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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-1", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-1 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-1
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6930
* Accuracy: 0.5047
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-2", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-2 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-2
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6932
* Accuracy: 0.4931
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-3", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-3 | null | [
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"distilbert",
"text-classification",
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"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-3
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6914
* Accuracy: 0.5195
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-4", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-4 | null | [
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"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-4
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6921
* Accuracy: 0.5107
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-5", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-5 | null | [
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"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-5
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8419
* Accuracy: 0.6172
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-6", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-6 | null | [
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"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-6
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5336
* Accuracy: 0.7523
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-7
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-7", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-7 | null | [
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"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-7
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6950
* Accuracy: 0.4618
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-8
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-8", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-8 | null | [
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"distilbert",
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"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-8
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6925
* Accuracy: 0.5200
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-9
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-9", "results": []}]} | SetFit/distilbert-base-uncased__sst2__train-8-9 | null | [
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"distilbert",
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"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst2\_\_train-8-9
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6925
* Accuracy: 0.5140
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst5__all-train
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst5__all-train", "results": []}]} | SetFit/distilbert-base-uncased__sst5__all-train | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_sst5\_\_all-train
============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3757
* Accuracy: 0.5045
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_pre... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__all-train
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased__subj__all-train", "results": []}]} | SetFit/distilbert-base-uncased__subj__all-train | null | [
"transformers",
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"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_all-train
============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3193
* Accuracy: 0.9485
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-0", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-0 | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-0
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4440
* Accuracy: 0.789
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-1", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-1 | null | [
"transformers",
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-1
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5488
* Accuracy: 0.791
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-2", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-2 | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-2
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3081
* Accuracy: 0.8755
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
44,
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-3", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-3 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-3
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3496
* Accuracy: 0.859
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
44,
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-4", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-4 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-4
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3305
* Accuracy: 0.8565
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
44,
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-5", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-5 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-5
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6927
* Accuracy: 0.506
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-6", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-6 | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-6
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6075
* Accuracy: 0.7485
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-7
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-7", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-7 | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-7
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2766
* Accuracy: 0.8845
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-8
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-8", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-8 | null | [
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"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-8
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3160
* Accuracy: 0.8735
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
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"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-9
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__subj__train-8-9", "results": []}]} | SetFit/distilbert-base-uncased__subj__train-8-9 | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_\_subj\_\_train-8-9
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4865
* Accuracy: 0.778
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_preci... | [
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null | transformers |
# Small-E-Czech
Small-E-Czech is an [Electra](https://arxiv.org/abs/2003.10555)-small model pretrained on a Czech web corpus created at [Seznam.cz](https://www.seznam.cz/) and introduced in an [IAAI 2022 paper](https://arxiv.org/abs/2112.01810). Like other pretrained models, it should be finetuned on a downstream tas... | {"language": "cs", "license": "cc-by-4.0"} | Seznam/small-e-czech | null | [
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"electra",
"cs",
"arxiv:2003.10555",
"arxiv:2112.01810",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.10555",
"2112.01810"
] | [
"cs"
] | TAGS
#transformers #pytorch #tf #electra #cs #arxiv-2003.10555 #arxiv-2112.01810 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Small-E-Czech
Small-E-Czech is an Electra-small model pretrained on a Czech web corpus created at URL and introduced in an IAAI 2022 paper. Like other pretrained models, it should be finetuned on a downstream task of interest before use. At URL, it has helped improve web search ranking, query typo correction or cli... | [
"# Small-E-Czech\n\nSmall-E-Czech is an Electra-small model pretrained on a Czech web corpus created at URL and introduced in an IAAI 2022 paper. Like other pretrained models, it should be finetuned on a downstream task of interest before use. At URL, it has helped improve web search ranking, query typo correction ... | [
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summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mode-bart-deutsch
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the m... | {"language": "de", "license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "mode-bart-deutsch", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "mlsum de", "type": "mlsum", "args": "d... | Shahm/bart-german | null | [
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #onnx #safetensors #bart #text2text-generation #generated_from_trainer #summarization #de #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# mode-bart-deutsch
This model is a fine-tuned version of facebook/bart-base on the mlsum de dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2152
- Rouge1: 41.698
- Rouge2: 31.3548
- Rougel: 38.2817
- Rougelsum: 39.6349
- Gen Len: 63.1723
## Model description
More information needed
#... | [
"# mode-bart-deutsch\n\nThis model is a fine-tuned version of facebook/bart-base on the mlsum de dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2152\n- Rouge1: 41.698\n- Rouge2: 31.3548\n- Rougel: 38.2817\n- Rougelsum: 39.6349\n- Gen Len: 63.1723",
"## Model description\n\nMore info... | [
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summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-seven-epoch-base-german
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the mlsum de dat... | {"language": "de", "license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-seven-epoch-base-german", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "mlsum de", "type": "mlsum", "... | Shahm/t5-small-german | null | [
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"text2text-generation",
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"dataset:mlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #de #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-seven-epoch-base-german
This model is a fine-tuned version of t5-small on the mlsum de dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5491
- Rouge1: 42.3787
- Rouge2: 32.0253
- Rougel: 38.9529
- Rougelsum: 40.4544
- Gen Len: 47.7873
## Model description
More information needed
#... | [
"# t5-seven-epoch-base-german\n\nThis model is a fine-tuned version of t5-small on the mlsum de dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.5491\n- Rouge1: 42.3787\n- Rouge2: 32.0253\n- Rougel: 38.9529\n- Rougelsum: 40.4544\n- Gen Len: 47.7873",
"## Model description\n\nMore info... | [
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text-generation | transformers |
# Spongebob DialoGPT model | {"tags": ["conversational"]} | Shakaw/DialoGPT-small-spongebot | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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null | transformers | # ChineseBERT-base
This repository contains code, model, dataset for **ChineseBERT** at ACL2021.
paper:
**[ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038)**
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
code: ... | {} | ShannonAI/ChineseBERT-base | null | [
"transformers",
"pytorch",
"arxiv:2106.16038",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16038"
] | [] | TAGS
#transformers #pytorch #arxiv-2106.16038 #endpoints_compatible #region-us
| # ChineseBERT-base
This repository contains code, model, dataset for ChineseBERT at ACL2021.
paper:
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
code:
ChineseBERT github link
## Model descrip... | [
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... | [
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null | transformers | # ChineseBERT-large
This repository contains code, model, dataset for **ChineseBERT** at ACL2021.
paper:
**[ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038)**
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
code:... | {} | ShannonAI/ChineseBERT-large | null | [
"transformers",
"pytorch",
"arxiv:2106.16038",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16038"
] | [] | TAGS
#transformers #pytorch #arxiv-2106.16038 #endpoints_compatible #region-us
| # ChineseBERT-large
This repository contains code, model, dataset for ChineseBERT at ACL2021.
paper:
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
code:
ChineseBERT github link
## Model descri... | [
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text-classification | transformers |
[](https://colab.research.google.com/drive/1dqeUwS_DZ-urrmYzB29nTCBUltwJxhbh?usp=sharing)
# 22 Language Identifier - BERT
This model is trained to identify the following 22 different languages.
- Arabic
- Chinese
- Dutch
- English
- Est... | {"metrics": ["accuracy"], "widget": [{"text": "In war resolution, in defeat defiance, in victory magnanimity"}, {"text": "en la guerra resoluci\u00f3n en la derrota desaf\u00edo en la victoria magnanimidad"}]} | SharanSMenon/22-languages-bert-base-cased | null | [
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"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
![Open In Colab](URL
# 22 Language Identifier - BERT
This model is trained to identify the following 22 different languages.
- Arabic
- Chinese
- Dutch
- English
- Estonian
- French
- Hindi
- Indonesian
- Japanese
- Korean
- Latin
- Persian
- Portugese
- Pushto
- Romanian
- Russian
- Spanish
- Swedi... | [
"# 22 Language Identifier - BERT\n\nThis model is trained to identify the following 22 different languages. \n\n\n- Arabic \n- Chinese \n- Dutch \n- English \n- Estonian \n- French\n- Hindi\n- Indonesian \n- Japanese \n- Korean \n- Latin \n- Persian \n- Portugese \n- Pushto\n- Romanian \n- Russian \n- Spanish \n- S... | [
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text2text-generation | null |
## RusEnQA
QA for Russian and English based on the [rugpt3xl](https://huggingface.co/sberbank-ai/rugpt3xl) model
### Fine-tuning format:
```
"<s>paragraph: "+eng_context+"\nlang: rus\nquestion: "+rus_question+' answer: '+ rus_answer+"</s>"
```
### About ruGPT-3 XL model
Model was trained with 512 sequence length ... | {"language": ["ru", "en"], "tags": ["PyTorch", "Transformers", "gpt2", "squad", "lm-head", "casual-lm"], "pipeline_tag": "text2text-generation", "thumbnail": "https://github.com/RussianNLP/RusEnQA"} | Shavrina/RusEnQA | null | [
"PyTorch",
"Transformers",
"gpt2",
"squad",
"lm-head",
"casual-lm",
"text2text-generation",
"ru",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
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#PyTorch #Transformers #gpt2 #squad #lm-head #casual-lm #text2text-generation #ru #en #region-us
|
## RusEnQA
QA for Russian and English based on the rugpt3xl model
### Fine-tuning format:
### About ruGPT-3 XL model
Model was trained with 512 sequence length using Deepspeed and Megatron code by SberDevices team, on 80B tokens dataset for 4 epochs. After that model was finetuned 1 epoch with sequence length 204... | [
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text-generation | transformers |
# SHAY0 Dialo GPT Model | {"tags": ["conversational"]} | ShayoGun/DialoGPT-small-shayo | 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
|
# SHAY0 Dialo GPT Model | [
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text-generation | transformers |
# Harry Potter DialGPT Model | {"tags": ["conversational"]} | Sheel/DialoGPT-small-harrypotter | 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
|
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text-generation | transformers |
# Mikasa DialoGPT Model | {"tags": ["conversational"]} | Sheerwin02/DialoGPT-medium-mikasa | 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
|
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] |
text-generation | transformers |
#isla DialoGPT Model
| {"tags": ["conversational"]} | Sheerwin02/DialoGPT-small-isla | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
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"autotrain_compatible",
"endpoints_compatible",
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|
#isla DialoGPT Model
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null | null | This is a repo with gather thoughts and experiments on the state-of-the-art techniques in NLP. | {} | ShenSeanchen/NLP | null | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# superglue-boolq
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "superglue-boolq", "results": []}]} | ShengdingHu/superglue-boolq | null | [
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| superglue-boolq
===============
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2098
* Accuracy: 76.7584
* Average Metrics: 76.7584
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Shenyancheng/distilbert-base-uncased-finetuned-ner | null | [
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| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0620
* Precision: 0.9267
* Recall: 0.9371
* F1: 0.9319
* Accuracy: 0.9838
Model des... | [
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text-generation | null |
# My Awesome Model | {"tags": ["conversational"]} | Sherman/DialoGPT-medium-joey | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Shike/DialoGPT_medium_harrypotter | null | [
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text-generation | transformers |
# My Hero Academia DialoGPT Model | {"tags": ["conversational"]} | Shinx/DialoGPT-medium-myheroacademia | null | [
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | NaturesDisaster/DialoGPT-large-Neku | null | [
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null | null | tags:
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text-generation | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | NaturesDisaster/DialoGPT-small-Neku | null | [
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text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | ShiroNeko/DialoGPT-small-rick | null | [
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question-answering | null | model card | {"language": "en", "license": "mit", "tags": ["exbert", "my-tag"], "datasets": ["dataset1", "scan-web"], "pipeline_tag": "question-answering"} | Shiyu/my-repo | null | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-roberta-depression
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unk... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "I feel so low and numb, don't feel like doing anything. Just passing my days"}, {"text": "Sleep is my greatest and most comforting escape whenever I wake up these days. The literal very first emotion I feel is just mise... | ShreyaR/finetuned-roberta-depression | null | [
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| finetuned-roberta-depression
============================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1385
* Accuracy: 0.9745
Model description
-----------------
More information needed
Intended uses & limitations... | [
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text-generation | transformers |
#goku DialoGPT Model
| {"tags": ["conversational"]} | Shubham-Kumar-DTU/DialoGPT-small-goku | null | [
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#goku DialoGPT Model
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT
This model is a fine-tuned version of [micro... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT", "results": []}]} | Shushant/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT | null | [
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| BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel\_PubmedBERT
====================================================================================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on an unknown dataset.
It achieves the following... | [
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fill-mask | transformers | # NepNewsBERT
## Masked Language Model for nepali language trained on nepali news scrapped from different nepali news website. The data set contained about 10 million of nepali sentences mainly related to nepali news.
## Usage
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.f... | {} | Shushant/NepNewsBERT | null | [
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| # NepNewsBERT
## Masked Language Model for nepali language trained on nepali news scrapped from different nepali news website. The data set contained about 10 million of nepali sentences mainly related to nepali news.
## Usage
from transformers import AutoTokenizer, AutoModelForMaskedLM
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-v1.1-biomedicalQuestionAnswering
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dm... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "biobert-v1.1-biomedicalQuestionAnswering", "results": []}]} | Shushant/biobert-v1.1-biomedicalQuestionAnswering | null | [
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| biobert-v1.1-biomedicalQuestionAnswering
========================================
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9009
Model description
-----------------
More information needed
Intended uses... | [
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fill-mask | transformers | # NEPALI BERT
## Masked Language Model for nepali language trained on nepali news scrapped from different nepali news website. The data set contained about 10 million of nepali sentences mainly related to nepali news.
This model is a fine-tuned version of [Bert Base Uncased](https://huggingface.co/bert-base-uncased) o... | {"language": ["ne"], "license": "mit", "library_name": "transformers", "datasets": ["Shushant/nepali"], "metrics": ["perplexity"], "pipeline_tag": "fill-mask"} | Shushant/nepaliBERT | null | [
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#transformers #pytorch #bert #fill-mask #ne #dataset-Shushant/nepali #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # NEPALI BERT
## Masked Language Model for nepali language trained on nepali news scrapped from different nepali news website. The data set contained about 10 million of nepali sentences mainly related to nepali news.
This model is a fine-tuned version of Bert Base Uncased on dataset composed of different news scrappe... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 164469
## Validation Metrics
- Loss: 0.05527503043413162
- Accuracy: 0.9853049228508449
- Precision: 0.991044776119403
- Recall: 0.9793510324483776
- AUC: 0.9966895139869654
- F1: 0.9851632047477745
## Usage
You can use cURL to access... | {"language": "en", "tags": "autonlp", "datasets": ["Shuvam/autonlp-data-college_classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | Shuvam/autonlp-college_classification-164469 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 164469
## Validation Metrics
- Loss: 0.05527503043413162
- Accuracy: 0.9853049228508449
- Precision: 0.991044776119403
- Recall: 0.9793510324483776
- AUC: 0.9966895139869654
- F1: 0.9851632047477745
## Usage
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text-generation | transformers | This model is a fine-tuned version of Microsoft/DialoGPT-medium trained to created sarcastic responses from the dataset "Sarcasm on Reddit" located [here](https://www.kaggle.com/danofer/sarcasm). | {"pipeline_tag": "conversational"} | SilentMyuth/sarcastic-model | null | [
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| This model is a fine-tuned version of Microsoft/DialoGPT-medium trained to created sarcastic responses from the dataset "Sarcasm on Reddit" located here. | [] | [
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | SilentMyuth/stableben | null | [
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text-generation | transformers | Hewlo | {} | SilentMyuth/stablejen | null | [
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text-generation | transformers | conver = pipeline("conversational")
---
tags:
- conversational
---
# Harry potter DialoGPT model | {} | Sin/DialoGPT-small-zai | null | [
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"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
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#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| conver = pipeline("conversational")
---
tags:
- conversational
---
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question-answering | transformers | # Muril Large Squad2
This model is finetuned for QA task on Squad2 from [Muril Large checkpoint](https://huggingface.co/google/muril-large-cased).
## Hyperparameters
```
Batch Size: 4
Grad Accumulation Steps = 8
Total epochs = 3
MLM Checkpoint = google/muril-large-cased
max_seq_len = 256
learning_rate = 1e-5
lr_schedu... | {} | Sindhu/muril-large-squad2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| # Muril Large Squad2
This model is finetuned for QA task on Squad2 from Muril Large checkpoint.
## Hyperparameters
## Squad 2 Evaluation stats:
Generated from the official Squad2 evaluation script
## Limitations
MuRIL is specifically trained to work on 18 Indic languages and English. This model is not expected to... | [
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question-answering | transformers |
# Rembert Squad2
This model is finetuned for QA task on Squad2 from [Rembert checkpoint](https://huggingface.co/google/rembert).
## Hyperparameters
```
Batch Size: 4
Grad Accumulation Steps = 8
Total epochs = 3
MLM Checkpoint = "rembert"
max_seq_len = 256
learning_rate = 1e-5
lr_schedule = LinearWarmup
warmup_ratio =... | {"language": ["multilingual"], "tags": ["question-answering"], "datasets": ["squad2"], "metrics": ["squad2"]} | Sindhu/rembert-squad2 | null | [
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"multilingual",
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"multilingual"
] | TAGS
#transformers #pytorch #rembert #question-answering #multilingual #dataset-squad2 #endpoints_compatible #region-us
|
# Rembert Squad2
This model is finetuned for QA task on Squad2 from Rembert checkpoint.
## Hyperparameters
## Squad 2 Evaluation stats:
Metrics generated from the official Squad2 evaluation script
For any questions, you can reach out to me on Twitter | [
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text-generation | transformers |
# The Vampire Diaries DialoGPT Model | {"tags": ["conversational"]} | SirBastianXVII/DialoGPT-small-TVD | null | [
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text-generation | transformers |
# Trump Insults GPT Bot | {"tags": ["conversational"]} | Sired/DialoGPT-small-trumpbot | null | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wangchanberta-base-att-spm-uncased-finetuned-th-squad
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-s... | {"tags": ["generated_from_trainer"], "datasets": ["thaiqa_squad"], "widget": [{"text": "\u0e2a\u0e42\u0e21\u0e2a\u0e23\u0e40\u0e23\u0e2d\u0e31\u0e25\u0e21\u0e32\u0e14\u0e23\u0e34\u0e14\u0e01\u0e48\u0e2d\u0e15\u0e31\u0e49\u0e07\u0e02\u0e36\u0e49\u0e19\u0e43\u0e19\u0e1b\u0e35\u0e43\u0e14", "context": "\u0e2a\u0e42\u0e21\... | Sirinya/wangchanberta-th-squad_test1 | null | [
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|
# wangchanberta-base-att-spm-uncased-finetuned-th-squad
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the thaiqa_squad dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch... | [
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text-generation | transformers | # DialoGPT Trained on a customized various spiritual texts and mixed with various different character personalities.
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on the energy complex known as Ra. Some text has been changed from the original with the inten... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | Siyris/DialoGPT-medium-SIY | null | [
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| # DialoGPT Trained on a customized various spiritual texts and mixed with various different character personalities.
This is an instance of microsoft/DialoGPT-medium trained on the energy complex known as Ra. Some text has been changed from the original with the intention of making it fit our discord server better. I'v... | [
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text-generation | transformers | # DialoGPT Trained on a customized version of The Law of One.
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on the energy complex known as Ra. Some text has been changed from the original with the intention of making it fit our discord server better.
I buil... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | Siyris/SIY | null | [
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"license:mit",
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| # DialoGPT Trained on a customized version of The Law of One.
This is an instance of microsoft/DialoGPT-medium trained on the energy complex known as Ra. Some text has been changed from the original with the intention of making it fit our discord server better.
I built a Discord AI chatbot based on this model for inter... | [
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question-answering | transformers |
# BERT base Japanese - JaQuAD
## Description
A Japanese Question Answering model fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD).
Please refer [BERT base Japanese](https://huggingface.co/cl-tohoku/bert-base-japanese) for details about the pre-training model.
The codes for the fine-tunin... | {"language": "ja", "license": "cc-by-sa-3.0", "tags": ["question-answering", "extractive-qa"], "datasets": ["SkelterLabsInc/JaQuAD"], "metrics": ["Exact match", "F1 score"], "pipeline_tag": ["None"]} | SkelterLabsInc/bert-base-japanese-jaquad | null | [
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"dataset:SkelterLabsInc/JaQuAD",
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|
# BERT base Japanese - JaQuAD
## Description
A Japanese Question Answering model fine-tuned on JaQuAD.
Please refer BERT base Japanese for details about the pre-training model.
The codes for the fine-tuning are available at SkelterLabsInc/JaQuAD
## Evaluation results
On the development set.
On the test set.
... | [
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text2text-generation | transformers |
**Model Overview**
This is the model presented in the paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/).
The model itself is [BART (base)](https://huggingface.co/facebook/bart-base) model trained on parallel detoxification dataset ParaDetox achiving SOTA results fo... | {"language": ["en"], "license": "openrail++", "tags": ["detoxification"], "datasets": ["s-nlp/paradetox"], "licenses": ["cc-by-nc-sa"]} | s-nlp/bart-base-detox | null | [
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"detoxification",
"en",
"dataset:s-nlp/paradetox",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #detoxification #en #dataset-s-nlp/paradetox #license-openrail++ #autotrain_compatible #endpoints_compatible #region-us
|
Model Overview
This is the model presented in the paper "ParaDetox: Detoxification with Parallel Data".
The model itself is BART (base) model trained on parallel detoxification dataset ParaDetox achiving SOTA results for detoxification task. More details, code and data can be found here.
How to use
Citation
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #detoxification #en #dataset-s-nlp/paradetox #license-openrail++ #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
59
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] |
text-generation | transformers | # Model Details
This is a conditional language model based on [gpt2-medium](https://huggingface.co/gpt2-medium/) but with a vocabulary from [t5-base](https://huggingface.co/t5-base), for compatibility with T5-based paraphrasers such as [t5-paranmt-detox](https://huggingface.co/SkolkovoInstitute/t5-paranmt-detox). Th... | {"language": ["en"], "tags": ["text-generation", "conditional-text-generation"]} | s-nlp/gpt2-base-gedi-detoxification | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conditional-text-generation",
"en",
"arxiv:2109.08914",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.08914"
] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conditional-text-generation #en #arxiv-2109.08914 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Details
This is a conditional language model based on gpt2-medium but with a vocabulary from t5-base, for compatibility with T5-based paraphrasers such as t5-paranmt-detox. The model is conditional on two styles, 'toxic' and 'normal', and was fine-tuned on the dataset from the Jigsaw toxic comment classifica... | [
"# Model Details\n \n\nThis is a conditional language model based on gpt2-medium but with a vocabulary from t5-base, for compatibility with T5-based paraphrasers such as t5-paranmt-detox. The model is conditional on two styles, 'toxic' and 'normal', and was fine-tuned on the dataset from the Jigsaw toxic comment cl... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conditional-text-generation #en #arxiv-2109.08914 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Details\n \n\nThis is a conditional language model based on gpt2-medium but with a vocabulary from t5-base, for co... | [
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conditional-text-generation #en #arxiv-2109.08914 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Details\n \n\nThis is a conditional language model based on gpt2-medium but with a vocabulary from t5-base, for compatib... |
text-classification | transformers |
The model has been trained to predict for English sentences, whether they are formal or informal.
Base model: `roberta-base`
Datasets: [GYAFC](https://github.com/raosudha89/GYAFC-corpus) from [Rao and Tetreault, 2018](https://aclanthology.org/N18-1012) and [online formality corpus](http://www.seas.upenn.edu/~nlp/re... | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["formality"], "datasets": ["GYAFC", "Pavlick-Tetreault-2016"]} | s-nlp/roberta-base-formality-ranker | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"formality",
"en",
"dataset:GYAFC",
"dataset:Pavlick-Tetreault-2016",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #formality #en #dataset-GYAFC #dataset-Pavlick-Tetreault-2016 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| The model has been trained to predict for English sentences, whether they are formal or informal.
Base model: 'roberta-base'
Datasets: GYAFC from Rao and Tetreault, 2018 and online formality corpus from Pavlick and Tetreault, 2016.
Data augmentation: changing texts to upper or lower case; removing all punctuation... | [] | [
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] |
text-classification | transformers | ## Toxicity Classification Model (but for the first part of the data)
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by **Jigsaw** ([Jigsaw 2018](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge), [Jigsa... | {"language": ["en"], "tags": ["toxic comments classification"], "licenses": ["cc-by-nc-sa"]} | s-nlp/roberta_first_toxicity_classifier | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"toxic comments classification",
"en",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #toxic comments classification #en #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| ## Toxicity Classification Model (but for the first part of the data)
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into tw... | [
"## Toxicity Classification Model (but for the first part of the data)\nThis model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it i... | [
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text-classification | transformers |
## Toxicity Classification Model
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by **Jigsaw** ([Jigsaw 2018](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge), [Jigsaw 2019](https://www.kaggle.com/c/ji... | {"language": ["en"], "tags": ["toxic comments classification"], "licenses": ["cc-by-nc-sa"]} | s-nlp/roberta_toxicity_classifier | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"toxic comments classification",
"en",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #toxic comments classification #en #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Toxicity Classification Model
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa mod... | [
"## Toxicity Classification Model\n\nThis model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by Jigsaw (Jigsaw 2018, Jigsaw 2019, Jigsaw 2020), containing around 2 million examples. We split it into two parts and fine-tune a RoBER... | [
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text-classification | transformers | This model is a clone of [SkolkovoInstitute/roberta_toxicity_classifier](https://huggingface.co/SkolkovoInstitute/roberta_toxicity_classifier) trained on a disjoint dataset.
While `roberta_toxicity_classifier` is used for evaluation of detoxification algorithms, `roberta_toxicity_classifier_v1` can be used within the... | {} | s-nlp/roberta_toxicity_classifier_v1 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:1911.00536",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.00536"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #arxiv-1911.00536 #autotrain_compatible #endpoints_compatible #region-us
| This model is a clone of SkolkovoInstitute/roberta_toxicity_classifier trained on a disjoint dataset.
While 'roberta_toxicity_classifier' is used for evaluation of detoxification algorithms, 'roberta_toxicity_classifier_v1' can be used within these algorithms, as in the paper Text Detoxification using Large Pre-train... | [] | [
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] | [
38
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] |
text2text-generation | transformers | This is the detoxification baseline model trained on the [train](https://github.com/skoltech-nlp/russe_detox_2022/blob/main/data/input/train.tsv) part of "RUSSE 2022: Russian Text Detoxification Based on Parallel Corpora" competition. The source sentences are Russian toxic messages from Odnoklassniki, Pikabu, and Twitt... | {"language": ["ru"], "license": "openrail++", "tags": ["text-generation-inference"], "datasets": ["s-nlp/ru_paradetox"]} | s-nlp/ruT5-base-detox | null | [
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"safetensors",
"t5",
"text2text-generation",
"text-generation-inference",
"ru",
"dataset:s-nlp/ru_paradetox",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #text-generation-inference #ru #dataset-s-nlp/ru_paradetox #license-openrail++ #autotrain_compatible #endpoints_compatible #region-us
| This is the detoxification baseline model trained on the train part of "RUSSE 2022: Russian Text Detoxification Based on Parallel Corpora" competition. The source sentences are Russian toxic messages from Odnoklassniki, Pikabu, and Twitter platforms. The base model is ruT5 provided from Sber.
How to use
| [] | [
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] | [
64
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] |
text-classification | transformers |
This is a model for evaluation of naturalness of short Russian texts. It has been trained to distinguish human-written texts from their corrupted versions.
Corruption sources: random replacement, deletion, addition, shuffling, and re-inflection of words and characters, random changes of capitalization, round-trip tr... | {"language": ["ru"], "tags": ["fluency"]} | s-nlp/rubert-base-corruption-detector | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"fluency",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bert #text-classification #fluency #ru #autotrain_compatible #endpoints_compatible #region-us
|
This is a model for evaluation of naturalness of short Russian texts. It has been trained to distinguish human-written texts from their corrupted versions.
Corruption sources: random replacement, deletion, addition, shuffling, and re-inflection of words and characters, random changes of capitalization, round-trip tr... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #fluency #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
33
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #fluency #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
Bert-based classifier (finetuned from [Conversational Rubert](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)) trained on merge of Russian Language Toxic Comments [dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments/metadata) collected from 2ch.hk and Toxic Russian Comments [d... | {"language": ["ru"], "tags": ["toxic comments classification"], "licenses": ["cc-by-nc-sa"]} | s-nlp/russian_toxicity_classifier | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"toxic comments classification",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #toxic comments classification #ru #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bert-based classifier (finetuned from Conversational Rubert) trained on merge of Russian Language Toxic Comments dataset collected from URL and Toxic Russian Comments dataset collected from URL.
The datasets were merged, shuffled, and split into train, dev, test splits in 80-10-10 proportion.
The metrics obtained fro... | [] | [
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] |
text2text-generation | transformers | This is a paraphraser based on [ceshine/t5-paraphrase-paws-msrp-opinosis](https://huggingface.co/ceshine/t5-paraphrase-paws-msrp-opinosis)
and additionally fine-tuned on [ParaNMT](https://arxiv.org/abs/1711.05732) filtered for the task of detoxification.
The model was trained for the paper [Text Detoxification using L... | {"license": "openrail++", "datasets": ["s-nlp/paranmt_for_detox"]} | s-nlp/t5-paranmt-detox | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"dataset:s-nlp/paranmt_for_detox",
"arxiv:1711.05732",
"arxiv:1911.00536",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1711.05732",
"1911.00536"
] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #dataset-s-nlp/paranmt_for_detox #arxiv-1711.05732 #arxiv-1911.00536 #license-openrail++ #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a paraphraser based on ceshine/t5-paraphrase-paws-msrp-opinosis
and additionally fine-tuned on ParaNMT filtered for the task of detoxification.
The model was trained for the paper Text Detoxification using Large Pre-trained Neural Models.
An example of its use and the code for its training is given in URL | [] | [
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] |
text2text-generation | transformers | This is a paraphraser based on [ceshine/t5-paraphrase-paws-msrp-opinosis](https://huggingface.co/ceshine/t5-paraphrase-paws-msrp-opinosis)
and additionally fine-tuned on [ParaNMT](https://arxiv.org/abs/1711.05732).
The model was trained for the paper [Text Detoxification using Large Pre-trained Neural Models](https://... | {} | s-nlp/t5-paraphrase-paws-msrp-opinosis-paranmt | null | [
"transformers",
"pytorch",
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"text2text-generation",
"arxiv:1711.05732",
"arxiv:1911.00536",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1711.05732",
"1911.00536"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1711.05732 #arxiv-1911.00536 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a paraphraser based on ceshine/t5-paraphrase-paws-msrp-opinosis
and additionally fine-tuned on ParaNMT.
The model was trained for the paper Text Detoxification using Large Pre-trained Neural Models.
An example of its use is given in URL | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-1711.05732 #arxiv-1911.00536 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
text-classification | transformers |
XLMRoberta-based classifier trained on XFORMAL.
all
| | precision | recall | f1-score | support |
|--------------|-----------|----------|----------|---------|
| 0 | 0.744912 | 0.927790 | 0.826354 | 108019 |
| 1 | 0.889088 | 0.645630 | 0.748048 | 96845 |
| accuracy | ... | {"language": ["en", "fr", "it", "pt"], "license": "cc-by-nc-sa-4.0", "tags": ["formal or informal classification"], "licenses": ["cc-by-nc-sa"]} | s-nlp/xlmr_formality_classifier | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"formal or informal classification",
"en",
"fr",
"it",
"pt",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"fr",
"it",
"pt"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #formal or informal classification #en #fr #it #pt #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| XLMRoberta-based classifier trained on XFORMAL.
all
en
fr
it
pt
How to use
----------
Licensing Information
---------------------
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](URL).
[](URL)
| [] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #formal or informal classification #en #fr #it #pt #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
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] |
text-classification | transformers |
## General concept of the model
#### Proposed usage
The **'inappropriateness'** substance we tried to collect in the dataset and detect with the model **is NOT a substitution of toxicity**, it is rather a derivative of toxicity. So the model based on our dataset could serve as **an additional layer of inappropriaten... | {"language": ["ru"], "tags": ["toxic comments classification"], "licenses": ["cc-by-nc-sa"]} | apanc/russian-inappropriate-messages | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"toxic comments classification",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #toxic comments classification #ru #autotrain_compatible #endpoints_compatible #region-us
| General concept of the model
----------------------------
#### Proposed usage
The 'inappropriateness' substance we tried to collect in the dataset and detect with the model is NOT a substitution of toxicity, it is rather a derivative of toxicity. So the model based on our dataset could serve as an additional layer ... | [
"#### Proposed usage\n\n\nThe 'inappropriateness' substance we tried to collect in the dataset and detect with the model is NOT a substitution of toxicity, it is rather a derivative of toxicity. So the model based on our dataset could serve as an additional layer of inappropriateness filtering after toxicity and ob... | [
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text-classification | transformers |
## General concept of the model
This model is trained on the dataset of sensitive topics of the Russian language. The concept of sensitive topics is described [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. Please note th... | {"language": ["ru"], "tags": ["toxic comments classification"], "licenses": ["cc-by-nc-sa"]} | apanc/russian-sensitive-topics | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"toxic comments classification",
"ru",
"arxiv:2103.05345",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.05345"
] | [
"ru"
] | TAGS
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| General concept of the model
----------------------------
This model is trained on the dataset of sensitive topics of the Russian language. The concept of sensitive topics is described in this article presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. Please note that this article describes t... | [] | [
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text-generation | transformers |
# Harry Potter DialogGPT Model | {"tags": ["conversational"]} | Skywhy/DialoGPT-medium-Churchyy | null | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878
## Validation Metrics
- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
- Weighted F1: 0.8742964006840133... | {"language": "en", "tags": "autonlp", "datasets": ["Smone55/autonlp-data-au_topics"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 208.0823957145878} | Smone55/autonlp-au_topics-452311620 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878
## Validation Metrics
- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
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text-generation | transformers |
#StupidEdwin | {"tags": ["conversational"]} | Snaky/StupidEdwin | null | [
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text2text-generation | transformers | ## Schema Guided Dialogue Output Plan Constructor
| {} | SoLID/sgd-output-plan-constructor | null | [
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#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Schema Guided Dialogue Output Plan Constructor
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text2text-generation | transformers |
Hyperparameters: 1 epoch, max_len_dict including domain classification task, and 1e-5 learning rate | {"language": ["eng"], "license": "afl-3.0", "tags": ["dialogue"], "datasets": ["schema guided dialogue"], "metrics": ["exactness"], "thumbnail": "https://townsquare.media/site/88/files/2020/06/C_Charlotte_RGB_7484.jpg"} | SoLID/sgd-t5-tod | null | [
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"dialogue",
"eng",
"license:afl-3.0",
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"eng"
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#transformers #pytorch #t5 #text2text-generation #dialogue #eng #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Hyperparameters: 1 epoch, max_len_dict including domain classification task, and 1e-5 learning rate | [] | [
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text-generation | transformers |
# Cartman DialoGPT Model | {"tags": ["conversational"]} | Soapsy/DialoGPT-mid-cartman | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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text-generation | null |
# My Awesome Model | {"tags": ["conversational"]} | SonMooSans/DialoGPT-small-joshua | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | SonMooSans/test | null | [
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"text-generation",
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model_index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | SongRb/distilbert-base-uncased-finetuned-cola | null | [
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"tensorboard",
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"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
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"endpoints_compatible",
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#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8549
* Matthews Correlation: 0.5332
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | SongRb/distilbert-base-uncased-finetuned-ner | null | [
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#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0746
* Precision: 0.9347
* Recall: 0.9426
* F1: 0.9386
* Accuracy: 0.9851
Model des... | [
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question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | Sonny/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
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"dataset:squad",
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#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
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fill-mask | transformers | This is a test model2. | {} | Sonny/dummy-model2 | null | [
"transformers",
"camembert",
"fill-mask",
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"region:us"
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#transformers #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
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text2text-generation | transformers | This is the model so far before time out
| {} | SophieTr/distil-pegasus-reddit | null | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tune-Pegasus-large
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "fine-tune-Pegasus-large", "results": []}]} | SophieTr/fine-tune-Pegasus-large | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# fine-tune-Pegasus-large
This model is a fine-tuned version of google/pegasus-large on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 11.0526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation dat... | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [sshleifer/distill-pegasus-xsum-16-4](https://huggingface.co/sshleifer/distill-peg... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "results", "results": []}]} | SophieTr/results | null | [
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#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of sshleifer/distill-pegasus-xsum-16-4 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4473
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More in... | [
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text-generation | transformers |
# Naruto DialoGPT Model | {"tags": ["conversational"]} | Sora4762/DialoGPT-small-naruto | null | [
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"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
|
# Naruto DialoGPT Model | [
"# Naruto DialoGPT Model"
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] |
text-generation | transformers |
# Naruto DialoGPT Model1.1 | {"tags": ["conversational"]} | Sora4762/DialoGPT-small-naruto1.1 | 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
|
# Naruto DialoGPT Model1.1 | [
"# Naruto DialoGPT Model1.1"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT
This model is a fine-tuned version of [Sotir... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT", "results": []}]} | Sotireas/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel\_PubmedBERT
====================================================================================
This model is a fine-tuned version of Sotireas/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel\_PubmedBERT on an unknown da... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
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36,
101,
5,
44
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"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Soumyajit1008/DialoGPT-small-harryPotterssen | 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
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Sourabh714/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1573
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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47,
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44
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"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
null | transformers |
### VAE with Pytorch-Lightning
This is inspired from vae-playground. This is an example where we test out vae and conv_vae models with multiple datasets
like MNIST, celeb-a and MNIST-Fashion datasets.
This also comes with an example streamlit app & deployed at huggingface.
## Model Training
You can tr... | {"license": "apache-2.0"} | Souranil/VAE | null | [
"transformers",
"pytorch",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #license-apache-2.0 #endpoints_compatible #region-us
|
### VAE with Pytorch-Lightning
This is inspired from vae-playground. This is an example where we test out vae and conv_vae models with multiple datasets
like MNIST, celeb-a and MNIST-Fashion datasets.
This also comes with an example streamlit app & deployed at huggingface.
## Model Training
You can tr... | [
"### VAE with Pytorch-Lightning\r\n\r\nThis is inspired from vae-playground. This is an example where we test out vae and conv_vae models with multiple datasets \r\nlike MNIST, celeb-a and MNIST-Fashion datasets.\r\n\r\nThis also comes with an example streamlit app & deployed at huggingface.",
"## Model Training\... | [
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"### VAE with Pytorch-Lightning\r\n\r\nThis is inspired from vae-playground. This is an example where we test out vae and conv_vae models with multiple datasets \r\nlike MNIST, celeb-a and MNIST-Fashion datasets.\r\n\r\nThis a... | [
25,
76,
105
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"TAGS\n#transformers #pytorch #license-apache-2.0 #endpoints_compatible #region-us \n### VAE with Pytorch-Lightning\r\n\r\nThis is inspired from vae-playground. This is an example where we test out vae and conv_vae models with multiple datasets \r\nlike MNIST, celeb-a and MNIST-Fashion datasets.\r\n\r\nThis also co... |
null | null | Log FiBER
This model is able to sentence embedding. | {} | Souvikcmsa/LogFiBER | null | [
"pytorch",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #region-us
| Log FiBER
This model is able to sentence embedding. | [] | [
"TAGS\n#pytorch #region-us \n"
] | [
10
] | [
"TAGS\n#pytorch #region-us \n"
] |
text-generation | transformers |
#Gandalf DialoGPT Model | {"tags": ["conversational"]} | SpacyGalaxy/DialoGPT-medium-Gandalf | 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
|
#Gandalf DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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39
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] |
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