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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
feature-extraction | transformers |
# Margin-MSE Trained DistilBert for Dense Passage Retrieval
We provide a retrieval trained DistilBert-based model (we call the architecture BERT_Dot). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to **re-rank a... | {"language": "en", "tags": ["dpr", "dense-passage-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/distilbert-dot-margin_mse-T2-msmarco | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"dpr",
"dense-passage-retrieval",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2010.02666",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.02666"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #feature-extraction #dpr #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2010.02666 #endpoints_compatible #region-us
| Margin-MSE Trained DistilBert for Dense Passage Retrieval
=========================================================
We provide a retrieval trained DistilBert-based model (we call the architecture BERT\_Dot). Our model is trained with Margin-MSE using a 3 teacher BERT\_Cat (concatenated BERT scoring) ensemble on MSMAR... | [
"### MSMARCO-DEV",
"### TREC-DL'19\n\n\nFor MRR and Recall we use the recommended binarization point of the graded relevance of 2. This might skew the results when compared to other binarization point numbers.\n\n\n\nFor more baselines, info and analysis, please see the paper: URL\n\n\nLimitations & Bias\n-------... | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #dpr #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2010.02666 #endpoints_compatible #region-us \n",
"### MSMARCO-DEV",
"### TREC-DL'19\n\n\nFor MRR and Recall we use the recommended binarization point of the graded rel... |
feature-extraction | transformers |
# DistilBert for Dense Passage Retrieval trained with Balanced Topic Aware Sampling (TAS-B)
We provide a retrieval trained DistilBert-based model (we call the *dual-encoder then dot-product scoring* architecture BERT_Dot) trained with Balanced Topic Aware Sampling on MSMARCO-Passage.
This instance was trained w... | {"language": "en", "tags": ["dpr", "dense-passage-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"dpr",
"dense-passage-retrieval",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2104.06967",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06967"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #feature-extraction #dpr #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2104.06967 #endpoints_compatible #has_space #region-us
| DistilBert for Dense Passage Retrieval trained with Balanced Topic Aware Sampling (TAS-B)
=========================================================================================
We provide a retrieval trained DistilBert-based model (we call the *dual-encoder then dot-product scoring* architecture BERT\_Dot) trained... | [
"### MSMARCO-DEV (7K)",
"### TREC-DL'19\n\n\nFor MRR and Recall we use the recommended binarization point of the graded relevance of 2. This might skew the results when compared to other binarization point numbers.",
"### TREC-DL'20\n\n\nFor MRR and Recall we use the recommended binarization point of the graded... | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #dpr #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2104.06967 #endpoints_compatible #has_space #region-us \n",
"### MSMARCO-DEV (7K)",
"### TREC-DL'19\n\n\nFor MRR and Recall we use the recommended binarization point o... |
null | transformers |
# Intra-Document Cascading (IDCM)
We provide a retrieval trained IDCM model. Our model is trained on MSMARCO-Document with up to 2000 tokens.
This instance can be used to **re-rank a candidate set** of long documents. The base BERT architecure is a 6-layer DistilBERT.
If you want to know more about our intra... | {"language": "en", "tags": ["document-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/idcm-distilbert-msmarco_doc | null | [
"transformers",
"pytorch",
"IDCM",
"document-retrieval",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2105.09816",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.09816"
] | [
"en"
] | TAGS
#transformers #pytorch #IDCM #document-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2105.09816 #endpoints_compatible #region-us
| Intra-Document Cascading (IDCM)
===============================
We provide a retrieval trained IDCM model. Our model is trained on MSMARCO-Document with up to 2000 tokens.
This instance can be used to re-rank a candidate set of long documents. The base BERT architecure is a 6-layer DistilBERT.
If you want to know... | [
"### MSMARCO-Document-DEV\n\n\nMRR@10: BM25, NDCG@10: .252\nMRR@10: IDCM, NDCG@10: .380",
"### TREC-DL'19 (Document Task)\n\n\nFor MRR we use the recommended binarization point of the graded relevance of 2. This might skew the results when compared to other binarization point numbers.\n\n\nMRR@10: BM25, NDCG@10: ... | [
"TAGS\n#transformers #pytorch #IDCM #document-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2105.09816 #endpoints_compatible #region-us \n",
"### MSMARCO-Document-DEV\n\n\nMRR@10: BM25, NDCG@10: .252\nMRR@10: IDCM, NDCG@10: .380",
"### TREC-DL'19 (Document Task)\n\n\nFor MRR we use the recommen... |
null | transformers |
# Margin-MSE Trained PreTTR
We provide a retrieval trained DistilBert-based PreTTR model (https://arxiv.org/abs/2004.14255). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to **re-rank a candidate set**. The arch... | {"language": "en", "tags": ["knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/prettr-distilbert-split_at_3-margin_mse-T2-msmarco | null | [
"transformers",
"pytorch",
"distilbert",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2004.14255",
"arxiv:2010.02666",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.14255",
"2010.02666"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #knowledge-distillation #en #dataset-ms_marco #arxiv-2004.14255 #arxiv-2010.02666 #endpoints_compatible #region-us
| Margin-MSE Trained PreTTR
=========================
We provide a retrieval trained DistilBert-based PreTTR model (URL Our model is trained with Margin-MSE using a 3 teacher BERT\_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to re-rank a candidate set. The architecture is a ... | [
"### MSMARCO-DEV\n\n\nHere, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible)\n\n\nMRR@10: BM25, NDCG@10: .194\nMRR@10: Margin-MSE PreTTR (Re-ranking), NDCG@10: .386\n\n\nFor more metrics, baselines, info and analysis, please see the paper: URL\n\n\nLimitations & B... | [
"TAGS\n#transformers #pytorch #distilbert #knowledge-distillation #en #dataset-ms_marco #arxiv-2004.14255 #arxiv-2010.02666 #endpoints_compatible #region-us \n",
"### MSMARCO-DEV\n\n\nHere, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible)\n\n\nMRR@10: BM25, NDCG... |
automatic-speech-recognition | transformers | # wav2vec-lt-lite
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "lt", split="test[:2%]")
processor = Wav2Vec... | {"language": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]} | seccily/wav2vec-lt-lite | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"lt",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #lt #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| # wav2vec-lt-lite
## Usage
The model can be used directly (without a language model) as follows:
Test Result: 59.47 | [
"# wav2vec-lt-lite",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\nTest Result: 59.47"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #lt #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec-lt-lite",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\nTest Result: 59.47"
] |
text2text-generation | transformers | # Turkish-question-paraphrase-generator
mT5 based pre-trained model to generate question paraphrases in Turkish language.
## Acknowledgement
In this project, which we undertook as an BLM3010 Computer Project of Yildiz Technical University, our goal was to conduct research on Turkish in area that has not been studied ... | {} | secometo/mt5-base-turkish-question-paraphrase-generator | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Turkish-question-paraphrase-generator
mT5 based pre-trained model to generate question paraphrases in Turkish language.
## Acknowledgement
In this project, which we undertook as an BLM3010 Computer Project of Yildiz Technical University, our goal was to conduct research on Turkish in area that has not been studied ... | [
"# Turkish-question-paraphrase-generator\nmT5 based pre-trained model to generate question paraphrases in Turkish language.",
"## Acknowledgement\nIn this project, which we undertook as an BLM3010 Computer Project of Yildiz Technical University, our goal was to conduct research on Turkish in area that has not bee... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Turkish-question-paraphrase-generator\nmT5 based pre-trained model to generate question paraphrases in Turkish language.",
"## Acknowledgement\nIn this project, whic... |
translation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transfor... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | seduerr/pai-tl | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Google's T5
Pretraining Dataset: C4
Other Community Checkpoints: here
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfe... | [
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and pract... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a da... |
text2text-generation | transformers | ‘contrast: ‘ | {} | seduerr/pai_con | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ‘contrast: ‘ | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | input_ = paraphrase: + str(input_) + ' </s>'
| {} | seduerr/pai_paraph | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| input_ = paraphrase: + str(input_) + ' </s>'
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
zero-shot-classification | transformers |
# SqueezeBERT | {"language": "en", "tags": ["squeezebert"], "datasets": ["mulit_nli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | seduerr/paiintent | null | [
"transformers",
"pytorch",
"squeezebert",
"zero-shot-classification",
"en",
"dataset:mulit_nli",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #squeezebert #zero-shot-classification #en #dataset-mulit_nli #endpoints_compatible #region-us
|
# SqueezeBERT | [
"# SqueezeBERT"
] | [
"TAGS\n#transformers #pytorch #squeezebert #zero-shot-classification #en #dataset-mulit_nli #endpoints_compatible #region-us \n",
"# SqueezeBERT"
] |
text2text-generation | transformers | hello
hello
| {} | seduerr/sentiment | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
hello
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Invoking more Creativity with Pawraphrases based on T5
## This micro-service allows to find paraphrases for a given text based on T5.
We explain how we finetune the architecture T5 with the dataset PAWS (both from Google) to get the capability of creating paraphrases (or ... | {} | seduerr/t5-pawraphrase | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Invoking more Creativity with Pawraphrases based on T5
## This micro-service allows to find paraphrases for a given text based on T5.
!Imgur
We explain how we finetune the architecture T5 with the dataset PAWS (both from Google) to get the capability of creating paraphrases (or pawphrases since we are using the P... | [
"# Invoking more Creativity with Pawraphrases based on T5",
"## This micro-service allows to find paraphrases for a given text based on T5.\n\n!Imgur\n\nWe explain how we finetune the architecture T5 with the dataset PAWS (both from Google) to get the capability of creating paraphrases (or pawphrases since we are... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Invoking more Creativity with Pawraphrases based on T5",
"## This micro-service allows to find paraphrases for a given text based on T5.\n\n!Imgur\n\nWe explain how w... |
translation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transfor... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | seduerr/t5-small-pytorch | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Google's T5
Pretraining Dataset: C4
Other Community Checkpoints: here
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfe... | [
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and pract... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a da... |
text2text-generation | transformers | # T5 Base with Paraphrases in German Language
This T5 base model has been trained with the German part of the PAWS-X data set.
It can be used as any T5 model and will generated paraphrases with the prompt keyword: 'paraphrase: '__GermanSentence__
Please contact me, if you need more information (sduerr@mit.edu).
Tha... | {} | seduerr/t5_base_paws_ger | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5 Base with Paraphrases in German Language
This T5 base model has been trained with the German part of the PAWS-X data set.
It can be used as any T5 model and will generated paraphrases with the prompt keyword: 'paraphrase: '__GermanSentence__
Please contact me, if you need more information (sduerr@URL).
Thank y... | [
"# T5 Base with Paraphrases in German Language\n\nThis T5 base model has been trained with the German part of the PAWS-X data set. \nIt can be used as any T5 model and will generated paraphrases with the prompt keyword: 'paraphrase: '__GermanSentence__\n\nPlease contact me, if you need more information (sduerr@URL)... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5 Base with Paraphrases in German Language\n\nThis T5 base model has been trained with the German part of the PAWS-X data set. \nIt can be used as any T5 model and wil... |
text-classification | transformers |
# Election Fraud Binary Classifier
- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855
## Validation Metrics
- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.... | {"language": "en", "tags": "coe", "datasets": ["sefaozalpadl/autonlp-data-election_relevancy_analysis"], "widget": [{"text": "@PressSec Response to Putin is laughable. He has Biden's number. He knows Biden can't hold up in a live debate, and the Chinese did a number on the U.S. too. Biden is making US the laughing stoc... | sefaozalpadl/election_relevancy_best | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"coe",
"en",
"dataset:sefaozalpadl/autonlp-data-election_relevancy_analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-election_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Election Fraud Binary Classifier
- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855
## Validation Metrics
- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.... | [
"# Election Fraud Binary Classifier\n\n- Problem type: Binary Classification\n- Model ID: 23315155\n- CO2 Emissions (in grams): 1.3248523193990855",
"## Validation Metrics\n\n- Loss: 0.4240806996822357\n- Accuracy: 0.8173913043478261\n- Precision: 0.8837209302325582\n- Recall: 0.8085106382978723\n- AUC: 0.8882580... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-election_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Election Fraud Binary Classifier\n\n- Problem type: Binary Classification\n- Model ID: 23315155\n- CO... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831
## Validation Metrics
- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870... | {"language": "en", "tags": "coe", "datasets": ["sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis"], "widget": [{"text": "take our country back. Stop the steal! #trump2020"}], "co2_eq_emissions": 0.6503024714880831} | sefaozalpadl/stop_the_steal_relevancy_analysis-binary | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"coe",
"en",
"dataset:sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831
## Validation Metrics
- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 23995359\n- CO2 Emissions (in grams): 0.6503024714880831",
"## Validation Metrics\n\n- Loss: 0.49598395824432373\n- Accuracy: 0.7907801418439716\n- Precision: 0.7841726618705036\n- Recall: 0.7898550724637681\n- AUC: 0.87741545893... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 23995359\n- C... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# koelectra-long-qa
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monolo... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "koelectra-long-qa", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}}]}]} | sehandev/koelectra-long-qa | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# koelectra-long-qa
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# koelectra-long-qa\n\nThis model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# koelectra-long-qa\n\nThis model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intende... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# koelectra-qa
This model was trained from scratch on an unkown dataset.
## Model description
More information needed
## Intend... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "koelectra-qa", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}}]}]} | sehandev/koelectra-qa | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# koelectra-qa
This model was trained from scratch on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparamete... | [
"# koelectra-qa\n\nThis model was trained from scratch on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameter... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# koelectra-qa\n\nThis model was trained from scratch on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed"... |
text2text-generation | transformers | # Abstractive stage of PLSUM
Abstractive stage of the Multi-document Extractive Summarization (MDAS) model for portuguese, PLSUM. To goal here is to create Wikipedia-like summaries from multiple sentences extracted in the previous stage of PLSUM (the extractive stage) from websites (input and output in portuguese).
P... | {} | seidel/plsum-base-ptt5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Abstractive stage of PLSUM
Abstractive stage of the Multi-document Extractive Summarization (MDAS) model for portuguese, PLSUM. To goal here is to create Wikipedia-like summaries from multiple sentences extracted in the previous stage of PLSUM (the extractive stage) from websites (input and output in portuguese).
P... | [
"# Abstractive stage of PLSUM\n\nAbstractive stage of the Multi-document Extractive Summarization (MDAS) model for portuguese, PLSUM. To goal here is to create Wikipedia-like summaries from multiple sentences extracted in the previous stage of PLSUM (the extractive stage) from websites (input and output in portugue... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Abstractive stage of PLSUM\n\nAbstractive stage of the Multi-document Extractive Summarization (MDAS) model for portuguese, PLSUM. To goal here is to create ... |
null | transformers |
# ouBioBERT-Base, Uncased
Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT) is a language model based on the BERT-Base (Devlin, et al., 2019) architecture. We pre-trained ouBioBERT on PubMed abstracts from the PubMed baseline (ftp://ftp.ncbi.nlm.nih.gov... | {"license": "apache-2.0", "tags": ["exbert"]} | seiya/oubiobert-base-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"exbert",
"arxiv:2005.07202",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.07202"
] | [] | TAGS
#transformers #pytorch #jax #bert #pretraining #exbert #arxiv-2005.07202 #license-apache-2.0 #endpoints_compatible #region-us
| ouBioBERT-Base, Uncased
=======================
Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT) is a language model based on the BERT-Base (Devlin, et al., 2019) architecture. We pre-trained ouBioBERT on PubMed abstracts from the PubMed baseline (ftp:... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #pretraining #exbert #arxiv-2005.07202 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | BERT model finetuned for masked language modeling on generics dataset by freezing all the weights of pretrained BERT except the last layer. The aim is to investigate if the model will overgeneralize generics and treat quantified statements such as 'All ducks lay eggs', 'All tigers have stripes' as if these are generics... | {} | sello-ralethe/bert-base-frozen-generics-mlm | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| BERT model finetuned for masked language modeling on generics dataset by freezing all the weights of pretrained BERT except the last layer. The aim is to investigate if the model will overgeneralize generics and treat quantified statements such as 'All ducks lay eggs', 'All tigers have stripes' as if these are generics... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# LaBSE
This is a port of the [LaBSE](https://tfhub.dev/google/LaBSE/1) model to PyTorch. It can be used to map 109 languages to a shared vector space.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-... | {"language": ["multilingual", "af", "sq", "am", "ar", "hy", "as", "az", "eu", "be", "bn", "bs", "bg", "my", "ca", "ceb", "zh", "co", "hr", "cs", "da", "nl", "en", "eo", "et", "fi", "fr", "fy", "gl", "ka", "de", "el", "gu", "ht", "ha", "haw", "he", "hi", "hmn", "hu", "is", "ig", "id", "ga", "it", "ja", "jv", "kn", "kk",... | sentence-transformers/LaBSE | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"multilingual",
"af",
"sq",
"am",
"ar",
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"as",
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"eu",
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"bg",
"my",
"ca",
"ceb",
"zh",
"co",
"hr",
"cs",
"da",
"nl",
"en",
"eo",... | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"af",
"sq",
"am",
"ar",
"hy",
"as",
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"fr",
"fy",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"ha",
"haw",
"he",
"hi",
... | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #multilingual #af #sq #am #ar #hy #as #az #eu #be #bn #bs #bg #my #ca #ceb #zh #co #hr #cs #da #nl #en #eo #et #fi #fr #fy #gl #ka #de #el #gu #ht #ha #haw #he #hi #hmn #hu #is #ig #id #ga #it #ja #jv #kn #kk #km #rw #ko #ku #k... |
# LaBSE
This is a port of the LaBSE model to PyTorch. It can be used to map 109 languages to a shared vector space.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated ev... | [
"# LaBSE\nThis is a port of the LaBSE model to PyTorch. It can be used to map 109 languages to a shared vector space.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nF... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #multilingual #af #sq #am #ar #hy #as #az #eu #be #bn #bs #bg #my #ca #ceb #zh #co #hr #cs #da #nl #en #eo #et #fi #fr #fy #gl #ka #de #el #gu #ht #ha #haw #he #hi #hmn #hu #is #ig #id #ga #it #ja #jv #kn #kk #km #rw #ko ... |
sentence-similarity | sentence-transformers |
# all-MiniLM-L12-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers]... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/all-MiniLM-L12-v1 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| all-MiniLM-L12-v1
=================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have se... | [
"### Pre-training\n\n\nWe use the pretrained 'microsoft/MiniLM-L12-H384-uncased'. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possib... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'micro... |
sentence-similarity | sentence-transformers |
# all-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers]... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search... | sentence-transformers/all-MiniLM-L12-v2 | null | [
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"rust",
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"feature-extraction",
"sentence-similarity",
"transformers",
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"dataset:s2orc",
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"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"d... | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #rust #bert #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dataset-m... | all-MiniLM-L12-v2
=================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have se... | [
"### Pre-training\n\n\nWe use the pretrained 'microsoft/MiniLM-L12-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each ... | [
"TAGS\n#sentence-transformers #pytorch #rust #bert #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dat... |
sentence-similarity | sentence-transformers |
# all-MiniLM-L6-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/all-MiniLM-L6-v1 | null | [
"sentence-transformers",
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"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| all-MiniLM-L6-v1
================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have sent... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreim... |
sentence-similarity | sentence-transformers |
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search... | sentence-transformers/all-MiniLM-L6-v2 | null | [
"sentence-transformers",
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"feature-extraction",
"sentence-similarity",
"transformers",
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"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_ne... | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #rust #bert #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #datas... | all-MiniLM-L6-v2
================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have sent... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #pytorch #tf #rust #bert #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli ... |
sentence-similarity | sentence-transformers |
# all-distilroberta-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transforme... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search... | sentence-transformers/all-distilroberta-v1 | null | [
"sentence-transformers",
"pytorch",
"rust",
"roberta",
"fill-mask",
"feature-extraction",
"sentence-similarity",
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"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code... | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #rust #roberta #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-s... | all-distilroberta-v1
====================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you h... | [
"### Pre-training\n\n\nWe use the pretrained 'distilroberta-base'. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pai... | [
"TAGS\n#sentence-transformers #pytorch #rust #roberta #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dat... |
sentence-similarity | sentence-transformers |
# all-mpnet-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers]... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/all-mpnet-base-v1 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
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"feature-extraction",
"sentence-similarity",
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"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
... | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| all-mpnet-base-v1
=================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have se... | [
"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-base'. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence p... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretr... |
sentence-similarity | sentence-transformers |
# all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers]... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search... | sentence-transformers/all-mpnet-base-v2 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net"... | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dat... | all-mpnet-base-v2
=================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have se... | [
"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-base' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sent... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snl... |
sentence-similarity | sentence-transformers |
# all-roberta-large-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transform... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/all-roberta-large-v1 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
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"arxiv:1904.06472",
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"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
... | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #roberta #fill-mask #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| all-roberta-large-v1
====================
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you ... | [
"### Pre-training\n\n\nWe use the pretrained 'roberta-large'. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs fr... | [
"TAGS\n#sentence-transformers #pytorch #roberta #fill-mask #feature-extraction #sentence-similarity #transformers #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pre... |
sentence-similarity | sentence-transformers |
# allenai-specter
This model is a conversion of the [AllenAI SPECTER](https://github.com/allenai/specter) model to [sentence-transformers](https://www.SBERT.net). It can be used to map the titles & abstracts of scientific publications to a vector space such that similar papers are close.
## Usage (Sentence-Transfor... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/allenai-specter | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# allenai-specter
This model is a conversion of the AllenAI SPECTER model to sentence-transformers. It can be used to map the titles & abstracts of scientific publications to a vector space such that similar papers are close.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-tra... | [
"# allenai-specter\n\nThis model is a conversion of the AllenAI SPECTER model to sentence-transformers. It can be used to map the titles & abstracts of scientific publications to a vector space such that similar papers are close.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have s... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# allenai-specter\n\nThis model is a conversion of the AllenAI SPECTER model to sentence-transformers. It can be used to map the titles & ab... |
sentence-similarity | sentence-transformers |
# average_word_embeddings_glove.6B.300d
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have ... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/average_word_embeddings_glove.6B.300d | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# average_word_embeddings_glove.6B.300d
This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers ins... | [
"# average_word_embeddings_glove.6B.300d\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transf... | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# average_word_embeddings_glove.6B.300d\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space an... |
sentence-similarity | sentence-transformers |
# average_word_embeddings_glove.840B.300d
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you hav... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/average_word_embeddings_glove.840B.300d | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# average_word_embeddings_glove.840B.300d
This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers i... | [
"# average_word_embeddings_glove.840B.300d\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tran... | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# average_word_embeddings_glove.840B.300d\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be ... |
sentence-similarity | sentence-transformers |
# average_word_embeddings_komninos
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sent... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/average_word_embeddings_komninos | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# average_word_embeddings_komninos
This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installe... | [
"# average_word_embeddings_komninos\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformer... | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# average_word_embeddings_komninos\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can... |
sentence-similarity | sentence-transformers |
# average_word_embeddings_levy_dependency
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you hav... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/average_word_embeddings_levy_dependency | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# average_word_embeddings_levy_dependency
This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers i... | [
"# average_word_embeddings_levy_dependency\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tran... | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# average_word_embeddings_levy_dependency\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be ... |
sentence-similarity | sentence-transformers |
# bert-base-nli-cls-token
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
This is a [sentence-transformers](https://www.S... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-base-nli-cls-token | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-nli-cls-token
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space a... | [
"# bert-base-nli-cls-token\n\n️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-nli-cls-token\n\n️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-max-tokens
This is a [sentence-tran... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-base-nli-max-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-base-nli-max-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimension... | [
"# sentence-transformers/bert-base-nli-max-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have senten... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/bert-base-nli-max-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 7... |
sentence-similarity | sentence-transformers | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-mean-tokens
This is a [sentence-tran... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-base-nli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"rust",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #rust #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| ️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-base-nli-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimension... | [
"# sentence-transformers/bert-base-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sente... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #rust #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/bert-base-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences &... |
sentence-similarity | sentence-transformers | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-stsb-mean-tokens
This is a [sentence... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-base-nli-stsb-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
| ️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-base-nli-stsb-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dime... | [
"# sentence-transformers/bert-base-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have ... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/bert-base-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-wikipedia-sections-mean-tokens
This is... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-base-wikipedia-sections-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-base-wikipedia-sections-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs t... | [
"# sentence-transformers/bert-base-wikipedia-sections-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/bert-base-wikipedia-sections-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragr... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-large-nli-cls-token
This is a [sentence-tra... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-large-nli-cls-token | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-large-nli-cls-token
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensi... | [
"# sentence-transformers/bert-large-nli-cls-token\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sente... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/bert-large-nli-cls-token\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 d... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-large-nli-max-tokens
This is a [sentence-tr... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-large-nli-max-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-large-nli-max-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimens... | [
"# sentence-transformers/bert-large-nli-max-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sent... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/bert-large-nli-max-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-large-nli-mean-tokens
This is a [sentence-t... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-large-nli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-large-nli-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimen... | [
"# sentence-transformers/bert-large-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sen... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/bert-large-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-large-nli-stsb-mean-tokens
This is a [sente... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/bert-large-nli-stsb-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/bert-large-nli-stsb-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 ... | [
"# sentence-transformers/bert-large-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you hav... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/bert-large-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a... |
sentence-similarity | sentence-transformers |
# sentence-transformers/clip-ViT-B-32-multilingual-v1
This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model. You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close. This model can be used for **image search** (users search through a l... | {"language": "multilingual", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/clip-ViT-B-32-multilingual-v1 | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"multilingual",
"arxiv:2004.09813",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.09813",
"1908.10084"
] | [
"multilingual"
] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #multilingual #arxiv-2004.09813 #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/clip-ViT-B-32-multilingual-v1
This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model. You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close. This model can be used for image search (users search through a large... | [
"# sentence-transformers/clip-ViT-B-32-multilingual-v1\n\nThis is a multi-lingual version of the OpenAI CLIP-ViT-B32 model. You can map text (in 50+ languages) and images to a common dense vector space such that images and the matching texts are close. This model can be used for image search (users search through a... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #multilingual #arxiv-2004.09813 #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/clip-ViT-B-32-multilingual-v1\n\nThis is a multi-lingual version of the Op... |
sentence-similarity | sentence-transformers |
# clip-ViT-B-32
This is the Image & Text model [CLIP](https://arxiv.org/abs/2103.00020), which maps text and images to a shared vector space. For applications of the models, have a look in our documentation [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html)
## Usage
Aft... | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/clip-ViT-B-32 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:2103.00020",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.00020"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-2103.00020 #endpoints_compatible #has_space #region-us
| clip-ViT-B-32
=============
This is the Image & Text model CLIP, which maps text and images to a shared vector space. For applications of the models, have a look in our documentation URL - Image Search
Usage
-----
After installing sentence-transformers ('pip install sentence-transformers'), the usage of this mode... | [] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-2103.00020 #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/distilbert-base-nli-max-tokens
This is a [senten... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | sentence-transformers/distilbert-base-nli-max-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/distilbert-base-nli-max-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 di... | [
"# sentence-transformers/distilbert-base-nli-max-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have ... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/distilbert-base-nli-max-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs... |
feature-extraction | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/distilbert-base-nli-mean-tokens
This is a [sente... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | sentence-transformers/distilbert-base-nli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/distilbert-base-nli-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 d... | [
"# sentence-transformers/distilbert-base-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/distilbert-base-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/distilbert-base-nli-stsb-mean-tokens
This is a [... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distilbert-base-nli-stsb-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/distilbert-base-nli-stsb-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a ... | [
"# sentence-transformers/distilbert-base-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/distilbert-base-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sente... |
sentence-similarity | sentence-transformers |
# sentence-transformers/distilbert-base-nli-stsb-quora-ranking
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model beco... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distilbert-base-nli-stsb-quora-ranking | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/distilbert-base-nli-stsb-quora-ranking
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have se... | [
"# sentence-transformers/distilbert-base-nli-stsb-quora-ranking\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when y... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/distilbert-base-nli-stsb-quora-ranking\n\nThis is a sentence-transformers model: It maps sentences & pa... |
sentence-similarity | sentence-transformers |
# sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this mo... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you... | [
"# sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes eas... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking\n\nThis is a sentence-transformers model: It maps senten... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/distilroberta-base-msmarco-v1
This is a [sentenc... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distilroberta-base-msmarco-v1 | null | [
"sentence-transformers",
"pytorch",
"tf",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/distilroberta-base-msmarco-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dim... | [
"# sentence-transformers/distilroberta-base-msmarco-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have s... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/distilroberta-base-msmarco-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/distilroberta-base-msmarco-v2
This is a [sentenc... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distilroberta-base-msmarco-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/distilroberta-base-msmarco-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dim... | [
"# sentence-transformers/distilroberta-base-msmarco-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have s... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/distilroberta-base-msmarco-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to ... |
sentence-similarity | sentence-transformers |
# sentence-transformers/distilroberta-base-paraphrase-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes ea... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distilroberta-base-paraphrase-v1 | null | [
"sentence-transformers",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/distilroberta-base-paraphrase-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence... | [
"# sentence-transformers/distilroberta-base-paraphrase-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you hav... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/distilroberta-base-paraphrase-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs ... |
sentence-similarity | sentence-transformers |
# sentence-transformers/distiluse-base-multilingual-cased-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model become... | {"language": ["multilingual", "ar", "zh", "nl", "en", "fr", "de", "it", "ko", "pl", "pt", "ru", "es", "tr"], "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distiluse-base-multilingual-cased-v1 | null | [
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"license:apache-2.0",
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"has_spac... | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
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"zh",
"nl",
"en",
"fr",
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"it",
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"es",
"tr"
] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #multilingual #ar #zh #nl #en #fr #de #it #ko #pl #pt #ru #es #tr #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/distiluse-base-multilingual-cased-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sent... | [
"# sentence-transformers/distiluse-base-multilingual-cased-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #multilingual #ar #zh #nl #en #fr #de #it #ko #pl #pt #ru #es #tr #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/distiluse-base-multilingual-cased-v1\n\n... |
sentence-similarity | sentence-transformers |
# sentence-transformers/distiluse-base-multilingual-cased-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model become... | {"language": ["multilingual", "ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "ko", "ku", "lt", "lv", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "th", "tr", "uk", "ur", "v... | sentence-transformers/distiluse-base-multilingual-cased-v2 | null | [
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"... | null | 2022-03-02T23:29:05+00:00 | [
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"mr",
"ms",
"my",
"nb",
"nl",
"pl",
"pt",
"r... | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #multilingual #ar #bg #ca #cs #da #de #el #en #es #et #fa #fi #fr #gl #gu #he #hi #hr #hu #hy #id #it #ja #ka #ko #ku #lt #lv #mk #mn #mr #ms #my #nb #nl #pl #pt #ro #ru #sk #sl #sq #sr #sv #th #tr #uk #ur #vi #arxiv-1908.1008... |
# sentence-transformers/distiluse-base-multilingual-cased-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sent... | [
"# sentence-transformers/distiluse-base-multilingual-cased-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #multilingual #ar #bg #ca #cs #da #de #el #en #es #et #fa #fi #fr #gl #gu #he #hi #hr #hu #hy #id #it #ja #ka #ko #ku #lt #lv #mk #mn #mr #ms #my #nb #nl #pl #pt #ro #ru #sk #sl #sq #sr #sv #th #tr #uk #ur #vi #arxiv-190... |
sentence-similarity | sentence-transformers |
# sentence-transformers/distiluse-base-multilingual-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes e... | {"language": "multilingual", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/distiluse-base-multilingual-cased | null | [
"sentence-transformers",
"pytorch",
"tf",
"rust",
"distilbert",
"feature-extraction",
"sentence-similarity",
"multilingual",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"multilingual"
] | TAGS
#sentence-transformers #pytorch #tf #rust #distilbert #feature-extraction #sentence-similarity #multilingual #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/distiluse-base-multilingual-cased
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentenc... | [
"# sentence-transformers/distiluse-base-multilingual-cased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you ha... | [
"TAGS\n#sentence-transformers #pytorch #tf #rust #distilbert #feature-extraction #sentence-similarity #multilingual #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/distiluse-base-multilingual-cased\n\nThis is a sentence-transformers model: It maps se... |
sentence-similarity | sentence-transformers |
# sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semanti... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/facebook-dpr-ctx_encoder-multiset-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
This is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model beco... | [
"# sentence-transformers/facebook-dpr-ctx_encoder-multiset-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this ... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/facebook-dpr-ctx_encoder-multiset-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentences & p... |
sentence-similarity | sentence-transformers |
# sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semant... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base
This is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model bec... | [
"# sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentences & para... |
sentence-similarity | sentence-transformers |
# sentence-transformers/facebook-dpr-question_encoder-multiset-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/facebook-dpr-question_encoder-multiset-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/facebook-dpr-question_encoder-multiset-base
This is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model... | [
"# sentence-transformers/facebook-dpr-question_encoder-multiset-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing ... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/facebook-dpr-question_encoder-multiset-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentence... |
sentence-similarity | sentence-transformers |
# sentence-transformers/facebook-dpr-question_encoder-single-nq-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or s... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/facebook-dpr-question_encoder-single-nq-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/facebook-dpr-question_encoder-single-nq-base
This is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this mode... | [
"# sentence-transformers/facebook-dpr-question_encoder-single-nq-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/facebook-dpr-question_encoder-single-nq-base\n\nThis is a port of the DPR Model to sentence-transformers model: It maps sentenc... |
sentence-similarity | sentence-transformers |
# sentence-transformers/gtr-t5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-base-1](https://tfhub.... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/gtr-t5-base | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:2112.07899",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.07899"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/gtr-t5-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model gtr-base-1 to PyTorch. When using this model, have a... | [
"# sentence-transformers/gtr-t5-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.\n\nThis model was converted from the Tensorflow model gtr-base-1 to PyTorch. When using this model... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/gtr-t5-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense ve... |
sentence-similarity | sentence-transformers |
# sentence-transformers/gtr-t5-large
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-large-1](https://tfhu... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/gtr-t5-large | null | [
"sentence-transformers",
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"sentence-similarity",
"en",
"arxiv:2112.07899",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.07899"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/gtr-t5-large
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model gtr-large-1 to PyTorch. When using this model, have... | [
"# sentence-transformers/gtr-t5-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.\n\nThis model was converted from the Tensorflow model gtr-large-1 to PyTorch. When using this mod... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/gtr-t5-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense v... |
sentence-similarity | sentence-transformers |
# sentence-transformers/gtr-t5-xl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-xl-1](https://tfhub.dev/... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/gtr-t5-xl | null | [
"sentence-transformers",
"pytorch",
"t5",
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"sentence-similarity",
"en",
"arxiv:2112.07899",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.07899"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/gtr-t5-xl
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model gtr-xl-1 to PyTorch. When using this model, have a loo... | [
"# sentence-transformers/gtr-t5-xl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.\n\nThis model was converted from the Tensorflow model gtr-xl-1 to PyTorch. When using this model, ha... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/gtr-t5-xl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vect... |
sentence-similarity | sentence-transformers |
# sentence-transformers/gtr-t5-xxl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-xxl-1](https://tfhub.de... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/gtr-t5-xxl | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:2112.07899",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.07899"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/gtr-t5-xxl
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model gtr-xxl-1 to PyTorch. When using this model, have a l... | [
"# sentence-transformers/gtr-t5-xxl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.\n\nThis model was converted from the Tensorflow model gtr-xxl-1 to PyTorch. When using this model, ... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/gtr-t5-xxl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vec... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-MiniLM-L-12-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when yo... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-MiniLM-L-12-v3 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-MiniLM-L-12-v3
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transform... | [
"# sentence-transformers/msmarco-MiniLM-L-12-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-MiniLM-L-12-v3\n\nThis is a sentence-transformers model: It maps sentences & paragrap... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-MiniLM-L-6-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-MiniLM-L-6-v3 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-MiniLM-L-6-v3
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# sentence-transformers/msmarco-MiniLM-L-6-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-MiniLM-L-6-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraph... |
sentence-similarity | sentence-transformers |
# msmarco-MiniLM-L12-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsof... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-MiniLM-L12-cos-v5 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #region-us
| msmarco-MiniLM-L12-cos-v5
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 500k (query, answer) pairs from the MS MARCO Passages dataset. For an introduction to semantic ... | [] | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# msmarco-MiniLM-L6-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-MiniLM-L6-cos-v5 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #region-us
| msmarco-MiniLM-L6-cos-v5
========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 500k (query, answer) pairs from the MS MARCO Passages dataset. For an introduction to semantic se... | [] | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# msmarco-bert-base-dot-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500K (query, answer) pairs from the [MS MARCO dataset](https://github.com/microsoft/MSMARCO-... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-bert-base-dot-v5 | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #has_space #region-us
| msmarco-bert-base-dot-v5
========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 500K (query, answer) pairs from the MS MARCO dataset. For an introduction to semantic search, hav... | [] | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-bert-co-condensor
This is a port of the [Luyu/co-condenser-marco-retriever](https://huggingface.co/Luyu/co-condenser-marco-retriever) model to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized f... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-bert-co-condensor | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2108.05540",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.05540"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2108.05540 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| sentence-transformers/msmarco-bert-co-condensor
===============================================
This is a port of the Luyu/co-condenser-marco-retriever model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.
It is... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2108.05540 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-base-dot-prod-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-base-dot-prod-v3 | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-distilbert-base-dot-prod-v3
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sente... | [
"# sentence-transformers/msmarco-distilbert-base-dot-prod-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you ... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-distilbert-base-dot-prod-v3\n\nThis is a sentence-transformers model: It maps sentences & paragrap... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-base-tas-b
This is a port of the [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is op... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["ms_marco"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-base-tas-b | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:ms_marco",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #en #dataset-ms_marco #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-distilbert-base-tas-b
This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy... | [
"# sentence-transformers/msmarco-distilbert-base-tas-b\n\nThis is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model be... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #en #dataset-ms_marco #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-distilbert-base-tas-b\n\nThis is a port of the DistilBert TAS-B Model to sentenc... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/msmarco-distilbert-base-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-trans... | [
"# sentence-transformers/msmarco-distilbert-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sent... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/msmarco-distilbert-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to ... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-base-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-base-v3 | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-distilbert-base-v3
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-trans... | [
"# sentence-transformers/msmarco-distilbert-base-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sent... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-distilbert-base-v3\n\nThis is a sentence-transformers model: It maps sentences & par... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-base-v4
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-base-v4 | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-distilbert-base-v4
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-trans... | [
"# sentence-transformers/msmarco-distilbert-base-v4\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sent... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-distilbert-base-v4\n\nThis is a sentence-transformers model: It maps sentences & par... |
sentence-similarity | sentence-transformers |
# msmarco-distilbert-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsof... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-cos-v5 | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #has_space #region-us
| msmarco-distilbert-cos-v5
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 500k (query, answer) pairs from the MS MARCO Passages dataset. For an introduction to semantic ... | [] | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# msmarco-distilbert-dot-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500K (query, answer) pairs from the [MS MARCO dataset](https://github.com/microsoft/MSMARCO... | {"language": ["en"], "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-dot-v5 | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| msmarco-distilbert-dot-v5
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 500K (query, answer) pairs from the MS MARCO dataset. For an introduction to semantic search, h... | [] | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #en #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Usi... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes eas... | [
"# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model b... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned\n\nThis is a sentence-transformers model: It m... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes... | [
"# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this mod... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch\n\nThis is a sentence-transformers model: ... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-distilroberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy ... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-distilroberta-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/msmarco-distilroberta-base-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-tr... | [
"# sentence-transformers/msmarco-distilroberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have s... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/msmarco-distilroberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraph... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-roberta-base-ance-firstp
This is a port of the [ANCE FirstP Model](https://github.com/microsoft/ANCE/) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic s... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-roberta-base-ance-firstp | null | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/msmarco-roberta-base-ance-firstp
This is a port of the ANCE FirstP Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model be... | [
"# sentence-transformers/msmarco-roberta-base-ance-firstp\n\nThis is a port of the ANCE FirstP Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing thi... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/msmarco-roberta-base-ance-firstp\n\nThis is a port of the ANCE FirstP Model to sentence-transformers model: It maps sentences & paragraphs... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-roberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-roberta-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/msmarco-roberta-base-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transfor... | [
"# sentence-transformers/msmarco-roberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentenc... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/msmarco-roberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a... |
sentence-similarity | sentence-transformers |
# sentence-transformers/msmarco-roberta-base-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/msmarco-roberta-base-v3 | null | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-roberta-base-v3
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transfor... | [
"# sentence-transformers/msmarco-roberta-base-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentenc... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-roberta-base-v3\n\nThis is a sentence-transformers model: It maps sentences & paragraph... |
sentence-similarity | sentence-transformers |
# multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search,... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "search_qa", "eli5", "natural_questions", "trivia_qa", "em... | sentence-transformers/multi-qa-MiniLM-L6-cos-v1 | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:na... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #en #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-search_qa #dataset-eli5 #dataset-natural_questions #dataset-trivia_qa #dataset-embedding-d... | multi-qa-MiniLM-L6-cos-v1
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, hav... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, ... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #en #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-search_qa #dataset-eli5 #dataset-natural_questions #dataset-trivia_qa #dataset-embed... |
sentence-similarity | sentence-transformers |
# multi-qa-MiniLM-L6-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search,... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/multi-qa-MiniLM-L6-dot-v1 | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #has_space #region-us
| multi-qa-MiniLM-L6-dot-v1
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, hav... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, ... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the ... |
sentence-similarity | sentence-transformers |
# multi-qa-distilbert-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "search_qa", "eli5", "natural_questions", "trivia_qa", "em... | sentence-transformers/multi-qa-distilbert-cos-v1 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #distilbert #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-search_qa #dataset-eli5 #dataset-natural_questions #dataset-trivia_qa #datase... | multi-qa-distilbert-cos-v1
==========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, h... | [
"### Pre-training\n\n\nWe use the pretrained 'distilbert-base-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) ... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-search_qa #dataset-eli5 #dataset-natural_questions #dataset-trivia_qa #... |
sentence-similarity | sentence-transformers |
# multi-qa-distilbert-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/multi-qa-distilbert-dot-v1 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #distilbert #fill-mask #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #has_space #region-us
| multi-qa-distilbert-dot-v1
==========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, h... | [
"### Pre-training\n\n\nWe use the pretrained 'distilbert-base-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) ... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #fill-mask #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'distilbert-base-uncased' model. Please refer to the model card for more detailed information about... |
sentence-similarity | sentence-transformers |
# multi-qa-mpnet-base-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/multi-qa-mpnet-base-cos-v1 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #has_space #region-us
| multi-qa-mpnet-base-cos-v1
==========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, h... | [
"### Pre-training\n\n\nWe use the pretrained 'mpnet-base' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.\nWe sa... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'mpnet-base' model. Please refer to the model card for more detailed information about the pre-training ... |
sentence-similarity | sentence-transformers |
# multi-qa-mpnet-base-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search... | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "search_qa", "eli5", "natural_questions", "trivia_qa", "em... | sentence-transformers/multi-qa-mpnet-base-dot-v1 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"da... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-search_qa #dataset-eli5 #dataset-natural_questions #dataset-trivia_qa #dataset-emb... | multi-qa-mpnet-base-dot-v1
==========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, h... | [
"### Pre-training\n\n\nWe use the pretrained 'mpnet-base' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.\nWe sa... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #fill-mask #feature-extraction #sentence-similarity #transformers #en #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-search_qa #dataset-eli5 #dataset-natural_questions #dataset-trivia_qa #datas... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-bert-base-cls-pooling
This is a [sentence-tr... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-bert-base-cls-pooling | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-bert-base-cls-pooling
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensi... | [
"# sentence-transformers/nli-bert-base-cls-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sente... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-bert-base-cls-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimen... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-bert-base-max-pooling
This is a [sentence-tr... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-bert-base-max-pooling | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-bert-base-max-pooling
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensi... | [
"# sentence-transformers/nli-bert-base-max-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sente... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-bert-base-max-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimen... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-bert-base
This is a [sentence-transformers](... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-bert-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-bert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense v... | [
"# sentence-transformers/nli-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transfor... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional d... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-bert-large-cls-pooling
This is a [sentence-t... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-bert-large-cls-pooling | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-bert-large-cls-pooling
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimen... | [
"# sentence-transformers/nli-bert-large-cls-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sen... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-bert-large-cls-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-bert-large-max-pooling
This is a [sentence-t... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-bert-large-max-pooling | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-bert-large-max-pooling
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimen... | [
"# sentence-transformers/nli-bert-large-max-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sen... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-bert-large-max-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-bert-large
This is a [sentence-transformers]... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-bert-large | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-bert-large
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense... | [
"# sentence-transformers/nli-bert-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transf... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-bert-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional den... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-distilbert-base-max-pooling
This is a [sente... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-distilbert-base-max-pooling | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-distilbert-base-max-pooling
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 d... | [
"# sentence-transformers/nli-distilbert-base-max-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-distilbert-base-max-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraph... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-distilbert-base
This is a [sentence-transfor... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-distilbert-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-distilbert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional d... | [
"# sentence-transformers/nli-distilbert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tr... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-distilbert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 d... |
sentence-similarity | sentence-transformers |
# sentence-transformers/nli-distilroberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-distilroberta-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/nli-distilroberta-base-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transf... | [
"# sentence-transformers/nli-distilroberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sente... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-distilroberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to... |
sentence-similarity | sentence-transformers |
# sentence-transformers/nli-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you hav... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-mpnet-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #mpnet #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/nli-mpnet-base-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers i... | [
"# sentence-transformers/nli-mpnet-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tran... | [
"TAGS\n#sentence-transformers #pytorch #tf #mpnet #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/nli-mpnet-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 7... |
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