pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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-timit-demo-colab90
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab90", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab90 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T11:17:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab90
================================
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: 0.6766
* Wer: 0.4479
Model description
-----------------
More information needed
Intended uses & ... | [
"### 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 #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: 8... |
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-timit-demo-colab92
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab92", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab92 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T11:40:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab92
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6596
- eval_wer: 0.4164
- eval_runtime: 55.6472
- eval_samples_per_second: 12.615
- eval_steps_per_second: 1.581
- epoch: 2.85
... | [
"# wav2vec2-base-timit-demo-colab92\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6596\n- eval_wer: 0.4164\n- eval_runtime: 55.6472\n- eval_samples_per_second: 12.615\n- eval_steps_per_second: 1.581\n- e... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab92\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following r... |
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-timit-demo-colab_1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab_1", "results": []}]} | fahadtouseef/wav2vec2-base-timit-demo-colab_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T11:46:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab\_1
=================================
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: 0.3233
* Wer: 0.2574
Model description
-----------------
More information needed
Intended uses ... | [
"### 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* 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: 0.0002\n* train\\_batch\\_size: 1... |
text-classification | transformers |
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar.
This model comes from the paper [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://arxiv.org/abs/2203.09509) and can be used to detect implicit hate speech.
Please visit ... | {"language": ["en"], "tags": ["text-classification"]} | tomh/toxigen_hatebert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09509",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-01T12:02:09+00:00 | [
"2203.09509"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-2203.09509 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar.
This model comes from the paper ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection and can be used to detect implicit hate speech.
Please visit the Github Repository for the traini... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-2203.09509 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | test | {} | hdmt/aligner-en-vi | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T12:10:41+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| test | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar.
This model comes from the paper [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://arxiv.org/abs/2203.09509) and can be used to detect implicit hate speech.
Please visit ... | {"language": ["en"], "tags": ["text-classification"]} | tomh/toxigen_roberta | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2203.09509",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T12:19:41+00:00 | [
"2203.09509"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-2203.09509 #autotrain_compatible #endpoints_compatible #region-us
|
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar.
This model comes from the paper ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection and can be used to detect implicit hate speech.
Please visit the Github Repository for the traini... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #arxiv-2203.09509 #autotrain_compatible #endpoints_compatible #region-us \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-base-timit-demo-colab53
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab53", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab53 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T13:11:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab53
================================
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: 3.2003
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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 #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: 8... |
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-timit-demo-colab647
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab647", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab647 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T13:42:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab647
=================================
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: 0.5534
* Wer: 0.4799
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #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: 8... |
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-timit-demo-colab1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab1", "results": []}]} | cuzeverynameistaken/wav2vec2-base-timit-demo-colab1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T13:53:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab1
===============================
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: 0.7170
* Wer: 0.4784
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* 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: 1... |
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-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "bert-finetuned-mrpc", "results": []}]} | Yanael/bert-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T13:54:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-finetuned-mrpc
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-mrpc\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-finetuned-mrpc\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model descripti... |
text2text-generation | transformers |
# 🍊 제주 방언 번역 모델 🍊
- 제주어 -> 표준어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : https://github.com/Goormnlpteam3/JeBERT
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- AI HUB(+아래아 문자)
## 3. Hyp... | {"license": "afl-3.0"} | kompactss/JeBERT_je_ko | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T14:03:18+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 제주 방언 번역 모델
- 제주어 -> 표준어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : URL
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- AI HUB(+아래아 문자)
## 3. Hyper Parameters
- Epoch : 10 epochs(be... | [
"# 제주 방언 번역 모델 \n - 제주어 -> 표준어\n - Made by. 구름 자연어처리 과정 3기 3조!!\n - github link : URL",
"## 1. Seq2Seq Transformer Model\n - encoder : BertConfig\n - decoder : BertConfig\n - Tokenizer : WordPiece Tokenizer",
"## 2. Dataset\n - Jit Dataset\n - AI HUB(+아래아 문자)",
"## 3. Hyper Parameters\n - ... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# 제주 방언 번역 모델 \n - 제주어 -> 표준어\n - Made by. 구름 자연어처리 과정 3기 3조!!\n - github link : URL",
"## 1. Seq2Seq Transformer Model\n - encoder : BertConfig\n - decod... |
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | jcai1/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T14:10:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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 the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
text2text-generation | transformers |
# 🍊 제주 방언 번역 모델 🍊
- 표준어 -> 제주어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : https://github.com/Goormnlpteam3/JeBERT
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- AI HUB(+아래아 문자)
## 3. Hy... | {"license": "afl-3.0"} | kompactss/JeBERT_ko_je | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T14:16:08+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 제주 방언 번역 모델
- 표준어 -> 제주어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : URL
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- AI HUB(+아래아 문자)
## 3. Hyper Parameters
- Epoch : 10 epochs(b... | [
"# 제주 방언 번역 모델 \n - 표준어 -> 제주어\n - Made by. 구름 자연어처리 과정 3기 3조!!\n - github link : URL",
"## 1. Seq2Seq Transformer Model\n - encoder : BertConfig\n - decoder : BertConfig\n - Tokenizer : WordPiece Tokenizer",
"## 2. Dataset\n - Jit Dataset\n - AI HUB(+아래아 문자)",
"## 3. Hyper Parameters\n - ... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# 제주 방언 번역 모델 \n - 표준어 -> 제주어\n - Made by. 구름 자연어처리 과정 3기 3조!!\n - github link : URL",
"## 1. Seq2Seq Transformer Model\n - encoder : BertConfig\n - decod... |
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. -->
# beto_full_train_3_epochs
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dc... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "beto_full_train_3_epochs", "results": []}]} | rjuez00/meddocan-beto-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T15:21:07+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# beto_full_train_3_epochs
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0445
- Precision: 0.9541
- Recall: 0.9481
- F1: 0.9511
- Accuracy: 0.9951
## Model description
More information needed
#... | [
"# beto_full_train_3_epochs\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0445\n- Precision: 0.9541\n- Recall: 0.9481\n- F1: 0.9511\n- Accuracy: 0.9951",
"## Model description\n\nMore infor... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# beto_full_train_3_epochs\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset.\nIt achieves the following results on the ... |
image-classification | transformers |
# rare-puppers
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/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | ipvikas/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T15:51:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
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
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\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",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
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-timit-demo-colab57
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab57", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab57 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T16:06:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab57
================================
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: 0.7328
* Wer: 0.4593
Model description
-----------------
More information needed
Intended uses & ... | [
"### 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 #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: 8... |
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. -->
# test
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-22... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["f1"], "base_model": "facebook/deit-tiny-patch16-224", "model-index": [{"name": "test", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | flyswot/test | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T16:30:58+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/deit-tiny-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| test
====
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2724
* F1: 0.1240
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.001",
"### Trai... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #base_model-facebook/deit-tiny-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used ... |
question-answering | transformers | Enter the Name of Emotion in the Question Field
Enter The Text from which emotion has to be extracted
Example 1-
Question - Guilty
Context - I shouted to my mom
Example 2 -
Question - Sad
Context - I felt betrayed when my girlfriend kissed another guy even though she was drunk
Note: Model is st... | {} | Nakul24/Spanbert-emotion-extraction | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T16:42:46+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| Enter the Name of Emotion in the Question Field
Enter The Text from which emotion has to be extracted
Example 1-
Question - Guilty
Context - I shouted to my mom
Example 2 -
Question - Sad
Context - I felt betrayed when my girlfriend kissed another guy even though she was drunk
Note: Model is st... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Peter Parker DialoGPT Model | {"tags": ["conversational"]} | dmoz47/DialoGPT-small-peterparker | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-01T16:45:00+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peter Parker DialoGPT Model | [
"# Peter Parker DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter Parker DialoGPT Model"
] |
null | null | The [MEDDOCAN dataset](https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN) has some entities not separated by a space but a dot. For example such is the case of Alicante.Villajoyosa which are two separate entities but with traditional tokenizers are only one Token. Spacy tokenizers also don't work, when I was trying to a... | {} | rjuez00/meddocan-flair-spanish-fast-bilstm-crf | null | [
"pytorch",
"region:us"
] | null | 2022-05-01T17:01:08+00:00 | [] | [] | TAGS
#pytorch #region-us
| The MEDDOCAN dataset has some entities not separated by a space but a dot. For example such is the case of Alicante.Villajoyosa which are two separate entities but with traditional tokenizers are only one Token. Spacy tokenizers also don't work, when I was trying to assign the entities two the tokens on training SpaCy ... | [] | [
"TAGS\n#pytorch #region-us \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-base-timit-demo-colab240
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab240", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab240 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T17:29:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab240
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6367
- eval_wer: 0.5855
- eval_runtime: 20.4889
- eval_samples_per_second: 6.931
- eval_steps_per_second: 0.879
- epoch: 14.08... | [
"# wav2vec2-base-timit-demo-colab240\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6367\n- eval_wer: 0.5855\n- eval_runtime: 20.4889\n- eval_samples_per_second: 6.931\n- eval_steps_per_second: 0.879\n- e... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab240\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048
## Validation Metrics
- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.99085869882... | {"language": "en", "tags": "autotrain", "datasets": ["agnihotri/autotrain-data-contract_type"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.07610944071640048} | agnihotri/cuad_contract_type | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:agnihotri/autotrain-data-contract_type",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T17:36:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-agnihotri/autotrain-data-contract_type #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048
## Validation Metrics
- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.99085869882... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 809725368\n- CO2 Emissions (in grams): 0.07610944071640048",
"## Validation Metrics\n\n- Loss: 0.05312908813357353\n- Accuracy: 0.9911504424778761\n- Macro F1: 0.9912087912087912\n- Micro F1: 0.9911504424778761\n- Weighted... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-agnihotri/autotrain-data-contract_type #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 809725368\n- CO2 Emis... |
text-classification | transformers | # Dummy Model
Following the Hugging Face course | {} | Yanael/dummy-model | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T18:30:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Dummy Model
Following the Hugging Face course | [
"# Dummy Model\n\nFollowing the Hugging Face course"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Dummy Model\n\nFollowing the Hugging Face course"
] |
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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | Gergoe/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-01T18:48:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2891
* Rouge1: 15.35
* Rouge2: 6.4925
* Rougel: 14.8921
* Rougelsum: 14.6312
Model description
----------... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #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*... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Conformer-Large-960h with Relative Position Embeddings + 4-gram
This model is identical to [Facebook's wav2vec2-conformer-rel-pos-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large-960h-ft), but is
augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's offici... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-conformer-rel-pos-large-960h-ft-4-gram", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Spe... | patrickvonplaten/wav2vec2-conformer-rel-pos-large-960h-ft-4-gram | null | [
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T19:27:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Conformer-Large-960h with Relative Position Embeddings + 4-gram
========================================================================
This model is identical to Facebook's wav2vec2-conformer-rel-pos-large-960h-ft, but is
augmented with an English 4-gram. The 'URL' of Librispeech's official ngrams is used.... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings + 4-gram
This model is identical to [Facebook's wav2vec2-conformer-rope-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rope-large-960h-ft), but is
augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngram... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-conformer-rope-large-960h-ft-4-gram", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech... | patrickvonplaten/wav2vec2-conformer-rope-large-960h-ft-4-gram | null | [
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-01T19:28:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings + 4-gram
======================================================================
This model is identical to Facebook's wav2vec2-conformer-rope-large-960h-ft, but is
augmented with an English 4-gram. The 'URL' of Librispeech's official ngrams is used.
Eval... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003... | SebastianS/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T20:12:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0452
* Accuracy: 0.9911
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-timit-demo-colab66
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab66", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab66 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T21:54:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab66
================================
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: 3.2675
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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 #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: 8... |
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-timit-demo-colab2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab2", "results": []}]} | sherry7144/wav2vec2-base-timit-demo-colab2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T22:01:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab2
===============================
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: 0.7746
* Wer: 0.5855
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* 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: 1... |
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. -->
# pega_70_articles
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "pega_70_articles", "results": []}]} | Worldman/pega_70_articles | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T22:16:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# pega_70_articles
This model is a fine-tuned version of google/pegasus-cnn_dailymail 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 hyperpar... | [
"# pega_70_articles\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail 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 proced... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# pega_70_articles\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on an unknown dataset.",
"## Model description\n\nMore informati... |
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. -->
# voodooMaestro/finetuned-stories
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknow... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "voodooMaestro/finetuned-stories", "results": []}]} | voodooMaestro/finetuned-stories | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T22:31:33+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| voodooMaestro/finetuned-stories
===============================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.9188
* Validation Loss: 1.5604
* Epoch: 0
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #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\\_rate': {'class\... |
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. -->
# bert-finetuned-squad1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad1", "results": []}]} | Ghost1/bert-finetuned-squad1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T23:04:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad1
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# bert-finetuned-squad1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore informatio... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 812425472
- CO2 Emissions (in grams): 0.007597570744740809
## Validation Metrics
- Loss: 0.5105093121528625
- Accuracy: 0.8268156424581006
- Macro F1: 0.6020923520923521
- Micro F1: 0.8268156424581006
- Weighted F1: 0.80213951163... | {"language": "en", "tags": "autotrain", "datasets": ["charly/autotrain-data-sentiment-4"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.007597570744740809} | charly/autotrain-sentiment-4-812425472 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:charly/autotrain-data-sentiment-4",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-01T23:36:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-charly/autotrain-data-sentiment-4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 812425472
- CO2 Emissions (in grams): 0.007597570744740809
## Validation Metrics
- Loss: 0.5105093121528625
- Accuracy: 0.8268156424581006
- Macro F1: 0.6020923520923521
- Micro F1: 0.8268156424581006
- Weighted F1: 0.80213951163... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 812425472\n- CO2 Emissions (in grams): 0.007597570744740809",
"## Validation Metrics\n\n- Loss: 0.5105093121528625\n- Accuracy: 0.8268156424581006\n- Macro F1: 0.6020923520923521\n- Micro F1: 0.8268156424581006\n- Weighted... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-charly/autotrain-data-sentiment-4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 812425472\n- CO2 Emissions (i... |
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-sst2
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": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}... | DioLiu/distilbert-base-uncased-finetuned-sst2 | 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 | 2022-05-02T01:28:34+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-sst2
======================================
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: 0.5963
* Accuracy: 0.8968
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-timit-demo-colab3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab3", "results": []}]} | sherry7144/wav2vec2-base-timit-demo-colab3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T02:14:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab3
===============================
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: 0.8344
* Wer: 0.6055
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* 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: 1... |
null | keras | This is CaiT model from [1]. It was first implemented in TensorFlow and then the original parameters from [2] were ported into the implementation. Refer to [3] for more details.
## References
[1] Going deeper with Image Transformers: https://arxiv.org/abs/2103.17239
[2] CaiT GitHub: https://github.com/facebookresear... | {} | probing-vits/cait_xxs24_224_classification | null | [
"keras",
"arxiv:2103.17239",
"has_space",
"region:us"
] | null | 2022-05-02T02:19:00+00:00 | [
"2103.17239"
] | [] | TAGS
#keras #arxiv-2103.17239 #has_space #region-us
| This is CaiT model from [1]. It was first implemented in TensorFlow and then the original parameters from [2] were ported into the implementation. Refer to [3] for more details.
## References
[1] Going deeper with Image Transformers: URL
[2] CaiT GitHub: URL
[3] CaiT-TF GitHub: URL | [
"## References\n\n[1] Going deeper with Image Transformers: URL\n\n[2] CaiT GitHub: URL\n\n[3] CaiT-TF GitHub: URL"
] | [
"TAGS\n#keras #arxiv-2103.17239 #has_space #region-us \n",
"## References\n\n[1] Going deeper with Image Transformers: URL\n\n[2] CaiT GitHub: URL\n\n[3] CaiT-TF GitHub: URL"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142
## Validation Metrics
- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.58731376634781... | {"language": "unk", "tags": "autotrain", "datasets": ["crcb/autotrain-data-go_emo_new"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 20.58663910106142} | crcb/emo_go_new | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:crcb/autotrain-data-go_emo_new",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T03:07:25+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-crcb/autotrain-data-go_emo_new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142
## Validation Metrics
- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.58731376634781... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 813325491\n- CO2 Emissions (in grams): 20.58663910106142",
"## Validation Metrics\n\n- Loss: 1.3628994226455688\n- Accuracy: 0.5920355494787216\n- Macro F1: 0.4844439507523978\n- Micro F1: 0.5920355494787216\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-crcb/autotrain-data-go_emo_new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 813325491\n- CO2 Emissions (... |
text-classification | transformers |
# Model for detection of Orientalization of Japan in newspaper articles
This model was based on the original [HerBERT](https://huggingface.co/allegro/herbert-base-cased) Base.
The model was finetuned on a set of Polish press articles mentioning Japan from the years 1818-1939 to recognize if an article (or any input... | {"language": "pl", "license": "cc-by-sa-4.0", "datasets": ["18th and 19th century articles mentioning Japan"]} | ptaszynski/japan-orientalization-pl | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"pl",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T04:25:36+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #bert #text-classification #pl #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model for detection of Orientalization of Japan in newspaper articles
This model was based on the original HerBERT Base.
The model was finetuned on a set of Polish press articles mentioning Japan from the years 1818-1939 to recognize if an article (or any input text) presents a genuine description of Japan, or wh... | [
"# Model for detection of Orientalization of Japan in newspaper articles\n\nThis model was based on the original HerBERT Base. \n\nThe model was finetuned on a set of Polish press articles mentioning Japan from the years 1818-1939 to recognize if an article (or any input text) presents a genuine description of Japa... | [
"TAGS\n#transformers #pytorch #bert #text-classification #pl #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model for detection of Orientalization of Japan in newspaper articles\n\nThis model was based on the original HerBERT Base. \n\nThe model was finetuned on a set of Poli... |
token-classification | spacy | ### Details: https://spacy.io/models/sv#sv_core_news_sm
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sv_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["sv"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/sv_core_news_sm | null | [
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T06:56:09+00:00 | [] | [
"sv"
] | TAGS
#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (381 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nSwedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (381 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nSwedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (381 labels for... |
token-classification | spacy | ### Details: https://spacy.io/models/sv#sv_core_news_md
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sv_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["sv"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/sv_core_news_md | null | [
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T06:56:23+00:00 | [] | [
"sv"
] | TAGS
#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (381 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nSwedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (381 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nSwedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (381 labels for... |
token-classification | spacy | ### Details: https://spacy.io/models/sv#sv_core_news_lg
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sv_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["sv"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/sv_core_news_lg | null | [
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T06:56:46+00:00 | [] | [
"sv"
] | TAGS
#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (381 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nSwedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (381 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nSwedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (381 labels for... |
null | null | Bienvenue | {} | Berry/model_name | null | [
"region:us"
] | null | 2022-05-02T07:16:34+00:00 | [] | [] | TAGS
#region-us
| Bienvenue | [] | [
"TAGS\n#region-us \n"
] |
token-classification | spacy | ### Details: https://spacy.io/models/ko#ko_core_news_sm
Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `ko_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["ko"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ko_core_news_sm | null | [
"spacy",
"token-classification",
"ko",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T07:18:16+00:00 | [] | [
"ko"
] | TAGS
#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (2028 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nKorean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2028 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nKorean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2028 labels for... |
token-classification | spacy | ### Details: https://spacy.io/models/ko#ko_core_news_md
Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `ko_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["ko"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ko_core_news_md | null | [
"spacy",
"token-classification",
"ko",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T07:18:31+00:00 | [] | [
"ko"
] | TAGS
#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (2028 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nKorean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2028 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nKorean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2028 labels for... |
token-classification | spacy | ### Details: https://spacy.io/models/ko#ko_core_news_lg
Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `ko_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["ko"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ko_core_news_lg | null | [
"spacy",
"token-classification",
"ko",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T07:19:03+00:00 | [] | [
"ko"
] | TAGS
#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (2028 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nKorean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2028 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nKorean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2028 labels for... |
question-answering | transformers |
**Exact Match** 83.19
**F1** 90.46
Checkout [linkbert-large-finetuned-squad](https://huggingface.co/niklaspm/linkbert-large-finetuned-squad) which achives F1:92.68 and EM:86.5
See [LinkBERT Paper](https://arxiv.org/abs/2203.15827) | {"license": "apache-2.0"} | niklaspm/linkbert-base-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T07:53:53+00:00 | [
"2203.15827"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us
|
Exact Match 83.19
F1 90.46
Checkout linkbert-large-finetuned-squad which achives F1:92.68 and EM:86.5
See LinkBERT Paper | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2203.15827 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
token-classification | spacy | ### Details: https://spacy.io/models/fi#fi_core_news_sm
Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `fi_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["fi"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/fi_core_news_sm | null | [
"spacy",
"token-classification",
"fi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T08:12:21+00:00 | [] | [
"fi"
] | TAGS
#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (2145 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nFinnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2145 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nFinnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2145 labels fo... |
token-classification | spacy | ### Details: https://spacy.io/models/fi#fi_core_news_md
Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `fi_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["fi"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/fi_core_news_md | null | [
"spacy",
"token-classification",
"fi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T08:12:33+00:00 | [] | [
"fi"
] | TAGS
#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (2145 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nFinnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2145 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nFinnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2145 labels fo... |
token-classification | spacy | ### Details: https://spacy.io/models/fi#fi_core_news_lg
Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `fi_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8... | {"language": ["fi"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/fi_core_news_lg | null | [
"spacy",
"token-classification",
"fi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-05-02T08:12:58+00:00 | [] | [
"fi"
] | TAGS
#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (2145 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nFinnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2145 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nFinnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (2145 labels fo... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.056186258092819436
## Validation Metrics
- Loss: 0.15057753026485443
- Accuracy: 0.9738805970149254
- Precision: 0.9469026548672567
- Recall: 0.9304347826086956
- AUC: 0.9891149437157905
- F1: 0... | {"language": "en", "tags": "autotrain", "datasets": ["tristantristantristan/autotrain-data-rumour_detection"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.056186258092819436} | tristantristantristan/rumor | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:tristantristantristan/autotrain-data-rumour_detection",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T08:27:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-tristantristantristan/autotrain-data-rumour_detection #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.056186258092819436
## Validation Metrics
- Loss: 0.15057753026485443
- Accuracy: 0.9738805970149254
- Precision: 0.9469026548672567
- Recall: 0.9304347826086956
- AUC: 0.9891149437157905
- F1: 0... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 813825547\n- CO2 Emissions (in grams): 0.056186258092819436",
"## Validation Metrics\n\n- Loss: 0.15057753026485443\n- Accuracy: 0.9738805970149254\n- Precision: 0.9469026548672567\n- Recall: 0.9304347826086956\n- AUC: 0.989114... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-tristantristantristan/autotrain-data-rumour_detection #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 813825547\n- C... |
zero-shot-classification | transformers |
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers... | {"datasets": ["snli", "anli", "multi_nli", "multi_nli_mismatch", "fever"], "pipeline_tag": "zero-shot-classification"} | HiTZ/A2T_RoBERTa_SMFA_ACE-arg | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"autotrain_compatible",
"endpoints_compatibl... | null | 2022-05-02T08:38:07+00:00 | [
"2104.14690",
"2203.13602"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# A2T Entailment model
Important: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.
Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero... | [
"# A2T Entailment model\n\nImportant: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.\n\n\nTextual Entailment (or Natural Language Inference) has turned out to be a good choice... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# A2T Entailment ... |
text-classification | transformers | # Distilbert-base-uncased-emotion
## Model description:
[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["Accuracy, F1 Score"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | nanopass/distilbert-base-uncased-emotion-2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T08:42:09+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #distilbert #text-classification #emotion #en #dataset-emotion #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Distilbert-base-uncased-emotion
===============================
Model description:
------------------
Distilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and a... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #distilbert #text-classification #emotion #en #dataset-emotion #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
The following is a fine-tuning of the BioBert models on the GAD dataset.
The model works by masking the gene string with "@GENE$" and the disease string with "@DISEASE$".
The output is a text classification that can either be:
- "LABEL0" if there is no relation
- "LABEL1" if there is a relation. | {"license": "afl-3.0", "widget": [{"text": "The case of a 72-year-old male with @DISEASE$ with poor insulin control (fasting hyperglycemia greater than 180 mg/dl) who had a long-standing polyuric syndrome is here presented. Hypernatremia and plasma osmolality elevated together with a low urinary osmolality led to the s... | JacopoBandoni/BioBertRelationGenesDiseases | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T09:25:29+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
The following is a fine-tuning of the BioBert models on the GAD dataset.
The model works by masking the gene string with "@GENE$" and the disease string with "@DISEASE$".
The output is a text classification that can either be:
- "LABEL0" if there is no relation
- "LABEL1" if there is a relation. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | Philip-Jan/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T09:50:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3328
- Accuracy: 0.8633
- F1: 0.8647
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3328\n- Accuracy: 0.8633\n- F1: 0.8647",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
multiple-choice | 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-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | jhoonk/bert-base-uncased-finetuned-swag | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T09:57:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-swag
================================
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0337
* Accuracy: 0.7888
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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 #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 5e-05\n* train\\_batch\\_size: 16\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": []}]} | jhoonk/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 | 2022-05-02T10:03:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1622
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
null | null | # A fine-tuned GPT-Neo Model for Tweet Generation
This model is a fine-tuned version of the 1.3B-parameter GPT-Neo model developed by EleutherAI. As the default GPT-Neo model did not receive any social media data during its pre-training, we fine-tuned it with tweets collected from Twitter from October to November 202... | {} | Nijana/gpt-neo-1.3B-climate_change_tweets | null | [
"region:us"
] | null | 2022-05-02T10:35:45+00:00 | [] | [] | TAGS
#region-us
| # A fine-tuned GPT-Neo Model for Tweet Generation
This model is a fine-tuned version of the 1.3B-parameter GPT-Neo model developed by EleutherAI. As the default GPT-Neo model did not receive any social media data during its pre-training, we fine-tuned it with tweets collected from Twitter from October to November 202... | [
"# A fine-tuned GPT-Neo Model for Tweet Generation \n\nThis model is a fine-tuned version of the 1.3B-parameter GPT-Neo model developed by EleutherAI. As the default GPT-Neo model did not receive any social media data during its pre-training, we fine-tuned it with tweets collected from Twitter from October to Novem... | [
"TAGS\n#region-us \n",
"# A fine-tuned GPT-Neo Model for Tweet Generation \n\nThis model is a fine-tuned version of the 1.3B-parameter GPT-Neo model developed by EleutherAI. As the default GPT-Neo model did not receive any social media data during its pre-training, we fine-tuned it with tweets collected from Twit... |
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-timit-demo-colab971
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab971", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab971 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T10:49:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab971
=================================
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: 0.6551
* Wer: 0.4448
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\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: 2... |
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-timit-demo-colab_2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab_2", "results": []}]} | fahadtouseef/wav2vec2-base-timit-demo-colab_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T10:50:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab\_2
=================================
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: 0.3801
* Wer: 0.3035
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* 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: 1... |
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | KoenBronstring/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T11:08:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3149
- Accuracy: 0.8733
- F1: 0.8758
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3149\n- Accuracy: 0.8733\n- F1: 0.8758",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
zero-shot-classification | transformers |
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers... | {"datasets": ["snli", "anli", "multi_nli", "multi_nli_mismatch", "fever"], "pipeline_tag": "zero-shot-classification"} | HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg_ACE-arg | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"autotrain_compatible",
"endpoints_compatibl... | null | 2022-05-02T11:08:43+00:00 | [
"2104.14690",
"2203.13602"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us
|
# A2T Entailment model
Important: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.
Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero... | [
"# A2T Entailment model\n\nImportant: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.\n\n\nTextual Entailment (or Natural Language Inference) has turned out to be a good choice... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us \n",
"# A2T Entailment model\n\nIm... |
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-sst2-newdata
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2-newdata", "results": []}]} | DioLiu/distilbert-base-uncased-finetuned-sst2-newdata | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T11:18:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2-newdata
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0588
* Accuracy: 0.9911
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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\\_b... |
token-classification | transformers |
This model predicts the punctuation of Dutch texts. We developed it to restore the punctuation of transcribed spoken language.
This model was trained on the [SoNaR Dataset](http://hdl.handle.net/10032/tm-a2-h5).
The model restores the following punctuation markers: **"." "," "?" "-" ":"**
## Sample Code
We provide ... | {"language": ["nl"], "license": "mit", "tags": ["punctuation prediction", "punctuation"], "datasets": "sonar", "metrics": ["f1"], "widget": [{"text": "Ondanks dat het nu bijna voorjaar is hebben we nog steds best koude dagen", "example_title": "Dutch Sample"}]} | oliverguhr/fullstop-dutch-sonar-punctuation-prediction | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"punctuation prediction",
"punctuation",
"nl",
"dataset:sonar",
"arxiv:2301.03319",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T11:20:47+00:00 | [
"2301.03319"
] | [
"nl"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #punctuation prediction #punctuation #nl #dataset-sonar #arxiv-2301.03319 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| This model predicts the punctuation of Dutch texts. We developed it to restore the punctuation of transcribed spoken language.
This model was trained on the SoNaR Dataset.
The model restores the following punctuation markers: "." "," "?" "-" ":"
Sample Code
-----------
We provide a simple python package that al... | [
"### Restore Punctuation\n\n\noutput\n\n\n\n> \n> hervatting van de zitting. ik verklaar de zitting van het europees parlement, die op vrijdag 17 december werd onderbroken, te zijn hervat.\n> \n> \n>",
"### Predict Labels\n\n\noutput\n\n\n\n> \n> [['hervatting', '0', 0.99998724], ['van', '0', 0.9999784], ['de', '... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #punctuation prediction #punctuation #nl #dataset-sonar #arxiv-2301.03319 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Restore Punctuation\n\n\noutput\n\n\n\n> \n> hervatting van de zitting. ik verklaar de z... |
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-timit-demo-colab3000
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab3000", "results": []}]} | hassnain/wav2vec2-base-timit-demo-colab3000 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T11:25:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab3000
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6852
- eval_wer: 0.3845
- eval_runtime: 71.297
- eval_samples_per_second: 9.846
- eval_steps_per_second: 1.234
- epoch: 24.22... | [
"# wav2vec2-base-timit-demo-colab3000\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6852\n- eval_wer: 0.3845\n- eval_runtime: 71.297\n- eval_samples_per_second: 9.846\n- eval_steps_per_second: 1.234\n- e... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab3000\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following... |
zero-shot-classification | transformers |
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers... | {"datasets": ["snli", "anli", "multi_nli", "multi_nli_mismatch", "fever"], "pipeline_tag": "zero-shot-classification"} | HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"autotrain_compatible",
"endpoints_compatibl... | null | 2022-05-02T11:25:23+00:00 | [
"2104.14690",
"2203.13602"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us
|
# A2T Entailment model
Important: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.
Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero... | [
"# A2T Entailment model\n\nImportant: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.\n\n\nTextual Entailment (or Natural Language Inference) has turned out to be a good choice... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us \n",
"# A2T Entailment model\n\nIm... |
zero-shot-classification | transformers |
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers... | {"datasets": ["snli", "anli", "multi_nli", "multi_nli_mismatch", "fever"], "pipeline_tag": "zero-shot-classification"} | HiTZ/A2T_RoBERTa_SMFA_ACE-arg_WikiEvents-arg | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"autotrain_compatible",
"endpoints_compatibl... | null | 2022-05-02T11:37:35+00:00 | [
"2104.14690",
"2203.13602"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us
|
# A2T Entailment model
Important: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.
Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero... | [
"# A2T Entailment model\n\nImportant: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.\n\n\nTextual Entailment (or Natural Language Inference) has turned out to be a good choice... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us \n",
"# A2T Entailment model\n\nIm... |
zero-shot-classification | transformers |
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers... | {"datasets": ["snli", "anli", "multi_nli", "multi_nli_mismatch", "fever"], "pipeline_tag": "zero-shot-classification"} | HiTZ/A2T_RoBERTa_SMFA_TACRED-re | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"autotrain_compatible",
"endpoints_compatibl... | null | 2022-05-02T11:52:23+00:00 | [
"2104.14690",
"2203.13602"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us
|
# A2T Entailment model
Important: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.
Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero... | [
"# A2T Entailment model\n\nImportant: These pretrained entailment models are intended to be used with the Ask2Transformers library but are also fully compatible with the 'ZeroShotTextClassificationPipeline' from Transformers.\n\n\nTextual Entailment (or Natural Language Inference) has turned out to be a good choice... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #dataset-snli #dataset-anli #dataset-multi_nli #dataset-multi_nli_mismatch #dataset-fever #arxiv-2104.14690 #arxiv-2203.13602 #autotrain_compatible #endpoints_compatible #region-us \n",
"# A2T Entailment model\n\nIm... |
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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [cardiffnlp... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T12:10:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DistilBERTFINAL\_ctxSentence\_TRAIN\_essays\_TEST\_NULL\_second\_train\_set\_null\_False
========================================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval... |
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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [card... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T12:12:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DistilBERTFINAL\_ctxSentence\_TRAIN\_webDiscourse\_TEST\_NULL\_second\_train\_set\_null\_False
==============================================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval... |
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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [cardif... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERTFINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERTFINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T12:14:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DistilBERTFINAL\_ctxSentence\_TRAIN\_editorials\_TEST\_NULL\_second\_train\_set\_null\_False
============================================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval... |
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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [cardiffnlp/tw... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False", "results": []}]} | ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T12:19:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DistilBERTFINAL\_ctxSentence\_TRAIN\_all\_TEST\_NULL\_second\_train\_set\_null\_False
=====================================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval... |
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 conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | kurama/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T12:33:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0617
* Precision: 0.9322
* Recall: 0.9485
* F1: 0.9403
* Accuracy: 0.9860
Model description
-----------------
More information ... | [
"### 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 73.8 | 73.7 |
| test | 74.4 | 74.3 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-nli-rua_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T12:48:52+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 73.8, F1macro: 73.7
Set: test, F1micro: 74.4, F1macro: 74.3
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-classification | null |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transfo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet", "imagenet-21k"]} | Matthijs/vit-base-patch16-224 | null | [
"coreml",
"vision",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | null | 2022-05-02T12:56:44+00:00 | [
"2010.11929"
] | [] | TAGS
#coreml #vision #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #license-apache-2.0 #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transfor... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Tr... | [
"TAGS\n#coreml #vision #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #license-apache-2.0 #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tun... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 69.9 | 69.9 |
| test | 68.8 | 68.8 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-rua_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T12:58:49+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 69.9, F1macro: 69.9
Set: test, F1micro: 68.8, F1macro: 68.8
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Umutoni DialoGPT Model | {"tags": ["conversational"]} | niprestige/GPT-small-DusabeBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T13:01:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Umutoni DialoGPT Model | [
"# Umutoni DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Umutoni DialoGPT 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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_french_second_train_set_NULL_True
This model is a fine-tuned version of [cardiffnlp/t... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_french_second_train_set_NULL_True", "results": []}]} | ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_french_second_train_set_NULL_True | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T13:03:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DistilBERTFINAL\_ctxSentence\_TRAIN\_all\_TEST\_french\_second\_train\_set\_NULL\_True
======================================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation s... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval... |
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. -->
# _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False
This model is a fine-tuned version of [cardiffnlp/twitter-rober... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "_ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False", "results": []}]} | ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T13:07:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| \_ctxSentence\_TRAIN\_all\_TEST\_french\_second\_train\_set\_french\_False
==========================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4936
* P... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval... |
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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | spasis/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T14:04:32+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1185
* Rouge1: 17.2081
* Rouge2: 8.8374
* Rougel: 16.8033
* Rougelsum: 16.663
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Trai... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
text-generation | transformers |
# Sergio bot DialoGPT Model | {"tags": ["conversational"]} | Shakerlicious/DialoGPT-small-descentbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T14:15:39+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sergio bot DialoGPT Model | [
"# Sergio bot DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sergio bot DialoGPT Model"
] |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/thai_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./r... | {"language": "th", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/thai_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"th",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T14:16:52+00:00 | [
"1804.00015"
] | [
"th"
] | TAGS
#espnet #audio #automatic-speech-recognition #th #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/thai\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Apr 18 11:05:12 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:... | [
"### 'espnet/thai\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Apr 18 11:05:12 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #th #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/thai\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnv... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/zh-CN_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./... | {"language": "zh-CN", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/zh-CN_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T14:24:05+00:00 | [
"1804.00015"
] | [
"zh-CN"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/zh-CN\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Apr 18 13:15:36 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12... | [
"### 'espnet/zh-CN\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Apr 18 13:15:36 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/zh-CN\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnviro... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/id_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run... | {"language": "id", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/id_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"id",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T14:30:01+00:00 | [
"1804.00015"
] | [
"id"
] | TAGS
#espnet #audio #automatic-speech-recognition #id #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/id\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Apr 18 11:07:50 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:28... | [
"### 'espnet/id\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Apr 18 11:07:50 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #id #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/id\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvir... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/greek_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./... | {"language": "el", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/greek_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"el",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T14:34:01+00:00 | [
"1804.00015"
] | [
"el"
] | TAGS
#espnet #audio #automatic-speech-recognition #el #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/greek\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Apr 17 19:51:46 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12... | [
"### 'espnet/greek\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Apr 17 19:51:46 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #el #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/greek\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEn... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/pt_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run... | {"language": "pt", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/pt_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"pt",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T14:37:14+00:00 | [
"1804.00015"
] | [
"pt"
] | TAGS
#espnet #audio #automatic-speech-recognition #pt #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/pt\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Apr 11 18:55:23 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:28... | [
"### 'espnet/pt\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Apr 11 18:55:23 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #pt #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/pt\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvir... |
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-timit-demo-colab_3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab_3", "results": []}]} | fahadtouseef/wav2vec2-base-timit-demo-colab_3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T14:40:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab\_3
=================================
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: 3.1942
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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* 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: 0.0003\n* train\\_batch\\_size: 1... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/tamil_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./... | {"language": "ta", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/tamil_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"ta",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T14:45:20+00:00 | [
"1804.00015"
] | [
"ta"
] | TAGS
#espnet #audio #automatic-speech-recognition #ta #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/tamil\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon May 2 11:41:47 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:... | [
"### 'espnet/tamil\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon May 2 11:41:47 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #ta #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/tamil\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEn... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/farsi_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./... | {"language": "fa", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/farsi_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"fa",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T14:49:22+00:00 | [
"1804.00015"
] | [
"fa"
] | TAGS
#espnet #audio #automatic-speech-recognition #fa #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/farsi\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon May 2 11:48:56 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:... | [
"### 'espnet/farsi\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon May 2 11:48:56 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #fa #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/farsi\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEn... |
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. -->
# layoutlmv3-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/la... | {"tags": ["generated_from_trainer"], "datasets": ["nielsr/funsd-layoutlmv3"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "layoutlmv3-finetuned-funsd", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "da... | nielsr/layoutlmv3-finetuned-funsd | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:nielsr/funsd-layoutlmv3",
"base_model:microsoft/layoutlmv3-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-02T15:18:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-nielsr/funsd-layoutlmv3 #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| layoutlmv3-finetuned-funsd
==========================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the nielsr/funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1164
* Precision: 0.9026
* Recall: 0.913
* F1: 0.9078
* Accuracy: 0.8330
The script for t... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* training\\_steps: 1000",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-nielsr/funsd-layoutlmv3 #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe follow... |
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. -->
# language-detection-fine-tuned-on-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "language-detection-fine-tuned-on-xlm-roberta-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "common_language", "type... | jerryKakooza/language-detection-fine-tuned-on-xlm-roberta-base | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:common_language",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T15:45:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-common_language #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| language-detection-fine-tuned-on-xlm-roberta-base
=================================================
This model is a fine-tuned version of xlm-roberta-base on the common\_language dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1642
* Accuracy: 0.9760
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-common_language #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* lea... |
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/1508957108892581889/eKjV... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/wliiyum/1651510930825/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/wliiyum | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T16:01:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
will i am
@wliiyum
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
-------------
... | [] | [
"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. -->
# pegasus-qmsum-meeting-summarization
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pe... | {"tags": ["generated_from_trainer"], "datasets": ["yawnick/QMSum"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-qmsum-meeting-summarization", "results": []}]} | mikeadimech/pegasus-qmsum-meeting-summarization | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:yawnick/QMSum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T16:05:40+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-qmsum-meeting-summarization
===================================
This model is a fine-tuned version of google/pegasus-xsum on the QMSum dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2331
* Rouge1: 32.7156
* Rouge2: 10.5699
* Rougel: 23.2759
* Rougelsum: 29.7903
* Gen Len: 61.65
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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 #pegasus #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size:... |
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-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | roshantushar/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T16:12:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base 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",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
text2text-generation | transformers |
# 🍊 제주 방언 번역 모델 🍊
- 표준어 -> 제주어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : https://github.com/Goormnlpteam3/JeBERT
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- AI HUB(+아래아 문자)_v2
## 3.... | {"license": "afl-3.0"} | kompactss/JeBERT_ko_je_v2 | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T16:30:31+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 제주 방언 번역 모델
- 표준어 -> 제주어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : URL
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- AI HUB(+아래아 문자)_v2
## 3. Hyper Parameters
- Epoch : 10 epoch... | [
"# 제주 방언 번역 모델 \n - 표준어 -> 제주어\n - Made by. 구름 자연어처리 과정 3기 3조!!\n - github link : URL",
"## 1. Seq2Seq Transformer Model\n - encoder : BertConfig\n - decoder : BertConfig\n - Tokenizer : WordPiece Tokenizer",
"## 2. Dataset\n - Jit Dataset\n - AI HUB(+아래아 문자)_v2",
"## 3. Hyper Parameters\n ... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# 제주 방언 번역 모델 \n - 표준어 -> 제주어\n - Made by. 구름 자연어처리 과정 3기 3조!!\n - github link : URL",
"## 1. Seq2Seq Transformer Model\n - encoder : BertConfig\n - decod... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-base-chinese-finetuned-job-resume
This model is a fine-tuned version of [ckiplab/gpt2-base-chinese](https://huggingface.co/... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-base-chinese-finetuned-job-resume", "results": []}]} | czw/gpt2-base-chinese-finetuned-job-resume | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T16:50:01+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-base-chinese-finetuned-job-resume
======================================
This model is a fine-tuned version of ckiplab/gpt2-base-chinese on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2658
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: 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 #gpt2 #text-generation #generated_from_trainer #license-gpl-3.0 #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: 2e-05\n* train\\_... |
text-generation | transformers |
# Sheldon Cooper DialoGPT Model | {"tags": ["conversational"]} | atomsspawn/DialoGPT-small-shelbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T16:55:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sheldon Cooper DialoGPT Model | [
"# Sheldon Cooper DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sheldon Cooper DialoGPT Model"
] |
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. -->
# layoutlmv3-finetuned-cord
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/lay... | {"tags": ["generated_from_trainer"], "datasets": ["cord"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "layoutlmv3-finetuned-cord", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "co... | nielsr/layoutlmv3-finetuned-cord | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord",
"base_model:microsoft/layoutlmv3-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-02T16:58:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-cord #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| layoutlmv3-finetuned-cord
=========================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the CORD dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1845
* Precision: 0.9620
* Recall: 0.9656
* F1: 0.9638
* Accuracy: 0.9682
The script for training can be found... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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* training\\_steps: 1000",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-cord #base_model-microsoft/layoutlmv3-base #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/tamil_slu`
This model was trained by Sujay S Kumar using tamil recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 395bda6123ae268f991e5ef1dab887b6e677974a
pip install -e .
cd egs2/tamil/asr1
./run.sh --skip_data_pr... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["tamil"]} | espnet/tamil_slu | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:tamil",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-02T17:00:45+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-tamil #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/tamil\_slu'
This model was trained by Sujay S Kumar using tamil recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Oct 3 20:59:46 EDT 2021'
* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.... | [
"### 'espnet/tamil\\_slu'\n\n\nThis model was trained by Sujay S Kumar using tamil recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Oct 3 20:59:46 EDT 2021'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'\n* espnet ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-tamil #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/tamil\\_slu'\n\n\nThis model was trained by Sujay S Kumar using tamil recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\... |
text-classification | transformers |
Base model: [lacai/roberta-large-dialog-narrative](https://huggingface.co/lacai/roberta-large-dialog-narrative)
Fine tuned as a progression model (to predict the acceptability of a dialogue) on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019):
Given a complete di... | {"license": "mit"} | LACAI/roberta-large-adapted-PFG-progression | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-02T17:09:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Base model: lacai/roberta-large-dialog-narrative
Fine tuned as a progression model (to predict the acceptability of a dialogue) on the Persuasion For Good Dataset (Wang et al., 2019):
Given a complete dialogue from (or in the style of) Persuasion For Good, the task is to predict a numeric score typically in the rang... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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/1445280995175911425/JkWN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hot_domme/1652063339945/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hot_domme | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-02T17:11:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
™STREET DON غعتس دتعد Steamin Hot
@hot\_domme
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.
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.