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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265909
- CO2 Emissions (in grams): 80.25874179679201
## Validation Metrics
- Loss: 5.950643062591553
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Typ... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 80.25874179679201} | teacookies/autonlp-more_fine_tune_24465520-26265909 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265909
- CO2 Emissions (in grams): 80.25874179679201
## Validation Metrics
- Loss: 5.950643062591553
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265909\n- CO2 Emissions (in grams): 80.25874179679201",
"## Validation Metrics\n\n- Loss: 5.950643062591553",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265909\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265910
- CO2 Emissions (in grams): 77.64468929470678
## Validation Metrics
- Loss: 5.950643062591553
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Typ... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 77.64468929470678} | teacookies/autonlp-more_fine_tune_24465520-26265910 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265910
- CO2 Emissions (in grams): 77.64468929470678
## Validation Metrics
- Loss: 5.950643062591553
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265910\n- CO2 Emissions (in grams): 77.64468929470678",
"## Validation Metrics\n\n- Loss: 5.950643062591553",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265910\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265911
- CO2 Emissions (in grams): 97.58591836686978
## Validation Metrics
- Loss: 6.2383246421813965
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-more_fine_tune_24465520"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 97.58591836686978} | teacookies/autonlp-more_fine_tune_24465520-26265911 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26265911
- CO2 Emissions (in grams): 97.58591836686978
## Validation Metrics
- Loss: 6.2383246421813965
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265911\n- CO2 Emissions (in grams): 97.58591836686978",
"## Validation Metrics\n\n- Loss: 6.2383246421813965",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-more_fine_tune_24465520 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 26265911\n- CO2 Emissions (in g... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465514
- CO2 Emissions (in grams): 54.44076291568145
## Validation Metrics
- Loss: 0.5786784887313843
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 54.44076291568145} | teacookies/autonlp-roberta-base-squad2-24465514 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465514
- CO2 Emissions (in grams): 54.44076291568145
## Validation Metrics
- Loss: 0.5786784887313843
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465514\n- CO2 Emissions (in grams): 54.44076291568145",
"## Validation Metrics\n\n- Loss: 0.5786784887313843",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465514\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465515
- CO2 Emissions (in grams): 56.45146749922553
## Validation Metrics
- Loss: 0.5932255387306213
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 56.45146749922553} | teacookies/autonlp-roberta-base-squad2-24465515 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465515
- CO2 Emissions (in grams): 56.45146749922553
## Validation Metrics
- Loss: 0.5932255387306213
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465515\n- CO2 Emissions (in grams): 56.45146749922553",
"## Validation Metrics\n\n- Loss: 0.5932255387306213",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465515\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465516
- CO2 Emissions (in grams): 65.5797497320557
## Validation Metrics
- Loss: 0.6545609831809998
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Typ... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 65.5797497320557} | teacookies/autonlp-roberta-base-squad2-24465516 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465516
- CO2 Emissions (in grams): 65.5797497320557
## Validation Metrics
- Loss: 0.6545609831809998
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465516\n- CO2 Emissions (in grams): 65.5797497320557",
"## Validation Metrics\n\n- Loss: 0.6545609831809998",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465516\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465517
- CO2 Emissions (in grams): 54.75747617143382
## Validation Metrics
- Loss: 0.6653227806091309
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 54.75747617143382} | teacookies/autonlp-roberta-base-squad2-24465517 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465517
- CO2 Emissions (in grams): 54.75747617143382
## Validation Metrics
- Loss: 0.6653227806091309
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465517\n- CO2 Emissions (in grams): 54.75747617143382",
"## Validation Metrics\n\n- Loss: 0.6653227806091309",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465517\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465518
- CO2 Emissions (in grams): 45.268576304018616
## Validation Metrics
- Loss: 0.5742421746253967
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 45.268576304018616} | teacookies/autonlp-roberta-base-squad2-24465518 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465518
- CO2 Emissions (in grams): 45.268576304018616
## Validation Metrics
- Loss: 0.5742421746253967
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465518\n- CO2 Emissions (in grams): 45.268576304018616",
"## Validation Metrics\n\n- Loss: 0.5742421746253967",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465518\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465519
- CO2 Emissions (in grams): 58.19097299648645
## Validation Metrics
- Loss: 0.566668689250946
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Typ... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 58.19097299648645} | teacookies/autonlp-roberta-base-squad2-24465519 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465519
- CO2 Emissions (in grams): 58.19097299648645
## Validation Metrics
- Loss: 0.566668689250946
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465519\n- CO2 Emissions (in grams): 58.19097299648645",
"## Validation Metrics\n\n- Loss: 0.566668689250946",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465519\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465520
- CO2 Emissions (in grams): 57.56554511511173
## Validation Metrics
- Loss: 0.6455457806587219
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 57.56554511511173} | teacookies/autonlp-roberta-base-squad2-24465520 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465520
- CO2 Emissions (in grams): 57.56554511511173
## Validation Metrics
- Loss: 0.6455457806587219
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465520\n- CO2 Emissions (in grams): 57.56554511511173",
"## Validation Metrics\n\n- Loss: 0.6455457806587219",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465520\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465521
- CO2 Emissions (in grams): 70.20260764805424
## Validation Metrics
- Loss: 0.6295848488807678
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 70.20260764805424} | teacookies/autonlp-roberta-base-squad2-24465521 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465521
- CO2 Emissions (in grams): 70.20260764805424
## Validation Metrics
- Loss: 0.6295848488807678
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465521\n- CO2 Emissions (in grams): 70.20260764805424",
"## Validation Metrics\n\n- Loss: 0.6295848488807678",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465521\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465522
- CO2 Emissions (in grams): 44.450538076574766
## Validation Metrics
- Loss: 0.5572742223739624
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 44.450538076574766} | teacookies/autonlp-roberta-base-squad2-24465522 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465522
- CO2 Emissions (in grams): 44.450538076574766
## Validation Metrics
- Loss: 0.5572742223739624
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465522\n- CO2 Emissions (in grams): 44.450538076574766",
"## Validation Metrics\n\n- Loss: 0.5572742223739624",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465522\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465523
- CO2 Emissions (in grams): 56.99866929988893
## Validation Metrics
- Loss: 0.5468788146972656
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 56.99866929988893} | teacookies/autonlp-roberta-base-squad2-24465523 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465523
- CO2 Emissions (in grams): 56.99866929988893
## Validation Metrics
- Loss: 0.5468788146972656
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465523\n- CO2 Emissions (in grams): 56.99866929988893",
"## Validation Metrics\n\n- Loss: 0.5468788146972656",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465523\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465524
- CO2 Emissions (in grams): 58.51753681929935
## Validation Metrics
- Loss: 0.5759999752044678
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Ty... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 58.51753681929935} | teacookies/autonlp-roberta-base-squad2-24465524 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465524
- CO2 Emissions (in grams): 58.51753681929935
## Validation Metrics
- Loss: 0.5759999752044678
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465524\n- CO2 Emissions (in grams): 58.51753681929935",
"## Validation Metrics\n\n- Loss: 0.5759999752044678",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465524\n- CO2 Emissions (in grams... |
question-answering | transformers |
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465525
- CO2 Emissions (in grams): 63.997230261104875
## Validation Metrics
- Loss: 0.5740988850593567
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-T... | {"language": "unk", "tags": ["autonlp", "question-answering"], "datasets": ["teacookies/autonlp-data-roberta-base-squad2"], "widget": [{"text": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}], "co2_eq_emissions": 63.997230261104875} | teacookies/autonlp-roberta-base-squad2-24465525 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"unk",
"dataset:teacookies/autonlp-data-roberta-base-squad2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 24465525
- CO2 Emissions (in grams): 63.997230261104875
## Validation Metrics
- Loss: 0.5740988850593567
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465525\n- CO2 Emissions (in grams): 63.997230261104875",
"## Validation Metrics\n\n- Loss: 0.5740988850593567",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #unk #dataset-teacookies/autonlp-data-roberta-base-squad2 #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- Model ID: 24465525\n- CO2 Emissions (in grams... |
null | transformers | # Hindi Image Captioning Model
This is an encoder-decoder image captioning model made with [VIT](https://huggingface.co/google/vit-base-patch16-224-in21k) encoder and [GPT2-Hindi](https://huggingface.co/surajp/gpt2-hindi) as a decoder. This is a first attempt at using ViT + GPT2-Hindi for image captioning task. We use... | {} | team-indain-image-caption/hindi-image-captioning | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #has_space #region-us
| # Hindi Image Captioning Model
This is an encoder-decoder image captioning model made with VIT encoder and GPT2-Hindi as a decoder. This is a first attempt at using ViT + GPT2-Hindi for image captioning task. We used the Flickr8k Hindi Dataset available on kaggle to train the model.
This model was trained using Huggi... | [
"# Hindi Image Captioning Model\n\nThis is an encoder-decoder image captioning model made with VIT encoder and GPT2-Hindi as a decoder. This is a first attempt at using ViT + GPT2-Hindi for image captioning task. We used the Flickr8k Hindi Dataset available on kaggle to train the model.\n\nThis model was trained us... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #has_space #region-us \n",
"# Hindi Image Captioning Model\n\nThis is an encoder-decoder image captioning model made with VIT encoder and GPT2-Hindi as a decoder. This is a first attempt at using ViT + GPT2-Hindi for image captioning task... |
text2text-generation | transformers | # Model Description:
To create t5-base-c4jfleg model, T5-base model is fine-tuned on the [**JFLEG dataset**](https://huggingface.co/datasets/jfleg) and [**C4 200M dataset**](https://huggingface.co/datasets/liweili/c4_200m) by taking around 3000 examples from each with the objective of grammar correction.
The original... | {} | team-writing-assistant/t5-base-c4jfleg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Model Description:
To create t5-base-c4jfleg model, T5-base model is fine-tuned on the JFLEG dataset and C4 200M dataset by taking around 3000 examples from each with the objective of grammar correction.
The original Google's [T5-base] model was pre-trained on C4 dataset.
The T5 model was presented in Exploring th... | [
"# Model Description:\nTo create t5-base-c4jfleg model, T5-base model is fine-tuned on the JFLEG dataset and C4 200M dataset by taking around 3000 examples from each with the objective of grammar correction.\n\n\nThe original Google's [T5-base] model was pre-trained on C4 dataset.\n\nThe T5 model was presented in E... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model Description:\nTo create t5-base-c4jfleg model, T5-base model is fine-tuned on the JFLEG dataset and C4 200M dataset by taking around ... |
automatic-speech-recognition | transformers |
# wav2vec2-xlsr-ft-cy
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on
the [Welsh Common Voice version 15 dataset](https://commonvoice.mozilla.org/cy/datasets).
## Usage
The wav2vec2-xlsr-ft-cy model can be used with or without the included KenLM language mode... | {"language": "cy", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "ken-lm", "robust-speech-event", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]} | techiaith/wav2vec2-xlsr-ft-cy | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"ken-lm",
"robust-speech-event",
"speech",
"cy",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"cy"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #ken-lm #robust-speech-event #speech #cy #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xlsr-ft-cy
Fine-tuned facebook/wav2vec2-large-xlsr-53 on
the Welsh Common Voice version 15 dataset.
## Usage
The wav2vec2-xlsr-ft-cy model can be used with or without the included KenLM language model as follows:
### without LM
### with LM
| [
"# wav2vec2-xlsr-ft-cy\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on \nthe Welsh Common Voice version 15 dataset.",
"## Usage\n\nThe wav2vec2-xlsr-ft-cy model can be used with or without the included KenLM language model as follows:",
"### without LM",
"### with LM"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #ken-lm #robust-speech-event #speech #cy #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-xlsr-ft-cy\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on \nthe Welsh Common Voice... |
text-classification | transformers | from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained('techthiyanes/Bert_Bahasa_Sentiment')
inputs = tokenizer("saya tidak", return_tensors="pt")
labels = torch.tensor([1]).uns... | {} | techthiyanes/Bert_Bahasa_Sentiment | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained('techthiyanes/Bert_Bahasa_Sentiment')
inputs = tokenizer("saya tidak", return_tensors="pt")
labels = URL([1]).unsqueeze(0)... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
audio-to-audio | generic |
# Audio to Audio repository template
This is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two required steps:
1. Specify the requirements by defining a `requirement... | {"library_name": "generic", "tags": ["audio-to-audio"]} | templates/audio-to-audio | null | [
"generic",
"audio-to-audio",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #audio-to-audio #has_space #region-us
|
# Audio to Audio repository template
This is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two required steps:
1. Specify the requirements by defining a 'URL' file.
... | [
"# Audio to Audio repository template\n\nThis is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two required steps:\n\n1. Specify the requirements by defining a 'URL... | [
"TAGS\n#generic #audio-to-audio #has_space #region-us \n",
"# Audio to Audio repository template\n\nThis is a template repository for Audio to Audio to support generic inference with Hugging Face Hub generic Inference API. Examples of Audio to Audio are Source Separation and Speech Enhancement. There are two requ... |
automatic-speech-recognition | generic |
# Automatic Speech Recognition repository template
This is a template repository for Automatic Speech Recognition to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `... | {"library_name": "generic", "tags": ["automatic-speech-recognition"]} | templates/automatic-speech-recognition | null | [
"generic",
"automatic-speech-recognition",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #automatic-speech-recognition #has_space #region-us
|
# Automatic Speech Recognition repository template
This is a template repository for Automatic Speech Recognition to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call... | [
"# Automatic Speech Recognition repository template\n\nThis is a template repository for Automatic Speech Recognition to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:\n\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' an... | [
"TAGS\n#generic #automatic-speech-recognition #has_space #region-us \n",
"# Automatic Speech Recognition repository template\n\nThis is a template repository for Automatic Speech Recognition to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:\n\n1. Specify the r... |
feature-extraction | generic |
# Feature Extraction repository template
This is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call... | {"library_name": "generic", "tags": ["feature-extraction"]} | templates/feature-extraction | null | [
"generic",
"feature-extraction",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #feature-extraction #region-us
|
# Feature Extraction repository template
This is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These me... | [
"# Feature Extraction repository template\n\nThis is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' methods.... | [
"TAGS\n#generic #feature-extraction #region-us \n",
"# Feature Extraction repository template\n\nThis is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file.\n2. ... |
image-classification | generic |
# Image Classification repository template
This is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__... | {"library_name": "generic", "tags": ["image-classification"]} | templates/image-classification | null | [
"generic",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #image-classification #region-us
|
# Image Classification repository template
This is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. Thes... | [
"# Image Classification repository template\n\nThis is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' meth... | [
"TAGS\n#generic #image-classification #region-us \n",
"# Image Classification repository template\n\nThis is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file... |
tabular-classification | generic | # Tabular Classification repository template
This is a template repository for tabular classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `... | {"library_name": "generic", "tags": ["tabular-classification"]} | templates/tabular-classification | null | [
"generic",
"tabular-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #tabular-classification #has_space #region-us
| # Tabular Classification repository template
This is a template repository for tabular classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. Th... | [
"# Tabular Classification repository template\n\nThis is a template repository for tabular classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' me... | [
"TAGS\n#generic #tabular-classification #has_space #region-us \n",
"# Tabular Classification repository template\n\nThis is a template repository for tabular classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n1. Specify the requirements by defini... |
text-classification | generic |
# Text Classification repository template
This is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__c... | {"library_name": "generic", "tags": ["text-classification"]} | templates/text-classification | null | [
"generic",
"text-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #text-classification #region-us
|
# Text Classification repository template
This is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These... | [
"# Text Classification repository template\n\nThis is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:\n\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' metho... | [
"TAGS\n#generic #text-classification #region-us \n",
"# Text Classification repository template\n\nThis is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:\n\n1. Specify the requirements by defining a 'URL' file.\... |
text-to-image | generic |
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods... | {"library_name": "generic", "tags": ["text-to-image"]} | templates/text-to-image | null | [
"generic",
"text-to-image",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #text-to-image #has_space #region-us
|
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These methods are c... | [
"# Text To Image repository template\n\nThis is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' methods. These metho... | [
"TAGS\n#generic #text-to-image #has_space #region-us \n",
"# Text To Image repository template\n\nThis is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n1. Specify the requirements by defining a 'URL' file.\n2. Implem... |
token-classification | generic |
# Token Classification repository template
This is a template repository for token classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__... | {"library_name": "generic", "tags": ["token-classification"]} | templates/token-classification | null | [
"generic",
"token-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #token-classification #region-us
|
# Token Classification repository template
This is a template repository for token classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. Thes... | [
"# Token Classification repository template\n\nThis is a template repository for token classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' meth... | [
"TAGS\n#generic #token-classification #region-us \n",
"# Token Classification repository template\n\nThis is a template repository for token classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n\n1. Specify the requirements by defining a 'URL' file... |
text-classification | transformers |
# Titlewave: bert-base-uncased
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See the [github repository](https://github.com/tennessejoyce/TitleWave) for more information.
This is one of two NLP models used in the Titlewave project, and its... | {"language": "en", "license": "cc-by-4.0", "widget": [{"text": "[Gmail API] How can I extract plain text from an email sent to me?"}]} | tennessejoyce/titlewave-bert-base-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Titlewave: bert-base-uncased
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See the github repository for more information.
This is one of two NLP models used in the Titlewave project, and its purpose is to classify whether question will ... | [
"# Titlewave: bert-base-uncased",
"## Model description\n\nTitlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See the github repository for more information.\nThis is one of two NLP models used in the Titlewave project, and its purpose is to classify whether que... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Titlewave: bert-base-uncased",
"## Model description\n\nTitlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See t... |
summarization | transformers |
# Titlewave: t5-base
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See https://github.com/tennessejoyce/TitleWave for more information.
This is one of two NLP models used in the Titlewave project, and its purpose is to suggests a new title... | {"language": "en", "license": "cc-by-4.0", "pipeline_tag": "summarization", "widget": [{"text": "Example question body."}]} | tennessejoyce/titlewave-t5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Titlewave: t5-base
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See URL for more information.
This is one of two NLP models used in the Titlewave project, and its purpose is to suggests a new title based on on the body of the question. ... | [
"# Titlewave: t5-base",
"## Model description\n\nTitlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See URL for more information.\nThis is one of two NLP models used in the Titlewave project, and its purpose is to suggests a new title based on on the body of the... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Titlewave: t5-base",
"## Model description\n\nTitlewave is a Chrome extension that helps you choose better titles for... |
text2text-generation | transformers | # Titlewave: t5-small
This is one of two models used in the Titlewave project. See https://github.com/tennessejoyce/TitleWave for more information.
This model was fine-tuned on a dataset of Stack Overflow posts, with a ConditionalGeneration head that summarizes the body of a question in order to suggest a title.
| {} | tennessejoyce/titlewave-t5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Titlewave: t5-small
This is one of two models used in the Titlewave project. See URL for more information.
This model was fine-tuned on a dataset of Stack Overflow posts, with a ConditionalGeneration head that summarizes the body of a question in order to suggest a title.
| [
"# Titlewave: t5-small\n\nThis is one of two models used in the Titlewave project. See URL for more information.\n\nThis model was fine-tuned on a dataset of Stack Overflow posts, with a ConditionalGeneration head that summarizes the body of a question in order to suggest a title."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Titlewave: t5-small\n\nThis is one of two models used in the Titlewave project. See URL for more information.\n\nThis model was fine-tuned on a dataset of Stack Overflo... |
text-to-speech | tensorflowtts |
# FastSpeech trained on LJSpeech (Eng)
This repository provides a pretrained [FastSpeech](https://arxiv.org/abs/1905.09263) trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First... | {"language": "eng", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["LJSpeech"], "widget": [{"text": "How are you?"}]} | tensorspeech/tts-fastspeech-ljspeech-en | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"eng",
"dataset:LJSpeech",
"arxiv:1905.09263",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1905.09263"
] | [
"eng"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #eng #dataset-LJSpeech #arxiv-1905.09263 #license-apache-2.0 #has_space #region-us
|
# FastSpeech trained on LJSpeech (Eng)
This repository provides a pretrained FastSpeech trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Converting yo... | [
"# FastSpeech trained on LJSpeech (Eng)\nThis repository provides a pretrained FastSpeech trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
"### C... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #eng #dataset-LJSpeech #arxiv-1905.09263 #license-apache-2.0 #has_space #region-us \n",
"# FastSpeech trained on LJSpeech (Eng)\nThis repository provides a pretrained FastSpeech trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you... |
text-to-speech | tensorflowtts |
# FastSpeech2 trained on Baker (Chinese)
This repository provides a pretrained [FastSpeech2](https://arxiv.org/abs/2006.04558) trained on Baker dataset (Ch). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First ... | {"language": "chinese", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["Baker"], "widget": [{"text": "\u8fd9\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u7aef\u5230\u7aef\u4e2d\u6587\u8bed\u97f3\u5408\u6210\u7cfb\u7edf"}]} | tensorspeech/tts-fastspeech2-baker-ch | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"dataset:Baker",
"arxiv:2006.04558",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.04558"
] | [
"chinese"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #dataset-Baker #arxiv-2006.04558 #license-apache-2.0 #has_space #region-us
|
# FastSpeech2 trained on Baker (Chinese)
This repository provides a pretrained FastSpeech2 trained on Baker dataset (Ch). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Converting you... | [
"# FastSpeech2 trained on Baker (Chinese)\nThis repository provides a pretrained FastSpeech2 trained on Baker dataset (Ch). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
"### Co... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #dataset-Baker #arxiv-2006.04558 #license-apache-2.0 #has_space #region-us \n",
"# FastSpeech2 trained on Baker (Chinese)\nThis repository provides a pretrained FastSpeech2 trained on Baker dataset (Ch). For a detail of the model, we encourage you to read ... |
text-to-speech | tensorflowtts |
# FastSpeech2 trained on KSS (Korean)
This repository provides a pretrained [FastSpeech2](https://arxiv.org/abs/2006.04558) trained on KSS dataset (Ko). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of al... | {"language": "ko", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["KSS"], "widget": [{"text": "\uc2e0\uc740 \uc6b0\ub9ac\uc758 \uc218\ud559 \ubb38\uc81c\uc5d0\ub294 \uad00\uc2ec\uc774 \uc5c6\ub2e4. \uc2e0\uc740 \ub2e4\ub9cc \uacbd\ud5d8\uc801\uc73c\ub85c \ud1b... | tensorspeech/tts-fastspeech2-kss-ko | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"ko",
"dataset:KSS",
"arxiv:2006.04558",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.04558"
] | [
"ko"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #ko #dataset-KSS #arxiv-2006.04558 #license-apache-2.0 #region-us
|
# FastSpeech2 trained on KSS (Korean)
This repository provides a pretrained FastSpeech2 trained on KSS dataset (Ko). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Converting your Tex... | [
"# FastSpeech2 trained on KSS (Korean)\nThis repository provides a pretrained FastSpeech2 trained on KSS dataset (Ko). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
"### Convert... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #ko #dataset-KSS #arxiv-2006.04558 #license-apache-2.0 #region-us \n",
"# FastSpeech2 trained on KSS (Korean)\nThis repository provides a pretrained FastSpeech2 trained on KSS dataset (Ko). For a detail of the model, we encourage you to read more about\nTe... |
text-to-speech | tensorflowtts |
# FastSpeech2 trained on LJSpeech (Eng)
This repository provides a pretrained [FastSpeech2](https://arxiv.org/abs/2006.04558) trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
Fir... | {"language": "eng", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["LJSpeech"], "widget": [{"text": "How are you?"}]} | tensorspeech/tts-fastspeech2-ljspeech-en | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"eng",
"dataset:LJSpeech",
"arxiv:2006.04558",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.04558"
] | [
"eng"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #eng #dataset-LJSpeech #arxiv-2006.04558 #license-apache-2.0 #has_space #region-us
|
# FastSpeech2 trained on LJSpeech (Eng)
This repository provides a pretrained FastSpeech2 trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Converting ... | [
"# FastSpeech2 trained on LJSpeech (Eng)\nThis repository provides a pretrained FastSpeech2 trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
"###... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #eng #dataset-LJSpeech #arxiv-2006.04558 #license-apache-2.0 #has_space #region-us \n",
"# FastSpeech2 trained on LJSpeech (Eng)\nThis repository provides a pretrained FastSpeech2 trained on LJSpeech dataset (ENG). For a detail of the model, we encourage y... |
text-to-speech | tensorflowtts |
# Multi-band MelGAN trained on Baker (Ch)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on Baker dataset (ch). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS... | {"language": "ch", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "mel-to-wav"], "datasets": ["Baker"], "widget": [{"text": "\u8fd9\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u7aef\u5230\u7aef\u4e2d\u6587\u8bed\u97f3\u5408\u6210\u7cfb\u7edf"}]} | tensorspeech/tts-mb_melgan-baker-ch | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"ch",
"dataset:Baker",
"arxiv:2005.05106",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.05106"
] | [
"ch"
] | TAGS
#tensorflowtts #audio #text-to-speech #mel-to-wav #ch #dataset-Baker #arxiv-2005.05106 #license-apache-2.0 #has_space #region-us
|
# Multi-band MelGAN trained on Baker (Ch)
This repository provides a pretrained Multi-band MelGAN trained on Baker dataset (ch). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Convert... | [
"# Multi-band MelGAN trained on Baker (Ch)\nThis repository provides a pretrained Multi-band MelGAN trained on Baker dataset (ch). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #mel-to-wav #ch #dataset-Baker #arxiv-2005.05106 #license-apache-2.0 #has_space #region-us \n",
"# Multi-band MelGAN trained on Baker (Ch)\nThis repository provides a pretrained Multi-band MelGAN trained on Baker dataset (ch). For a detail of the model, we encourage yo... |
text-to-speech | tensorflowtts |
# Multi-band MelGAN trained on KSS (Korean)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS... | {"language": "ko", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "mel-to-wav"], "datasets": ["KSS"], "widget": [{"text": "\uc2e0\uc740 \uc6b0\ub9ac\uc758 \uc218\ud559 \ubb38\uc81c\uc5d0\ub294 \uad00\uc2ec\uc774 \uc5c6\ub2e4. \uc2e0\uc740 \ub2e4\ub9cc \uacbd\ud5d8\uc801\uc73c\ub85c \ud1b5... | tensorspeech/tts-mb_melgan-kss-ko | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"ko",
"dataset:KSS",
"arxiv:2005.05106",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.05106"
] | [
"ko"
] | TAGS
#tensorflowtts #audio #text-to-speech #mel-to-wav #ko #dataset-KSS #arxiv-2005.05106 #license-apache-2.0 #region-us
|
# Multi-band MelGAN trained on KSS (Korean)
This repository provides a pretrained Multi-band MelGAN trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Convert... | [
"# Multi-band MelGAN trained on KSS (Korean)\nThis repository provides a pretrained Multi-band MelGAN trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #mel-to-wav #ko #dataset-KSS #arxiv-2005.05106 #license-apache-2.0 #region-us \n",
"# Multi-band MelGAN trained on KSS (Korean)\nThis repository provides a pretrained Multi-band MelGAN trained on KSS dataset (ko). For a detail of the model, we encourage you to read mor... |
text-to-speech | tensorflowtts |
# Multi-band MelGAN trained on LJSpeech (En)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install Tensor... | {"language": "en", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "mel-to-wav"], "datasets": ["ljspeech"], "widget": [{"text": "Hello, how are you doing?"}]} | tensorspeech/tts-mb_melgan-ljspeech-en | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"en",
"dataset:ljspeech",
"arxiv:2005.05106",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.05106"
] | [
"en"
] | TAGS
#tensorflowtts #audio #text-to-speech #mel-to-wav #en #dataset-ljspeech #arxiv-2005.05106 #license-apache-2.0 #has_space #region-us
|
# Multi-band MelGAN trained on LJSpeech (En)
This repository provides a pretrained Multi-band MelGAN trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### ... | [
"# Multi-band MelGAN trained on LJSpeech (En)\nThis repository provides a pretrained Multi-band MelGAN trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following comman... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #mel-to-wav #en #dataset-ljspeech #arxiv-2005.05106 #license-apache-2.0 #has_space #region-us \n",
"# Multi-band MelGAN trained on LJSpeech (En)\nThis repository provides a pretrained Multi-band MelGAN trained on LJSpeech dataset (Eng). For a detail of the model, we en... |
text-to-speech | tensorflowtts |
# Multi-band MelGAN trained on Synpaflex (Fr)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on Synpaflex dataset (French). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install T... | {"language": "fr", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "mel-to-wav"], "datasets": ["synpaflex"], "widget": [{"text": "Oh, je voudrais tant que tu te souviennes Des jours heureux quand nous \u00e9tions amis"}]} | tensorspeech/tts-mb_melgan-synpaflex-fr | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"fr",
"dataset:synpaflex",
"arxiv:2005.05106",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.05106"
] | [
"fr"
] | TAGS
#tensorflowtts #audio #text-to-speech #mel-to-wav #fr #dataset-synpaflex #arxiv-2005.05106 #license-apache-2.0 #region-us
|
# Multi-band MelGAN trained on Synpaflex (Fr)
This repository provides a pretrained Multi-band MelGAN trained on Synpaflex dataset (French). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
... | [
"# Multi-band MelGAN trained on Synpaflex (Fr)\nThis repository provides a pretrained Multi-band MelGAN trained on Synpaflex dataset (French). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following c... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #mel-to-wav #fr #dataset-synpaflex #arxiv-2005.05106 #license-apache-2.0 #region-us \n",
"# Multi-band MelGAN trained on Synpaflex (Fr)\nThis repository provides a pretrained Multi-band MelGAN trained on Synpaflex dataset (French). For a detail of the model, we encoura... |
text-to-speech | tensorflowtts |
# Multi-band MelGAN trained on Thorsten (Ger)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on Thorsten dataset (ger). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install Tenso... | {"language": "ger", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "mel-to-wav"], "datasets": ["Thorsten"], "widget": [{"text": "M\u00f6chtest du das meiner Frau erkl\u00e4ren? Nein? Ich auch nicht."}]} | tensorspeech/tts-mb_melgan-thorsten-ger | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"ger",
"dataset:Thorsten",
"arxiv:2005.05106",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.05106"
] | [
"ger"
] | TAGS
#tensorflowtts #audio #text-to-speech #mel-to-wav #ger #dataset-Thorsten #arxiv-2005.05106 #license-apache-2.0 #region-us
|
# Multi-band MelGAN trained on Thorsten (Ger)
This repository provides a pretrained Multi-band MelGAN trained on Thorsten dataset (ger). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
###... | [
"# Multi-band MelGAN trained on Thorsten (Ger)\nThis repository provides a pretrained Multi-band MelGAN trained on Thorsten dataset (ger). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following comma... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #mel-to-wav #ger #dataset-Thorsten #arxiv-2005.05106 #license-apache-2.0 #region-us \n",
"# Multi-band MelGAN trained on Thorsten (Ger)\nThis repository provides a pretrained Multi-band MelGAN trained on Thorsten dataset (ger). For a detail of the model, we encourage y... |
text-to-speech | tensorflowtts |
# MelGAN trained on LJSpeech (En)
This repository provides a pretrained [MelGAN](https://arxiv.org/abs/1910.06711) trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, ... | {"language": "en", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "mel-to-wav"], "datasets": ["ljspeech"], "widget": [{"text": "Hello, how are you doing?"}]} | tensorspeech/tts-melgan-ljspeech-en | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"mel-to-wav",
"en",
"dataset:ljspeech",
"arxiv:1910.06711",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.06711"
] | [
"en"
] | TAGS
#tensorflowtts #audio #text-to-speech #mel-to-wav #en #dataset-ljspeech #arxiv-1910.06711 #license-apache-2.0 #has_space #region-us
|
# MelGAN trained on LJSpeech (En)
This repository provides a pretrained MelGAN trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Converting your Text t... | [
"# MelGAN trained on LJSpeech (En)\nThis repository provides a pretrained MelGAN trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
"### Converting... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #mel-to-wav #en #dataset-ljspeech #arxiv-1910.06711 #license-apache-2.0 #has_space #region-us \n",
"# MelGAN trained on LJSpeech (En)\nThis repository provides a pretrained MelGAN trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read mo... |
text-to-speech | tensorflowtts |
# Tacotron 2 with Guided Attention trained on Baker (Chinese)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Baker dataset (Ch). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https... | {"language": "ch", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["baker"], "widget": [{"text": "\u8fd9\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u7aef\u5230\u7aef\u4e2d\u6587\u8bed\u97f3\u5408\u6210\u7cfb\u7edf"}]} | tensorspeech/tts-tacotron2-baker-ch | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"ch",
"dataset:baker",
"arxiv:1712.05884",
"arxiv:1710.08969",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1712.05884",
"1710.08969"
] | [
"ch"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #ch #dataset-baker #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #has_space #region-us
|
# Tacotron 2 with Guided Attention trained on Baker (Chinese)
This repository provides a pretrained Tacotron2 trained with Guided Attention on Baker dataset (Ch). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with th... | [
"# Tacotron 2 with Guided Attention trained on Baker (Chinese)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Baker dataset (Ch). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTT... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #ch #dataset-baker #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #has_space #region-us \n",
"# Tacotron 2 with Guided Attention trained on Baker (Chinese)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Baker dat... |
text-to-speech | tensorflowtts |
# Tacotron 2 with Guided Attention trained on KSS (Korean)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on KSS dataset (KO). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://gi... | {"language": "ko", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["kss"], "widget": [{"text": "\uc2e0\uc740 \uc6b0\ub9ac\uc758 \uc218\ud559 \ubb38\uc81c\uc5d0\ub294 \uad00\uc2ec\uc774 \uc5c6\ub2e4. \uc2e0\uc740 \ub2e4\ub9cc \uacbd\ud5d8\uc801\uc73c\ub85c \ud1b... | tensorspeech/tts-tacotron2-kss-ko | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"ko",
"dataset:kss",
"arxiv:1712.05884",
"arxiv:1710.08969",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1712.05884",
"1710.08969"
] | [
"ko"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #ko #dataset-kss #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #region-us
|
# Tacotron 2 with Guided Attention trained on KSS (Korean)
This repository provides a pretrained Tacotron2 trained with Guided Attention on KSS dataset (KO). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the fol... | [
"# Tacotron 2 with Guided Attention trained on KSS (Korean)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on KSS dataset (KO). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS wit... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #ko #dataset-kss #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #region-us \n",
"# Tacotron 2 with Guided Attention trained on KSS (Korean)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on KSS dataset (KO). For a d... |
text-to-speech | tensorflowtts |
# Tacotron 2 with Guided Attention trained on LJSpeech (En)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](htt... | {"language": "en", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["ljspeech"], "widget": [{"text": "Hello, how are you doing?"}]} | tensorspeech/tts-tacotron2-ljspeech-en | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"en",
"dataset:ljspeech",
"arxiv:1712.05884",
"arxiv:1710.08969",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1712.05884",
"1710.08969"
] | [
"en"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #en #dataset-ljspeech #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #has_space #region-us
|
# Tacotron 2 with Guided Attention trained on LJSpeech (En)
This repository provides a pretrained Tacotron2 trained with Guided Attention on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with ... | [
"# Tacotron 2 with Guided Attention trained on LJSpeech (En)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlow... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #en #dataset-ljspeech #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #has_space #region-us \n",
"# Tacotron 2 with Guided Attention trained on LJSpeech (En)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on LJSpeech... |
text-to-speech | tensorflowtts |
# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](ht... | {"language": "fr", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["synpaflex"], "widget": [{"text": "Oh, je voudrais tant que tu te souviennes Des jours heureux quand nous \u00e9tions amis"}]} | tensorspeech/tts-tacotron2-synpaflex-fr | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"fr",
"dataset:synpaflex",
"arxiv:1712.05884",
"arxiv:1710.08969",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1712.05884",
"1710.08969"
] | [
"fr"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #fr #dataset-synpaflex #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #region-us
|
# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)
This repository provides a pretrained Tacotron2 trained with Guided Attention on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with... | [
"# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlo... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #fr #dataset-synpaflex #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #region-us \n",
"# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Synpaflex dataset... |
text-to-speech | tensorflowtts |
# Tacotron 2 with Guided Attention trained on Thorsten (Ger)
This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Thorsten dataset (Ger). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](ht... | {"language": "german", "license": "apache-2.0", "tags": ["tensorflowtts", "audio", "text-to-speech", "text-to-mel"], "datasets": ["Thorsten"], "widget": [{"text": "M\u00f6chtest du das meiner Frau erkl\u00e4ren? Nein? Ich auch nicht."}]} | tensorspeech/tts-tacotron2-thorsten-ger | null | [
"tensorflowtts",
"audio",
"text-to-speech",
"text-to-mel",
"dataset:Thorsten",
"arxiv:1712.05884",
"arxiv:1710.08969",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1712.05884",
"1710.08969"
] | [
"german"
] | TAGS
#tensorflowtts #audio #text-to-speech #text-to-mel #dataset-Thorsten #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #region-us
|
# Tacotron 2 with Guided Attention trained on Thorsten (Ger)
This repository provides a pretrained Tacotron2 trained with Guided Attention on Thorsten dataset (Ger). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with... | [
"# Tacotron 2 with Guided Attention trained on Thorsten (Ger)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Thorsten dataset (Ger). For a detail of the model, we encourage you to read more about\nTensorFlowTTS.",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlo... | [
"TAGS\n#tensorflowtts #audio #text-to-speech #text-to-mel #dataset-Thorsten #arxiv-1712.05884 #arxiv-1710.08969 #license-apache-2.0 #region-us \n",
"# Tacotron 2 with Guided Attention trained on Thorsten (Ger)\nThis repository provides a pretrained Tacotron2 trained with Guided Attention on Thorsten dataset (Ger)... |
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": []}]} | terri1102/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 | 2022-03-02T23:29:05+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.
It achieves the following results on the evaluation set:
* Loss: 0.4275
* Wer: 0.3380
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text-generation | transformers |
#Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | terter/rick-bot-test-v2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick Sanchez DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
Fine-tuning [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model on sentences from Wiki auto annotated with PER, LOC, ORG tags [corus/WiNER](https://pypi.org/project/corus/#reference)
language: RU
NER Class:
- PER
- LOC
- ORG
license: mit
| {"widget": [{"text": "\u0412 \u0433\u043e\u0440\u043e\u0434 \u0421\u0435\u0440\u0433\u0438\u0435\u0432 \u041f\u043e\u0441\u0430\u0434 \u043f\u0440\u0438\u0435\u0445\u0430\u043b \u041a\u0443\u0440\u0442 \u041a\u043e\u0431\u0435\u0439\u043d."}]} | tesemnikov-av/NER-RUBERT-Per-Loc-Org | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
Fine-tuning cointegrated/rubert-tiny model on sentences from Wiki auto annotated with PER, LOC, ORG tags corus/WiNER
language: RU
NER Class:
- PER
- LOC
- ORG
license: mit
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
NER Toxic models
Fine-tuning [cointegrated/rubert-tiny-toxicity](https://huggingface.co/cointegrated/rubert-tiny-toxicity) model on data from [toxic_dataset_ner](https://huggingface.co/datasets/tesemnikov-av/toxic_dataset_ner)
language: RU
```python
!pip install transformers > /dev/null
from transformers... | {"widget": [{"text": "\u041d\u0443 \u0442\u044b \u0438 \u043f\u0440\u0438\u0434\u0443\u0440\u043e\u043a!!"}]} | tesemnikov-av/rubert-ner-toxicity | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
NER Toxic models
Fine-tuning cointegrated/rubert-tiny-toxicity model on data from toxic_dataset_ner
language: RU
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | hello
hello
| {} | teshnizi/bert-lossy | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| hello
hello
| [] | [
"TAGS\n#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-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": []}]} | testimonial/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 | 2022-03-02T23:29:05+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.
It achieves the following results on the evaluation set:
* Loss: 0.4688
* Wer: 0.3417
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
token-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 19126711
- CO2 Emissions (in grams): 1.8458289701133035
## Validation Metrics
- Loss: 0.054593171924352646
- Accuracy: 0.9790668170284748
- Precision: 0.8029411764705883
- Recall: 0.6026490066225165
- F1: 0.6885245901639344
## Usage
You c... | {"language": "en", "tags": "autonlp", "datasets": ["testing/autonlp-data-ingredient_sentiment_analysis"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.8458289701133035} | testing/autonlp-ingredient_sentiment_analysis-19126711 | null | [
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"autotrain_compatible",
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"region:us"
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"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 19126711
- CO2 Emissions (in grams): 1.8458289701133035
## Validation Metrics
- Loss: 0.054593171924352646
- Accuracy: 0.9790668170284748
- Precision: 0.8029411764705883
- Recall: 0.6026490066225165
- F1: 0.6885245901639344
## Usage
You c... | [
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"## Validation Metrics\n\n- Loss: 0.054593171924352646\n- Accuracy: 0.9790668170284748\n- Precision: 0.8029411764705883\n- Recall: 0.6026490066225165\n- F1: 0.688524590163934... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 19126711\n- CO2 Emission... |
image-classification | generic |
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training
## Training
The model was trained as follows
```python
path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_f... | {"library_name": "generic", "tags": ["image-classification"]} | testorg2/fastai_cat_vs_dog_fork_3 | null | [
"generic",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #image-classification #region-us
|
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI Quick Start. Example training
## Training
The model was trained as follows
| [
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] |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becom... | {"language": "multilingual", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | testorg2/larger_fork | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"multilingual",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"multilingual"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #multilingual #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sen... | [
"# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
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sentence-similarity | sentence-transformers |
# teven/roberta_kelm_tekgen
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becom... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | teven/roberta_kelm_tekgen | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# teven/roberta_kelm_tekgen
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
T... | [
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text-classification | transformers | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | {} | textattack/albert-base-v2-CoLA | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | [
"## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince this was a classification task, the model was trained with a cross-entropy loss function. \nThe bes... | [
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text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... | {} | textattack/albert-base-v2-MRPC | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this wa... | [
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text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classi... | {} | textattack/albert-base-v2-QQP | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classi... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 5e-05, and a maximum sequence length of 128. \nSince this wa... | [
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text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classi... | {} | textattack/albert-base-v2-RTE | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classi... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince this wa... | [
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text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 64.
Since this was a classif... | {} | textattack/albert-base-v2-SST-2 | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 64.
Since this was a classif... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 3e-05, and a maximum sequence length of 64. \nSince this was... | [
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text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a regres... | {} | textattack/albert-base-v2-STS-B | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a regres... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince this wa... | [
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"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was ... |
text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a classi... | {} | textattack/albert-base-v2-WNLI | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a classi... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 2e-05, and a maximum sequence length of 256. \nSince this wa... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was ... |
text-classification | transformers | ## TextAttack Model CardThis `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... | {} | textattack/albert-base-v2-ag-news | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model CardThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... | [
"## TextAttack Model CardThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this w... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model CardThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. The model was... |
text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... | {} | textattack/albert-base-v2-imdb | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this wa... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model was ... |
text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this w... | {} | textattack/albert-base-v2-rotten-tomatoes | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this w... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSi... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the rotten_tomatoes dataset loaded using the 'nlp' library. The... |
fill-mask | transformers | ## albert-base-v2 fine-tuned with TextAttack on the rotten_tomatoes dataset
This `albert-base-v2` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learnin... | {} | textattack/albert-base-v2-rotten_tomatoes | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## albert-base-v2 fine-tuned with TextAttack on the rotten_tomatoes dataset
This 'albert-base-v2' model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learnin... | [
"## albert-base-v2 fine-tuned with TextAttack on the rotten_tomatoes dataset\n \n This 'albert-base-v2' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \n for 10 epochs with a batch size of 128, ... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## albert-base-v2 fine-tuned with TextAttack on the rotten_tomatoes dataset\n \n This 'albert-base-v2' model was fine-tuned for sequence classificationusing TextAttack \n and the rot... |
text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the snli dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 64.
Since this was a classif... | {} | textattack/albert-base-v2-snli | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the snli dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 64.
Since this was a classif... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the snli dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 2e-05, and a maximum sequence length of 64. \nSince this was... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the snli dataset loaded using the 'nlp' library. The model was ... |
text-classification | transformers | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 512.
Since this was... | {} | textattack/albert-base-v2-yelp-polarity | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 512.
Since this was... | [
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the yelp_polarity dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 3e-05, and a maximum sequence length of 512. \nSinc... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'albert-base-v2' model was fine-tuned for sequence classification using TextAttack \nand the yelp_polarity dataset loaded using the 'nlp' library. The m... |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-cased` model was fine-tuned for sequence classificationusing TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence length of 128.
... | {} | textattack/bert-base-cased-STS-B | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## TextAttack Model Card
This 'bert-base-cased' model was fine-tuned for sequence classificationusing TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence length of 128.
... | [
"## TextAttack Model Card \n This 'bert-base-cased' model was fine-tuned for sequence classificationusing TextAttack \n and the glue dataset loaded using the 'nlp' library. The model was fine-tuned \n for 3 epochs with a batch size of 128, a learning \n rate of 1e-05, and a maximum sequence length of... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## TextAttack Model Card \n This 'bert-base-cased' model was fine-tuned for sequence classificationusing TextAttack \n and the glue dataset loaded using the 'nlp' library. The model ... |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a cla... | {} | textattack/bert-base-uncased-MRPC | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a cla... | [
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 256. \nSince this... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The mode... |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... | {} | textattack/bert-base-uncased-RTE | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... | [
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 8, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this ... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The mode... |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a cla... | {} | textattack/bert-base-uncased-WNLI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a cla... | [
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 5e-05, and a maximum sequence length of 256. \nSince this... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The mode... |
text-classification | transformers | ## TextAttack Model CardThis `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a c... | {} | textattack/bert-base-uncased-ag-news | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model CardThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a c... | [
"## TextAttack Model CardThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince thi... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model CardThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. The mod... |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a cla... | {} | textattack/bert-base-uncased-imdb | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a cla... | [
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The mode... |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence lengt... | {} | textattack/bert-base-uncased-rotten-tomatoes | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'bert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 10 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence lengt... | [
"## TextAttack Model Card \n This 'bert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \n for 10 epochs with a batch size of 16, a learning \n rate of 2e-05, and a maximum seque... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card \n This 'bert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the '... |
fill-mask | transformers | ## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset
This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a le... | {} | textattack/bert-base-uncased-rotten_tomatoes | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset
This 'bert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 10 epochs with a batch size of 64, a le... | [
"## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset\n \n This 'bert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \n for 10 epochs with a batch size of... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset\n \n This 'bert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack \n an... |
text-classification | transformers | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this ... | {} | textattack/bert-base-uncased-yelp-polarity | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this ... | [
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the yelp_polarity dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 5e-05, and a maximum sequence length of 256. \nS... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'bert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the yelp_polarity dataset loaded using the 'nlp' library.... |
null | transformers | ## TextAttack Model Card
This `distilbert-base-cased` model was fine-tuned for sequence classificationusing TextAttack
and the snli dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 256, a learning
rate of 2e-05, and a maximum sequence length of 12... | {} | textattack/distilbert-base-cased-snli | null | [
"transformers",
"pytorch",
"distilbert",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'distilbert-base-cased' model was fine-tuned for sequence classificationusing TextAttack
and the snli dataset loaded using the 'nlp' library. The model was fine-tuned
for 3 epochs with a batch size of 256, a learning
rate of 2e-05, and a maximum sequence length of 12... | [
"## TextAttack Model Card \n This 'distilbert-base-cased' model was fine-tuned for sequence classificationusing TextAttack \n and the snli dataset loaded using the 'nlp' library. The model was fine-tuned \n for 3 epochs with a batch size of 256, a learning \n rate of 2e-05, and a maximum sequence len... | [
"TAGS\n#transformers #pytorch #distilbert #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card \n This 'distilbert-base-cased' model was fine-tuned for sequence classificationusing TextAttack \n and the snli dataset loaded using the 'nlp' library. The model was fine-tuned \n for ... |
text-classification | transformers | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | {} | textattack/distilbert-base-uncased-CoLA | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | [
"## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince this was a classification task, the model was trained with a cross-entropy loss function. \nThe bes... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 3e-05, and a max... |
text-classification | transformers | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was... | {} | textattack/distilbert-base-uncased-MRPC | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was... | [
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 2e-05, and a maximum sequence length of 256. \nSinc... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. T... |
text-classification | transformers | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was... | {} | textattack/distilbert-base-uncased-RTE | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was... | [
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSinc... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. T... |
text-classification | transformers | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 128, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this wa... | {} | textattack/distilbert-base-uncased-WNLI | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 128, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this wa... | [
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 128, a learning \nrate of 2e-05, and a maximum sequence length of 256. \nSin... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. T... |
text-classification | transformers | ## TextAttack Model CardThis `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this w... | {} | textattack/distilbert-base-uncased-ag-news | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model CardThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this w... | [
"## TextAttack Model CardThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSin... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model CardThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. ... |
text-classification | transformers | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was... | {} | textattack/distilbert-base-uncased-imdb | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was... | [
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSinc... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'distilbert-base-uncased' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. T... |
text-classification | transformers | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence... | {} | textattack/distilbert-base-uncased-rotten-tomatoes | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'distilbert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence... | [
"## TextAttack Model Card \n This 'distilbert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \n for 3 epochs with a batch size of 128, a learning \n rate of 1e-05, and a maximum... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card \n This 'distilbert-base-uncased' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded usin... |
text2text-generation | transformers | ## TextAttack Model CardSince this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://githu... | {} | textattack/facebook-bart-base-RTE | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| ## TextAttack Model CardSince this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out TextAttack on Github.
| [
"## TextAttack Model CardSince this was a classification task, the model was trained with a cross-entropy loss function.\nThe best score the model achieved on this task was 0.7256317689530686, as measured by the\neval set accuracy, found after 4 epochs.\n\nFor more information, check out TextAttack on Github."
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"## TextAttack Model CardSince this was a classification task, the model was trained with a cross-entropy loss function.\nThe best score the model achieved on this task was 0.7256317689530686, as ... |
text2text-generation | transformers | ## TextAttack Model Cardrate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 4 epochs.
For more inform... | {} | textattack/facebook-bart-base-glue-RTE | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| ## TextAttack Model Cardrate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 4 epochs.
For more inform... | [
"## TextAttack Model Cardrate of 2e-05, and a maximum sequence length of 128.\nSince this was a classification task, the model was trained with a cross-entropy loss function.\nThe best score the model achieved on this task was 0.7256317689530686, as measured by the\neval set accuracy, found after 4 epochs.\n\nFor m... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"## TextAttack Model Cardrate of 2e-05, and a maximum sequence length of 128.\nSince this was a classification task, the model was trained with a cross-entropy loss function.\nThe best score the m... |
text-classification | transformers | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | {} | textattack/roberta-base-CoLA | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | [
"## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this was a classification task, the model was trained with a cross-entropy loss function. \nThe bes... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Cardand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 2e-05, and a m... |
text-classification | transformers | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a classifi... | {} | textattack/roberta-base-MRPC | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'roberta-base' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a classifi... | [
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 3e-05, and a maximum sequence length of 256. \nSince this was ... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model ... |
text-classification | transformers | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classifi... | {} | textattack/roberta-base-RTE | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'roberta-base' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classifi... | [
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this was ... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model ... |
text-classification | transformers | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a regressio... | {} | textattack/roberta-base-STS-B | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'roberta-base' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a regressio... | [
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 8, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this was a... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model ... |
text-classification | transformers | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a classifi... | {} | textattack/roberta-base-WNLI | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'roberta-base' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a classifi... | [
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 5e-05, and a maximum sequence length of 256. \nSince this was ... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model ... |
text-classification | transformers | ## TextAttack Model CardThis `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classi... | {} | textattack/roberta-base-ag-news | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model CardThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classi... | [
"## TextAttack Model CardThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 5e-05, and a maximum sequence length of 128. \nSince this was... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model CardThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the ag_news dataset loaded using the 'nlp' library. The model... |
text-classification | transformers | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classifi... | {} | textattack/roberta-base-imdb | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'roberta-base' model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classifi... | [
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 64, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince this was ... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'roberta-base' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model ... |
text-classification | transformers | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of ... | {} | textattack/roberta-base-rotten-tomatoes | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'roberta-base' model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 10 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of ... | [
"## TextAttack Model Card \n This 'roberta-base' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \n for 10 epochs with a batch size of 64, a learning \n rate of 2e-05, and a maximum sequence l... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card \n This 'roberta-base' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the 'nl... |
fill-mask | transformers | ## roberta-base fine-tuned with TextAttack on the rotten_tomatoes dataset
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learning
... | {} | textattack/roberta-base-rotten_tomatoes | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## roberta-base fine-tuned with TextAttack on the rotten_tomatoes dataset
This 'roberta-base' model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learning
... | [
"## roberta-base fine-tuned with TextAttack on the rotten_tomatoes dataset\n \n This 'roberta-base' model was fine-tuned for sequence classificationusing TextAttack \n and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \n for 10 epochs with a batch size of 128, a le... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## roberta-base fine-tuned with TextAttack on the rotten_tomatoes dataset\n \n This 'roberta-base' model was fine-tuned for sequence classificationusing TextAttack \n and the r... |
text-generation | transformers | ## TextAttack Model Cardfor 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7976989453499521, as measured by the
eval... | {} | textattack/xlnet-base-cased-CoLA | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Cardfor 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7976989453499521, as measured by the
eval... | [
"## TextAttack Model Cardfor 5 epochs with a batch size of 32, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince this was a classification task, the model was trained with a cross-entropy loss function. \nThe best score the model achieved on this task was 0.7976989453499521, as measured by t... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Cardfor 5 epochs with a batch size of 32, a learning \nrate of 3e-05, and a maximum sequence length of 128. \nSince this was a classification task, the model was traine... |
text-generation | transformers | ## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a clas... | {} | textattack/xlnet-base-cased-MRPC | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a clas... | [
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 5e-05, and a maximum sequence length of 256. \nSince this ... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fin... |
text-generation | transformers | ## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... | {} | textattack/xlnet-base-cased-RTE | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... | [
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 128. \nSince this ... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fin... |
text-generation | transformers | ## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a regre... | {} | textattack/xlnet-base-cased-STS-B | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a regre... | [
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 8, a learning \nrate of 5e-05, and a maximum sequence length of 128. \nSince this w... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fin... |
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