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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T14:48:36+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
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. -->
# bart-paraphrasing-mlm
This model is a fine-tuned version of [gayanin/bart-paraphrase-pubmed-1.1](https://huggingface.co/gayanin/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrasing-mlm", "results": []}]} | gayanin/bart-paraphrasing-mlm | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T14:50:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrasing-mlm
=====================
This model is a fine-tuned version of gayanin/bart-paraphrase-pubmed-1.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5510
* Rouge2 Precision: 0.7148
* Rouge2 Recall: 0.5223
* Rouge2 Fmeasure: 0.5866
Model description
-------... | [
"### 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: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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\... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | sudoparsa/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T15:38:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0894
* Accuracy: 0.9828
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #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: 3e-05\n* train\\_batch\\_... |
text2text-generation | transformers |
# An Arabic abstractive text summarization model
A BERT2BERT-based model whose parameters are initialized with AraBERT weights and which has been fine-tuned on a dataset of 84,764 paragraph-summary pairs.
Paper: [Arabic abstractive text summarization using RNN-based and transformer-based architectures](https://www.sc... | {"language": ["ar"], "tags": ["AraBERT", "BERT", "BERT2BERT", "MSA", "Arabic Text Summarization", "Arabic News Title Generation", "Arabic Paraphrasing"], "widget": [{"text": "\u0634\u0647\u062f\u062a \u0645\u062f\u064a\u0646\u0629 \u0637\u0631\u0627\u0628\u0644\u0633\u060c \u0645\u0633\u0627\u0621 \u0623\u0645\u0633 \u... | malmarjeh/bert2bert | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"AraBERT",
"BERT",
"BERT2BERT",
"MSA",
"Arabic Text Summarization",
"Arabic News Title Generation",
"Arabic Paraphrasing",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-07T15:44:16+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #AraBERT #BERT #BERT2BERT #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# An Arabic abstractive text summarization model
A BERT2BERT-based model whose parameters are initialized with AraBERT weights and which has been fine-tuned on a dataset of 84,764 paragraph-summary pairs.
Paper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.
Dataset: link.... | [
"# An Arabic abstractive text summarization model\nA BERT2BERT-based model whose parameters are initialized with AraBERT weights and which has been fine-tuned on a dataset of 84,764 paragraph-summary pairs.\n\nPaper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.\n\nDatas... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #AraBERT #BERT #BERT2BERT #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# An Arabic abstractive text summarization model\nA BERT2BER... |
text2text-generation | transformers | byt5 finetuned on MNLI dataset for 3 epochs, with lr=1e-4
valid matched acc = 0.80
| {} | Splend1dchan/byt5small-glue-mnli | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-07T15:57:56+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| byt5 finetuned on MNLI dataset for 3 epochs, with lr=1e-4
valid matched acc = 0.80
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mrbalazs5/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "mrbalazs5/bert-finetuned-squad", "results": []}]} | mrbalazs5/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T16:04:24+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| mrbalazs5/bert-finetuned-squad
==============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7151
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 66546, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espn... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["sinhala"]} | espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer | null | [
"espnet",
"tensorboard",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-07T16:09:26+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer'
This model was trained by Karthik using DSTC2/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet.",
... |
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... | fenixobia/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T17:07:59+00:00 | [] | [] | TAGS
#transformers #pytorch #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: 0.7808
* Matthews Correlation: 0.5596
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 #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\\_rate: 2e-0... |
null | transformers |
# Document Image Transformer (base-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) ... | {"tags": ["dit"], "inference": false} | microsoft/dit-base | null | [
"transformers",
"pytorch",
"beit",
"dit",
"arxiv:2203.02378",
"region:us"
] | null | 2022-03-07T17:18:46+00:00 | [
"2203.02378"
] | [] | TAGS
#transformers #pytorch #beit #dit #arxiv-2203.02378 #region-us
|
# Document Image Transformer (base-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first released in t... | [
"# Document Image Transformer (base-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first release... | [
"TAGS\n#transformers #pytorch #beit #dit #arxiv-2203.02378 #region-us \n",
"# Document Image Transformer (base-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper DiT: Self-supervis... |
text2text-generation | transformers |
# AI Interviewer Question-Asking Model
For a Senior Project at Calvin University
Created by: Hyechan Jun, Ha-Ram Koo, and Advait Scaria
This model is fine-tuned on facebook/bart-base to generate sequences ending in a question mark (?). It is a remake of an earlier model that had errors in its training and validatio... | {"datasets": ["INTERVIEW: NPR Media Dialog Transcripts"]} | hyechanjun/interview-question-remake | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-07T17:48:30+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# AI Interviewer Question-Asking Model
For a Senior Project at Calvin University
Created by: Hyechan Jun, Ha-Ram Koo, and Advait Scaria
This model is fine-tuned on facebook/bart-base to generate sequences ending in a question mark (?). It is a remake of an earlier model that had errors in its training and validatio... | [
"# AI Interviewer Question-Asking Model\n\nFor a Senior Project at Calvin University\n\nCreated by: Hyechan Jun, Ha-Ram Koo, and Advait Scaria\n\nThis model is fine-tuned on facebook/bart-base to generate sequences ending in a question mark (?). It is a remake of an earlier model that had errors in its training and... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# AI Interviewer Question-Asking Model\n\nFor a Senior Project at Calvin University\n\nCreated by: Hyechan Jun, Ha-Ram Koo, and Advait Scaria\n\nThis model is fine-tuned on facebook/ba... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/librispeech_conformer`
This model was trained by Yifan Peng using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c3569453a408fd4ff4173d9c1d2062c88d1fc060
pip install -e .
cd egs2/librispeech/asr1
./r... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | pyf98/librispeech_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-07T18:16:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/librispeech\_conformer'
This model was trained by Yifan Peng using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Mar 7 12:26:10 EST 2022'
* python version: '3.9.7 (default, Sep 16 2021, 13:0... | [
"### 'pyf98/librispeech\\_conformer'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Mar 7 12:26:10 EST 2022'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"### 'pyf98/librispeech\\_conformer'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======... |
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. -->
# mbart-large-cc25-finetuned-source-to-target
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingfac... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mbart-large-cc25-finetuned-source-to-target", "results": []}]} | z5ying/mbart-large-cc25-finetuned-source-to-target | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T18:25:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mbart-large-cc25-finetuned-source-to-target
This model is a fine-tuned version of facebook/mbart-large-cc25 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# mbart-large-cc25-finetuned-source-to-target\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 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 #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart-large-cc25-finetuned-source-to-target\n\nThis model is a fine-tuned version of facebook/mbart-large-cc25 on an unknown dataset.",
"## Model descrip... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/librispeech_conformer_layerdrop0.1_last6`
This model was trained by Yifan Peng using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c3569453a408fd4ff4173d9c1d2062c88d1fc060
pip install -e .
cd egs2/l... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | pyf98/librispeech_conformer_layerdrop0.1_last6 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-07T18:37:56+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/librispeech\_conformer\_layerdrop0.1\_last6'
This model was trained by Yifan Peng using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Mar 7 12:21:40 EST 2022'
* python version: '3.9.7 (defau... | [
"### 'pyf98/librispeech\\_conformer\\_layerdrop0.1\\_last6'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Mar 7 12:21:40 EST 2022'\n* python version: '3.9.7 (default, Sep 16 2... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/librispeech\\_conformer\\_layerdrop0.1\\_last6'\n\n\nThis model was trained by Yifan Peng using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESU... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-English
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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.
This model has been fin... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "en", "hf-asr-leaderboard", "mozilla-foundation/common_voice_6_0", "robust-speech-event", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "mozilla-foundation/common_voice_6_0"], "metrics": ["wer", "cer"], "mod... | abidlabs/speech-text | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"lice... | null | 2022-03-07T19:09:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #en #hf-asr-leaderboard #mozilla-foundation/common_voice_6_0 #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #dataset-mozilla-foundation/common_voice_6_0 #license-apache-2.0 #model-index #endpoints_compatible #has_... | Wav2Vec2-Large-XLSR-53-English
==============================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on English using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :)
The ... | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #en #hf-asr-leaderboard #mozilla-foundation/common_voice_6_0 #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #dataset-mozilla-foundation/common_voice_6_0 #license-apache-2.0 #model-index #endpoints_compatible... |
null | transformers |
# Document Image Transformer (large-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378)... | {"tags": ["dit"], "inference": false} | microsoft/dit-large | null | [
"transformers",
"pytorch",
"beit",
"dit",
"arxiv:2203.02378",
"region:us"
] | null | 2022-03-07T20:09:02+00:00 | [
"2203.02378"
] | [] | TAGS
#transformers #pytorch #beit #dit #arxiv-2203.02378 #region-us
|
# Document Image Transformer (large-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first released in ... | [
"# Document Image Transformer (large-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first releas... | [
"TAGS\n#transformers #pytorch #beit #dit #arxiv-2203.02378 #region-us \n",
"# Document Image Transformer (large-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper DiT: Self-supervi... |
text2text-generation | transformers |
# Creators
- [Stefan Schweter](https://github.com/stefan-it) ([schweter.ml](https://schweter.ml))
- [Philip May](https://may.la) ([Deutsche Telekom](https://www.telekom.de/))
- [Philipp Schmid](https://www.philschmid.de/) ([Hugging Face](https://huggingface.co/))
# Evaluation
Evaluation was done on a summarization ta... | {"language": "de", "license": "mit", "tags": ["german", "deutsch"]} | GermanT5/t5-efficient-gc4-german-base-nl36 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"german",
"deutsch",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-07T20:17:23+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #german #deutsch #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Creators
- Stefan Schweter (URL)
- Philip May (Deutsche Telekom)
- Philipp Schmid (Hugging Face)
# Evaluation
Evaluation was done on a summarization task with:
- train data: Swisstext
- test data: MLSUM
- GPUs: 4 (V100)
for details see: <URL
# Tips for training on GPUs
This model is too big to fit on a normal 16G... | [
"# Creators\n- Stefan Schweter (URL)\n- Philip May (Deutsche Telekom)\n- Philipp Schmid (Hugging Face)",
"# Evaluation\nEvaluation was done on a summarization task with:\n- train data: Swisstext\n- test data: MLSUM\n- GPUs: 4 (V100)\n\nfor details see: <URL",
"# Tips for training on GPUs\nThis model is too big ... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #german #deutsch #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Creators\n- Stefan Schweter (URL)\n- Philip May (Deutsche Telekom)\n- Philipp Schmid (Hugging Face)",
"# E... |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-triviaqa-large-batch | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T20:17:41+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
# MIT Indoor Scenes
Fine tune [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the data [MIT Indoor Scenes](https://www.kaggle.com/itsahmad/indoor-scenes-cvpr-2019)
| {"license": "apache-2.0"} | vincentclaes/mit-indoor-scenes | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-07T20:24:00+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# MIT Indoor Scenes
Fine tune google/vit-base-patch16-224-in21k on the data MIT Indoor Scenes
| [
"# MIT Indoor Scenes\r\n\r\nFine tune google/vit-base-patch16-224-in21k on the data MIT Indoor Scenes"
] | [
"TAGS\n#transformers #pytorch #vit #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MIT Indoor Scenes\r\n\r\nFine tune google/vit-base-patch16-224-in21k on the data MIT Indoor Scenes"
] |
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"], "datasets": ["conll2003"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | MikhailGalperin/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T20:29:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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 the conll2003 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-ner\n\nThis model is a fine-tuned version of distilbert-base-uncased on the conll2003 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 #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-ner\n\nThis model is a fine-tuned version of distilbert-base-uncased on the c... |
image-classification | transformers |
# Document Image Transformer (base-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/), a dataset consisting of 400,000 grayscale images in 16 class... | {"tags": ["dit", "vision", "image-classification"], "datasets": ["rvl_cdip"], "widget": [{"src": "https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/coca_cola_advertisement.png", "example_title": "Advertisement"}, {"src": "https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/sc... | microsoft/dit-base-finetuned-rvlcdip | null | [
"transformers",
"pytorch",
"beit",
"image-classification",
"dit",
"vision",
"dataset:rvl_cdip",
"arxiv:2203.02378",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-07T20:48:42+00:00 | [
"2203.02378"
] | [] | TAGS
#transformers #pytorch #beit #image-classification #dit #vision #dataset-rvl_cdip #arxiv-2203.02378 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Document Image Transformer (base-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It was intr... | [
"# Document Image Transformer (base-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It wa... | [
"TAGS\n#transformers #pytorch #beit #image-classification #dit #vision #dataset-rvl_cdip #arxiv-2203.02378 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Document Image Transformer (base-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006)... |
image-segmentation | keras |
# Semantic-Segmentation-of-Teeth-in-Panoramic-X-ray-Image
The aim of this study is automatic semantic segmentation and measurement total length of teeth in one-shot panoramic x-ray image by using deep learning method with U-Net Model and binary image analysis in order to provide diagnostic information for the manageme... | {"tags": ["segmentation", "dentalimaging", "medicalimaging", "image-segmentation"], "datasets": ["SerdarHelli/SegmentationOfTeethPanoramicXRayImages"], "metrics": ["f1", "accuracy"]} | SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net | null | [
"keras",
"segmentation",
"dentalimaging",
"medicalimaging",
"image-segmentation",
"dataset:SerdarHelli/SegmentationOfTeethPanoramicXRayImages",
"has_space",
"region:us"
] | null | 2022-03-07T20:50:27+00:00 | [] | [] | TAGS
#keras #segmentation #dentalimaging #medicalimaging #image-segmentation #dataset-SerdarHelli/SegmentationOfTeethPanoramicXRayImages #has_space #region-us
|
# Semantic-Segmentation-of-Teeth-in-Panoramic-X-ray-Image
The aim of this study is automatic semantic segmentation and measurement total length of teeth in one-shot panoramic x-ray image by using deep learning method with U-Net Model and binary image analysis in order to provide diagnostic information for the manageme... | [
"# Semantic-Segmentation-of-Teeth-in-Panoramic-X-ray-Image\nThe aim of this study is automatic semantic segmentation and measurement total length of teeth in one-shot panoramic x-ray image by using deep learning method with U-Net Model and binary image analysis in order to provide diagnostic information for the man... | [
"TAGS\n#keras #segmentation #dentalimaging #medicalimaging #image-segmentation #dataset-SerdarHelli/SegmentationOfTeethPanoramicXRayImages #has_space #region-us \n",
"# Semantic-Segmentation-of-Teeth-in-Panoramic-X-ray-Image\nThe aim of this study is automatic semantic segmentation and measurement total length of... |
image-classification | transformers |
# Document Image Transformer (large-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/), a dataset consisting of 400,000 grayscale images in 16 clas... | {"tags": ["dit"], "datasets": ["rvl_cdip"], "inference": false} | microsoft/dit-large-finetuned-rvlcdip | null | [
"transformers",
"pytorch",
"beit",
"image-classification",
"dit",
"dataset:rvl_cdip",
"arxiv:2203.02378",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-07T21:02:12+00:00 | [
"2203.02378"
] | [] | TAGS
#transformers #pytorch #beit #image-classification #dit #dataset-rvl_cdip #arxiv-2203.02378 #autotrain_compatible #has_space #region-us
|
# Document Image Transformer (large-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It was int... | [
"# Document Image Transformer (large-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It w... | [
"TAGS\n#transformers #pytorch #beit #image-classification #dit #dataset-rvl_cdip #arxiv-2203.02378 #autotrain_compatible #has_space #region-us \n",
"# Document Image Transformer (large-sized model) \n\nDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 ... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1386970823681052680/oA_4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lilbratmia-littlehorney-plusbibi1/1646689525715/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lilbratmia-littlehorney-plusbibi1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-07T21:35:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Bibi und Anna & Vanny\_Bunny™ & Mia
@lilbratmia-littlehorney-plusbibi1
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# t5-small-paraphrasing-mlm
This model is a fine-tuned version of [gayanin/t5-small-paraphrase-pubmed](https://huggingface.co/gaya... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-paraphrasing-mlm", "results": []}]} | gayanin/t5-small-paraphrasing-mlm | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-07T21:54:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-paraphrasing-mlm
=========================
This model is a fine-tuned version of gayanin/t5-small-paraphrase-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7030
* Rouge2 Precision: 0.6576
* Rouge2 Recall: 0.4712
* Rouge2 Fmeasure: 0.532
Model description
... | [
"### 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: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-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... |
text-generation | transformers |
This is a totally safe and groundbreaking model.
GPT3 performance with under 10Mo model.
| {"pipeline_tag": "text-generation"} | Narsil/totallysafe | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-07T22:13:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #endpoints_compatible #text-generation-inference #region-us
|
This is a totally safe and groundbreaking model.
GPT3 performance with under 10Mo model.
| [] | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#mskeen m e4 16h 0k DialoGPT Model | {"tags": ["conversational"]} | zenham/mskeen_m_e4_16h | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-07T22:51:25+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#mskeen m e4 16h 0k DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
See original GitHub repo for more details [here](https://github.com/megagonlabs/cocosum)
| {"license": "bsd-3-clause"} | megagonlabs/cocosum-cont-self | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-07T23:27:29+00:00 | [] | [] | TAGS
#license-bsd-3-clause #region-us
|
See original GitHub repo for more details here
| [] | [
"TAGS\n#license-bsd-3-clause #region-us \n"
] |
null | null |
See original GitHub repo for more details [here](https://github.com/megagonlabs/cocosum)
| {"license": "bsd-3-clause"} | megagonlabs/cocosum-cont-few | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-07T23:29:46+00:00 | [] | [] | TAGS
#license-bsd-3-clause #region-us
|
See original GitHub repo for more details here
| [] | [
"TAGS\n#license-bsd-3-clause #region-us \n"
] |
null | null |
See original GitHub repo for more details [here](https://github.com/megagonlabs/cocosum)
| {"license": "bsd-3-clause"} | megagonlabs/cocosum-comm-self | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-07T23:31:25+00:00 | [] | [] | TAGS
#license-bsd-3-clause #region-us
|
See original GitHub repo for more details here
| [] | [
"TAGS\n#license-bsd-3-clause #region-us \n"
] |
null | null |
See original GitHub repo for more details [here](https://github.com/megagonlabs/cocosum)
| {"license": "bsd-3-clause"} | megagonlabs/cocosum-comm-few | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-07T23:32:02+00:00 | [] | [] | TAGS
#license-bsd-3-clause #region-us
|
See original GitHub repo for more details here
| [] | [
"TAGS\n#license-bsd-3-clause #region-us \n"
] |
text-generation | transformers |
#khemx m e4 16h 0k DialoGPT Model | {"tags": ["conversational"]} | zenham/khemx_m_e4_16h | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-07T23:33:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#khemx m e4 16h 0k DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmp9eavpdw4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp9eavpdw4", "results": []}]} | smartiros/BERT_for_sentiment_50k_2_epochs_preprocessed_v1 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-07T23:39:06+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| tmp9eavpdw4
===========
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1333
* Train Accuracy: 0.9487
* Validation Loss: 0.7282
* Validation Accuracy: 0.7929
* Epoch: 1
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
automatic-speech-recognition | 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. -->
# wav2vec2-base-cv
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-cv", "results": []}]} | jiobiala24/wav2vec2-base-cv | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T00:03:37+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-cv
================
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1562
* Wer: 0.3804
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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: 0.0001\n* train\\_batch\... |
text-classification | transformers |
## LinkBERT-large
LinkBERT-large model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://gith... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "exbert", "linkbert", "feature-extraction", "fill-mask", "question-answering", "text-classification", "token-classification"], "datasets": ["wikipedia", "bookcorpus"]} | michiyasunaga/LinkBERT-large | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:... | null | 2022-03-08T01:42:14+00:00 | [
"2203.15827"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #exbert #linkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us
| LinkBERT-large
--------------
LinkBERT-large model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository.
Model description
-----------------
... | [
"### How to use\n\n\nTo use the model to get the features of a given text in PyTorch:\n\n\nFor fine-tuning, you can use this repository or follow any other BERT fine-tuning codebases.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, LinkBERT achieves the following results.\n\n\n... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #exbert #linkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nTo use the model to get th... |
text-generation | transformers |
#wail m e4 16h 2k DialoGPT Model | {"tags": ["conversational"]} | zenham/wail_m_e4_16h_2k | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T02:16:34+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#wail m e4 16h 2k DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# t5-small-finetuned-es-to-pt
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the tatoeba dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tatoeba"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-es-to-pt", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "tatoeba", "type": "tatoeba", "... | oskrmiguel/t5-small-finetuned-es-to-pt | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:tatoeba",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T02:54:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-tatoeba #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-es-to-pt
===========================
This model is a fine-tuned version of t5-small on the tatoeba dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5557
* Bleu: 15.0473
* Gen Len: 15.8693
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: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-tatoeba #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
text-classification | transformers | ## my first model
fine-tuned from distillbert | {} | liujr1980/mmodels | null | [
"transformers",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T03:01:46+00:00 | [] | [] | TAGS
#transformers #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## my first model
fine-tuned from distillbert | [
"## my first model\nfine-tuned from distillbert"
] | [
"TAGS\n#transformers #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## my first model\nfine-tuned from distillbert"
] |
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. -->
# sec-bert-finetuned-finance-classification
This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/n... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["financial-sentiment-analysis", "sentiment-analysis", "sentence_50agree", "generated_from_trainer", "sentiment", "finance"], "datasets": ["financial_phrasebank", "Kaggle_Self_label", "nickmuchi/financial-classification"], "metrics": ["accuracy", "f1", "precision"... | nickmuchi/sec-bert-finetuned-finance-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"onnx",
"bert",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"sentence_50agree",
"generated_from_trainer",
"sentiment",
"finance",
"en",
"dataset:financial_phrasebank",
"dataset:Kaggle_Self_label",
"dataset:nickm... | null | 2022-03-08T03:30:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #onnx #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #generated_from_trainer #sentiment #finance #en #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #license-cc-by-sa-4.0 #model-... | sec-bert-finetuned-finance-classification
=========================================
This model is a fine-tuned version of nlpaueb/sec-bert-base on the sentence\_50Agree financial-phrasebank + Kaggle Dataset, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #onnx #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #generated_from_trainer #sentiment #finance #en #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #license-cc-by-sa-4.0 #... |
null | null | # This is the test model | {} | hadehuang/testmodel | null | [
"region:us"
] | null | 2022-03-08T03:31:52+00:00 | [] | [] | TAGS
#region-us
| # This is the test model | [
"# This is the test model"
] | [
"TAGS\n#region-us \n",
"# This is the test model"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1421952831796350976/rFuw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/fitdollar/1646716677087/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/fitdollar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T05:17:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Fit$
@fitdollar
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | 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. -->
# bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
This model is a fine-tuned version of [Ameer05/model-token-rep... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch", "results": []}]} | Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T05:33:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
=================================================================
This model is a fine-tuned version of Ameer05/model-token-repo on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5216
* Rouge1: 59.5791
* Rouge2... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #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: 5.6e-05\n* train\\_batch\\_s... |
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. -->
# bart-large-cnn-10k-pad-early-lit
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-10k-pad-early-lit", "results": []}]} | cammy/bart-large-cnn-10k-pad-early-lit | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T05:46:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-10k-pad-early-lit
================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3758
* Rouge1: 27.7351
* Rouge2: 13.1664
* Rougel: 21.6559
* Rougelsum: 24.648
* Gen Len: 69.343
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #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: 1\n* eval\\_... |
automatic-speech-recognition | 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. -->
# wav2vec2-base-cv-10000
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-cv](https://huggingface.co/jiobiala24/wav... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-cv-10000", "results": []}]} | jiobiala24/wav2vec2-base-cv-10000 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T05:58:28+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-cv-10000
======================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-cv on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3393
* Wer: 0.3684
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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: 0.0001\n* train\\_batch\... |
null | null |
---
thumbnail: Refer to https://github.com/jungjee/RawNet for full documentation
tags:
- Speaker recognition
- Speaker verification
- RawNet
- RawNet3
license: "mit"
datasets:
- VoxCeleb1
- VoxCeleb2
metrics:
- EER 0.89% on Vox1-O
- minDCF 0.0659 on Vox1-O
---
| {"license": "mit"} | jungjee/RawNet3 | null | [
"license:mit",
"region:us"
] | null | 2022-03-08T06:15:15+00:00 | [] | [] | TAGS
#license-mit #region-us
|
---
thumbnail: Refer to URL for full documentation
tags:
- Speaker recognition
- Speaker verification
- RawNet
- RawNet3
license: "mit"
datasets:
- VoxCeleb1
- VoxCeleb2
metrics:
- EER 0.89% on Vox1-O
- minDCF 0.0659 on Vox1-O
---
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-classification | transformers |
## BioLinkBERT-large
BioLinkBERT-large model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are availab... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "exbert", "linkbert", "biolinkbert", "feature-extraction", "fill-mask", "question-answering", "text-classification", "token-classification"], "datasets": ["pubmed"], "widget": [{"text": "Sunitinib is a tyrosine kinase inhibitor"}]} | michiyasunaga/BioLinkBERT-large | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"biolinkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:pubmed",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T06:20:38+00:00 | [
"2203.15827"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #exbert #linkbert #biolinkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-pubmed #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us
| BioLinkBERT-large
-----------------
BioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository.
This model achieves state-of-the-ar... | [
"### How to use\n\n\nTo use the model to get the features of a given text in PyTorch:\n\n\nFor fine-tuning, you can use this repository or follow any other BERT fine-tuning codebases.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, LinkBERT achieves the following results.\n\n\n... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #exbert #linkbert #biolinkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-pubmed #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nTo use the model to get the features... |
table-question-answering | transformers |
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretrain... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"], "datasets": ["wikisql"]} | microsoft/tapex-large-finetuned-wikisql | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2107.07653",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-08T06:41:10+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| TAPEX (large-sized model)
=========================
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
Model description
-----------------
TAPEX (Table Pre-... | [
"### How to Use\n\n\nHere is how to use this model in transformers:",
"### How to Eval\n\n\nPlease find the eval script here.",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to Use\n\n\nHere is how to use this model in transformers:",
"### How to Eva... |
text-classification | transformers |
## LinkBERT-base
LinkBERT-base model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "exbert", "linkbert", "feature-extraction", "fill-mask", "question-answering", "text-classification", "token-classification"], "datasets": ["wikipedia", "bookcorpus"]} | michiyasunaga/LinkBERT-base | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:... | null | 2022-03-08T07:21:51+00:00 | [
"2203.15827"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #exbert #linkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us
| LinkBERT-base
-------------
LinkBERT-base model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository.
Model description
-----------------
Lin... | [
"### How to use\n\n\nTo use the model to get the features of a given text in PyTorch:\n\n\nFor fine-tuning, you can use this repository or follow any other BERT fine-tuning codebases.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, LinkBERT achieves the following results.\n\n\n... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #exbert #linkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nTo use the model to get th... |
text-classification | transformers |
## BioLinkBERT-base
BioLinkBERT-base model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "exbert", "linkbert", "biolinkbert", "feature-extraction", "fill-mask", "question-answering", "text-classification", "token-classification"], "datasets": ["pubmed"], "widget": [{"text": "Sunitinib is a tyrosine kinase inhibitor"}]} | michiyasunaga/BioLinkBERT-base | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"linkbert",
"biolinkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"en",
"dataset:pubmed",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"re... | null | 2022-03-08T07:22:12+00:00 | [
"2203.15827"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #exbert #linkbert #biolinkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-pubmed #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| BioLinkBERT-base
----------------
BioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository.
This model achieves state-of-the-art p... | [
"### How to use\n\n\nTo use the model to get the features of a given text in PyTorch:\n\n\nFor fine-tuning, you can use this repository or follow any other BERT fine-tuning codebases.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, LinkBERT achieves the following results.\n\n\n... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #exbert #linkbert #biolinkbert #fill-mask #question-answering #text-classification #token-classification #en #dataset-pubmed #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nTo use the model to get t... |
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. -->
# t5-small-med-term-mlm
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-med-term-mlm", "results": []}]} | gayanin/t5-small-med-term-mlm | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T07:26:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-med-term-mlm
=====================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4736
* Rouge2 Precision: 0.7731
* Rouge2 Recall: 0.5541
* Rouge2 Fmeasure: 0.6251
Model description
-----------------
More informat... | [
"### 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: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-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... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | SGrannemann/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T07:43:47+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the followin... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1386970823681052680/oA_4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/betonkoepfin-littlehorney-plusbibi1/1646725560421/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/betonkoepfin-littlehorney-plusbibi1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T07:44:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Bibi und Anna & Betty S. & Vanny\_Bunny™
@betonkoepfin-littlehorney-plusbibi1
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was devel... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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-custom
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-custom", "results": []}]} | kamilali/distilbert-base-uncased-finetuned-custom | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T07:58:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-custom
========================================
This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7808
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #question-answering #generated_from_trainer #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\\_size: 1\n* eval\\_batc... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1386970823681052680/oA_4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/desertblooom-littlehorney-plusbibi1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T08:02:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Bibi und Anna & Wüstenblume & Vanny\_Bunny™
@desertblooom-littlehorney-plusbibi1
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was de... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | 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. -->
# bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-10-1
This model is a fine-tuned version of [Ameer05... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-10-1", "results": []}]} | Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-10-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T08:28:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-10-1
=================================================================================
This model is a fine-tuned version of Ameer05/model-token-repo on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #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\\_siz... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | SGrannemann/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T08:47:55+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-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:
* Train Loss: 0.0225
* Validation Loss: 0.0519
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
summarization | 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. -->
# bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1
This model is a fine-tuned version of [Ameer0... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1", "results": []}]} | Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T08:57:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1
==================================================================================
This model is a fine-tuned version of Ameer05/model-token-repo on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss:... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #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\\_siz... |
question-answering | transformers |
# bert-base for QA with qasper
Train from bert-base-uncased.
How to use by python code:
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
# Load model with pipeline
model_name = "z-uo/bert-qasper"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
# ... | {"language": "en", "datasets": ["z-uo/qasper-squad"]} | z-uo/bert-qasper | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"dataset:z-uo/qasper-squad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T09:28:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #dataset-z-uo/qasper-squad #endpoints_compatible #region-us
|
# bert-base for QA with qasper
Train from bert-base-uncased.
How to use by python code:
| [
"# bert-base for QA with qasper\nTrain from bert-base-uncased.\n\nHow to use by python code:"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-z-uo/qasper-squad #endpoints_compatible #region-us \n",
"# bert-base for QA with qasper\nTrain from bert-base-uncased.\n\nHow to use by python code:"
] |
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-devops1-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-devops1-ner", "results": []}]} | akshaychaudhary/distilbert-base-uncased-finetuned-devops1-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T09:29:06+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-devops1-ner
=============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9870
* Precision: 0.0572
* Recall: 0.2689
* F1: 0.0944
* Accuracy: 0.7842
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 3e-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. -->
# biobert-base-cased-v1.2-finetuned-ner-Concat_CRAFT_es
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](h... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner-Concat_CRAFT_es", "results": []}]} | StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-Concat_CRAFT_es | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T09:29:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2-finetuned-ner-Concat\_CRAFT\_es
=======================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2079
* Precision: 0.8487
* Recall: 0.8443
* F... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-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: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
automatic-speech-recognition | 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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-rnd-2-layer | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T10:17:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2188
* Wer: 0.9238
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | frahman/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T10:26:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1352
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
audio-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. -->
# wav2vec2-base-100k-voxpopuli-finetuned-gtzan
This model is a fine-tuned version of [facebook/wav2vec2-base-100k-voxpopuli](https... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-100k-voxpopuli-finetuned-gtzan", "results": []}]} | lewtun/wav2vec2-base-100k-voxpopuli-finetuned-gtzan | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T10:30:56+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| wav2vec2-base-100k-voxpopuli-finetuned-gtzan
============================================
This model is a fine-tuned version of facebook/wav2vec2-base-100k-voxpopuli on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9408
* Accuracy: 0.86
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #license-cc-by-nc-4.0 #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: 2\n* eval\\_batch\\_s... |
null | null | ... | {} | cwtpc/kiddee3-finetuned-th-to-en | null | [
"region:us"
] | null | 2022-03-08T10:30:57+00:00 | [] | [] | TAGS
#region-us
| ... | [] | [
"TAGS\n#region-us \n"
] |
audio-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. -->
# wav2vec2-base-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks", "results": []}]} | alirezafarashah/wav2vec2-base-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T11:33:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-ks
================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0982
* Accuracy: 0.9825
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #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: 3e-05\n* train\\_batch\\_... |
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. -->
# bart-med-term-mlm
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-med-term-mlm", "results": []}]} | gayanin/bart-med-term-mlm | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T12:09:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-med-term-mlm
=================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2506
* Rouge2 Precision: 0.8338
* Rouge2 Recall: 0.6005
* Rouge2 Fmeasure: 0.6775
Model description
-----------------
More inform... | [
"### 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: 4\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_1... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | AlekseyKorshuk/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T12:40:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the wnut\_17 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------... | [
"### 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: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1197820815636672513/JSCZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/feufillet-greatestquotes-hostagekiller/1646746104400/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/feufillet-greatestquotes-hostagekiller | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T13:26:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
URL & HUSSY2K. & Great Minds Quotes
@feufillet-greatestquotes-hostagekiller
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was develop... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# RoBERTa Turkish medium BPE 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-bpe-16k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T13:44:50+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium BPE 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hi... | [
"# RoBERTa Turkish medium BPE 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 heads, a... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium BPE 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The mode... |
fill-mask | transformers |
# RoBERTa Turkish medium Word-level 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-word-16k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T13:51:17+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Word-level 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and... | [
"# RoBERTa Turkish medium Word-level 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 h... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Word-level 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. T... |
fill-mask | transformers |
# RoBERTa Turkish medium WordPiece 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-wp-16k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T14:03:17+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium WordPiece 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and ... | [
"# RoBERTa Turkish medium WordPiece 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 he... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium WordPiece 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. Th... |
fill-mask | transformers |
# RoBERTa Turkish medium Morph-level 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-morph-16k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T14:07:49+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Morph-level 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | [
"# RoBERTa Turkish medium Morph-level 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Morph-level 16k (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. ... |
fill-mask | transformers |
# RoBERTa Turkish medium Character-level (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"], "datasets": ["oscar"]} | ctoraman/RoBERTa-TR-medium-char | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T14:19:31+00:00 | [
"2204.08832"
] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa Turkish medium Character-level (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, an... | [
"# RoBERTa Turkish medium Character-level (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. \nThe pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. \n\nModel architecture is similar to bert-medium (8 layers, 8 ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #dataset-oscar #arxiv-2204.08832 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa Turkish medium Character-level (uncased)\n\nPretrained model on Turkish language using a masked language modeling (MLM) objective. ... |
image-classification | transformers |
# vit-world-landmarks
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | mmgyorke/vit-world-landmarks | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T14:40:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vit-world-landmarks
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### arc de triomphe
!arc de triomphe
#### big ben
!big ben
#### la sagrada familia
!la sagrada famil... | [
"# vit-world-landmarks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### arc de triomphe\n\n!arc de triomphe",
"#### big ben\n\n!big ben",
"#### la sagra... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit-world-landmarks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any ... |
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. -->
# bart-large-cnn-100-pad-early-lit
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-100-pad-early-lit", "results": []}]} | cammy/bart-large-cnn-100-pad-early-lit | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T15:00:54+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-100-pad-early-lit
================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1460
* Rouge1: 25.4944
* Rouge2: 7.9048
* Rougel: 16.2879
* Rougelsum: 20.883
* Gen Len: 64.3
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #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: 1\n* eval\\_... |
null | null | 1 | {} | shuoyingzhao/CSI5140PROJECT | null | [
"region:us"
] | null | 2022-03-08T15:17:15+00:00 | [] | [] | TAGS
#region-us
| 1 | [] | [
"TAGS\n#region-us \n"
] |
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | Rawat29/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T15:23:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8512
Model description
-----------------
More information needed
Intended uses & limita... | [
"### 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 #roberta #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: ... |
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-german-europeana-cased-germeval_14
This model is a fine-tuned version of [dbmdz/distilbert-base-german-europeana... | {"language": ["de"], "license": "mit", "datasets": ["germeval_14"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-german-europeana-cased-germeval_14", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "germeval_14... | HuggingAlex1247/distilbert-base-german-europeana-cased-germeval_14 | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"de",
"dataset:germeval_14",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T15:23:57+00:00 | [] | [
"de"
] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #de #dataset-germeval_14 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-german-europeana-cased-germeval_14
This model is a fine-tuned version of dbmdz/distilbert-base-german-europeana-cased on the germeval_14 dataset.
It achieves the following results on the evaluation set:
- precision: 0.7437
- recall: 0.7571
- f1: 0.7504
- accuracy: 0.9541
## Model description
Mor... | [
"# distilbert-base-german-europeana-cased-germeval_14\n\nThis model is a fine-tuned version of dbmdz/distilbert-base-german-europeana-cased on the germeval_14 dataset.\nIt achieves the following results on the evaluation set:\n- precision: 0.7437\n- recall: 0.7571\n- f1: 0.7504\n- accuracy: 0.9541",
"## Model des... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #de #dataset-germeval_14 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-german-europeana-cased-germeval_14\n\nThis model is a fine-tuned version of dbmdz/distilbert-base-german-europea... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": "en", "tags": ["distilbert", "long context"]} | ccdv/lsg-distilbert-base-uncased-4096 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"long context",
"custom_code",
"en",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-03-08T15:40:18+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #long context #custom_code #en #arxiv-2210.15497 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training global tokens
This model is adapted from disti... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\n\nThis mode... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #long context #custom_code #en #arxiv-2210.15497 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/co... |
automatic-speech-recognition | 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. -->
# wav2vec2-large-xls-r-300m-de-with-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-de-with-lm", "results": []}]} | Noricum/wav2vec2-large-xls-r-300m-de-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T15:45:28+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-de-with-lm
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# wav2vec2-large-xls-r-300m-de-with-lm\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None 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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-de-with-lm\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Model description\n\nMo... |
text-generation | null |
# My Awesome Model
| {"tags": ["conversational"]} | RTM/vilang | null | [
"conversational",
"region:us"
] | null | 2022-03-08T15:50:31+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
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-8-50-0.01
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-8-50-0.01", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"... | daisyxie21/bert-base-uncased-8-50-0.01 | 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 | 2022-03-08T16:10:03+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
| bert-base-uncased-8-50-0.01
===========================
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: 0.9219
* Matthews Correlation: 0.0
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.01\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: 50",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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\\_rat... |
null | null |
# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning
<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a><a href="https://pytorchlightning.ai/">
<img alt="Lightning"... | {"license": "mit", "tags": ["focus-prediction", "microscopy", "pytorch"], "metrics": ["MAE", "PLCC", "SRCC", "R2"], "name": "K-POP"} | 13hannes11/master_thesis_models | null | [
"tensorboard",
"focus-prediction",
"microscopy",
"pytorch",
"license:mit",
"region:us"
] | null | 2022-03-08T16:31:24+00:00 | [] | [] | TAGS
#tensorboard #focus-prediction #microscopy #pytorch #license-mit #region-us
|
# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning
<a href="URL alt="PyTorch" src="URL href="URL
<img alt="Lightning" src="URL
<a href="URL alt="Config: Hydra" src="URL
## Description
This repository contains the models and training pipeline for my master thesis... | [
"# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning\n\n<a href=\"URL alt=\"PyTorch\" src=\"URL href=\"URL\n<img alt=\"Lightning\" src=\"URL\n<a href=\"URL alt=\"Config: Hydra\" src=\"URL",
"## Description\n\nThis repository contains the models and training pip... | [
"TAGS\n#tensorboard #focus-prediction #microscopy #pytorch #license-mit #region-us \n",
"# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning\n\n<a href=\"URL alt=\"PyTorch\" src=\"URL href=\"URL\n<img alt=\"Lightning\" src=\"URL\n<a href=\"URL alt=\"Config: Hyd... |
null | 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. -->
# dit-base-manuscripts
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["masked-image-modeling", "generated_from_trainer"], "base_model": "facebook/deit-base-distilled-patch16-224", "model-index": [{"name": "dit-base-manuscripts", "results": []}]} | davanstrien/dit-base-manuscripts | null | [
"transformers",
"pytorch",
"tensorboard",
"deit",
"masked-image-modeling",
"generated_from_trainer",
"base_model:facebook/deit-base-distilled-patch16-224",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T17:22:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deit #masked-image-modeling #generated_from_trainer #base_model-facebook/deit-base-distilled-patch16-224 #license-apache-2.0 #endpoints_compatible #region-us
| dit-base-manuscripts
====================
This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the davanstrien/iiif\_manuscripts\_label\_ge\_50 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1266
Model description
-----------------
More information neede... | [
"### 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: 1333\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #deit #masked-image-modeling #generated_from_trainer #base_model-facebook/deit-base-distilled-patch16-224 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
question-answering | transformers |
# roberta-base for QA with qasper
Train from deepset/roberta-base-squad2.
How to use by python code:
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
# Load model with pipeline
model_name = "z-uo/roberta-qasper"
nlp = pipeline('question-answering', model=model_name, tokenizer... | {"language": "en", "tags": ["question_answering"], "datasets": ["z-uo/qasper-squad"]} | z-uo/roberta-qasper | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"question_answering",
"en",
"dataset:z-uo/qasper-squad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T18:23:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #question_answering #en #dataset-z-uo/qasper-squad #endpoints_compatible #region-us
|
# roberta-base for QA with qasper
Train from deepset/roberta-base-squad2.
How to use by python code:
| [
"# roberta-base for QA with qasper\nTrain from deepset/roberta-base-squad2.\n\nHow to use by python code:"
] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #question_answering #en #dataset-z-uo/qasper-squad #endpoints_compatible #region-us \n",
"# roberta-base for QA with qasper\nTrain from deepset/roberta-base-squad2.\n\nHow to use by python code:"
] |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-squadv2-large-batch | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T18:23:33+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-triviaqa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T18:38:16+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ---
language: en
license: apache-2.0
---
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
... | {} | allenai/aspire-sentence-embedder | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2111.08366",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T19:36:18+00:00 | [
"2111.08366"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2111.08366 #endpoints_compatible #region-us
| ---
language: en
license: apache-2.0
---
## Overview
Model included in a paper for modeling fine grained similarity between documents:
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper... | [
"## Overview\r\r\n\r\r\nModel included in a paper for modeling fine grained similarity between documents:\r\r\n\r\r\nTitle: \"Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity\"\r\r\n\r\r\nAuthors: Sheshera Mysore, Arman Cohan, Tom Hope\r\r\n\r\r\nPaper: URL\r\r\n\r\r\nGithub... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2111.08366 #endpoints_compatible #region-us \n",
"## Overview\r\r\n\r\r\nModel included in a paper for modeling fine grained similarity between documents:\r\r\n\r\r\nTitle: \"Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Docume... |
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-natural-questions
This model is a fine-tuned version of [distilbert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["natural_questions"], "model-index": [{"name": "distilbert-base-uncased-finetuned-natural-questions", "results": []}]} | datarpit/distilbert-base-uncased-finetuned-natural-questions | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:natural_questions",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T20:12:53+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-natural-questions
===================================================
This model is a fine-tuned version of distilbert-base-uncased on the natural\_questions dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6267
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-natural_questions #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\\_si... |
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. -->
# lib_balanced_gpt_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Mode... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "lib_balanced_gpt_model", "results": []}]} | akozlo/lib_bal | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-08T20:14:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# lib_balanced_gpt_model
This model is a fine-tuned version of gpt2 on an unknown 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... | [
"# lib_balanced_gpt_model\n\nThis model is a fine-tuned version of gpt2 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",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# lib_balanced_gpt_model\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore informat... |
automatic-speech-recognition | 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. -->
# xtreme_s_xlsr_minds14_fr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["accuracy"], "model-index": [{"name": "xtreme_s_xlsr_minds14_fr", "results": []}]} | anton-l/xtreme_s_xlsr_minds14_fr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"automatic-speech-recognition",
"google/xtreme_s",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T20:17:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #automatic-speech-recognition #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_minds14\_fr
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - MINDS14.FR-FR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3922
* Accuracy: 0.9135
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #automatic-speech-recognition #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
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. -->
# spanish-TinyBERT-betito-finetuned-xnli-es
This model is a fine-tuned version of [mrm8488/spanish-TinyBERT-betito](https://huggin... | {"tags": ["generated_from_trainer"], "datasets": ["xnli"], "metrics": ["accuracy"], "model-index": [{"name": "spanish-TinyBERT-betito-finetuned-xnli-es", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "xnli", "type": "xnli", "args": "es"}, "metrics": [{"type": "... | mrm8488/spanish-TinyBERT-betito-finetuned-xnli-es | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:xnli",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-08T20:55:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| spanish-TinyBERT-betito-finetuned-xnli-es
=========================================
This model is a fine-tuned version of mrm8488/spanish-TinyBERT-betito on the xnli dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7104
* Accuracy: 0.7475
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.50838112218154e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 64\n* seed: 13\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5083... |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-nq | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T21:35:09+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 8,
'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #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. -->
# 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": []}]} | kj141/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 | 2022-03-08T22:43:49+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.
## 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 the squad 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 #dataset-squad #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 the squad dataset.",
"## Mode... |
fill-mask | transformers |
# roberta-base-ukrainian
## Model Description
This is a RoBERTa model pre-trained on [Корпус UberText](https://lang.org.ua/uk/corpora/#anchor4). You can fine-tune `roberta-base-ukrainian` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-ukrainian-upos), dependency-parsing... | {"language": ["uk"], "license": "cc-by-sa-4.0", "tags": ["ukrainian", "masked-lm", "ubertext"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/roberta-base-ukrainian | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ukrainian",
"masked-lm",
"ubertext",
"uk",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-08T23:25:41+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ukrainian #masked-lm #ubertext #uk #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-ukrainian
## Model Description
This is a RoBERTa model pre-trained on Корпус UberText. You can fine-tune 'roberta-base-ukrainian' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# roberta-base-ukrainian",
"## Model Description\n\nThis is a RoBERTa model pre-trained on Корпус UberText. You can fine-tune 'roberta-base-ukrainian' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ukrainian #masked-lm #ubertext #uk #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-ukrainian",
"## Model Description\n\nThis is a RoBERTa model pre-trained on Корпус UberText. You can fine-tune 'roberta-base-ukr... |
null | null | data origin https://recipenlg.cs.put.poznan.pl/dataset
create environment
```
conda env create -v -f Recipe-Creator.yml
conda activate Recipe-Creator
``` | {} | franz96521/Recipe-Creator | null | [
"region:us"
] | null | 2022-03-08T23:36:06+00:00 | [] | [] | TAGS
#region-us
| data origin URL
create environment
| [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
# roberta-base-ukrainian-upos
## Model Description
This is a RoBERTa model pre-trained on Корпус UberText for POS-tagging and dependency-parsing, derived from [roberta-base-ukrainian](https://huggingface.co/KoichiYasuoka/roberta-base-ukrainian). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/... | {"language": ["uk"], "license": "cc-by-sa-4.0", "tags": ["ukrainian", "token-classification", "pos", "ubertext", "dependency-parsing"], "datasets": ["universal_dependencies", "ukr-models/Ukr-Synth"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0421\u0432\u043e\u0431\u043e\u0434\u0430 \u0456 \u043d\u0... | KoichiYasuoka/roberta-base-ukrainian-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"ukrainian",
"pos",
"ubertext",
"dependency-parsing",
"uk",
"dataset:universal_dependencies",
"dataset:ukr-models/Ukr-Synth",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T00:17:30+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #roberta #token-classification #ukrainian #pos #ubertext #dependency-parsing #uk #dataset-universal_dependencies #dataset-ukr-models/Ukr-Synth #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-ukrainian-upos
## Model Description
This is a RoBERTa model pre-trained on Корпус UberText for POS-tagging and dependency-parsing, derived from roberta-base-ukrainian. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Depende... | [
"# roberta-base-ukrainian-upos",
"## Model Description\n\nThis is a RoBERTa model pre-trained on Корпус UberText for POS-tagging and dependency-parsing, derived from roberta-base-ukrainian. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #ukrainian #pos #ubertext #dependency-parsing #uk #dataset-universal_dependencies #dataset-ukr-models/Ukr-Synth #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-ukrainian-upos",
"## Model Description\n\... |
automatic-speech-recognition | 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. -->
# wav2vec2-demo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-demo", "results": []}]} | M-Quan/wav2vec2-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T01:26:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-demo
=============
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4239
* Wer: 0.3508
Model description
-----------------
More information needed
Intended uses & limitations
--------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 3... |
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. -->
# ss_ver1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ss_ver1", "results": []}]} | jcai1/ss_ver1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-09T01:28:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ss\_ver1
========
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More inform... | [
"### 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: 1",
"### 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: 5e-05\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-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... | aaraki/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 | 2022-03-09T01:56:17+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: 0.5026
* Matthews Correlation: 0.4097
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: 1",
"### 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... |
image-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. -->
# vit-base-xray-pneumonia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["chest xrays"], "metrics": ["accuracy"], "widget": [{"src": "https://drive.google.com/uc?id=1yqnhD4Wjt4Y_NGLtijTGGaaw9GL497kQ", "example_title": "PNEUMONIA"}, {"src": "https://drive.google.com/uc?id=1xjcIEDb8kuSd4wF44gCEg... | nickmuchi/vit-base-xray-pneumonia | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-09T02:04:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-base-xray-pneumonia
=======================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the chest-xray-pneumonia dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3387
* Accuracy: 0.9006
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* tra... |
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