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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
audio-classification | transformers |
# Wav2Vec2-Large for Emotion Recognition
## Model description
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Emotion Recognition task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/emotion).
The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), wh... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "IEMOCAP clip \"happy\"", "src": "https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav"}, {"example_title": "IEMOCAP clip \"neutral... | superb/wav2vec2-large-superb-er | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Large for Emotion Recognition
======================================
Model description
-----------------
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Emotion Recognition task.
The base model is wav2vec2-large-lv60, which is pretrained on 16kHz
sampled speech audio. When using the model mak... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Wav2Vec2-Large for Intent Classification
## Model description
This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands).
The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-larg... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2"], "datasets": ["superb"]} | superb/wav2vec2-large-superb-ic | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Large for Intent Classification
========================================
Model description
-----------------
This is a ported version of S3PRL's Wav2Vec2 for the SUPERB Intent Classification task.
The base model is wav2vec2-large-lv60, which is pretrained on 16kHz
sampled speech audio. When using the mod... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Wav2Vec2-Large for Keyword Spotting
## Model description
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands).
The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), ... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "Speech Commands \"down\"", "src": "https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav"}, {"example_title": "Speech Commands \"go\"", "... | superb/wav2vec2-large-superb-ks | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Large for Keyword Spotting
===================================
Model description
-----------------
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Keyword Spotting task.
The base model is wav2vec2-large-lv60, which is pretrained on 16kHz
sampled speech audio. When using the model make sure th... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Wav2Vec2-Large for Speaker Identification
## Model description
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1).
The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-l... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb Speaker id10004", "src"... | superb/wav2vec2-large-superb-sid | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large for Speaker Identification
=========================================
Model description
-----------------
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task.
The base model is wav2vec2-large-lv60, which is pretrained on 16kHz
sampled speech audio. When using the ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
question-answering | null |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | superb-test-user/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"pytorch",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#pytorch #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
token-classification | transformers | just to test | {} | superman/testingmodel | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| just to test | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers | # Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Distilbert_Base fine-tuned on SQuAD2.0 and custom QA dataset
This model is [twmkn9/distilbert-base-uncased-squad2] trained on additional custom dataset as:
```
!python3 run_squad.py --model_type distilbert \
--model_name_or_pat... | {} | superspray/distilbert_base_squad2_custom_dataset | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us
| # Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Distilbert_Base fine-tuned on SQuAD2.0 and custom QA dataset
This model is [twmkn9/distilbert-base-uncased-squad2] trained on additional custom dataset as:
We used Google Colab for training the model, | [
"# Question & Answering Model for 'Save Your Minutes' from Dobby-AI\nDistilbert_Base fine-tuned on SQuAD2.0 and custom QA dataset\n\nThis model is [twmkn9/distilbert-base-uncased-squad2] trained on additional custom dataset as:\n\nWe used Google Colab for training the model,"
] | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us \n",
"# Question & Answering Model for 'Save Your Minutes' from Dobby-AI\nDistilbert_Base fine-tuned on SQuAD2.0 and custom QA dataset\n\nThis model is [twmkn9/distilbert-base-uncased-squad2] trained on additional custo... |
question-answering | transformers | # Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA dataset
This model is [ahotrod/electra_large_discriminator_squad2_512](https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512/blob/main/README.md)
trained on additional ... | {} | superspray/electra_large_discriminator_squad2_custom_dataset | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us
| # Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA dataset
This model is ahotrod/electra_large_discriminator_squad2_512
trained on additional custom dataset as:
We used Google Colab for training the model, | [
"# Question & Answering Model for 'Save Your Minutes' from Dobby-AI \n\nElectra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA dataset\n\nThis model is ahotrod/electra_large_discriminator_squad2_512\n trained on additional custom dataset as:\n \n\t\t\t\t\t\t\nWe used Google Colab for training the model,"
... | [
"TAGS\n#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us \n",
"# Question & Answering Model for 'Save Your Minutes' from Dobby-AI \n\nElectra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA dataset\n\nThis model is ahotrod/electra_large_discriminator_squad2_512\n traine... |
fill-mask | transformers |
# RoBERTa-hindi-guj-san
## Model description
Multillingual RoBERTa like model trained on Wikipedia articles of Hindi, Sanskrit, Gujarati languages. The tokenizer was trained on combined text.
However, Hindi text was used to pre-train the model and then it was fine-tuned on Sanskrit and Gujarati Text combined hoping... | {"language": ["hi", "sa", "gu"], "license": "mit", "tags": ["Indic"], "datasets": ["Wikipedia (Hindi, Sanskrit, Gujarati)"], "metrics": ["perplexity"]} | surajp/RoBERTa-hindi-guj-san | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"Indic",
"hi",
"sa",
"gu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"sa",
"gu"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #Indic #hi #sa #gu #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| RoBERTa-hindi-guj-san
=====================
Model description
-----------------
Multillingual RoBERTa like model trained on Wikipedia articles of Hindi, Sanskrit, Gujarati languages. The tokenizer was trained on combined text.
However, Hindi text was used to pre-train the model and then it was fine-tuned on Sanskri... | [
"### Configuration\n\n\n\nIntended uses & limitations\n---------------------------",
"#### How to use\n\n\nTraining data\n-------------\n\n\nCleaned wikipedia articles in Hindi, Sanskrit and Gujarati on Kaggle. It contains training as well as evaluation text.\nUsed in iNLTK\n\n\n* Hindi\n* Gujarati\n* Sanskrit\n\... | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #Indic #hi #sa #gu #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Configuration\n\n\n\nIntended uses & limitations\n---------------------------",
"#### How to use\n\n\nTraining data\n-------------\n\n\n... |
fill-mask | transformers |
# RoBERTa trained on Sanskrit (SanBERTa)
**Mode size** (after training): **340MB**
### Dataset:
[Wikipedia articles](https://www.kaggle.com/disisbig/sanskrit-wikipedia-articles) (used in [iNLTK](https://github.com/goru001/nlp-for-sanskrit)).
It contains evaluation set.
[Sanskrit scraps from CLTK](http://cltk.org/)... | {"language": "sa"} | surajp/SanBERTa | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"sa",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sa"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #sa #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa trained on Sanskrit (SanBERTa)
======================================
Mode size (after training): 340MB
### Dataset:
Wikipedia articles (used in iNLTK).
It contains evaluation set.
Sanskrit scraps from CLTK
### Configuration
### Training :
* On TPU
* For language modelling
* Iteratively increasin... | [
"### Dataset:\n\n\nWikipedia articles (used in iNLTK).\nIt contains evaluation set.\n\n\nSanskrit scraps from CLTK",
"### Configuration",
"### Training :\n\n\n* On TPU\n* For language modelling\n* Iteratively increasing '--block\\_size' from 128 to 256 over epochs",
"### Evaluation\n\n\n\nExample of usage:\n-... | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #sa #autotrain_compatible #endpoints_compatible #region-us \n",
"### Dataset:\n\n\nWikipedia articles (used in iNLTK).\nIt contains evaluation set.\n\n\nSanskrit scraps from CLTK",
"### Configuration",
"### Training :\n\n\n* On TPU\n* For lan... |
feature-extraction | transformers |
# ALBERT-base-Sanskrit
Explaination Notebook Colab: [SanskritALBERT.ipynb](https://colab.research.google.com/github/parmarsuraj99/suraj-parmar/blob/master/_notebooks/2020-05-02-SanskritALBERT.ipynb)
Size of the model is **46MB**
Example of usage:
```
tokenizer = AutoTokenizer.from_pretrained("surajp/albert-base-... | {"language": "sa"} | surajp/albert-base-sanskrit | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"feature-extraction",
"sa",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sa"
] | TAGS
#transformers #pytorch #safetensors #albert #feature-extraction #sa #endpoints_compatible #region-us
|
# ALBERT-base-Sanskrit
Explaination Notebook Colab: URL
Size of the model is 46MB
Example of usage:
> Created by Suraj Parmar/@parmarsuraj99
> Made with <span style="color: #e25555;">♥</span> in India
| [
"# ALBERT-base-Sanskrit\n\n\nExplaination Notebook Colab: URL\n\nSize of the model is 46MB\n\nExample of usage:\n\n\n\n\n\n> Created by Suraj Parmar/@parmarsuraj99\n\n> Made with <span style=\"color: #e25555;\">♥</span> in India"
] | [
"TAGS\n#transformers #pytorch #safetensors #albert #feature-extraction #sa #endpoints_compatible #region-us \n",
"# ALBERT-base-Sanskrit\n\n\nExplaination Notebook Colab: URL\n\nSize of the model is 46MB\n\nExample of usage:\n\n\n\n\n\n> Created by Suraj Parmar/@parmarsuraj99\n\n> Made with <span style=\"color: #... |
text-classification | transformers | # SMS Classifier
Finetuned 'distilbert-large' model for classifying SMS messages. Look at SMS dataset in this hub for your own version. | {} | sureshs/distilbert-large-sms-spam | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # SMS Classifier
Finetuned 'distilbert-large' model for classifying SMS messages. Look at SMS dataset in this hub for your own version. | [
"# SMS Classifier\n\nFinetuned 'distilbert-large' model for classifying SMS messages. Look at SMS dataset in this hub for your own version."
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# SMS Classifier\n\nFinetuned 'distilbert-large' model for classifying SMS messages. Look at SMS dataset in this hub for your own version."
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_abbreviation_detection_roberta_lar` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `transformer`, `abbreviationDetection` |
| **Components** | `transformer`, `abbreviationDetection` |
| **Vectors** | 0 keys, 0 unique vectors... | {"language": ["en"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons."}, {"text": "RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1."}, {"text": "Images were acquired usi... | surrey-nlp/en_abbreviation_detection_roberta_lar | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (3 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] |
question-answering | transformers |
# Swedish BERT Fine-tuned on SQuAD v2
This model is a fine-tuning checkpoint of Swedish BERT on SQuAD v2.
## Training data
Fine-tuning was done based on the pre-trained model [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased).
Training and dev datasets are our
[Swedish translation of S... | {"language": ["sv"], "license": "apache-2.0", "tags": ["squad"], "datasets": ["susumu2357/squad_v2_sv"], "metrics": ["squad_v2"]} | susumu2357/bert-base-swedish-squad2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"squad",
"sv",
"dataset:susumu2357/squad_v2_sv",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #squad #sv #dataset-susumu2357/squad_v2_sv #license-apache-2.0 #endpoints_compatible #region-us
|
# Swedish BERT Fine-tuned on SQuAD v2
This model is a fine-tuning checkpoint of Swedish BERT on SQuAD v2.
## Training data
Fine-tuning was done based on the pre-trained model KB/bert-base-swedish-cased.
Training and dev datasets are our
Swedish translation of SQuAD v2.
Here is the HuggingFace Datasets.
## Hype... | [
"# Swedish BERT Fine-tuned on SQuAD v2\n\nThis model is a fine-tuning checkpoint of Swedish BERT on SQuAD v2.",
"## Training data\n\nFine-tuning was done based on the pre-trained model KB/bert-base-swedish-cased.\n\nTraining and dev datasets are our\nSwedish translation of SQuAD v2. \n\nHere is the HuggingFace Da... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #squad #sv #dataset-susumu2357/squad_v2_sv #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Swedish BERT Fine-tuned on SQuAD v2\n\nThis model is a fine-tuning checkpoint of Swedish BERT on SQuAD v2.",
"## Training data\n\nFine-tuning w... |
token-classification | 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. -->
# BERT_NER_Ep5-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "BERT_NER_Ep5-finetuned-ner", "results": []}]} | suwani/BERT_NER_Ep5-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_NER\_Ep5-finetuned-ner
============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3553
* Precision: 0.6526
* Recall: 0.7248
* F1: 0.6868
* Accuracy: 0.9004
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | 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. -->
# BERT_NER_Ep5_PAD_50-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "BERT_NER_Ep5_PAD_50-finetuned-ner", "results": []}]} | suwani/BERT_NER_Ep5_PAD_50-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_NER\_Ep5\_PAD\_50-finetuned-ner
=====================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3893
* Precision: 0.6540
* Recall: 0.7348
* F1: 0.6920
* Accuracy: 0.9006
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | 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. -->
# BERT_NER_Ep5_PAD_75-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "BERT_NER_Ep5_PAD_75-finetuned-ner", "results": []}]} | suwani/BERT_NER_Ep5_PAD_75-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_NER\_Ep5\_PAD\_75-finetuned-ner
=====================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3504
* Precision: 0.6469
* Recall: 0.7246
* F1: 0.6835
* Accuracy: 0.9013
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | 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. -->
# BERT_NER_Ep6_PAD_50-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "BERT_NER_Ep6_PAD_50-finetuned-ner", "results": []}]} | suwani/BERT_NER_Ep6_PAD_50-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_NER\_Ep6\_PAD\_50-finetuned-ner
=====================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3741
* Precision: 0.6510
* Recall: 0.7399
* F1: 0.6926
* Accuracy: 0.9020
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | suwani/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2787
* Precision: 0.6403
* Recall: 0.6929
* F1: 0.6655
* Accuracy: 0.9100
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
token-classification | 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. -->
# try_connll-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "try_connll-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "typ... | suwani/try_connll-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| try\_connll-finetuned-ner
=========================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0596
* Precision: 0.9283
* Recall: 0.9372
* F1: 0.9328
* Accuracy: 0.9841
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | suzuki/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2962
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text-generation | 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. -->
# gpt2-finetuned-nft-shakes-seuss-2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": []} | sv/gpt2-finetuned-nft-shakes-seuss-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-finetuned-nft-shakes-seuss-2
=================================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9547
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text-generation | 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. -->
# gpt2-finetuned-nft-shakes-seuss
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
I... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": []} | sv/gpt2-finetuned-nft-shakes-seuss | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-finetuned-nft-shakes-seuss
===============================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8505
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text-generation | 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. -->
# gpt2-finetuned-nft-shakes
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achi... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": []} | sv/gpt2-finetuned-nft-shakes | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-finetuned-nft-shakes
=========================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7566
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text-generation | 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. -->
# gpt2-nft-poetry
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the f... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": []} | sv/gpt2-nft-poetry | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-nft-poetry
===============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0243
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
feature-extraction | transformers | # SVALabs - German Uncased Electra Bi-Encoder
In this repository, we present our german, uncased bi-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the [german-nlp-group](https://huggingface.co/german-nlp-group/electra-base-german-uncased) and finetuned as a... | {} | svalabs/bi-electra-ms-marco-german-uncased | null | [
"transformers",
"pytorch",
"electra",
"feature-extraction",
"arxiv:1908.10084",
"arxiv:1611.09268",
"arxiv:2104.08663",
"arxiv:2104.12741",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084",
"1611.09268",
"2104.08663",
"2104.12741"
] | [] | TAGS
#transformers #pytorch #electra #feature-extraction #arxiv-1908.10084 #arxiv-1611.09268 #arxiv-2104.08663 #arxiv-2104.12741 #endpoints_compatible #region-us
| SVALabs - German Uncased Electra Bi-Encoder
===========================================
In this repository, we present our german, uncased bi-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the german-nlp-group and finetuned as a bi-encoder for Passage Ret... | [
"### Model Details",
"### Performance\n\n\nWe evaluated our model on the GermanDPR testset and followed the benchmark framework of BEIR.\nIn order to compare our results, we conducted an evaluation on the same test data with BM25 and presented the results in the table below.\nWe took every paragraph with negative... | [
"TAGS\n#transformers #pytorch #electra #feature-extraction #arxiv-1908.10084 #arxiv-1611.09268 #arxiv-2104.08663 #arxiv-2104.12741 #endpoints_compatible #region-us \n",
"### Model Details",
"### Performance\n\n\nWe evaluated our model on the GermanDPR testset and followed the benchmark framework of BEIR.\nIn or... |
text-classification | transformers | # SVALabs - German Uncased Electra Cross-Encoder
In this repository, we present our german, uncased cross-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the [german-nlp-group](https://huggingface.co/german-nlp-group/electra-base-german-uncased) and finetune... | {} | svalabs/cross-electra-ms-marco-german-uncased | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"arxiv:1908.10084",
"arxiv:1611.09268",
"arxiv:2104.08663",
"arxiv:2104.12741",
"arxiv:2010.02666",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084",
"1611.09268",
"2104.08663",
"2104.12741",
"2010.02666"
] | [] | TAGS
#transformers #pytorch #electra #text-classification #arxiv-1908.10084 #arxiv-1611.09268 #arxiv-2104.08663 #arxiv-2104.12741 #arxiv-2010.02666 #autotrain_compatible #endpoints_compatible #region-us
| SVALabs - German Uncased Electra Cross-Encoder
==============================================
In this repository, we present our german, uncased cross-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the german-nlp-group and finetuned as a cross-encoder for... | [
"### Model Details",
"### Performance\n\n\nWe evaluated our model on the GermanDPR testset and followed the benchmark framework of BEIR.\nIn order to compare our results, we conducted an evaluation on the same test data with BM25 and presented the results in the table below.\nWe took every paragraph with negative... | [
"TAGS\n#transformers #pytorch #electra #text-classification #arxiv-1908.10084 #arxiv-1611.09268 #arxiv-2104.08663 #arxiv-2104.12741 #arxiv-2010.02666 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model Details",
"### Performance\n\n\nWe evaluated our model on the GermanDPR testset and followe... |
zero-shot-classification | transformers |
# SVALabs - Gbert Large Zeroshot Nli
In this repository, we present our German zeroshot classification model.
This model was trained on the basis of the German BERT large model from [deepset.ai](https://huggingface.co/deepset/gbert-large) and finetuned for natural language inference based on 847.862 machine-tran... | {"language": "German", "tags": ["text-classification", "pytorch", "nli", "de"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Ich habe ein Problem mit meinem Iphone das so schnell wie m\u00f6glich gel\u00f6st werden muss.", "candidate_labels": "Computer, Handy, Tablet, dringend, nicht dringend", "hyp... | svalabs/gbert-large-zeroshot-nli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"nli",
"de",
"zero-shot-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"German"
] | TAGS
#transformers #pytorch #bert #text-classification #nli #de #zero-shot-classification #autotrain_compatible #endpoints_compatible #region-us
| SVALabs - Gbert Large Zeroshot Nli
==================================
In this repository, we present our German zeroshot classification model.
This model was trained on the basis of the German BERT large model from URL and finetuned for natural language inference based on 847.862 machine-translated nli sentence pai... | [
"### Model Details",
"### Performance\n\n\nWe evaluated our model for the nli task using the TEST set of the German part of the xnli dataset.\n\n\nXNLI TEST-Set Accuracy: 85.6%",
"### Zeroshot Text Classification Task Benchmark\n\n\nWe further tested our model for a zeroshot text classification task using a par... | [
"TAGS\n#transformers #pytorch #bert #text-classification #nli #de #zero-shot-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model Details",
"### Performance\n\n\nWe evaluated our model for the nli task using the TEST set of the German part of the xnli dataset.\n\n\nXNLI TEST-Set... |
token-classification | 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. -->
# XLMR-ENIS-finetuned-conll_ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-EN... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-conll_ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_n... | svanhvit/XLMR-ENIS-finetuned-conll_ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-conll\_ner
==============================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0713
* Precision: 0.8755
* Recall: 0.8426
* F1: 0.8587
* Accuracy: 0.9861
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
token-classification | 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. -->
# XLMR-ENIS-finetuned-ner-finetuned-conll_ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS-finetuned-ner](https://hug... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner-finetuned-conll_ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name... | svanhvit/XLMR-ENIS-finetuned-ner-finetuned-conll_ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner-finetuned-conll\_ner
============================================
This model is a fine-tuned version of vesteinn/XLMR-ENIS-finetuned-ner on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0770
* Precision: 0.8720
* Recall: 0.8430
* F1: 0.8573
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
feature-extraction | transformers | # MBG-ClinicalBERT
A model based on ClinicalBERT and additionally pre-trained on Bulgarian medical and clinical texts.
ClinicalBERT - https://github.com/EmilyAlsentzer/clinicalBERT
## Model Details
* Model type: BERT-based model
* Languages(s): Bulgarian
* Domain: Clinical texts
* Description: Model based on Clinic... | {"language": ["bg"], "license": "afl-3.0"} | svassileva/mbg-clinicalbert | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"bg",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bg"
] | TAGS
#transformers #pytorch #bert #feature-extraction #bg #license-afl-3.0 #endpoints_compatible #region-us
| # MBG-ClinicalBERT
A model based on ClinicalBERT and additionally pre-trained on Bulgarian medical and clinical texts.
ClinicalBERT - URL
## Model Details
* Model type: BERT-based model
* Languages(s): Bulgarian
* Domain: Clinical texts
* Description: Model based on ClinicalBERT and additionally pre-trained on Bulg... | [
"# MBG-ClinicalBERT\n\nA model based on ClinicalBERT and additionally pre-trained on Bulgarian medical and clinical texts.\n\nClinicalBERT - URL",
"## Model Details\n\n* Model type: BERT-based model\n* Languages(s): Bulgarian\n* Domain: Clinical texts\n* Description: Model based on ClinicalBERT and additionally p... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #bg #license-afl-3.0 #endpoints_compatible #region-us \n",
"# MBG-ClinicalBERT\n\nA model based on ClinicalBERT and additionally pre-trained on Bulgarian medical and clinical texts.\n\nClinicalBERT - URL",
"## Model Details\n\n* Model type: BERT-based mode... |
token-classification | transformers |
### Model and entities
`roberta_classics_ner` is a domain-specific RoBERTa-based model for named entity recognition in Classical Studies. It recognises bibliographical entities, such as:
| id | label | desciption | Example |
| --- | ------------- | ------------... | {"language": ["en"], "tags": ["classics", "citation mining"], "widget": [{"text": "Homer's Iliad opens with an invocation to the muse (1. 1)."}]} | sven-nm/roberta_classics_ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"classics",
"citation mining",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #classics #citation mining #en #autotrain_compatible #endpoints_compatible #region-us
| ### Model and entities
'roberta\_classics\_ner' is a domain-specific RoBERTa-based model for named entity recognition in Classical Studies. It recognises bibliographical entities, such as:
### Example
### Dataset
'roberta\_classics\_ner' was fine-tuned and evaluated on 'EpiBau', a dataset which has not been re... | [
"### Model and entities\n\n\n'roberta\\_classics\\_ner' is a domain-specific RoBERTa-based model for named entity recognition in Classical Studies. It recognises bibliographical entities, such as:",
"### Example",
"### Dataset\n\n\n'roberta\\_classics\\_ner' was fine-tuned and evaluated on 'EpiBau', a dataset w... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #classics #citation mining #en #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model and entities\n\n\n'roberta\\_classics\\_ner' is a domain-specific RoBERTa-based model for named entity recognition in Classical Studies. It recognises ... |
text2text-generation | 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. -->
# opus-mt-ru-en-finetuned-en-to-ru
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "opus-mt-ru-en-finetuned-en-to-ru", "results": []}]} | svsokol/opus-mt-ru-en-finetuned-en-to-ru | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-ru-en-finetuned-en-to-ru
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on the wmt16 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# opus-mt-ru-en-finetuned-en-to-ru\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on the wmt16 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-ru-en-finetuned-en-to-ru\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on the wmt16 dataset.",
"## M... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `sw005320/Shinji_Watanabe_laborotv_asr_train_blstm`
This model was trained by Shinji Watanabe using laborotv recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 9963fc53747c26417023546d3449e92884f13be0
pip install -e .
cd e... | {"language": "jp", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["laborotv"]} | sw005320/Shinji_Watanabe_laborotv_asr_train_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"jp",
"dataset:laborotv",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"jp"
] | TAGS
#espnet #audio #automatic-speech-recognition #jp #dataset-laborotv #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'sw005320/Shinji\_Watanabe\_laborotv\_asr\_train\_blstm'
This model was trained by Shinji Watanabe using laborotv recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri May 14 08:32:17 EDT 2021'
* python version: '3.8.... | [
"### 'sw005320/Shinji\\_Watanabe\\_laborotv\\_asr\\_train\\_blstm'\n\n\nThis model was trained by Shinji Watanabe using laborotv recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri May 14 08:32:17 EDT 2021'\n* python version: '3.8.5 (default... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #jp #dataset-laborotv #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'sw005320/Shinji\\_Watanabe\\_laborotv\\_asr\\_train\\_blstm'\n\n\nThis model was trained by Shinji Watanabe using laborotv recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `sw005320/aidatatang_200zh_conformer`
This model was trained by Shinji Watanabe using aidatatang_200zh recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 8ab3d9f2191f250cb62deff222d2e6addb3842dc
pip install -e .
cd egs2/ai... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aidatatang_200zh"]} | sw005320/aidatatang_200zh_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aidatatang_200zh",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aidatatang_200zh #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'sw005320/aidatatang\_200zh\_conformer'
This model was trained by Shinji Watanabe using aidatatang\_200zh recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri Dec 24 23:34:58 EST 2021'
* python version: '3.8.5 (defau... | [
"### 'sw005320/aidatatang\\_200zh\\_conformer'\n\n\nThis model was trained by Shinji Watanabe using aidatatang\\_200zh recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri Dec 24 23:34:58 EST 2021'\n* python version: '3.8.5 (default, Sep 4 20... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aidatatang_200zh #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'sw005320/aidatatang\\_200zh\\_conformer'\n\n\nThis model was trained by Shinji Watanabe using aidatatang\\_200zh recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\n... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | swapnil165/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
text-generation | transformers | pipeline_tag:conversational | {} | swapnil2911/DialoGPT-small-arya | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| pipeline_tag:conversational | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | pipeline_tag: conversational | {} | swapnil2911/DialoGPT-test-arya | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| pipeline_tag: conversational | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | pipeline_tag: conversational | {} | swapnil2911/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| pipeline_tag: conversational | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers |
# King Jamesify
This seq2seq model is my first experiment for "translating" modern English to the famous KJV Bible style.
The model is based on Google's "T5 Efficient Base" model. It was fine-tuned for 3 epochs on a NET to KJV dataset. | {"language": "en", "license": "apache-2.0", "tags": ["Bible", "KJV"]} | swcrazyfan/KingJamesify-T5-Base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Bible",
"KJV",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #Bible #KJV #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# King Jamesify
This seq2seq model is my first experiment for "translating" modern English to the famous KJV Bible style.
The model is based on Google's "T5 Efficient Base" model. It was fine-tuned for 3 epochs on a NET to KJV dataset. | [
"# King Jamesify\r\nThis seq2seq model is my first experiment for \"translating\" modern English to the famous KJV Bible style.\r\n\r\nThe model is based on Google's \"T5 Efficient Base\" model. It was fine-tuned for 3 epochs on a NET to KJV dataset."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #Bible #KJV #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# King Jamesify\r\nThis seq2seq model is my first experiment for \"translating\" modern English to the famous KJV Bible style.\r\n\r\n... |
text2text-generation | transformers | This model was fine-tuned to “translate” any English text into 17th-century style English.
The name comes from the dataset used for fine-tuning. Namely, modern Bible text as input and and the famous King James Bible as the output.
To test, use “kingify: “ at the beginning of anything you want to translate.
Gen... | {"license": "apache-2.0"} | swcrazyfan/KingJamesify-T5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"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 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model was fine-tuned to “translate” any English text into 17th-century style English.
The name comes from the dataset used for fine-tuning. Namely, modern Bible text as input and and the famous King James Bible as the output.
To test, use “kingify: “ at the beginning of anything you want to translate.
Gen... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | null | BERT Implication | {} | sybae/BertPractice | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| BERT Implication | [] | [
"TAGS\n#region-us \n"
] |
text-classification | 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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | sylviachency/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9155
* Matthews Correlation: 0.5235
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
zero-shot-classification | transformers |
A cross-attention NLI model trained for zero-shot and few-shot text classification.
The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base), trained with the code from [here](https://github.com/facebookresearch/anli);
on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.... | {"language": ["en"], "tags": ["zero-shot-classification"], "datasets": ["SNLI", "MNLI"]} | symanto/mpnet-base-snli-mnli | null | [
"transformers",
"pytorch",
"safetensors",
"mpnet",
"text-classification",
"zero-shot-classification",
"en",
"dataset:SNLI",
"dataset:MNLI",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #mpnet #text-classification #zero-shot-classification #en #dataset-SNLI #dataset-MNLI #autotrain_compatible #endpoints_compatible #region-us
|
A cross-attention NLI model trained for zero-shot and few-shot text classification.
The base model is mpnet-base, trained with the code from here;
on SNLI and MNLI.
Usage:
| [] | [
"TAGS\n#transformers #pytorch #safetensors #mpnet #text-classification #zero-shot-classification #en #dataset-SNLI #dataset-MNLI #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
A Siamese network model trained for zero-shot and few-shot text classification.
The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base).
It was trained on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
This is a [sentence-transformers](https:/... | {"language": ["en"], "tags": ["zero-shot-classification", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["SNLI", "MNLI"], "pipeline_tag": "sentence-similarity"} | symanto/sn-mpnet-base-snli-mnli | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"zero-shot-classification",
"sentence-similarity",
"transformers",
"en",
"dataset:SNLI",
"dataset:MNLI",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #zero-shot-classification #sentence-similarity #transformers #en #dataset-SNLI #dataset-MNLI #endpoints_compatible #has_space #region-us
|
A Siamese network model trained for zero-shot and few-shot text classification.
The base model is mpnet-base.
It was trained on SNLI and MNLI.
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space.
## Usage (Sentence-Transformers)
Using this model becomes eas... | [
"## 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:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer ... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #zero-shot-classification #sentence-similarity #transformers #en #dataset-SNLI #dataset-MNLI #endpoints_compatible #has_space #region-us \n",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers ins... |
sentence-similarity | sentence-transformers |
A Siamese network model trained for zero-shot and few-shot text classification.
The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base).
It was trained on [SNLI](https://nlp.stanford.edu/projects/snli/), [MNLI](https://cims.nyu.edu/~sbowman/multinli/), [ANLI](https://github.com/facebookresearch/... | {"language": ["ar", "bg", "de", "el", "en", "es", "fr", "ru", "th", "tr", "ur", "vn", "zh"], "tags": ["zero-shot-classification", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["SNLI", "MNLI", "ANLI", "XNLI"], "pipeline_tag": "sentence-similarity"} | symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli | null | [
"sentence-transformers",
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"xlm-roberta",
"feature-extraction",
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"dataset:ANLI",
"dat... | null | 2022-03-02T23:29:05+00:00 | [] | [
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"zh"
] | TAGS
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|
A Siamese network model trained for zero-shot and few-shot text classification.
The base model is xlm-roberta-base.
It was trained on SNLI, MNLI, ANLI and XNLI.
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space.
## Usage (Sentence-Transformers)
Using this... | [
"## 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:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer ... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #zero-shot-classification #sentence-similarity #transformers #ar #bg #de #el #en #es #fr #ru #th #tr #ur #vn #zh #dataset-SNLI #dataset-MNLI #dataset-ANLI #dataset-XNLI #endpoints_compatible #has_space #region-us \n",
"## Usage (Sentence-Tran... |
zero-shot-classification | transformers |
A cross-attention NLI model trained for zero-shot and few-shot text classification.
The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), trained with the code from [here](https://github.com/facebookresearch/anli);
on [SNLI](https://nlp.stanford.edu/projects/snli/), [MNLI](https://cims.nyu.e... | {"language": ["ar", "bg", "de", "el", "en", "es", "fr", "ru", "th", "tr", "ur", "vn", "zh", "multilingual"], "tags": ["zero-shot-classification"], "datasets": ["SNLI", "MNLI", "ANLI", "XNLI"]} | symanto/xlm-roberta-base-snli-mnli-anli-xnli | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"zero-shot-classification",
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"ru",
"th",
"tr",
"ur",
"vn",
"zh",
"multilingual",
"dataset:SNLI",
"dataset:MNLI",
"dataset:ANLI",
"dataset:XNLI",
"autotrain_compatib... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"ru",
"th",
"tr",
"ur",
"vn",
"zh",
"multilingual"
] | TAGS
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|
A cross-attention NLI model trained for zero-shot and few-shot text classification.
The base model is xlm-roberta-base, trained with the code from here;
on SNLI, MNLI, ANLI and XNLI.
Usage:
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #zero-shot-classification #ar #bg #de #el #en #es #fr #ru #th #tr #ur #vn #zh #multilingual #dataset-SNLI #dataset-MNLI #dataset-ANLI #dataset-XNLI #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# checkpoint-500-finetuned-squad
This model was trained from scratch on the squad dataset.
## Model description
More information... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "checkpoint-500-finetuned-squad", "results": []}]} | tabo/checkpoint-500-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# checkpoint-500-finetuned-squad
This model was trained from scratch on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# checkpoint-500-finetuned-squad\n\nThis model was trained from scratch on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"# checkpoint-500-finetuned-squad\n\nThis model was trained from scratch on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intend... |
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. -->
# distilbert-base-uncased-finetuned-squad2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad2", "results": []}]} | tabo/distilbert-base-uncased-finetuned-squad2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad2
========================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1606
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
token-classification | flair |
## Slovene Part-of-speech (PoS) Tagging for Flair
This is a Slovene part-of-speech (PoS) tagger trained on the [Slovenian UD Treebank](https://github.com/UniversalDependencies/UD_Slovenian-SSJ) using Flair NLP framework.
The tagger is trained using a combination of forward Slovene contextual string embeddings, backw... | {"language": "sl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Danes je lep dan."}]} | tadejmagajna/flair-sl-pos | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"sl",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sl"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #sl #region-us
|
## Slovene Part-of-speech (PoS) Tagging for Flair
This is a Slovene part-of-speech (PoS) tagger trained on the Slovenian UD Treebank using Flair NLP framework.
The tagger is trained using a combination of forward Slovene contextual string embeddings, backward Slovene contextual string embeddings and classic Slovene ... | [
"## Slovene Part-of-speech (PoS) Tagging for Flair\n\nThis is a Slovene part-of-speech (PoS) tagger trained on the Slovenian UD Treebank using Flair NLP framework.\n\nThe tagger is trained using a combination of forward Slovene contextual string embeddings, backward Slovene contextual string embeddings and classic ... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #sl #region-us \n",
"## Slovene Part-of-speech (PoS) Tagging for Flair\n\nThis is a Slovene part-of-speech (PoS) tagger trained on the Slovenian UD Treebank using Flair NLP framework.\n\nThe tagger is trained using a combination of forward Sloven... |
text-generation | transformers | # KoGPT2-Transformers
KoGPT2 on Huggingface Transformers
### KoGPT2-Transformers
- [SKT-AI 에서 공개한 KoGPT2 (ver 1.0)](https://github.com/SKT-AI/KoGPT2)를 [Transformers](https://github.com/huggingface/transformers)에서 사용하도록 하였습니다.
- **SKT-AI 에서 KoGPT2 2.0을 공개하였습니다. https://huggingface.co/skt/kogpt2-base-v2/**
### Demo... | {} | taeminlee/kogpt2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # KoGPT2-Transformers
KoGPT2 on Huggingface Transformers
### KoGPT2-Transformers
- SKT-AI 에서 공개한 KoGPT2 (ver 1.0)를 Transformers에서 사용하도록 하였습니다.
- SKT-AI 에서 KoGPT2 2.0을 공개하였습니다. URL
### Demo
- 일상 대화 챗봇 : URL:36200/dialo
- 화장품 리뷰 생성 : URL:36200/ctrl
### Example
| [
"# KoGPT2-Transformers\n\nKoGPT2 on Huggingface Transformers",
"### KoGPT2-Transformers\n\n- SKT-AI 에서 공개한 KoGPT2 (ver 1.0)를 Transformers에서 사용하도록 하였습니다.\n\n- SKT-AI 에서 KoGPT2 2.0을 공개하였습니다. URL",
"### Demo\n\n- 일상 대화 챗봇 : URL:36200/dialo\n- 화장품 리뷰 생성 : URL:36200/ctrl",
"### Example"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# KoGPT2-Transformers\n\nKoGPT2 on Huggingface Transformers",
"### KoGPT2-Transformers\n\n- SKT-AI 에서 공개한 KoGPT2 (ver 1.0)를 Transformers에서 사용하... |
text-classification | 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. -->
# bert-injury-classifier
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluat... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-injury-classifier", "results": []}]} | tal-yifat/bert-injury-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-injury-classifier
======================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6915
* Accuracy: 0.5298
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_b... |
text-generation | 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. -->
# injury-report-distilgpt2-test
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "injury-report-distilgpt2-test", "results": []}]} | tal-yifat/injury-report-distilgpt2-test | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| injury-report-distilgpt2-test
=============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5243
Model description
-----------------
More information needed
Intended uses & limitations
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
fill-mask | 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. -->
# injury-report-test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the No... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "injury-report-test", "results": []}]} | tal-yifat/injury-report-test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| injury-report-test
==================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5697
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"datasets": ["glue", "multi_nli", "tals/vitaminc"]} | tals/albert-base-mnli | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:glue",
"dataset:multi_nli",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #dataset-glue #dataset-multi_nli #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-glue #dataset-multi_nli #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more ... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"language": "python", "datasets": ["fever", "glue", "tals/vitaminc"]} | tals/albert-base-vitaminc-fever | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"python"
] | TAGS
#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more deta... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"datasets": ["glue", "multi_nli", "tals/vitaminc"]} | tals/albert-base-vitaminc-mnli | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:glue",
"dataset:multi_nli",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #dataset-glue #dataset-multi_nli #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-glue #dataset-multi_nli #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more ... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"language": "python", "datasets": ["fever", "glue", "tals/vitaminc"]} | tals/albert-base-vitaminc | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"python"
] | TAGS
#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more deta... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"language": "python", "datasets": ["fever", "glue", "tals/vitaminc"]} | tals/albert-base-vitaminc_flagging | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"python"
] | TAGS
#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more deta... |
null | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"language": "python", "datasets": ["fever", "glue", "tals/vitaminc"]} | tals/albert-base-vitaminc_rationale | null | [
"transformers",
"pytorch",
"albert",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"python"
] | TAGS
#transformers #pytorch #albert #dataset-fever #dataset-glue #dataset-tals/vitaminc #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #dataset-fever #dataset-glue #dataset-tals/vitaminc #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, plea... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"datasets": ["tals/vitaminc", "fever"]} | tals/albert-base-vitaminc_wnei-fever | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:tals/vitaminc",
"dataset:fever",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #dataset-tals/vitaminc #dataset-fever #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-tals/vitaminc #dataset-fever #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"language": "python", "datasets": ["fever", "glue", "tals/vitaminc"]} | tals/albert-xlarge-vitaminc-fever | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"python"
] | TAGS
#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more deta... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"datasets": ["glue", "multi_nli", "tals/vitaminc"]} | tals/albert-xlarge-vitaminc-mnli | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"text-classification",
"dataset:glue",
"dataset:multi_nli",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #albert #text-classification #dataset-glue #dataset-multi_nli #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #text-classification #dataset-glue #dataset-multi_nli #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 2... |
text-classification | transformers | # Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
``... | {"language": "python", "datasets": ["fever", "glue", "tals/vitaminc"]} | tals/albert-xlarge-vitaminc | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"python"
] | TAGS
#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us
| # Details
Model used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').
For more details see: URL
When using this model, please cite the paper.
# BibTeX entry and citation info
| [
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more details see: URL\n\nWhen using this model, please cite the paper.",
"# BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #dataset-fever #dataset-glue #dataset-tals/vitaminc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Details\nModel used in Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21').\n\nFor more deta... |
fill-mask | transformers | # roberta_python
---
language: code
datasets:
- code_search_net
- Fraser/python-lines
tags:
- python
- code
- masked-lm
widget:
- text "assert 6 == sum([i for i in range(<mask>)])"
---
# Details
This is a roBERTa-base model trained on the python part of [CodeSearchNet](https://github.com/github/CodeSearchNet) and reach... | {} | tals/roberta_python | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2106.05784",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.05784"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2106.05784 #autotrain_compatible #endpoints_compatible #region-us
| # roberta_python
---
language: code
datasets:
- code_search_net
- Fraser/python-lines
tags:
- python
- code
- masked-lm
widget:
- text "assert 6 == sum([i for i in range(<mask>)])"
---
# Details
This is a roBERTa-base model trained on the python part of CodeSearchNet and reached a dev perplexity of 3.296
This model wa... | [
"# roberta_python\n---\nlanguage: code\ndatasets:\n- code_search_net\n- Fraser/python-lines\ntags:\n- python\n- code\n- masked-lm\nwidget:\n- text \"assert 6 == sum([i for i in range(<mask>)])\"\n---",
"# Details\nThis is a roBERTa-base model trained on the python part of CodeSearchNet and reached a dev perplexit... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2106.05784 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_python\n---\nlanguage: code\ndatasets:\n- code_search_net\n- Fraser/python-lines\ntags:\n- python\n- code\n- masked-lm\nwidget:\n- text \"assert 6 == sum([i for i in range(<ma... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Hindi-Marathi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. When using this model, make sure that your speech input is sampled at 16kHz.
## Installation
```bash
pip install git+https://github.com/huggingface/transformers.git datasets libros... | {"language": ["mr", "hi"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hindi", "marathi"], "datasets": ["openslr", "interspeech_2021_asr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Hindi-Marathi by Tanmay Laud", "results": [{... | tanmaylaud/wav2vec2-large-xlsr-hindi-marathi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hindi",
"marathi",
"mr",
"hi",
"dataset:openslr",
"dataset:interspeech_2021_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr",
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hindi #marathi #mr #hi #dataset-openslr #dataset-interspeech_2021_asr #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Hindi-Marathi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. When using this model, make sure that your speech input is sampled at 16kHz.
## Installation
## Eval dataset:
## Usage
The model can be used directly (without a language model)... | [
"# Wav2Vec2-Large-XLSR-53-Hindi-Marathi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi and Marathi using the OpenSLR SLR64 datasets. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Installation",
"## Eval dataset:",
"## Usage\n The model can be used directly (without a... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hindi #marathi #mr #hi #dataset-openslr #dataset-interspeech_2021_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Hindi-Marathi\nFine-tuned facebook/wav2vec2-large-x... |
automatic-speech-recognition | transformers |
# Wav2vec2-Large-English
Fine-tuned [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
| {} | tanmayplanet32/english-model | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
# Wav2vec2-Large-English
Fine-tuned facebook/wav2vec2-large on English using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
| [
"# Wav2vec2-Large-English\n\nFine-tuned facebook/wav2vec2-large on English using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# Wav2vec2-Large-English\n\nFine-tuned facebook/wav2vec2-large on English using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz."
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-Bengali
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using the [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/).
When using this model, make sure that your speech input is sampled at 16kHz.
## U... | {"language": "Bengali", "license": "cc-by-sa-4.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["OpenSLR"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Bengali by Tanmoy Sarkar", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | tanmoyio/wav2vec2-large-xlsr-bengali | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:OpenSLR",
"license:cc-by-sa-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Bengali"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-OpenSLR #license-cc-by-sa-4.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-Bengali
Fine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using the Bengali ASR training data set containing ~196K utterances.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
Dataset must be downloaded from this website and preprocessed accordingly. For examp... | [
"# Wav2Vec2-Large-XLSR-Bengali\nFine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using the Bengali ASR training data set containing ~196K utterances.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nDataset must be downloaded from this website and preprocessed according... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-OpenSLR #license-cc-by-sa-4.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Bengali\nFine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using the Bengali ASR training da... |
null | null | My first model in Hugging Face | {} | tanphan/distilBERT | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| My first model in Hugging Face | [] | [
"TAGS\n#region-us \n"
] |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | tarikul/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\... |
fill-mask | transformers |
---
# EstBERT
### What's this?
The EstBERT model is a pretrained BERT<sub>Base</sub> model exclusively trained on Estonian cased corpus on both 128 and 512 sequence length of data.
### How to use?
You can use the model transformer library both in tensorflow and pytorch version.
```
from transformers import AutoTo... | {"language": "et", "license": "cc-by-4.0", "widget": [{"text": "Miks [MASK] ei taha mind kuulata?"}]} | tartuNLP/EstBERT | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"et",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #et #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
---
EstBERT
=======
### What's this?
The EstBERT model is a pretrained BERTBase model exclusively trained on Estonian cased corpus on both 128 and 512 sequence length of data.
### How to use?
You can use the model transformer library both in tensorflow and pytorch version.
You can also download the pretra... | [
"### What's this?\n\n\nThe EstBERT model is a pretrained BERTBase model exclusively trained on Estonian cased corpus on both 128 and 512 sequence length of data.",
"### How to use?\n\n\nYou can use the model transformer library both in tensorflow and pytorch version.\n\n\nYou can also download the pretrained mode... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #et #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### What's this?\n\n\nThe EstBERT model is a pretrained BERTBase model exclusively trained on Estonian cased corpus on both 128 and 512 sequence length of data.",
... |
token-classification | transformers |
# EstBERT_NER
## Model description
EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by [Tkachenko et al](https://www.aclweb.org/anthology/W13-2412.pdf). It can recognize three types of entities: locations (LOC), organ... | {"language": "et", "license": "cc-by-4.0", "widget": [{"text": "Eesti President on Alar Karis."}]} | tartuNLP/EstBERT_NER | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"et",
"arxiv:2011.04784",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.04784"
] | [
"et"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #et #arxiv-2011.04784 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# EstBERT_NER
## Model description
EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by Tkachenko et al. It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER).
## How to use... | [
"# EstBERT_NER",
"## Model description \n\nEstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by Tkachenko et al. It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER).",
"... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #et #arxiv-2011.04784 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# EstBERT_NER",
"## Model description \n\nEstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. Thi... |
text-generation | transformers |
---
# gpt-est-base
This is the base-size [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2) model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl) for 3 epochs. Previously named "gpt-4-est-base", renamed to avoid click-baiting.
[Reference](https://d... | {"tags": ["generated_from_trainer"], "widget": [{"text": ">wiki< mis on GPT? Vastus:"}], "model-index": [{"name": "gpt-est-base", "results": []}]} | tartuNLP/gpt-for-est-base | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
---
# gpt-est-base
This is the base-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl) for 3 epochs. Previously named "gpt-4-est-base", renamed to avoid click-baiting.
Reference
### Format
For training data was prepended with a text domain tag, a... | [
"# gpt-est-base\n\nThis is the base-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl) for 3 epochs. Previously named \"gpt-4-est-base\", renamed to avoid click-baiting.\n\nReference",
"### Format\n\nFor training data was prepended with a text domain ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt-est-base\n\nThis is the base-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl) ... |
text-generation | transformers |
# gpt-est-large
This is the large-size [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2) model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl). Previously named "gpt-4-est-large", renamed to avoid click-baiting.
[Reference](https://doi.org/10.22364/bjmc... | {"tags": ["generated_from_trainer"], "widget": [{"text": ">wiki< mis on GPT? Vastus:"}], "model-index": [{"name": "gpt-est-large", "results": []}]} | tartuNLP/gpt-for-est-large | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt-est-large
This is the large-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl). Previously named "gpt-4-est-large", renamed to avoid click-baiting.
Reference
### Format
For training data was prepended with a text domain tag, and it should be adde... | [
"# gpt-est-large\n\nThis is the large-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl + Common Crawl). Previously named \"gpt-4-est-large\", renamed to avoid click-baiting.\n\nReference",
"### Format\n\nFor training data was prepended with a text domain tag, and i... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt-est-large\n\nThis is the large-size GPT2 model, trained from scratch on 2.2 billion words (Estonian National Corpus + News Crawl +... |
text-classification | 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. -->
# bert-base-uncased-airlines
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-airlines", "results": []}]} | tasosk/bert-base-uncased-airlines | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-airlines
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3458
* Accuracy: 0.9021
* F1: 0.9022
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\... |
text-classification | 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. -->
# distilbert-base-uncased-airlines
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-airlines", "results": []}]} | tasosk/distilbert-base-uncased-airlines | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-airlines
================================
This model is a fine-tuned version of distilbert-base-uncased on the tasosk/airlines dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3174
* Accuracy: 0.9288
* F1: 0.9289
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
null | transformers | # Spider
This is the unsupervised pretrained model discussed in our paper [Learning to Retrieve Passages without Supervision](https://arxiv.org/abs/2112.07708).
## Usage
We used weight sharing for the query encoder and passage encoder, so the same model should be applied for both.
**Note**! We format the passages s... | {} | tau/spider | null | [
"transformers",
"pytorch",
"dpr",
"arxiv:2112.07708",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.07708"
] | [] | TAGS
#transformers #pytorch #dpr #arxiv-2112.07708 #endpoints_compatible #region-us
| # Spider
This is the unsupervised pretrained model discussed in our paper Learning to Retrieve Passages without Supervision.
## Usage
We used weight sharing for the query encoder and passage encoder, so the same model should be applied for both.
Note! We format the passages similar to DPR, i.e. the title and the te... | [
"# Spider\n\nThis is the unsupervised pretrained model discussed in our paper Learning to Retrieve Passages without Supervision.",
"## Usage\n\nWe used weight sharing for the query encoder and passage encoder, so the same model should be applied for both.\n\nNote! We format the passages similar to DPR, i.e. the t... | [
"TAGS\n#transformers #pytorch #dpr #arxiv-2112.07708 #endpoints_compatible #region-us \n",
"# Spider\n\nThis is the unsupervised pretrained model discussed in our paper Learning to Retrieve Passages without Supervision.",
"## Usage\n\nWe used weight sharing for the query encoder and passage encoder, so the same... |
question-answering | transformers |
# Splinter base model (with pretrained QASS-layer weights)
Splinter-base is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/splinter... | {"language": "en", "license": "apache-2.0", "tags": ["splinter", "SplinterModel"]} | tau/splinter-base-qass | null | [
"transformers",
"pytorch",
"splinter",
"question-answering",
"SplinterModel",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #splinter #question-answering #SplinterModel #en #license-apache-2.0 #endpoints_compatible #region-us
|
# Splinter base model (with pretrained QASS-layer weights)
Splinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.
Note: This model does contain the pretrained weights... | [
"# Splinter base model (with pretrained QASS-layer weights)\n\nSplinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.\n\nNote: This model does contain the pretrained... | [
"TAGS\n#transformers #pytorch #splinter #question-answering #SplinterModel #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Splinter base model (with pretrained QASS-layer weights)\n\nSplinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selec... |
question-answering | transformers |
# Splinter base model
Splinter-base is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/splinter). The model is case-sensitive.
No... | {"language": "en", "license": "apache-2.0", "tags": ["splinter", "SplinterModel"]} | tau/splinter-base | null | [
"transformers",
"pytorch",
"splinter",
"question-answering",
"SplinterModel",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #splinter #question-answering #SplinterModel #en #license-apache-2.0 #endpoints_compatible #region-us
|
# Splinter base model
Splinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.
Note: This model doesn't contain the pretrained weights for the QASS layer (see paper f... | [
"# Splinter base model \n\nSplinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.\n\nNote: This model doesn't contain the pretrained weights for the QASS layer (see... | [
"TAGS\n#transformers #pytorch #splinter #question-answering #SplinterModel #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Splinter base model \n\nSplinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original re... |
question-answering | transformers |
# Splinter large model, (with pretrained QASS-layer weights)
Splinter-large is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/sp... | {"language": "en", "license": "apache-2.0", "tags": ["splinter", "SplinterModel"]} | tau/splinter-large-qass | null | [
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"pytorch",
"splinter",
"question-answering",
"SplinterModel",
"en",
"arxiv:2108.05857",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.05857"
] | [
"en"
] | TAGS
#transformers #pytorch #splinter #question-answering #SplinterModel #en #arxiv-2108.05857 #license-apache-2.0 #endpoints_compatible #region-us
|
# Splinter large model, (with pretrained QASS-layer weights)
Splinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.
Note (1): This model does contain the pretrain... | [
"# Splinter large model, (with pretrained QASS-layer weights) \n \n\nSplinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.\n\nNote (1): This model does contain the... | [
"TAGS\n#transformers #pytorch #splinter #question-answering #SplinterModel #en #arxiv-2108.05857 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Splinter large model, (with pretrained QASS-layer weights) \n \n\nSplinter-large is the pretrained model discussed in the paper Few-Shot Question Answering ... |
question-answering | transformers |
# Splinter large model
Splinter-large is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/splinter). The model is case-sensitive.
... | {"language": "en", "license": "apache-2.0", "tags": ["splinter", "SplinterModel"]} | tau/splinter-large | null | [
"transformers",
"pytorch",
"splinter",
"question-answering",
"SplinterModel",
"en",
"arxiv:2108.05857",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.05857"
] | [
"en"
] | TAGS
#transformers #pytorch #splinter #question-answering #SplinterModel #en #arxiv-2108.05857 #license-apache-2.0 #endpoints_compatible #region-us
|
# Splinter large model
Splinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.
Note (1): This model doesn't contain the pretrained weights for the QASS layer (see ... | [
"# Splinter large model \n \n\nSplinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive.\n\nNote (1): This model doesn't contain the pretrained weights for the QASS la... | [
"TAGS\n#transformers #pytorch #splinter #question-answering #SplinterModel #en #arxiv-2108.05857 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Splinter large model \n \n\nSplinter-large is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL ... |
text2text-generation | transformers |
# T5-V1.1-large-rss
This model is [T5-v1.1-large](https://huggingface.co/google/t5-v1_1-large) finetuned on RSS dataset. The model was finetuned as part of
["How Optimal is Greedy Decoding for Extractive Question Answering?"](https://arxiv.org/abs/2108.05857), while the RSS pretraining method was introduced in [this ... | {"language": "en", "datasets": ["c4", "wikipedia"], "metrics": ["f1"]} | tau/t5-v1_1-large-rss | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"arxiv:2108.05857",
"arxiv:2101.00438",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.05857",
"2101.00438"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2108.05857 #arxiv-2101.00438 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-V1.1-large-rss
=================
This model is T5-v1.1-large finetuned on RSS dataset. The model was finetuned as part of
"How Optimal is Greedy Decoding for Extractive Question Answering?", while the RSS pretraining method was introduced in this paper.
Model description
-----------------
The original T5-v1.1-... | [
"### How to use\n\n\nYou can use this model directly but it is recommended to format the input to be aligned with that of the training scheme and as a text-question context:\n\n\nThe generated answer is then '\"<extra\\_id\\_0> 2009<extra\\_id\\_1>\"', while the one generated by the original T5-v1.1-large is '\"<ex... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2108.05857 #arxiv-2101.00438 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly but it is recommended to format the input to be... |
fill-mask | transformers | # TavBERT base model
A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
```python
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForM... | {"language": "he", "tags": ["roberta", "language model"], "datasets": ["oscar"]} | tau/tavbert-he | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"language model",
"he",
"dataset:oscar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #roberta #fill-mask #language model #he #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us
| # TavBERT base model
A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
## Training data
OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences).
| [
"# TavBERT base model\nA Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).",
"### How to use",
"## Training data\nOSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences)."
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"# TavBERT base model\nA Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et a... |
text-classification | transformers |
# tf-allociné
A french sentiment analysis model, based on [CamemBERT](https://camembert-model.fr/), and finetuned on a large-scale dataset scraped from [Allociné.fr](http://www.allocine.fr/) user reviews.
## Results
| Validation Accuracy | Validation F1-Score | Test Accuracy | Test F1-Score |
|--------------------:... | {"language": "fr"} | tblard/tf-allocine | null | [
"transformers",
"tf",
"camembert",
"text-classification",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #tf #camembert #text-classification #fr #autotrain_compatible #endpoints_compatible #region-us
| tf-allociné
===========
A french sentiment analysis model, based on CamemBERT, and finetuned on a large-scale dataset scraped from Allociné.fr user reviews.
Results
-------
The dataset and the evaluation code are available on this repo.
Usage
-----
Author
------
Théophile Blard – :email: URL@URL
If you u... | [] | [
"TAGS\n#transformers #tf #camembert #text-classification #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | 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. -->
# test-train
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "test-train", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": [{"type":... | tbochens/test-train | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| test-train
==========
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7268
* Accuracy: 0.8456
* F1: 0.8927
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
fill-mask | transformers | #### MathBERT model (custom vocab)
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
#### Model description
MathBERT is a transformers model pretrained on a large corpus of ... | {} | tbs17/MathBERT-custom | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| #### MathBERT model (custom vocab)
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
#### Model description
MathBERT is a transformers model pretrained on a large corpus of ... | [
"#### MathBERT model (custom vocab)\n\nPretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.",
"#### Model description\nMathBERT is a transformers model pretrained on a large... | [
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"#### MathBERT model (custom vocab)\n\nPretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference bet... |
fill-mask | transformers | #### MathBERT model (original vocab)
*Disclaimer: the format of the documentation follows the official BERT model readme.md*
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.... | {} | tbs17/MathBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| #### MathBERT model (original vocab)
*Disclaimer: the format of the documentation follows the official BERT model URL*
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
###... | [
"#### MathBERT model (original vocab)\n\n*Disclaimer: the format of the documentation follows the official BERT model URL*\n\nPretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and Engli... | [
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text2text-generation | transformers |
[](https://arxiv.org/abs/2105.12995)
This model is used to generate paraphrases. It has been trained on a mix of 3 different paraphrase detection datasets: MSR, Quora, Google-PAWS.
We use this model in our ACL'21 Paper ["PROTAUGMENT: Unsupervised diverse short-texts... | {"language": "en", "tags": ["Paraphase Generation", "Data Augmentation"], "datasets": ["Quora", "MSR", "Google-PAWS"]} | tdopierre/ProtAugment-ParaphraseGenerator | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"Paraphase Generation",
"Data Augmentation",
"en",
"dataset:Quora",
"dataset:MSR",
"dataset:Google-PAWS",
"arxiv:2105.12995",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.12995"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #Paraphase Generation #Data Augmentation #en #dataset-Quora #dataset-MSR #dataset-Google-PAWS #arxiv-2105.12995 #autotrain_compatible #endpoints_compatible #region-us
|
![acl](URL
This model is used to generate paraphrases. It has been trained on a mix of 3 different paraphrase detection datasets: MSR, Quora, Google-PAWS.
We use this model in our ACL'21 Paper "PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning"
Jointly used with generatio... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #Paraphase Generation #Data Augmentation #en #dataset-Quora #dataset-MSR #dataset-Google-PAWS #arxiv-2105.12995 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265897
- CO2 Emissions (in grams): 81.7509252560808
## Validation Metrics
- Loss: 0.5754176378250122
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Typ... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 81.7509252560808} | teacookies/autonlp-more_fine_tune_24465520-26265897 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265897
- CO2 Emissions (in grams): 81.7509252560808
## Validation Metrics
- Loss: 0.5754176378250122
## Usage
You can use cURL to access this model:
Or Python API:
| [
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"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
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"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265897\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265898
- CO2 Emissions (in grams): 82.78379967029494
## Validation Metrics
- Loss: 0.5732079148292542
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 82.78379967029494} | teacookies/autonlp-more_fine_tune_24465520-26265898 | null | [
"transformers",
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"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265898
- CO2 Emissions (in grams): 82.78379967029494
## Validation Metrics
- Loss: 0.5732079148292542
## Usage
You can use cURL to access this model:
Or Python API:
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question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265899
- CO2 Emissions (in grams): 124.66009281731397
## Validation Metrics
- Loss: 0.7011443972587585
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 124.66009281731397} | teacookies/autonlp-more_fine_tune_24465520-26265899 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265899
- CO2 Emissions (in grams): 124.66009281731397
## Validation Metrics
- Loss: 0.7011443972587585
## Usage
You can use cURL to access this model:
Or Python API:
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"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
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"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265899\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265900
- CO2 Emissions (in grams): 123.16270720220912
## Validation Metrics
- Loss: 0.6387976408004761
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 123.16270720220912} | teacookies/autonlp-more_fine_tune_24465520-26265900 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265900
- CO2 Emissions (in grams): 123.16270720220912
## Validation Metrics
- Loss: 0.6387976408004761
## Usage
You can use cURL to access this model:
Or Python API:
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"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
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"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265900\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265901
- CO2 Emissions (in grams): 80.04360178242067
## Validation Metrics
- Loss: 0.5551259517669678
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 80.04360178242067} | teacookies/autonlp-more_fine_tune_24465520-26265901 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265901
- CO2 Emissions (in grams): 80.04360178242067
## Validation Metrics
- Loss: 0.5551259517669678
## Usage
You can use cURL to access this model:
Or Python API:
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"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
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"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265901\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265902
- CO2 Emissions (in grams): 83.78453848505326
## Validation Metrics
- Loss: 0.5470030903816223
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 83.78453848505326} | teacookies/autonlp-more_fine_tune_24465520-26265902 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265902
- CO2 Emissions (in grams): 83.78453848505326
## Validation Metrics
- Loss: 0.5470030903816223
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265902\n- CO2 Emissions (in grams): 83.78453848505326",
"## Validation Metrics\n\n- Loss: 0.5470030903816223",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265902\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265903
- CO2 Emissions (in grams): 108.13983395548236
## Validation Metrics
- Loss: 0.6330059170722961
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 108.13983395548236} | teacookies/autonlp-more_fine_tune_24465520-26265903 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265903
- CO2 Emissions (in grams): 108.13983395548236
## Validation Metrics
- Loss: 0.6330059170722961
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265903\n- CO2 Emissions (in grams): 108.13983395548236",
"## Validation Metrics\n\n- Loss: 0.6330059170722961",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265903\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265904
- CO2 Emissions (in grams): 108.63800043275934
## Validation Metrics
- Loss: 0.5807144045829773
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 108.63800043275934} | teacookies/autonlp-more_fine_tune_24465520-26265904 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265904
- CO2 Emissions (in grams): 108.63800043275934
## Validation Metrics
- Loss: 0.5807144045829773
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265904\n- CO2 Emissions (in grams): 108.63800043275934",
"## Validation Metrics\n\n- Loss: 0.5807144045829773",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265904\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265905
- CO2 Emissions (in grams): 103.35758036182682
## Validation Metrics
- Loss: 0.5223112106323242
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 103.35758036182682} | teacookies/autonlp-more_fine_tune_24465520-26265905 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265905
- CO2 Emissions (in grams): 103.35758036182682
## Validation Metrics
- Loss: 0.5223112106323242
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265905\n- CO2 Emissions (in grams): 103.35758036182682",
"## Validation Metrics\n\n- Loss: 0.5223112106323242",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265905\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265906
- CO2 Emissions (in grams): 83.00580438705762
## Validation Metrics
- Loss: 0.5259918570518494
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 83.00580438705762} | teacookies/autonlp-more_fine_tune_24465520-26265906 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265906
- CO2 Emissions (in grams): 83.00580438705762
## Validation Metrics
- Loss: 0.5259918570518494
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265906\n- CO2 Emissions (in grams): 83.00580438705762",
"## Validation Metrics\n\n- Loss: 0.5259918570518494",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265906\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265907
- CO2 Emissions (in grams): 103.5636883689371
## Validation Metrics
- Loss: 0.6072460412979126
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 103.5636883689371} | teacookies/autonlp-more_fine_tune_24465520-26265907 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265907
- CO2 Emissions (in grams): 103.5636883689371
## Validation Metrics
- Loss: 0.6072460412979126
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265907\n- CO2 Emissions (in grams): 103.5636883689371",
"## Validation Metrics\n\n- Loss: 0.6072460412979126",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265907\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265908
- CO2 Emissions (in grams): 96.32087452115675
## Validation Metrics
- Loss: 0.5696008801460266
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 96.32087452115675} | teacookies/autonlp-more_fine_tune_24465520-26265908 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265908
- CO2 Emissions (in grams): 96.32087452115675
## Validation Metrics
- Loss: 0.5696008801460266
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265908\n- CO2 Emissions (in grams): 96.32087452115675",
"## Validation Metrics\n\n- Loss: 0.5696008801460266",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265908\n- CO2 Emissions (in g... |
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