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| [ | |
| { "name":"sentence-transformers/all-MiniLM-L6-v2", | |
| "model":"sentence-transformers/all-MiniLM-L6-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 3.8 million downloads from Huggingface", | |
| "sota_link":"https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":"True", | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/paraphrase-MiniLM-L6-v2", | |
| "model":"sentence-transformers/paraphrase-MiniLM-L6-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 2 million downloads from Huggingface", | |
| "sota_link":"https://huggingface.co./sentence-transformers/paraphrase-MiniLM-L6-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":"True", | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/bert-base-nli-mean-tokens", | |
| "model":"sentence-transformers/bert-base-nli-mean-tokens", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 700,000 downloads from Huggingface", | |
| "sota_link":"https://huggingface.co./sentence-transformers/bert-base-nli-mean-tokens" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":"True", | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/all-mpnet-base-v2", | |
| "model":"sentence-transformers/all-mpnet-base-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 500,000 downloads from Huggingface", | |
| "sota_link":"https://huggingface.co./sentence-transformers/all-mpnet-base-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":"True", | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/all-MiniLM-L12-v2", | |
| "model":"sentence-transformers/all-MiniLM-L12-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 500,000 downloads from Huggingface", | |
| "sota_link":"https://huggingface.co./sentence-transformers/all-MiniLM-L12-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":"True", | |
| "class":"HFModel"}, | |
| { "name":"SGPT-125M", | |
| "model":"Muennighoff/SGPT-125M-weightedmean-nli-bitfit", | |
| "fork_url":"https://github.com/taskswithcode/sgpt", | |
| "orig_author_url":"https://github.com/Muennighoff", | |
| "orig_author":"Niklas Muennighoff", | |
| "sota_info": { | |
| "task":"#1 in multiple information retrieval & search tasks(smaller variant)", | |
| "sota_link":"https://paperswithcode.com/paper/sgpt-gpt-sentence-embeddings-for-semantic" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2202.08904v5", | |
| "mark":"True", | |
| "class":"SGPTModel"}, | |
| { "name":"SIMCSE-base" , | |
| "model":"princeton-nlp/sup-simcse-roberta-base", | |
| "fork_url":"https://github.com/taskswithcode/SimCSE", | |
| "orig_author_url":"https://github.com/princeton-nlp", | |
| "orig_author":"Princeton Natural Language Processing", | |
| "sota_info": { | |
| "task":"Within top 10 in multiple semantic textual similarity tasks(smaller variant)", | |
| "sota_link":"https://paperswithcode.com/paper/simcse-simple-contrastive-learning-of" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2104.08821v4", | |
| "mark":"True", | |
| "class":"SimCSEModel","sota_link":"https://paperswithcode.com/sota/semantic-textual-similarity-on-sick"}, | |
| { "name":"GPT-3-175B (text-similarity-davinci-001)" , | |
| "model":"text-similarity-davinci-001", | |
| "fork_url":"https://openai.com/api/", | |
| "orig_author_url":"https://openai.com/api/", | |
| "orig_author":"OpenAI", | |
| "sota_info": { | |
| "task":"GPT-3 achieves strong zero-shot and few-shot performance on many NLP datasets etc.", | |
| "sota_link":"https://paperswithcode.com/method/gpt-3" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2005.14165v4", | |
| "mark":"True", | |
| "custom_load":"False", | |
| "Note":"Custom file upload requires OpenAI API access to create embeddings. For API access, use this link ", | |
| "alt_url":"https://openai.com/api/", | |
| "class":"OpenAIModel","sota_link":"https://arxiv.org/abs/2005.14165v4"}, | |
| { "name":"GPT-3-6.7B (text-similarity-curie-001)" , | |
| "model":"text-similarity-curie-001", | |
| "fork_url":"https://openai.com/api/", | |
| "orig_author_url":"https://openai.com/api/", | |
| "orig_author":"OpenAI", | |
| "sota_info": { | |
| "task":"GPT-3 achieves strong zero-shot and few-shot performance on many NLP datasets etc.", | |
| "sota_link":"https://paperswithcode.com/method/gpt-3" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2005.14165v4", | |
| "mark":"True", | |
| "custom_load":"False", | |
| "Note":"Custom file upload requires OpenAI API access to create embeddings. For API access, use this link ", | |
| "alt_url":"https://openai.com/api/", | |
| "class":"OpenAIModel","sota_link":"https://arxiv.org/abs/2005.14165v4"}, | |
| { "name":"GPT-3-1.3B (text-similarity-babbage-001)" , | |
| "model":"text-similarity-babbage-001", | |
| "fork_url":"https://openai.com/api/", | |
| "orig_author_url":"https://openai.com/api/", | |
| "orig_author":"OpenAI", | |
| "sota_info": { | |
| "task":"GPT-3 achieves strong zero-shot and few-shot performance on many NLP datasets etc.", | |
| "sota_link":"https://paperswithcode.com/method/gpt-3" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2005.14165v4", | |
| "mark":"True", | |
| "custom_load":"False", | |
| "Note":"Custom file upload requires OpenAI API access to create embeddings. For API access, use this link ", | |
| "alt_url":"https://openai.com/api/", | |
| "class":"OpenAIModel","sota_link":"https://arxiv.org/abs/2005.14165v4"}, | |
| { "name":"GPT-3-350M (text-similarity-ada-001)" , | |
| "model":"text-similarity-ada-001", | |
| "fork_url":"https://openai.com/api/", | |
| "orig_author_url":"https://openai.com/api/", | |
| "orig_author":"OpenAI", | |
| "sota_info": { | |
| "task":"GPT-3 achieves strong zero-shot and few-shot performance on many NLP datasets etc.", | |
| "sota_link":"https://paperswithcode.com/method/gpt-3" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2005.14165v4", | |
| "mark":"True", | |
| "custom_load":"False", | |
| "Note":"Custom file upload requires OpenAI API access to create embeddings. For API access, use this link ", | |
| "alt_url":"https://openai.com/api/", | |
| "class":"OpenAIModel","sota_link":"https://arxiv.org/abs/2005.14165v4"} | |
| ] | |