name: MTEB ImdbClassification\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 96.75399999999999\n - type: ap\n value: 95.29389839242187\n - type: f1\n value: 96.75348377433475\n - task:\n type: Retrieval\n dataset:\n type: mteb/msmarco\n name: MTEB MSMARCO\n config: default\n split: dev\n revision: c5a29a104738b98a9e76336939199e264163d4a0\n metrics:\n - type: map_at_1\n value: 25.176\n - type: map_at_10\n value: 38.598\n - type: map_at_100\n value: 39.707\n - type: map_at_1000\n value: 39.744\n - type: map_at_3\n value: 34.566\n - type: map_at_5\n value: 36.863\n - type: mrr_at_1\n value: 25.874000000000002\n - type: mrr_at_10\n value: 39.214\n - type: mrr_at_100\n value: 40.251\n - type: mrr_at_1000\n value: 40.281\n - type: mrr_at_3\n value: 35.291\n - type: mrr_at_5\n value: 37.545\n - type: ndcg_at_1\n value: 25.874000000000002\n - type: ndcg_at_10\n value: 45.98\n - type: ndcg_at_100\n value: 51.197\n - type: ndcg_at_1000\n value: 52.073\n - type: ndcg_at_3\n value: 37.785999999999994\n - type: ndcg_at_5\n value: 41.870000000000005\n - type: precision_at_1\n value: 25.874000000000002\n - type: precision_at_10\n value: 7.181\n - type: precision_at_100\n value: 0.979\n - type: precision_at_1000\n value: 0.106\n - type: precision_at_3\n value: 16.051000000000002\n - type: precision_at_5\n value: 11.713\n - type: recall_at_1\n value: 25.176\n - type: recall_at_10\n value: 68.67699999999999\n - type: recall_at_100\n value: 92.55\n - type: recall_at_1000\n value: 99.164\n - type: recall_at_3\n value: 46.372\n - type: recall_at_5\n value: 56.16\n - task:\n type: Classification\n dataset:\n type: mteb/mtop_domain\n name: MTEB MTOPDomainClassification (en)\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 99.03784769721841\n - type: f1\n value: 98.97791641821495\n - task:\n type: Classification\n dataset:\n type: mteb/mtop_intent\n name: MTEB MTOPIntentClassification (en)\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 91.88326493388054\n - type: f1\n value: 73.74809928034335\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_massive_intent\n name: MTEB MassiveIntentClassification (en)\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 85.41358439811701\n - type: f1\n value: 83.503679460639\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_massive_scenario\n name: MTEB MassiveScenarioClassification (en)\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 89.77135171486215\n - type: f1\n value: 88.89843747468366\n - task:\n type: Clustering\n dataset:\n type: mteb/medrxiv-clustering-p2p\n name: MTEB MedrxivClusteringP2P\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 46.22695362087359\n - task:\n type: Clustering\n dataset:\n type: mteb/medrxiv-clustering-s2s\n name: MTEB MedrxivClusteringS2S\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 44.132372165849425\n - task:\n type: Reranking\n dataset:\n type: mteb/mind_small\n name: MTEB MindSmallReranking\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 33.35680810650402\n - type: mrr\n value: 34.72625715637218\n - task:\n type: Retrieval\n dataset:\n type: mteb/nfcorpus\n name: MTEB NFCorpus\n config: default\n split: test\n revision: ec0fa4fe99da2ff19ca1214b7966684033a58814\n metrics:\n - type: map_at_1\n value: 7.165000000000001\n - type: map_at_10\n value: 15.424\n - type: map_at_100\n value: 20.28\n - type: map_at_1000\n value: 22.065\n - type: map_at_3\n value: 11.236\n - type: map_at_5\n value: 13.025999999999998\n - type: mrr_at_1\n value: 51.702999999999996\n - type: mrr_at_10\n value: 59.965\n - type: mrr_at_100\n value: 60.667\n - type: mrr_at_1000\n value: 60.702999999999996\n - type: mrr_at_3\n value: 58.772000000000006\n - type: mrr_at_5\n value: 59.267\n - type: ndcg_at_1\n value: 49.536\n - type: ndcg_at_10\n value: 40.6\n - type: ndcg_at_100\n value: 37.848\n - type: ndcg_at_1000\n value: 46.657\n - type: ndcg_at_3\n value: 46.117999999999995\n - type: ndcg_at_5\n value: 43.619\n - type: precision_at_1\n value: 51.393\n - type: precision_at_10\n value: 30.31\n - type: precision_at_100\n value: 9.972\n - type: precision_at_1000\n value: 2.329\n - type: precision_at_3\n value: 43.137\n - type: precision_at_5\n value: 37.585\n - type: recall_at_1\n value: 7.165000000000001\n - type: recall_at_10\n value: 19.689999999999998\n - type: recall_at_100\n value: 39.237\n - type: recall_at_1000\n value: 71.417\n - type: recall_at_3\n value: 12.247\n - type: recall_at_5\n value: 14.902999999999999\n - task:\n type: Retrieval\n dataset:\n type: mteb/nq\n name: MTEB NQ\n config: default\n split: test\n revision: b774495ed302d8c44a3a7ea25c90dbce03968f31\n metrics:\n - type: map_at_1\n value: 42.653999999999996\n - type: map_at_10\n value: 59.611999999999995\n - type: map_at_100\n value: 60.32300000000001\n - type: map_at_1000\n value: 60.336\n - type: map_at_3\n value: 55.584999999999994\n - type: map_at_5\n value: 58.19\n - type: mrr_at_1\n value: 47.683\n - type: mrr_at_10\n value: 62.06700000000001\n - type: mrr_at_100\n value: 62.537\n - type: mrr_at_1000\n value: 62.544999999999995\n - type: mrr_at_3\n value: 59.178\n - type: mrr_at_5\n value: 61.034\n - type: ndcg_at_1\n value: 47.654\n - type: ndcg_at_10\n value: 67.001\n - type: ndcg_at_100\n value: 69.73899999999999\n - type: ndcg_at_1000\n value: 69.986\n - type: ndcg_at_3\n value: 59.95700000000001\n - type: ndcg_at_5\n value: 64.025\n - type: precision_at_1\n value: 47.654\n - type: precision_at_10\n value: 10.367999999999999\n - type: precision_at_100\n value: 1.192\n - type: precision_at_1000\n value: 0.121\n - type: precision_at_3\n value: 26.651000000000003\n - type: precision_at_5\n value: 18.459\n - type: recall_at_1\n value: 42.653999999999996\n - type: recall_at_10\n value: 86.619\n - type: recall_at_100\n value: 98.04899999999999\n - type: recall_at_1000\n value: 99.812\n - type: recall_at_3\n value: 68.987\n - type: recall_at_5\n value: 78.158\n - task:\n type: Retrieval\n dataset:\n type: mteb/quora\n name: MTEB QuoraRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 72.538\n - type: map_at_10\n value: 86.702\n - type: map_at_100\n value: 87.31\n - type: map_at_1000\n value: 87.323\n - type: map_at_3\n value: 83.87\n - type: map_at_5\n value: 85.682\n - type: mrr_at_1\n value: 83.31\n - type: mrr_at_10\n value: 89.225\n - type: mrr_at_100\n value: 89.30399999999999\n - type: mrr_at_1000\n value: 89.30399999999999\n - type: mrr_at_3\n value: 88.44300000000001\n - type: mrr_at_5\n value: 89.005\n - type: ndcg_at_1\n value: 83.32000000000001\n - type: ndcg_at_10\n value: 90.095\n - type: ndcg_at_100\n value: 91.12\n - type: ndcg_at_1000\n value: 91.179\n - type: ndcg_at_3\n value: 87.606\n - type: ndcg_at_5\n value: 89.031\n - type: precision_at_1\n value: 83.32000000000001\n - type: precision_at_10\n value: 13.641\n - type: precision_at_100\n value: 1.541\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 38.377\n - type: precision_at_5\n value: 25.162000000000003\n - type: recall_at_1\n value: 72.538\n - type: recall_at_10\n value: 96.47200000000001\n - type: recall_at_100\n value: 99.785\n - type: recall_at_1000\n value: 99.99900000000001\n - type: recall_at_3\n value: 89.278\n - type: recall_at_5\n value: 93.367\n - task:\n type: Clustering\n dataset:\n type: mteb/reddit-clustering\n name: MTEB RedditClustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 73.55219145406065\n - task:\n type: Clustering\n dataset:\n type: mteb/reddit-clustering-p2p\n name: MTEB RedditClusteringP2P\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 74.13437105242755\n - task:\n type: Retrieval\n dataset:\n type: mteb/scidocs\n name: MTEB SCIDOCS\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.873\n - type: map_at_10\n value: 17.944\n - type: map_at_100\n value: 21.171\n - type: map_at_1000\n value: 21.528\n - type: map_at_3\n value: 12.415\n - type: map_at_5\n value: 15.187999999999999\n - type: mrr_at_1\n value: 33.800000000000004\n - type: mrr_at_10\n value: 46.455\n - type: mrr_at_100\n value: 47.378\n - type: mrr_at_1000\n value: 47.394999999999996\n - type: mrr_at_3\n value: 42.367\n - type: mrr_at_5\n value: 44.972\n - type: ndcg_at_1\n value: 33.800000000000004\n - type: ndcg_at_10\n value: 28.907\n - type: ndcg_at_100\n value: 39.695\n - type: ndcg_at_1000\n value: 44.582\n - type: ndcg_at_3\n value: 26.949\n - type: ndcg_at_5\n value: 23.988\n - type: precision_at_1\n value: 33.800000000000004\n - type: precision_at_10\n value: 15.079999999999998\n - type: precision_at_100\n value: 3.056\n - type: precision_at_1000\n value: 0.42100000000000004\n - type: precision_at_3\n value: 25.167\n - type: precision_at_5\n value: 21.26\n - type: recall_at_1\n value: 6.873\n - type: recall_at_10\n value: 30.568\n - type: recall_at_100\n value: 62.062\n - type: recall_at_1000\n value: 85.37700000000001\n - type: recall_at_3\n value: 15.312999999999999\n - type: recall_at_5\n value: 21.575\n - task:\n type: STS\n dataset:\n type: mteb/sickr-sts\n name: MTEB SICK-R\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 82.37009118256057\n - type: cos_sim_spearman\n value: 79.27986395671529\n - type: euclidean_pearson\n value: 79.18037715442115\n - type: euclidean_spearman\n value: 79.28004791561621\n - type: manhattan_pearson\n value: 79.34062972800541\n - type: manhattan_spearman\n value: 79.43106695543402\n - task:\n type: STS\n dataset:\n type: mteb/sts12-sts\n name: MTEB STS12\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 87.48474767383833\n - type: cos_sim_spearman\n value: 79.54505388752513\n - type: euclidean_pearson\n value: 83.43282704179565\n - type: euclidean_spearman\n value: 79.54579919925405\n - type: manhattan_pearson\n value: 83.77564492427952\n - type: manhattan_spearman\n value: 79.84558396989286\n - task:\n type: STS\n dataset:\n type: mteb/sts13-sts\n name: MTEB STS13\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 88.803698035802\n - type: cos_sim_spearman\n value: 88.83451367754881\n - type: euclidean_pearson\n value: 88.28939285711628\n - type: euclidean_spearman\n value: 88.83528996073112\n - type: manhattan_pearson\n value: 88.28017412671795\n - type: manhattan_spearman\n value: 88.9228828016344\n - task:\n type: STS\n dataset:\n type: mteb/sts14-sts\n name: MTEB STS14\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 85.27469288153428\n - type: cos_sim_spearman\n value: 83.87477064876288\n - type: euclidean_pearson\n value: 84.2601737035379\n - type: euclidean_spearman\n value: 83.87431082479074\n - type: manhattan_pearson\n value: 84.3621547772745\n - type: manhattan_spearman\n value: 84.12094375000423\n - task:\n type: STS\n dataset:\n type: mteb/sts15-sts\n name: MTEB STS15\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 88.12749863201587\n - type: cos_sim_spearman\n value: 88.54287568368565\n - type: euclidean_pearson\n value: 87.90429700607999\n - type: euclidean_spearman\n value: 88.5437689576261\n - type: manhattan_pearson\n value: 88.19276653356833\n - type: manhattan_spearman\n value: 88.99995393814679\n - task:\n type: STS\n dataset:\n type: mteb/sts16-sts\n name: MTEB STS16\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 85.68398747560902\n - type: cos_sim_spearman\n value: 86.48815303460574\n - type: euclidean_pearson\n value: 85.52356631237954\n - type: euclidean_spearman\n value: 86.486391949551\n - type: manhattan_pearson\n value: 85.67267981761788\n - type: manhattan_spearman\n value: 86.7073696332485\n - task:\n type: STS\n dataset:\n type: mteb/sts17-crosslingual-sts\n name: MTEB STS17 (en-en)\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 88.9057107443124\n - type: cos_sim_spearman\n value: 88.7312168757697\n - type: euclidean_pearson\n value: 88.72810439714794\n - type: euclidean_spearman\n value: 88.71976185854771\n - type: manhattan_pearson\n value: 88.50433745949111\n - type: manhattan_spearman\n value: 88.51726175544195\n - task:\n type: STS\n dataset:\n type: mteb/sts22-crosslingual-sts\n name: MTEB STS22 (en)\n config: en\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 67.59391795109886\n - type: cos_sim_spearman\n value: 66.87613008631367\n - type: euclidean_pearson\n value: 69.23198488262217\n - type: euclidean_spearman\n value: 66.85427723013692\n - type: manhattan_pearson\n value: 69.50730124841084\n - type: manhattan_spearman\n value: 67.10404669820792\n - task:\n type: STS\n dataset:\n type: mteb/stsbenchmark-sts\n name: MTEB STSBenchmark\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 87.0820605344619\n - type: cos_sim_spearman\n value: 86.8518089863434\n - type: euclidean_pearson\n value: 86.31087134689284\n - type: euclidean_spearman\n value: 86.8518520517941\n - type: manhattan_pearson\n value: 86.47203796160612\n - type: manhattan_spearman\n value: 87.1080149734421\n - task:\n type: Reranking\n dataset:\n type: mteb/scidocs-reranking\n name: MTEB SciDocsRR\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 89.09255369305481\n - type: mrr\n value: 97.10323445617563\n - task:\n type: Retrieval\n dataset:\n type: mteb/scifact\n name: MTEB SciFact\n config: default\n split: test\n revision: 0228b52cf27578f30900b9e5271d331663a030d7\n metrics:\n - type: map_at_1\n value: 61.260999999999996\n - type: map_at_10\n value: 74.043\n - type: map_at_100\n value: 74.37700000000001\n - type: map_at_1000\n value: 74.384\n - type: map_at_3\n value: 71.222\n - type: map_at_5\n value: 72.875\n - type: mrr_at_1\n value: 64.333\n - type: mrr_at_10\n value: 74.984\n - type: mrr_at_100\n value: 75.247\n - type: mrr_at_1000\n value: 75.25500000000001\n - type: mrr_at_3\n value: 73.167\n - type: mrr_at_5\n value: 74.35000000000001\n - type: ndcg_at_1\n value: 64.333\n - type: ndcg_at_10\n value: 79.06\n - type: ndcg_at_100\n value: 80.416\n - type: ndcg_at_1000\n value: 80.55600000000001\n - type: ndcg_at_3\n value: 74.753\n - type: ndcg_at_5\n value: 76.97500000000001\n - type: precision_at_1\n value: 64.333\n - type: precision_at_10\n value: 10.567\n - type: precision_at_100\n value: 1.1199999999999999\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 29.889\n - type: precision_at_5\n value: 19.533\n - type: recall_at_1\n value: 61.260999999999996\n - type: recall_at_10\n value: 93.167\n - type: recall_at_100\n value: 99.0\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 81.667\n - type: recall_at_5\n value: 87.394\n - task:\n type: PairClassification\n dataset:\n type: mteb/sprintduplicatequestions-pairclassification\n name: MTEB SprintDuplicateQuestions\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.71980198019801\n - type: cos_sim_ap\n value: 92.81616007802704\n - type: cos_sim_f1\n value: 85.17548454688318\n - type: cos_sim_precision\n value: 89.43894389438944\n - type: cos_sim_recall\n value: 81.3\n - type: dot_accuracy\n value: 99.71980198019801\n - type: dot_ap\n value: 92.81398760591358\n - type: dot_f1\n value: 85.17548454688318\n - type: dot_precision\n value: 89.43894389438944\n - type: dot_recall\n value: 81.3\n - type: euclidean_accuracy\n value: 99.71980198019801\n - type: euclidean_ap\n value: 92.81560637245072\n - type: euclidean_f1\n value: 85.17548454688318\n - type: euclidean_precision\n value: 89.43894389438944\n - type: euclidean_recall\n value: 81.3\n - type: manhattan_accuracy\n value: 99.73069306930694\n - type: manhattan_ap\n value: 93.14005487480794\n - type: manhattan_f1\n value: 85.56263269639068\n - type: manhattan_precision\n value: 91.17647058823529\n - type: manhattan_recall\n value: 80.60000000000001\n - type: max_accuracy\n value: 99.73069306930694\n - type: max_ap\n value: 93.14005487480794\n - type: max_f1\n value: 85.56263269639068\n - task:\n type: Clustering\n dataset:\n type: mteb/stackexchange-clustering\n name: MTEB StackExchangeClustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 79.86443362395185\n - task:\n type: Clustering\n dataset:\n type: mteb/stackexchange-clustering-p2p\n name: MTEB StackExchangeClusteringP2P\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 49.40897096662564\n - task:\n type: Reranking\n dataset:\n type: mteb/stackoverflowdupquestions-reranking\n name: MTEB StackOverflowDupQuestions\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 55.66040806627947\n - type: mrr\n value: 56.58670475766064\n - task:\n type: Summarization\n dataset:\n type: mteb/summeval\n name: MTEB SummEval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.51015090598575\n - type: cos_sim_spearman\n value: 31.35016454939226\n - type: dot_pearson\n value: 31.5150068731\n - type: dot_spearman\n value: 31.34790869023487\n - task:\n type: Retrieval\n dataset:\n type: mteb/trec-covid\n name: MTEB TRECCOVID\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.254\n - type: map_at_10\n value: 2.064\n - type: map_at_100\n value: 12.909\n - type: map_at_1000\n value: 31.761\n - type: map_at_3\n value: 0.738\n - type: map_at_5\n value: 1.155\n - type: mrr_at_1\n value: 96.0\n - type: mrr_at_10\n value: 98.0\n - type: mrr_at_100\n value: 98.0\n - type: mrr_at_1000\n value: 98.0\n - type: mrr_at_3\n value: 98.0\n - type: mrr_at_5\n value: 98.0\n - type: ndcg_at_1\n value: 93.0\n - type: ndcg_at_10\n value: 82.258\n - type: ndcg_at_100\n value: 64.34\n - type: ndcg_at_1000\n value: 57.912\n - type: ndcg_at_3\n value: 90.827\n - type: ndcg_at_5\n value: 86.79\n - type: precision_at_1\n value: 96.0\n - type: precision_at_10\n value: 84.8\n - type: precision_at_100\n value: 66.0\n - type: precision_at_1000\n value: 25.356\n - type: precision_at_3\n value: 94.667\n - type: precision_at_5\n value: 90.4\n - type: recall_at_1\n value: 0.254\n - type: recall_at_10\n value: 2.1950000000000003\n - type: recall_at_100\n value: 16.088\n - type: recall_at_1000\n value: 54.559000000000005\n - type: recall_at_3\n value: 0.75\n - type: recall_at_5\n value: 1.191\n - task:\n type: Retrieval\n dataset:\n type: mteb/touche2020\n name: MTEB Touche2020\n config: default\n split: test\n revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f\n metrics:\n - type: map_at_1\n value: 2.976\n - type: map_at_10\n value: 11.389000000000001\n - type: map_at_100\n value: 18.429000000000002\n - type: map_at_1000\n value: 20.113\n - type: map_at_3\n value: 6.483\n - type: map_at_5\n value: 8.770999999999999\n - type: mrr_at_1\n value: 40.816\n - type: mrr_at_10\n value: 58.118\n - type: mrr_at_100\n value: 58.489999999999995\n - type: mrr_at_1000\n value: 58.489999999999995\n - type: mrr_at_3\n value: 53.061\n - type: mrr_at_5\n value: 57.041\n - type: ndcg_at_1\n value: 40.816\n - type: ndcg_at_10\n value: 30.567\n - type: ndcg_at_100\n value: 42.44\n - type: ndcg_at_1000\n value: 53.480000000000004\n - type: ndcg_at_3\n value: 36.016\n - type: ndcg_at_5\n value: 34.257\n - type: precision_at_1\n value: 42.857\n - type: precision_at_10\n value: 25.714\n - type: precision_at_100\n value: 8.429\n - type: precision_at_1000\n value: 1.5939999999999999\n - type: precision_at_3\n value: 36.735\n - type: precision_at_5\n value: 33.878\n - type: recall_at_1\n value: 2.976\n - type: recall_at_10\n value: 17.854999999999997\n - type: recall_at_100\n value: 51.833\n - type: recall_at_1000\n value: 86.223\n - type: recall_at_3\n value: 7.887\n - type: recall_at_5\n value: 12.026\n - task:\n type: Classification\n dataset:\n type: mteb/toxic_conversations_50k\n name: MTEB ToxicConversationsClassification\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 85.1174\n - type: ap\n value: 30.169441069345748\n - type: f1\n value: 69.79254701873245\n - task:\n type: Classification\n dataset:\n type: mteb/tweet_sentiment_extraction\n name: MTEB TweetSentimentExtractionClassification\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 72.58347481607245\n - type: f1\n value: 72.74877295564937\n - task:\n type: Clustering\n dataset:\n type: mteb/twentynewsgroups-clustering\n name: MTEB TwentyNewsgroupsClustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 53.90586138221305\n - task:\n type: PairClassification\n dataset:\n type: mteb/twittersemeval2015-pairclassification\n name: MTEB TwitterSemEval2015\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 87.35769207844072\n - type: cos_sim_ap\n value: 77.9645072410354\n - type: cos_sim_f1\n value: 71.32352941176471\n - type: cos_sim_precision\n value: 66.5903890160183\n - type: cos_sim_recall\n value: 76.78100263852242\n - type: dot_accuracy\n value: 87.37557370209214\n - type: dot_ap\n value: 77.96250046429908\n - type: dot_f1\n value: 71.28932757557064\n - type: dot_precision\n value: 66.95249130938586\n - type: dot_recall\n value: 76.22691292875989\n - type: euclidean_accuracy\n value: 87.35173153722357\n - type: euclidean_ap\n value: 77.96520460741593\n - type: euclidean_f1\n value: 71.32470733210104\n - type: euclidean_precision\n value: 66.91329479768785\n - type: euclidean_recall\n value: 76.35883905013192\n - type: manhattan_accuracy\n value: 87.25636287774931\n - type: manhattan_ap\n value: 77.77752485611796\n - type: manhattan_f1\n value: 71.18148599269183\n - type: manhattan_precision\n value: 66.10859728506787\n - type: manhattan_recall\n value: 77.0976253298153\n - type: max_accuracy\n value: 87.37557370209214\n - type: max_ap\n value: 77.96520460741593\n - type: max_f1\n value: 71.32470733210104\n - task:\n type: PairClassification\n dataset:\n type: mteb/twitterurlcorpus-pairclassification\n name: MTEB TwitterURLCorpus\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 89.38176737687739\n - type: cos_sim_ap\n value: 86.58811861657401\n - type: cos_sim_f1\n value: 79.09430644097604\n - type: cos_sim_precision\n value: 75.45085977911366\n - type: cos_sim_recall\n value: 83.10748383122882\n - type: dot_accuracy\n value: 89.38370784336554\n - type: dot_ap\n value: 86.58840606004333\n - type: dot_f1\n value: 79.10179860068133\n - type: dot_precision\n value: 75.44546153308643\n - type: dot_recall\n value: 83.13058207576223\n - type: euclidean_accuracy\n value: 89.38564830985369\n - type: euclidean_ap\n value: 86.58820721061164\n - type: euclidean_f1\n value: 79.09070942235888\n - type: euclidean_precision\n value: 75.38729937194697\n - type: euclidean_recall\n value: 83.17677856482906\n - type: manhattan_accuracy\n value: 89.40699344122326\n - type: manhattan_ap\n value: 86.60631843011362\n - type: manhattan_f1\n value: 79.14949970570925\n - type: manhattan_precision\n value: 75.78191039729502\n - type: manhattan_recall\n value: 82.83030489682784\n - type: max_accuracy\n value: 89.40699344122326\n - type: max_ap\n value: 86.60631843011362\n - type: max_f1\n value: 79.14949970570925\n - task:\n type: STS\n dataset:\n type: C-MTEB/AFQMC\n name: MTEB AFQMC\n config: default\n split: validation\n revision: b44c3b011063adb25877c13823db83bb193913c4\n metrics:\n - type: cos_sim_pearson\n value: 65.58442135663871\n - type: cos_sim_spearman\n value: 72.2538631361313\n - type: euclidean_pearson\n value: 70.97255486607429\n - type: euclidean_spearman\n value: 72.25374250228647\n - type: manhattan_pearson\n value: 70.83250199989911\n - type: manhattan_spearman\n value: 72.14819496536272\n - task:\n type: STS\n dataset:\n type: C-MTEB/ATEC\n name: MTEB ATEC\n config: default\n split: test\n revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865\n metrics:\n - type: cos_sim_pearson\n value: 59.99478404929932\n - type: cos_sim_spearman\n value: 62.61836216999812\n - type: euclidean_pearson\n value: 66.86429811933593\n - type: euclidean_spearman\n value: 62.6183520374191\n - type: manhattan_pearson\n value: 66.8063778911633\n - type: manhattan_spearman\n value: 62.569607573241115\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_reviews_multi\n name: MTEB AmazonReviewsClassification (zh)\n config: zh\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 53.98400000000001\n - type: f1\n value: 51.21447361350723\n - task:\n type: STS\n dataset:\n type: C-MTEB/BQ\n name: MTEB BQ\n config: default\n split: test\n revision: e3dda5e115e487b39ec7e618c0c6a29137052a55\n metrics:\n - type: cos_sim_pearson\n value: 79.11941660686553\n - type: cos_sim_spearman\n value: 81.25029594540435\n - type: euclidean_pearson\n value: 82.06973504238826\n - type: euclidean_spearman\n value: 81.2501989488524\n - type: manhattan_pearson\n value: 82.10094630392753\n - type: manhattan_spearman\n value: 81.27987244392389\n - task:\n type: Clustering\n dataset:\n type: C-MTEB/CLSClusteringP2P\n name: MTEB CLSClusteringP2P\n config: default\n split: test\n revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476\n metrics:\n - type: v_measure\n value: 47.07270168705156\n - task:\n type: Clustering\n dataset:\n type: C-MTEB/CLSClusteringS2S\n name: MTEB CLSClusteringS2S\n config: default\n split: test\n revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f\n metrics:\n - type: v_measure\n value: 45.98511703185043\n - task:\n type: Reranking\n dataset:\n type: C-MTEB/CMedQAv1-reranking\n name: MTEB CMedQAv1\n config: default\n split: test\n revision: 8d7f1e942507dac42dc58017c1a001c3717da7df\n metrics:\n - type: map\n value: 88.19895157194931\n - type: mrr\n value: 90.21424603174603\n - task:\n type: Reranking\n dataset:\n type: C-MTEB/CMedQAv2-reranking\n name: MTEB CMedQAv2\n config: default\n split: test\n revision: 23d186750531a14a0357ca22cd92d712fd512ea0\n metrics:\n - type: map\n value: 88.03317320980119\n - type: mrr\n value: 89.9461507936508\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/CmedqaRetrieval\n name: MTEB CmedqaRetrieval\n config: default\n split: dev\n revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301\n metrics:\n - type: map_at_1\n value: 29.037000000000003\n - type: map_at_10\n value: 42.001\n - type: map_at_100\n value: 43.773\n - type: map_at_1000\n value: 43.878\n - type: map_at_3\n value: 37.637\n - type: map_at_5\n value: 40.034\n - type: mrr_at_1\n value: 43.136\n - type: mrr_at_10\n value: 51.158\n - type: mrr_at_100\n value: 52.083\n - type: mrr_at_1000\n value: 52.12\n - type: mrr_at_3\n value: 48.733\n - type: mrr_at_5\n value: 50.025\n - type: ndcg_at_1\n value: 43.136\n - type: ndcg_at_10\n value: 48.685\n - type: ndcg_at_100\n value: 55.513\n - type: ndcg_at_1000\n value: 57.242000000000004\n - type: ndcg_at_3\n value: 43.329\n - type: ndcg_at_5\n value: 45.438\n - type: precision_at_1\n value: 43.136\n - type: precision_at_10\n value: 10.56\n - type: precision_at_100\n value: 1.6129999999999998\n - type: precision_at_1000\n value: 0.184\n - type: precision_at_3\n value: 24.064\n - type: precision_at_5\n value: 17.269000000000002\n - type: recall_at_1\n value: 29.037000000000003\n - type: recall_at_10\n value: 59.245000000000005\n - type: recall_at_100\n value: 87.355\n - type: recall_at_1000\n value: 98.74000000000001\n - type: recall_at_3\n value: 42.99\n - type: recall_at_5\n value: 49.681999999999995\n - task:\n type: PairClassification\n dataset:\n type: C-MTEB/CMNLI\n name: MTEB Cmnli\n config: default\n split: validation\n revision: 41bc36f332156f7adc9e38f53777c959b2ae9766\n metrics:\n - type: cos_sim_accuracy\n value: 82.68190018039687\n - type: cos_sim_ap\n value: 90.18017125327886\n - type: cos_sim_f1\n value: 83.64080906868193\n - type: cos_sim_precision\n value: 79.7076890489303\n - type: cos_sim_recall\n value: 87.98223053542202\n - type: dot_accuracy\n value: 82.68190018039687\n - type: dot_ap\n value: 90.18782350103646\n - type: dot_f1\n value: 83.64242087729039\n - type: dot_precision\n value: 79.65313028764805\n - type: dot_recall\n value: 88.05237315875614\n - type: euclidean_accuracy\n value: 82.68190018039687\n - type: euclidean_ap\n value: 90.1801957900632\n - type: euclidean_f1\n value: 83.63636363636364\n - type: euclidean_precision\n value: 79.52772506852203\n - type: euclidean_recall\n value: 88.19265840542437\n - type: manhattan_accuracy\n value: 82.14070956103427\n - type: manhattan_ap\n value: 89.96178420101427\n - type: manhattan_f1\n value: 83.21087838578791\n - type: manhattan_precision\n value: 78.35605121850475\n - type: manhattan_recall\n value: 88.70703764320785\n - type: max_accuracy\n value: 82.68190018039687\n - type: max_ap\n value: 90.18782350103646\n - type: max_f1\n value: 83.64242087729039\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/CovidRetrieval\n name: MTEB CovidRetrieval\n config: default\n split: dev\n revision: 1271c7809071a13532e05f25fb53511ffce77117\n metrics:\n - type: map_at_1\n value: 72.234\n - type: map_at_10\n value: 80.10000000000001\n - type: map_at_100\n value: 80.36\n - type: map_at_1000\n value: 80.363\n - type: map_at_3\n value: 78.315\n - type: map_at_5\n value: 79.607\n - type: mrr_at_1\n value: 72.392\n - type: mrr_at_10\n value: 80.117\n - type: mrr_at_100\n value: 80.36999999999999\n - type: mrr_at_1000\n value: 80.373\n - type: mrr_at_3\n value: 78.469\n - type: mrr_at_5\n value: 79.633\n - type: ndcg_at_1\n value: 72.392\n - type: ndcg_at_10\n value: 83.651\n - type: ndcg_at_100\n value: 84.749\n - type: ndcg_at_1000\n value: 84.83000000000001\n - type: ndcg_at_3\n value: 80.253\n - type: ndcg_at_5\n value: 82.485\n - type: precision_at_1\n value: 72.392\n - type: precision_at_10\n value: 9.557\n - type: precision_at_100\n value: 1.004\n - type: precision_at_1000\n value: 0.101\n - type: precision_at_3\n value: 28.732000000000003\n - type: precision_at_5\n value: 18.377\n - type: recall_at_1\n value: 72.234\n - type: recall_at_10\n value: 94.573\n - type: recall_at_100\n value: 99.368\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 85.669\n - type: recall_at_5\n value: 91.01700000000001\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/DuRetrieval\n name: MTEB DuRetrieval\n config: default\n split: dev\n revision: a1a333e290fe30b10f3f56498e3a0d911a693ced\n metrics:\n - type: map_at_1\n value: 26.173999999999996\n - type: map_at_10\n value: 80.04\n - type: map_at_100\n value: 82.94500000000001\n - type: map_at_1000\n value: 82.98100000000001\n - type: map_at_3\n value: 55.562999999999995\n - type: map_at_5\n value: 69.89800000000001\n - type: mrr_at_1\n value: 89.5\n - type: mrr_at_10\n value: 92.996\n - type: mrr_at_100\n value: 93.06400000000001\n - type: mrr_at_1000\n value: 93.065\n - type: mrr_at_3\n value: 92.658\n - type: mrr_at_5\n value: 92.84599999999999\n - type: ndcg_at_1\n value: 89.5\n - type: ndcg_at_10\n value: 87.443\n - type: ndcg_at_100\n value: 90.253\n - type: ndcg_at_1000\n value: 90.549\n - type: ndcg_at_3\n value: 85.874\n - type: ndcg_at_5\n value: 84.842\n - type: precision_at_1\n value: 89.5\n - type: precision_at_10\n value: 41.805\n - type: precision_at_100\n value: 4.827\n - type: precision_at_1000\n value: 0.49\n - type: precision_at_3\n value: 76.85\n - type: precision_at_5\n value: 64.8\n - type: recall_at_1\n value: 26.173999999999996\n - type: recall_at_10\n value: 89.101\n - type: recall_at_100\n value: 98.08099999999999\n - type: recall_at_1000\n value: 99.529\n - type: recall_at_3\n value: 57.902\n - type: recall_at_5\n value: 74.602\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/EcomRetrieval\n name: MTEB EcomRetrieval\n config: default\n split: dev\n revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9\n metrics:\n - type: map_at_1\n value: 56.10000000000001\n - type: map_at_10\n value: 66.15299999999999\n - type: map_at_100\n value: 66.625\n - type: map_at_1000\n value: 66.636\n - type: map_at_3\n value: 63.632999999999996\n - type: map_at_5\n value: 65.293\n - type: mrr_at_1\n value: 56.10000000000001\n - type: mrr_at_10\n value: 66.15299999999999\n - type: mrr_at_100\n value: 66.625\n - type: mrr_at_1000\n value: 66.636\n - type: mrr_at_3\n value: 63.632999999999996\n - type: mrr_at_5\n value: 65.293\n - type: ndcg_at_1\n value: 56.10000000000001\n - type: ndcg_at_10\n value: 71.146\n - type: ndcg_at_100\n value: 73.27799999999999\n - type: ndcg_at_1000\n value: 73.529\n - type: ndcg_at_3\n value: 66.09\n - type: ndcg_at_5\n value: 69.08999999999999\n - type: precision_at_1\n value: 56.10000000000001\n - type: precision_at_10\n value: 8.68\n - type: precision_at_100\n value: 0.964\n - type: precision_at_1000\n value: 0.098\n - type: precision_at_3\n value: 24.4\n - type: precision_at_5\n value: 16.1\n - type: recall_at_1\n value: 56.10000000000001\n - type: recall_at_10\n value: 86.8\n - type: recall_at_100\n value: 96.39999999999999\n - type: recall_at_1000\n value: 98.3\n - type: recall_at_3\n value: 73.2\n - type: recall_at_5\n value: 80.5\n - task:\n type: Classification\n dataset:\n type: C-MTEB/IFlyTek-classification\n name: MTEB IFlyTek\n config: default\n split: validation\n revision: 421605374b29664c5fc098418fe20ada9bd55f8a\n metrics:\n - type: accuracy\n value: 54.52096960369373\n - type: f1\n value: 40.930845295808695\n - task:\n type: Classification\n dataset:\n type: C-MTEB/JDReview-classification\n name: MTEB JDReview\n config: default\n split: test\n revision: b7c64bd89eb87f8ded463478346f76731f07bf8b\n metrics:\n - type: accuracy\n value: 86.51031894934334\n - type: ap\n value: 55.9516014323483\n - type: f1\n value: 81.54813679326381\n - task:\n type: STS\n dataset:\n type: C-MTEB/LCQMC\n name: MTEB LCQMC\n config: default\n split: test\n revision: 17f9b096f80380fce5ed12a9be8be7784b337daf\n metrics:\n - type: cos_sim_pearson\n value: 69.67437838574276\n - type: cos_sim_spearman\n value: 73.81314174653045\n - type: euclidean_pearson\n value: 72.63430276680275\n - type: euclidean_spearman\n value: 73.81358736777001\n - type: manhattan_pearson\n value: 72.58743833842829\n - type: manhattan_spearman\n value: 73.7590419009179\n - task:\n type: Reranking\n dataset:\n type: C-MTEB/Mmarco-reranking\n name: MTEB MMarcoReranking\n config: default\n split: dev\n revision: None\n metrics:\n - type: map\n value: 31.648613483640254\n - type: mrr\n value: 30.37420634920635\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/MMarcoRetrieval\n name: MTEB MMarcoRetrieval\n config: default\n split: dev\n revision: 539bbde593d947e2a124ba72651aafc09eb33fc2\n metrics:\n - type: map_at_1\n value: 73.28099999999999\n - type: map_at_10\n value: 81.977\n - type: map_at_100\n value: 82.222\n - type: map_at_1000\n value: 82.22699999999999\n - type: map_at_3\n value: 80.441\n - type: map_at_5\n value: 81.46600000000001\n - type: mrr_at_1\n value: 75.673\n - type: mrr_at_10\n value: 82.41000000000001\n - type: mrr_at_100\n value: 82.616\n - type: mrr_at_1000\n value: 82.621\n - type: mrr_at_3\n value: 81.094\n - type: mrr_at_5\n value: 81.962\n - type: ndcg_at_1\n value: 75.673\n - type: ndcg_at_10\n value: 85.15599999999999\n - type: ndcg_at_100\n value: 86.151\n - type: ndcg_at_1000\n value: 86.26899999999999\n - type: ndcg_at_3\n value: 82.304\n - type: ndcg_at_5\n value: 84.009\n - type: precision_at_1\n value: 75.673\n - type: precision_at_10\n value: 10.042\n - type: precision_at_100\n value: 1.052\n - type: precision_at_1000\n value: 0.106\n - type: precision_at_3\n value: 30.673000000000002\n - type: precision_at_5\n value: 19.326999999999998\n - type: recall_at_1\n value: 73.28099999999999\n - type: recall_at_10\n value: 94.446\n - type: recall_at_100\n value: 98.737\n - type: recall_at_1000\n value: 99.649\n - type: recall_at_3\n value: 86.984\n - type: recall_at_5\n value: 91.024\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_massive_intent\n name: MTEB MassiveIntentClassification (zh-CN)\n config: zh-CN\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 81.08607935440484\n - type: f1\n value: 78.24879986066307\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_massive_scenario\n name: MTEB MassiveScenarioClassification (zh-CN)\n config: zh-CN\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 86.05917955615332\n - type: f1\n value: 85.05279279434997\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/MedicalRetrieval\n name: MTEB MedicalRetrieval\n config: default\n split: dev\n revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6\n metrics:\n - type: map_at_1\n value: 56.2\n - type: map_at_10\n value: 62.57899999999999\n - type: map_at_100\n value: 63.154999999999994\n - type: map_at_1000\n value: 63.193\n - type: map_at_3\n value: 61.217\n - type: map_at_5\n value: 62.012\n - type: mrr_at_1\n value: 56.3\n - type: mrr_at_10\n value: 62.629000000000005\n - type: mrr_at_100\n value: 63.205999999999996\n - type: mrr_at_1000\n value: 63.244\n - type: mrr_at_3\n value: 61.267\n - type: mrr_at_5\n value: 62.062\n - type: ndcg_at_1\n value: 56.2\n - type: ndcg_at_10\n value: 65.592\n - type: ndcg_at_100\n value: 68.657\n - type: ndcg_at_1000\n value: 69.671\n - type: ndcg_at_3\n value: 62.808\n - type: ndcg_at_5\n value: 64.24499999999999\n - type: precision_at_1\n value: 56.2\n - type: precision_at_10\n value: 7.5\n - type: precision_at_100\n value: 0.899\n - type: precision_at_1000\n value: 0.098\n - type: precision_at_3\n value: 22.467000000000002\n - type: precision_at_5\n value: 14.180000000000001\n - type: recall_at_1\n value: 56.2\n - type: recall_at_10\n value: 75.0\n - type: recall_at_100\n value: 89.9\n - type: recall_at_1000\n value: 97.89999999999999\n - type: recall_at_3\n value: 67.4\n - type: recall_at_5\n value: 70.89999999999999\n - task:\n type: Classification\n dataset:\n type: C-MTEB/MultilingualSentiment-classification\n name: MTEB MultilingualSentiment\n config: default\n split: validation\n revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a\n metrics:\n - type: accuracy\n value: 76.87666666666667\n - type: f1\n value: 76.7317686219665\n - task:\n type: PairClassification\n dataset:\n type: C-MTEB/OCNLI\n name: MTEB Ocnli\n config: default\n split: validation\n revision: 66e76a618a34d6d565d5538088562851e6daa7ec\n metrics:\n - type: cos_sim_accuracy\n value: 79.64266377910124\n - type: cos_sim_ap\n value: 84.78274442344829\n - type: cos_sim_f1\n value: 81.16947472745292\n - type: cos_sim_precision\n value: 76.47058823529412\n - type: cos_sim_recall\n value: 86.48363252375924\n - type: dot_accuracy\n value: 79.64266377910124\n - type: dot_ap\n value: 84.7851404063692\n - type: dot_f1\n value: 81.16947472745292\n - type: dot_precision\n value: 76.47058823529412\n - type: dot_recall\n value: 86.48363252375924\n - type: euclidean_accuracy\n value: 79.64266377910124\n - type: euclidean_ap\n value: 84.78068373762378\n - type: euclidean_f1\n value: 81.14794656110837\n - type: euclidean_precision\n value: 76.35009310986965\n - type: euclidean_recall\n value: 86.58922914466737\n - type: manhattan_accuracy\n value: 79.48023822414727\n - type: manhattan_ap\n value: 84.72928897427576\n - type: manhattan_f1\n value: 81.32084770823064\n - type: manhattan_precision\n value: 76.24768946395564\n - type: manhattan_recall\n value: 87.11721224920802\n - type: max_accuracy\n value: 79.64266377910124\n - type: max_ap\n value: 84.7851404063692\n - type: max_f1\n value: 81.32084770823064\n - task:\n type: Classification\n dataset:\n type: C-MTEB/OnlineShopping-classification\n name: MTEB OnlineShopping\n config: default\n split: test\n revision: e610f2ebd179a8fda30ae534c3878750a96db120\n metrics:\n - type: accuracy\n value: 94.3\n - type: ap\n value: 92.8664032274438\n - type: f1\n value: 94.29311102997727\n - task:\n type: STS\n dataset:\n type: C-MTEB/PAWSX\n name: MTEB PAWSX\n config: default\n split: test\n revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1\n metrics:\n - type: cos_sim_pearson\n value: 48.51392279882909\n - type: cos_sim_spearman\n value: 54.06338895994974\n - type: euclidean_pearson\n value: 52.58480559573412\n - type: euclidean_spearman\n value: 54.06417276612201\n - type: manhattan_pearson\n value: 52.69525121721343\n - type: manhattan_spearman\n value: 54.048147455389675\n - task:\n type: STS\n dataset:\n type: C-MTEB/QBQTC\n name: MTEB QBQTC\n config: default\n split: test\n revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7\n metrics:\n - type: cos_sim_pearson\n value: 29.728387290757325\n - type: cos_sim_spearman\n value: 31.366121633635284\n - type: euclidean_pearson\n value: 29.14588368552961\n - type: euclidean_spearman\n value: 31.36764411112844\n - type: manhattan_pearson\n value: 29.63517350523121\n - type: manhattan_spearman\n value: 31.94157020583762\n - task:\n type: STS\n dataset:\n type: mteb/sts22-crosslingual-sts\n name: MTEB STS22 (zh)\n config: zh\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 63.64868296271406\n - type: cos_sim_spearman\n value: 66.12800618164744\n - type: euclidean_pearson\n value: 63.21405767340238\n - type: euclidean_spearman\n value: 66.12786567790748\n - type: manhattan_pearson\n value: 64.04300276525848\n - type: manhattan_spearman\n value: 66.5066857145652\n - task:\n type: STS\n dataset:\n type: C-MTEB/STSB\n name: MTEB STSB\n config: default\n split: test\n revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0\n metrics:\n - type: cos_sim_pearson\n value: 81.2302623912794\n - type: cos_sim_spearman\n value: 81.16833673266562\n - type: euclidean_pearson\n value: 79.47647843876024\n - type: euclidean_spearman\n value: 81.16944349524972\n - type: manhattan_pearson\n value: 79.84947238492208\n - type: manhattan_spearman\n value: 81.64626599410026\n - task:\n type: Reranking\n dataset:\n type: C-MTEB/T2Reranking\n name: MTEB T2Reranking\n config: default\n split: dev\n revision: 76631901a18387f85eaa53e5450019b87ad58ef9\n metrics:\n - type: map\n value: 67.80129586475687\n - type: mrr\n value: 77.77402311635554\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/T2Retrieval\n name: MTEB T2Retrieval\n config: default\n split: dev\n revision: 8731a845f1bf500a4f111cf1070785c793d10e64\n metrics:\n - type: map_at_1\n value: 28.666999999999998\n - type: map_at_10\n value: 81.063\n - type: map_at_100\n value: 84.504\n - type: map_at_1000\n value: 84.552\n - type: map_at_3\n value: 56.897\n - type: map_at_5\n value: 70.073\n - type: mrr_at_1\n value: 92.087\n - type: mrr_at_10\n value: 94.132\n - type: mrr_at_100\n value: 94.19800000000001\n - type: mrr_at_1000\n value: 94.19999999999999\n - type: mrr_at_3\n value: 93.78999999999999\n - type: mrr_at_5\n value: 94.002\n - type: ndcg_at_1\n value: 92.087\n - type: ndcg_at_10\n value: 87.734\n - type: ndcg_at_100\n value: 90.736\n - type: ndcg_at_1000\n value: 91.184\n - type: ndcg_at_3\n value: 88.78\n - type: ndcg_at_5\n value: 87.676\n - type: precision_at_1\n value: 92.087\n - type: precision_at_10\n value: 43.46\n - type: precision_at_100\n value: 5.07\n - type: precision_at_1000\n value: 0.518\n - type: precision_at_3\n value: 77.49000000000001\n - type: precision_at_5\n value: 65.194\n - type: recall_at_1\n value: 28.666999999999998\n - type: recall_at_10\n value: 86.632\n - type: recall_at_100\n value: 96.646\n - type: recall_at_1000\n value: 98.917\n - type: recall_at_3\n value: 58.333999999999996\n - type: recall_at_5\n value: 72.974\n - task:\n type: Classification\n dataset:\n type: C-MTEB/TNews-classification\n name: MTEB TNews\n config: default\n split: validation\n revision: 317f262bf1e6126357bbe89e875451e4b0938fe4\n metrics:\n - type: accuracy\n value: 52.971999999999994\n - type: f1\n value: 50.2898280984929\n - task:\n type: Clustering\n dataset:\n type: C-MTEB/ThuNewsClusteringP2P\n name: MTEB ThuNewsClusteringP2P\n config: default\n split: test\n revision: 5798586b105c0434e4f0fe5e767abe619442cf93\n metrics:\n - type: v_measure\n value: 86.0797948663824\n - task:\n type: Clustering\n dataset:\n type: C-MTEB/ThuNewsClusteringS2S\n name: MTEB ThuNewsClusteringS2S\n config: default\n split: test\n revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d\n metrics:\n - type: v_measure\n value: 85.10759092255017\n - task:\n type: Retrieval\n dataset:\n type: C-MTEB/VideoRetrieval\n name: MTEB VideoRetrieval\n config: default\n split: dev\n revision: 58c2597a5943a2ba48f4668c3b90d796283c5639\n metrics:\n - type: map_at_1\n value: 65.60000000000001\n - type: map_at_10\n value: 74.773\n - type: map_at_100\n value: 75.128\n - type: map_at_1000\n value: 75.136\n - type: map_at_3\n value: 73.05\n - type: map_at_5\n value: 74.13499999999999\n - type: mrr_at_1\n value: 65.60000000000001\n - type: mrr_at_10\n value: 74.773\n - type: mrr_at_100\n value: 75.128\n - type: mrr_at_1000\n value: 75.136\n - type: mrr_at_3\n value: 73.05\n - type: mrr_at_5\n value: 74.13499999999999\n - type: ndcg_at_1\n value: 65.60000000000001\n - type: ndcg_at_10\n value: 78.84299999999999\n - type: ndcg_at_100\n value: 80.40899999999999\n - type: ndcg_at_1000\n value: 80.57\n - type: ndcg_at_3\n value: 75.40599999999999\n - type: ndcg_at_5\n value: 77.351\n - type: precision_at_1\n value: 65.60000000000001\n - type: precision_at_10\n value: 9.139999999999999\n - type: precision_at_100\n value: 0.984\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 27.400000000000002\n - type: precision_at_5\n value: 17.380000000000003\n - type: recall_at_1\n value: 65.60000000000001\n - type: recall_at_10\n value: 91.4\n - type: recall_at_100\n value: 98.4\n - type: recall_at_1000\n value: 99.6\n - type: recall_at_3\n value: 82.19999999999999\n - type: recall_at_5\n value: 86.9\n - task:\n type: Classification\n dataset:\n type: C-MTEB/waimai-classification\n name: MTEB Waimai\n config: default\n split: test\n revision: 339287def212450dcaa9df8c22bf93e9980c7023\n metrics:\n - type: accuracy\n value: 89.47\n - type: ap\n value: 75.59561751845389\n - type: f1\n value: 87.95207751382563\n---\n\n## gte-Qwen2-7B-instruct\n\n**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024).\n\nRecently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models.\n\nThe model incorporates several key advancements:\n\n- Integration of bidirectional attention mechanisms, enriching its contextual understanding.\n- Instruction tuning, applied solely on the query side for streamlined efficiency\n- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.\n\n\n## Model Information\n- Model Size: 7B\n- Embedding Dimension: 3584\n- Max Input Tokens: 32k\n\n## Requirements\n```\ntransformers>=4.39.2\nflash_attn>=2.5.6\n```\n## Usage \n\n### Sentence Transformers\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-7B-instruct\", trust_remote_code=True)\n# In case you want to reduce the maximum length:\nmodel.max_seq_length = 8192\n\nqueries = [\n \"how much protein should a female eat\",\n \"summit define\",\n]\ndocuments = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\",\n]\n\nquery_embeddings = model.encode(queries, prompt_name=\"query\")\ndocument_embeddings = model.encode(documents)\n\nscores = (query_embeddings @ document_embeddings.T) * 100\nprint(scores.tolist())\n```\n\nObserve the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt=\"Instruct: ...\\nQuery: \"` to use a custom prompt of your choice.\n\n### Transformers\n\n```python\nimport torch\nimport torch.nn.functional as F\n\nfrom torch import Tensor\nfrom transformers import AutoTokenizer, AutoModel\n\n\ndef last_token_pool(last_hidden_states: Tensor,\n attention_mask: Tensor) -> Tensor:\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\n\ndef get_detailed_instruct(task_description: str, query: str) -> str:\n return f'Instruct: {task_description}\\nQuery: {query}'\n\n\n# Each query must come with a one-sentence instruction that describes the task\ntask = 'Given a web search query, retrieve relevant passages that answer the query'\nqueries = [\n get_detailed_instruct(task, 'how much protein should a female eat'),\n get_detailed_instruct(task, 'summit define')\n]\n# No need to add instruction for retrieval documents\ndocuments = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n]\ninput_texts = queries + documents\n\ntokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)\nmodel = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)\n\nmax_length = 8192\n\n# Tokenize the input texts\nbatch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')\noutputs = model(**batch_dict)\nembeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])\n\n# normalize embeddings\nembeddings = F.normalize(embeddings, p=2, dim=1)\nscores = (embeddings[:2] @ embeddings[2:].T) * 100\nprint(scores.tolist())\n```\n\n## Evaluation\n\n### MTEB & C-MTEB\n\nYou can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese):\n\n| Model Name | MTEB(56) | C-MTEB(35) | \n|:----:|:---------:|:----------:| \n| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | \n| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - |\n| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - |\n| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - |\n| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - |\n| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | \n| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 |\n| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 |\n| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | \n| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | \n| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | \n| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 |\n| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - |\n| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** |\n\n### GTE Models\n\nThe gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture). \n\n| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |\n|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:|\n| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB |\n| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB |\n| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB |\n| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB |\n| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |\n| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |\n| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB |\n| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB |\n| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB |\n| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB |\n\n## Citation\n\nIf you find our paper or models helpful, please consider cite:\n\n```\n@article{li2023towards,\n title={Towards general text embeddings with multi-stage contrastive learning},\n author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},\n journal={arXiv preprint arXiv:2308.03281},\n year={2023}\n}\n```\n\n",
"related_quantizations": []
},
"tags": [
"gguf",
"arxiv:2308.03281",
"endpoints_compatible",
"region:us",
"conversational"
],
"likes": 6,
"downloads": 2000,
"gated": false,
"private": false,
"last_modified": "2024-06-22T15:22:37.000Z",
"created_at": "2024-06-22T13:20:44.000Z",
"pipeline_tag": "",
"library_name": ""
}
Source payload excerpt (from Hugging Face API)
{
"_id": "6676cfac1192e834e954795f",
"id": "RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf",
"modelId": "RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf",
"sha": "77ec93a57cf1abdac377196841239b823fb7c1c6",
"createdAt": "2024-06-22T13:20:44.000Z",
"lastModified": "2024-06-22T15:22:37.000Z",
"author": "RichardErkhov",
"downloads": 2000,
"likes": 6,
"gated": false,
"private": false,
"pipeline_tag": "",
"library_name": "",
"siblings_count": 24
}