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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-regression-w-m-vote-epoch-3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:21:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-regression-w-m-vote-epoch-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:22:35+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
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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. -->
# Cybonto-distilbert-base-uncased-finetuned-ner-FewNerd
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["few_nerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Cybonto-distilbert-base-uncased-finetuned-ner-FewNerd", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset"... | theResearchNinja/Cybonto-distilbert-base-uncased-finetuned-ner-FewNerd | null | [
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:26:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-few_nerd #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Cybonto-distilbert-base-uncased-finetuned-ner-FewNerd
=====================================================
This model is a fine-tuned version of distilbert-base-uncased on the few\_nerd dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2091
* Precision: 0.7422
* Recall: 0.7830
* F1: 0.762... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-few_nerd #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-strict-epoch-1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:29:06+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm3-wikihow2
This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm2-wikihow2](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm3-wikihow2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", ... | Chikashi/t5-small-finetuned-cnndm3-wikihow2 | null | [
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T15:30:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm3-wikihow2
==================================
This model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm2-wikihow2 on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6265
* Rouge1: 24.6704
* Rouge2: 11.9038
* Rougel: 20.3622
* Rouge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-strict-epoch-2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:32:15+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
# Roberta for depression signs detection
This model is a fine-tuned version the <a href="https://huggingface.co/cardiffnlp/twitter-roberta-base">cardiffnlp/twitter-roberta-base</a> model. It has been trained using a recently published corpus: <a href="https://competitions.codalab.org/competitions/36410#learn_the_deta... | {"language": "en", "datasets": ["Shared task on Detecting Signs of Depression from Social Media Text at LT-EDI 2022-ACL 2022"], "metrics": ["Macro F1-Score"]} | paulagarciaserrano/roberta-depression-detection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:34:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
| Roberta for depression signs detection
======================================
This model is a fine-tuned version the <a href="URL model. It has been trained using a recently published corpus: <a href="URL task on Detecting Signs of Depression from Social Media Text at LT-EDI 2022-ACL 2022.
The obtained macro f1-sco... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-strict-epoch-3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:35:22+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-strict-epoch-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:41:38+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-nonstrict-epoch-1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
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"license:mit",
"autotrain_compatible",
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"region:us"
] | null | 2022-04-15T15:44:08+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-nonstrict-epoch-2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:46:17+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-nonstrict-epoch-3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:48:32+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-m-vote-nonstrict-epoch-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:50:19+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-strict-epoch-1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:53:35+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-strict-epoch-2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:55:21+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-strict-epoch-3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:57:00+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-strict-epoch-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T15:58:37+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:01:40+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:06:08+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:08:06+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
text-classification | transformers |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a ... | {"language": "es", "license": "mit", "widget": [{"text": "y porqu\u00e9 es lo que hay que hacer con los menas y con los adultos tambi\u00e9n!!!! NO a los inmigrantes ilegales!!!!"}]} | MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:09:54+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish"... | [
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several experiments that will be described in the upcoming paper \"Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Description\n\n\nThis model is a fine-tuned version of BETO (spanish bert) that has been trained on the *Datathon Against Racism* dataset (2022)\n\n\nWe performed several expe... |
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-toxic-clf
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-toxic-clf", "results": []}]} | profoz/distilbert-toxic-clf | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:13:54+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-toxic-clf
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# distilbert-toxic-clf\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-toxic-clf\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore inf... |
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-colab
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad-colab", "results": []}]} | Adrian/distilbert-base-uncased-finetuned-squad-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:32:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad-colab
=============================================
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: 1.1662
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text-classification | transformers | ## DistilbERT Toxic Classifier | {} | profoz/distilbert-toxic-classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:41:38+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## DistilbERT Toxic Classifier | [
"## DistilbERT Toxic Classifier"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## DistilbERT Toxic Classifier"
] |
feature-extraction | transformers |
## KRISSBERT
[https://arxiv.org/pdf/2112.07887.pdf](https://arxiv.org/pdf/2112.07887.pdf)
Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck ... | {"language": "en", "license": "mit", "tags": ["exbert"], "pipeline_tag": "feature-extraction", "widget": [{"text": "<ENT> ER </ENT> crowding has become a wide-spread problem."}]} | microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL | null | [
"transformers",
"pytorch",
"bert",
"exbert",
"feature-extraction",
"en",
"arxiv:2112.07887",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T16:50:38+00:00 | [
"2112.07887"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #exbert #feature-extraction #en #arxiv-2112.07887 #license-mit #endpoints_compatible #region-us
|
## KRISSBERT
URL
Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking h... | [
"## KRISSBERT \nURL\n\nEntity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity li... | [
"TAGS\n#transformers #pytorch #bert #exbert #feature-extraction #en #arxiv-2112.07887 #license-mit #endpoints_compatible #region-us \n",
"## KRISSBERT \nURL\n\nEntity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. ... |
null | espnet |
## ESPnet2 DIAR model
### `espnet/YushiUeda_librimix_diar_enh_2_3_spk`
This model was trained by YushiUeda using librimix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4f0f9a2435549211ef670354d09eb45883441b2d
pip install -e .
cd egs2/librimix... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["librimix"]} | espnet/YushiUeda_librimix_diar_enh_2_3_spk | null | [
"espnet",
"audio",
"diarization",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-15T17:28:45+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #diarization #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 DIAR model
------------------
### 'espnet/YushiUeda\_librimix\_diar\_enh\_2\_3\_spk'
This model was trained by YushiUeda using librimix recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri Mar 25 17:40:43 EDT 2022'
* python version: '3.7.11 (defaul... | [
"### 'espnet/YushiUeda\\_librimix\\_diar\\_enh\\_2\\_3\\_spk'\n\n\nThis model was trained by YushiUeda using librimix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri Mar 25 17:40:43 EDT 2022'\n* python version: '3.7.11 (default, Jul 27 2... | [
"TAGS\n#espnet #audio #diarization #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_librimix\\_diar\\_enh\\_2\\_3\\_spk'\n\n\nThis model was trained by YushiUeda using librimix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironm... |
null | espnet |
## ESPnet2 DIAR model
### `espnet/YushiUeda_librimix_diar_enh_2_3_spk_lmf`
This model was trained by YushiUeda using librimix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4f0f9a2435549211ef670354d09eb45883441b2d
pip install -e .
cd egs2/libr... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["librimix"]} | espnet/YushiUeda_librimix_diar_enh_2_3_spk_lmf | null | [
"espnet",
"audio",
"diarization",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-15T17:39:53+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #diarization #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 DIAR model
------------------
### 'espnet/YushiUeda\_librimix\_diar\_enh\_2\_3\_spk\_lmf'
This model was trained by YushiUeda using librimix recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat Mar 26 08:47:28 EDT 2022'
* python version: '3.7.11 (d... | [
"### 'espnet/YushiUeda\\_librimix\\_diar\\_enh\\_2\\_3\\_spk\\_lmf'\n\n\nThis model was trained by YushiUeda using librimix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat Mar 26 08:47:28 EDT 2022'\n* python version: '3.7.11 (default, Ju... | [
"TAGS\n#espnet #audio #diarization #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_librimix\\_diar\\_enh\\_2\\_3\\_spk\\_lmf'\n\n\nThis model was trained by YushiUeda using librimix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nila-yuki/final_lab
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nila-yuki/final_lab", "results": []}]} | nila-yuki/final_lab | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T17:47:57+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nila-yuki/final\_lab
====================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0240
* Validation Loss: 0.0593
* Epoch: 2
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text2text-generation | transformers | This model was finetuned on errorful sentences from the `train` subset of [UA-GEC](https://github.com/grammarly/ua-gec) corpus, introduced in [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://arxiv.org/abs/2103.16997) paper.
Only sentences containing errors were used; 8,874 s... | {"language": "uk", "tags": ["gec"], "widget": [{"text": "\u044f \u0439 \u043d\u0435 \u0434\u0443\u043c\u0430\u0432 \u0449\u043e \u043a\u043e\u043c\u043f'\u044e\u0442\u0435\u0440\u043d\u0430 \u043b\u0456\u043d\u0433\u0432\u0456\u0441\u0442\u0438\u043a\u0430 \u0446\u0435 \u043b\u0435\u0433\u043a\u043e\u043e."}]} | schhwmn/mt5-base-finetuned-ukr-gec | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"gec",
"uk",
"arxiv:2103.16997",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T17:48:40+00:00 | [
"2103.16997"
] | [
"uk"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #gec #uk #arxiv-2103.16997 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model was finetuned on errorful sentences from the 'train' subset of UA-GEC corpus, introduced in UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language paper.
Only sentences containing errors were used; 8,874 sentences for training and 987 sentences for validation. The training argume... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #gec #uk #arxiv-2103.16997 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ssavla2/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ssavla2/bert-finetuned-ner", "results": []}]} | ssavla2/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T17:52:18+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ssavla2/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0243
* Validation Loss: 0.0603
* Epoch: 2
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
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. -->
# Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17
This model is a fine-tuned version of [distilbert-base-uncased](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": ... | theResearchNinja/Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T18:03:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17
====================================================
This model is a fine-tuned version of distilbert-base-uncased on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5062
* Precision: 0.6603
* Recall: 0.4682
* F1: 0.5479
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
image-classification | transformers |
# VIT_Basic
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpic... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Zayn/VIT_Basic | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T18:10:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# VIT_Basic
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### chairs
!chairs
#### hot dog
!hot dog
#### ice cream
!ice cream
#### ladders
!ladders
#### tables
!tabl... | [
"# VIT_Basic\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### chairs\n\n!chairs",
"#### hot dog\n\n!hot dog",
"#### ice cream\n\n!ice cream",
"#### la... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# VIT_Basic\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues wit... |
token-classification | transformers |
This model is the combined camembert-base model, with the pretrained lilt checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding", with the visual backbone built from the pretrained checkpoint "microsoft/dit-base".
*Note:* This model shou... | {"language": ["fr"], "license": "mit", "tags": ["token-classification", "fill-mask"], "datasets": ["iit-cdip"]} | manu/lilt-camembert-dit-base-hf | null | [
"transformers",
"pytorch",
"liltrobertalike",
"fill-mask",
"token-classification",
"fr",
"dataset:iit-cdip",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T18:12:12+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #liltrobertalike #fill-mask #token-classification #fr #dataset-iit-cdip #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
This model is the combined camembert-base model, with the pretrained lilt checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding", with the visual backbone built from the pretrained checkpoint "microsoft/dit-base".
*Note:* This model shou... | [] | [
"TAGS\n#transformers #pytorch #liltrobertalike #fill-mask #token-classification #fr #dataset-iit-cdip #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | # Hopenet
- https://github.com/natanielruiz/deep-head-pose
- https://drive.google.com/file/d/1EJPu2sOAwrfuamTitTkw2xJ2ipmMsmD3/view
- https://drive.google.com/file/d/16OZdRULgUpceMKZV6U9PNFiigfjezsCY/view
- https://drive.google.com/file/d/1m25PrSE7g9D2q2XJVMR6IA7RaCvWSzCR/view
## Note
```python
import pa... | {} | public-data/Hopenet | null | [
"region:us",
"has_space"
] | null | 2022-04-15T19:03:56+00:00 | [] | [] | TAGS
#region-us #has_space
| # Hopenet
- URL
- URL
- URL
- URL
## Note
| [
"# Hopenet\n\n- URL\n - URL\n - URL\n - URL",
"## Note"
] | [
"TAGS\n#region-us #has_space \n",
"# Hopenet\n\n- URL\n - URL\n - URL\n - URL",
"## Note"
] |
text2text-generation | transformers | Input have to be constructed with prefix ": ", a word form, the colon and a POS, e.g.: `: effugere:VERB`. | {} | enelpol/evalatin2022-lemma-closed | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T19:20:25+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Input have to be constructed with prefix ": ", a word form, the colon and a POS, e.g.: ': effugere:VERB'. | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This model was finetuned on errorful sentences from the `train` subset of [UA-GEC](https://github.com/grammarly/ua-gec) corpus, introduced in [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://arxiv.org/abs/2103.16997) paper.
Only sentences containing errors were used; 8,874 s... | {"language": "uk", "tags": ["gec", "mbart-50"], "widget": [{"text": "\u044f \u0439 \u043d\u0435 \u0434\u0443\u043c\u0430\u0432 \u0449\u043e \u043a\u043e\u043c\u043f'\u044e\u0442\u0435\u0440\u043d\u0430 \u043b\u0456\u043d\u0433\u0432\u0456\u0441\u0442\u0438\u043a\u0430 \u0446\u0435 \u043b\u0435\u0433\u043a\u043e\u043e."... | schhwmn/mbart-large-50-finetuned-ukr-gec | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"gec",
"mbart-50",
"uk",
"arxiv:2103.16997",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-15T19:24:24+00:00 | [
"2103.16997"
] | [
"uk"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #gec #mbart-50 #uk #arxiv-2103.16997 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model was finetuned on errorful sentences from the 'train' subset of UA-GEC corpus, introduced in UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language paper.
Only sentences containing errors were used; 8,874 sentences for training and 987 sentences for validation. The training argume... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #gec #mbart-50 #uk #arxiv-2103.16997 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
unconditional-image-generation | transformers |
# Hugging NFT: theshiboshis
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/theshiboshis)... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/theshiboshis"]} | huggingnft/theshiboshis | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/theshiboshis",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T20:02:19+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/theshiboshis #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: theshiboshis
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: li... | [
"# Hugging NFT: theshiboshis",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available h... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/theshiboshis #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: theshiboshis",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the si... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | edonath/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T20:05:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4841
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 20220415-210530
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "20220415-210530", "results": []}]} | lilitket/20220415-210530 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T20:05:33+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| 20220415-210530
===============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6544
* Wer: 0.3881
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\... |
unconditional-image-generation | transformers |
# Hugging NFT: boredapeyachtclub
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/boredape... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/boredapeyachtclub"]} | huggingnft/boredapeyachtclub | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/boredapeyachtclub",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T20:48:09+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/boredapeyachtclub #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: boredapeyachtclub
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Spac... | [
"# Hugging NFT: boredapeyachtclub",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is availa... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/boredapeyachtclub #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: boredapeyachtclub",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed f... |
unconditional-image-generation | transformers |
# Hugging NFT: azuki
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/azuki).
Dataset is ... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/azuki"]} | huggingnft/azuki | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/azuki",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-15T20:52:23+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/azuki #license-mit #endpoints_compatible #has_space #region-us
|
# Hugging NFT: azuki
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link.
Pr... | [
"# Hugging NFT: azuki",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available here.\n\... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/azuki #license-mit #endpoints_compatible #has_space #region-us \n",
"# Hugging NFT: azuki",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site ... |
text-generation | transformers |
# dialog model
| {"tags": ["conversational"]} | Garsic/DialoGPT-medium-jill | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T21:13:11+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# dialog model
| [
"# dialog model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# dialog model"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | pdroberts/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T21:55:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1367
* F1: 0.8633
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm3-wikihow3
This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm3-wikihow2](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm3-wikihow3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "type": "wik... | Chikashi/t5-small-finetuned-cnndm3-wikihow3 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-15T22:11:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm3-wikihow3
==================================
This model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm3-wikihow2 on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3138
* Rouge1: 27.2654
* Rouge2: 10.5461
* Rougel: 23.2451
* Rougelsum: 2... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
text-classification | transformers |
<!-- 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. -->
# MBTI-ckiplab-bert
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## ... | {"language": ["zh"], "tags": ["MBTI", "zh", "zh-tw", "generated_from_trainer"], "model-index": [{"name": "MBTI-ckiplab-bert", "results": []}]} | theta/MBTI-ckiplab-bert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"MBTI",
"zh",
"zh-tw",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-15T23:52:23+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #MBTI #zh #zh-tw #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# MBTI-ckiplab-bert
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperpa... | [
"# MBTI-ckiplab-bert\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperpar... | [
"TAGS\n#transformers #pytorch #bert #text-classification #MBTI #zh #zh-tw #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# MBTI-ckiplab-bert\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended use... |
text-classification | transformers |
### Overview
Fine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the [Szeged Uncertainty Corpus](https://rgai.inf.u-szeged.hu/node/160) for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Bas... | {"language": ["en"], "license": "mit", "tags": ["uncertainty-detection", "social-media", "text-classification"], "widget": [{"text": "It seems like Bitcoin prices are heading into bearish territory.", "example_title": "Hedge Detection (Positive - Label 1)"}, {"text": "Bitcoin prices have fallen by 42% in the last 30 da... | ChrisLiewJY/BERTweet-Hedge | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"uncertainty-detection",
"social-media",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T00:09:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #uncertainty-detection #social-media #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Overview
Fine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the Szeged Uncertainty Corpus for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Based Training with the arithmetic mean of ... | [
"### Overview\n\n\nFine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the Szeged Uncertainty Corpus for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Based Training with the arithmetic ... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #uncertainty-detection #social-media #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\nFine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the Szeged Uncertainty Corpus for ... |
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. -->
# DSPFirst-Finetuning-1
This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](https://huggingface... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DSPFirst-Finetuning-1", "results": []}]} | ptran74/DSPFirst-Finetuning-1 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T00:12:32+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
| DSPFirst-Finetuning-1
=====================
This model is a fine-tuned version of ahotrod/electra\_large\_discriminator\_squad2\_512 on a generated Questions and Answers dataset from the DSPFirst textbook based on the SQuAD 2.0 format.
Dataset
=======
A visualization of the dataset can be found here. The split be... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* gradient\\_accumulation\\_steps: 86\n* total\\_train\\_batch\\_size: 516\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #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: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* gra... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dheerajdhanvee/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dheerajdhanvee/bert-finetuned-ner", "results": []}]} | dheerajdhanvee/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T00:35:53+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| dheerajdhanvee/bert-finetuned-ner
=================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0095
* Validation Loss: 0.0674
* Epoch: 4
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1695, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | chrisvinsen/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T00:37:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4617
* Wer: 0.3416
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hilmluo/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hilmluo/bert-finetuned-ner", "results": []}]} | hilmluo/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T00:44:17+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hilmluo/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0233
* Validation Loss: 0.0582
* Epoch: 2
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': '... |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_cubism | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-16T01:26:58+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us \n"
] |
fill-mask | transformers | This is a dummy model | {} | reinoudbosch/dummy-model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T01:45:03+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This is a dummy model | [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
unconditional-image-generation | pytorch |
dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art
Made by:-<br/>
[Jeronim Matije... | {"library_name": "pytorch", "tags": ["gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation"]} | huggan/projected_gan_pop_art_hana | null | [
"pytorch",
"gan",
"dcgan",
"projected-gan",
"huggan",
"unconditional-image-generation",
"region:us"
] | null | 2022-04-16T01:52:54+00:00 | [] | [] | TAGS
#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us
|
dataset: URL
trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images
fun stuff
check out the space demo: URL
Made by:-<br/>
Jeronim Matijević<br/>
Massimiliano Pappa<br/>
| [] | [
"TAGS\n#pytorch #gan #dcgan #projected-gan #huggan #unconditional-image-generation #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AdwayK/hugging_face_biobert_MLMAv2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AdwayK/hugging_face_biobert_MLMAv2", "results": []}]} | AdwayK/hugging_face_biobert_MLMAv2 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T02:14:51+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AdwayK/hugging\_face\_biobert\_MLMAv2
=====================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0
* Validation Loss: 0.0839
* Epoch: 9
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3390, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
null | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | huggan/lwg_aurora | null | [
"transformers",
"huggan",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T02:36:20+00:00 | [] | [] | TAGS
#transformers #huggan #gan #license-mit #endpoints_compatible #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#transformers #huggan #gan #license-mit #endpoints_compatible #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent is... |
text-classification | transformers | training_args = TrainingArguments(
output_dir="./results",
learning_rate=5e-5,
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
num_train_epochs=5,
weight_decay=0.01,
evaluation_strategy="epoch",
push_to_hub=True
) | {} | Raychanan/Longformer_Conflict | null | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T02:57:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #text-classification #autotrain_compatible #endpoints_compatible #region-us
| training_args = TrainingArguments(
output_dir="./results",
learning_rate=5e-5,
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
num_train_epochs=5,
weight_decay=0.01,
evaluation_strategy="epoch",
push_to_hub=True
) | [] | [
"TAGS\n#transformers #pytorch #tensorboard #longformer #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Kanye West AI - DialoGPT Small
Kanye West DialoGPT model built with lyrics from Kaggle (https://www.kaggle.com/datasets/convolutionalnn/kanye-west-lyrics-dataset) and resources from Lynn Zheng | {"tags": ["conversational"]} | mdm/DialoGPT-small-Kanye | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-16T03:34:24+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Kanye West AI - DialoGPT Small
Kanye West DialoGPT model built with lyrics from Kaggle (URL and resources from Lynn Zheng | [
"# Kanye West AI - DialoGPT Small\nKanye West DialoGPT model built with lyrics from Kaggle (URL and resources from Lynn Zheng"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kanye West AI - DialoGPT Small\nKanye West DialoGPT model built with lyrics from Kaggle (URL and resources from Lynn Zheng"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ytsai25/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ytsai25/bert-finetuned-ner", "results": []}]} | ytsai25/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T03:34:38+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ytsai25/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0240
* Validation Loss: 0.0613
* Epoch: 2
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AraT5-base-title-generation-finetuned-ar-xlsum
This model is a fine-tuned version of [UBC-NLP/AraT5-base-title-generation](https... | {"tags": ["summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "AraT5-base-title-generation-finetuned-ar-xlsum", "results": []}]} | eslamxm/AraT5-base-title-generation-finetuned-ar-wikilingua | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-16T03:50:45+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| AraT5-base-title-generation-finetuned-ar-xlsum
==============================================
This model is a fine-tuned version of UBC-NLP/AraT5-base-title-generation on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 4.8120
* Rouge-1: 23.29
* Rouge-2: 8.44
* Rouge-l: 20.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-commonvoice-demo-colab-1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-commonvoice-demo-colab-1", "results": []}]} | chrisvinsen/wav2vec2-base-commonvoice-demo-colab-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T03:58:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-commonvoice-demo-colab-1
======================================
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7289
* Wer: 0.7888
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* t... |
image-to-image | fastai |
GAN | {"tags": ["fastai", "image-to-image"]} | NDugar/horse_to_zebra_cycle_GAN | null | [
"fastai",
"image-to-image",
"has_space",
"region:us"
] | null | 2022-04-16T04:12:18+00:00 | [] | [] | TAGS
#fastai #image-to-image #has_space #region-us
|
GAN | [] | [
"TAGS\n#fastai #image-to-image #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | haohaoxuexi/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T04:21:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2239
* Accuracy: 0.923
* F1: 0.9233
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | V3RX2000/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T05:37:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4722
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
null | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | DrishtiSharma/lwg_chebakia | null | [
"transformers",
"huggan",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T05:53:49+00:00 | [] | [] | TAGS
#transformers #huggan #gan #license-mit #endpoints_compatible #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#transformers #huggan #gan #license-mit #endpoints_compatible #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent is... |
null | null | Hello | {"license": "afl-3.0"} | mkwng/test | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-04-16T06:15:12+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| Hello | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-commonvoice-demo-colab-2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-commonvoice-demo-colab-2", "results": []}]} | chrisvinsen/wav2vec2-base-commonvoice-demo-colab-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T06:22:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-commonvoice-demo-colab-2
======================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Wer: 1.0
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
fill-mask | transformers | # Bengali to English Word Aligner
Finetuned Model for **Bengali to English Word** which was build on `bert-base-multilingual-cased`
## Quick Start
Initialize to use it in your project
```python
tokenizer = AutoTokenizer.from_pretrained("musfiqdehan/bengali-english-word-aligner")
model = AutoModel.from_pretrained("mus... | {"license": "mit"} | musfiqdehan/bengali-english-word-aligner | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-16T06:26:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Bengali to English Word Aligner
Finetuned Model for Bengali to English Word which was build on 'bert-base-multilingual-cased'
## Quick Start
Initialize to use it in your project
## Bengali-English Word Alignment
![Open In Colab](URL
![Kaggle](URL
Install Dependencies
Import Necessary Libraries
Testing Word M... | [
"# Bengali to English Word Aligner\nFinetuned Model for Bengali to English Word which was build on 'bert-base-multilingual-cased'",
"## Quick Start\nInitialize to use it in your project",
"## Bengali-English Word Alignment\n\n![Open In Colab](URL\n\n![Kaggle](URL\n\nInstall Dependencies\n\nImport Necessary Libr... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Bengali to English Word Aligner\nFinetuned Model for Bengali to English Word which was build on 'bert-base-multilingual-cased'",
"## Quick Start\nInitialize to use it in your pro... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jadiker/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Jadiker/distilbert-base-uncased-finetuned-imdb", "results": []}]} | Jadiker/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T07:54:34+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Jadiker/distilbert-base-uncased-finetuned-imdb
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8518
* Validation Loss: 2.6184
* Epoch: 0
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | keras-io/nerf | null | [
"keras",
"region:us"
] | null | 2022-04-16T08:15:17+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-commonvoice-demo-colab-3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-commonvoice-demo-colab-3", "results": []}]} | chrisvinsen/wav2vec2-base-commonvoice-demo-colab-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T10:02:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-commonvoice-demo-colab-3
======================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6268
* Wer: 0.6391
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text2text-generation | transformers |
# LongT5 (transient-global attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.c... | {"language": "en", "license": "apache-2.0"} | google/long-t5-tglobal-base | null | [
"transformers",
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-16T10:05:48+00:00 | [
"2112.07916",
"1912.08777",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LongT5 (transient-global attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found... | [
"# LongT5 (transient-global attention, base-sized model)\n\nLongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be... | [
"TAGS\n#transformers #pytorch #jax #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LongT5 (transient-global attention, base-sized model)\n\nLongT5 model pre-trained on English lang... |
text2text-generation | transformers |
# LongT5 (transient-global attention, large-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.... | {"language": "en", "license": "apache-2.0"} | google/long-t5-tglobal-large | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-16T10:20:39+00:00 | [
"2112.07916",
"1912.08777",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| LongT5 (transient-global attention, large-sized model)
======================================================
LongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All... | [
"### How to use",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #longt5 #text2text-generation #en #arxiv-2112.07916 #arxiv-1912.08777 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use",
"### BibTeX entry and citation info"
] |
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. -->
# mBERT_all_ty_SQen_SQ20_1
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-m... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mBERT_all_ty_SQen_SQ20_1", "results": []}]} | krinal214/mBERT_all_ty_SQen_SQ20_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T11:07:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| mBERT\_all\_ty\_SQen\_SQ20\_1
=============================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5305
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Krishadow/biobert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Krishadow/biobert-finetuned-ner", "results": []}]} | Krishadow/biobert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T12:41:44+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Krishadow/biobert-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0450
* Validation Loss: 0.0593
* Epoch: 1
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': '... |
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-distil-bert-depression
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuned-distil-bert-depression", "results": []}]} | ShreyaR/finetuned-distil-bert-depression | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T12:54:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-distil-bert-depression
================================
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: 0.1695
* Accuracy: 0.9445
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 5e-05\n* train\\_b... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1587494876320448512/XH7s... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/discord/1668308516202/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/discord | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-16T13:48:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Discord
@discord
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309
## Validation Metrics
- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision:... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-go_emo"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 31.11935827749309} | crcb/goemos | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-go_emo",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T14:00:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-go_emo #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 748922872
- CO2 Emissions (in grams): 31.11935827749309
## Validation Metrics
- Loss: 0.17039568722248077
- Accuracy: 0.93625
- Macro F1: 0.9075787460059076
- Micro F1: 0.93625
- Weighted F1: 0.9371621543264445
- Macro Precision:... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 748922872\n- CO2 Emissions (in grams): 31.11935827749309",
"## Validation Metrics\n\n- Loss: 0.17039568722248077\n- Accuracy: 0.93625\n- Macro F1: 0.9075787460059076\n- Micro F1: 0.93625\n- Weighted F1: 0.9371621543264445\... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-go_emo #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 748922872\n- CO2 Emissions (in grams... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# yfu2307/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "yfu2307/bert-finetuned-ner", "results": []}]} | yfu2307/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T14:06:46+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| yfu2307/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0435
* Validation Loss: 0.1205
* Epoch: 4
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1695, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text2text-generation | transformers |
# Indonesia Recipe Ingredients Generator Model
**WARNING: inference on Huggingface might not run since the tokenizer used is not transformers's tokenizer.**
Feel free to test the model [in this space](https://huggingface.co/spaces/haryoaw/id-recigen)
😎 **Have fun on generating ingredients** 😎
This is a fine-tune... | {"language": "id", "license": "mit", "tags": ["bart", "id"]} | haryoaw/id-recigen-bart | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bart",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-16T14:09:44+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #bart #id #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Indonesia Recipe Ingredients Generator Model
WARNING: inference on Huggingface might not run since the tokenizer used is not transformers's tokenizer.
Feel free to test the model in this space
Have fun on generating ingredients
This is a fine-tuned model to generate the Indonesian food ingredients. One of my p... | [
"# Indonesia Recipe Ingredients Generator Model\n\nWARNING: inference on Huggingface might not run since the tokenizer used is not transformers's tokenizer.\n\nFeel free to test the model in this space\n\n Have fun on generating ingredients \n\nThis is a fine-tuned model to generate the Indonesian food ingredients.... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #bart #id #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Indonesia Recipe Ingredients Generator Model\n\nWARNING: inference on Huggingface might not run since the tokenizer used is not transformers's tokenizer.\n\nF... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# annaeze/lab9_2
This model is a fine-tuned version of [annaeze/lab9_1](https://huggingface.co/annaeze/lab9_1) on an unknown dataset.
It... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "annaeze/lab9_2", "results": []}]} | annaeze/lab9_2 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T14:17:44+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| annaeze/lab9\_2
===============
This model is a fine-tuned version of annaeze/lab9\_1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0642
* Validation Loss: 0.0854
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 669, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Krishadow/biobert-finetuned-ner-K
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Krishadow/biobert-finetuned-ner-K", "results": []}]} | Krishadow/biobert-finetuned-ner-K | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T14:31:35+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Krishadow/biobert-finetuned-ner-K
=================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0099
* Validation Loss: 0.0676
* Epoch: 4
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1695, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# satwiksstp/bert-finetuned-ner
This model was trained from scratch on an unknown dataset.
It achieves the following results on the eval... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "satwiksstp/bert-finetuned-ner", "results": []}]} | satwiksstp/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T14:42:16+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| satwiksstp/bert-finetuned-ner
=============================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0354
* Validation Loss: 0.0597
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'c... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cwan6830/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "cwan6830/bert-finetuned-ner", "results": []}]} | cwan6830/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T15:33:36+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| cwan6830/bert-finetuned-ner
===========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0247
* Validation Loss: 0.0564
* Epoch: 2
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AdwayK/hugging_face_biobert_MLMAv3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AdwayK/hugging_face_biobert_MLMAv3", "results": []}]} | AdwayK/hugging_face_biobert_MLMAv3 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T15:40:26+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AdwayK/hugging\_face\_biobert\_MLMAv3
=====================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0103
* Validation Loss: 0.0861
* Epoch: 4
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1695, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
null | null |
# trained using OSCAR dataset
vocab size 50000 | {"license": "osl-3.0"} | AswiN037/tamil-roberta-bpe-tokenizer-base | null | [
"license:osl-3.0",
"region:us"
] | null | 2022-04-16T15:50:08+00:00 | [] | [] | TAGS
#license-osl-3.0 #region-us
|
# trained using OSCAR dataset
vocab size 50000 | [
"# trained using OSCAR dataset\nvocab size 50000"
] | [
"TAGS\n#license-osl-3.0 #region-us \n",
"# trained using OSCAR dataset\nvocab size 50000"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AdwayK/biobert_on_ADR_as_NER
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evalu... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "AdwayK/biobert_on_ADR_as_NER", "results": []}]} | AdwayK/biobert_on_ADR_as_NER | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T16:02:07+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| AdwayK/biobert\_on\_ADR\_as\_NER
================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0413
* Validation Loss: 0.0811
* Epoch: 4
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 975, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_nam... |
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. -->
# DSPFirst-Finetuning-2
This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](https://huggingface... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "DSPFirst-Finetuning-2", "results": []}]} | ptran74/DSPFirst-Finetuning-2 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T16:02:45+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
| DSPFirst-Finetuning-2
=====================
This model is a fine-tuned version of ahotrod/electra\_large\_discriminator\_squad2\_512 on a generated Questions and Answers dataset from the DSPFirst textbook based on the SQuAD 2.0 format.
It achieves the following results on the evaluation set:
* Loss: 0.8057
* Exact:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* gradient\\_accumulation\\_steps: 86\n* total\\_train\\_batch\\_size: 516\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #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: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* gra... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AdwayK/base_bert_tuned_on_TAC2017_as_NER
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AdwayK/base_bert_tuned_on_TAC2017_as_NER", "results": []}]} | AdwayK/base_bert_tuned_on_TAC2017_as_NER | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T16:18:14+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AdwayK/base\_bert\_tuned\_on\_TAC2017\_as\_NER
==============================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0254
* Validation Loss: 0.0714
* Epoch: 4
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 975, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-bert-cased-first512-Conflict
`conv_text = '\n'.join([utt.text for utt in conv.get_chronological_utterance_list()])`
This ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy", "precision", "recall"], "model-index": [{"name": "bert-bert-cased-first512-Conflict", "results": []}]} | Raychanan/bert-bert-cased-first512-Conflict | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T16:22:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-bert-cased-first512-Conflict
=================================
'conv\_text = '\n'.join([URL for utt in conv.get\_chronological\_utterance\_list()])'
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6932
* F1: 0.666... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AdwayK/biobert_ncbi_disease_ner_tuned_on_TAC2017
This model is a fine-tuned version of [ugaray96/biobert_ncbi_disease_ner](https://hug... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "AdwayK/biobert_ncbi_disease_ner_tuned_on_TAC2017", "results": []}]} | AdwayK/biobert_ncbi_disease_ner_tuned_on_TAC2017 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T16:33:36+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| AdwayK/biobert\_ncbi\_disease\_ner\_tuned\_on\_TAC2017
======================================================
This model is a fine-tuned version of ugaray96/biobert\_ncbi\_disease\_ner on the TAC 2017 dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0343
* Validation Loss: 0.0679
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 975, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_nam... |
text-generation | transformers |
# GPT-2
Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-langua... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/gpt2-small-bg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"torch",
"custom_code",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-16T16:40:57+00:00 | [] | [
"bg"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #license-mit #autotrain_compatible #text-generation-inference #region-us
|
# GPT-2
Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
This is the SMALL version.
The training data is Bulgarian text from OSCAR, Chitanka and Wikipedia.
## Intended uses & limitations
... | [
"# GPT-2\n\nPretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\n\nThis is the SMALL version.\n\nThe training data is Bulgarian text from OSCAR, Chitanka and Wikipedia.",
"## Intended ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #license-mit #autotrain_compatible #text-generation-inference #region-us \n",
"# GPT-2\n\nPretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was... |
text-generation | transformers |
# GPT-2
Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-langua... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/gpt2-medium-bg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"torch",
"custom_code",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-16T17:06:49+00:00 | [] | [
"bg"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #license-mit #autotrain_compatible #text-generation-inference #region-us
|
# GPT-2
Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
## Model description
This is the MEDIUM version.
The training data is Bulgarian text from OSCAR, Chitanka and Wikipedia.
## Intended uses & limitations
... | [
"# GPT-2\n\nPretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"## Model description\n\nThis is the MEDIUM version.\n\nThe training data is Bulgarian text from OSCAR, Chitanka and Wikipedia.",
"## Intended... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #license-mit #autotrain_compatible #text-generation-inference #region-us \n",
"# GPT-2\n\nPretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was... |
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. -->
# roberta-finetuned-stance-assertive-hillary
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-finetuned-stance-assertive-hillary", "results": []}]} | michaellutz/roberta-finetuned-stance-assertive-hillary | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T17:13:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-finetuned-stance-assertive-hillary
This model is a fine-tuned version of roberta-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# roberta-finetuned-stance-assertive-hillary\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-finetuned-stance-assertive-hillary\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model descrip... |
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. -->
# ms-marco-finetuned-stance-assertive-hillary
This model is a fine-tuned version of [sentence-transformers/paraphrase-MiniLM-L6-v2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ms-marco-finetuned-stance-assertive-hillary", "results": []}]} | michaellutz/ms-marco-finetuned-stance-assertive-hillary | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T17:19:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ms-marco-finetuned-stance-assertive-hillary
This model is a fine-tuned version of sentence-transformers/paraphrase-MiniLM-L6-v2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
... | [
"# ms-marco-finetuned-stance-assertive-hillary\n\nThis model is a fine-tuned version of sentence-transformers/paraphrase-MiniLM-L6-v2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ms-marco-finetuned-stance-assertive-hillary\n\nThis model is a fine-tuned version of sentence-transformers/paraphrase-MiniLM-L6-v2 on an ... |
automatic-speech-recognition | espnet |
## ESPnet2 model
This model was trained by Dan Berrebbi using recipe in [espnet](https://github.com/espnet/espnet/).
# RESULTS
## Environments
- date: `Sat Apr 16 14:14:45 EDT 2022`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorc... | {"language": "fr", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["accented french (openslr56)"]} | espnet/accented_french_openslr57_ASR_transformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"fr",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-16T17:25:09+00:00 | [
"1804.00015"
] | [
"fr"
] | TAGS
#espnet #audio #automatic-speech-recognition #fr #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 model
-------------
This model was trained by Dan Berrebbi using recipe in espnet.
RESULTS
=======
Environments
------------
* date: 'Sat Apr 16 14:14:45 EDT 2022'
* python version: '3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.6a1'
* pytorch version: 'pytorch 1.11.0+c... | [
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #fr #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### WER",
"### CER",
"### TER\n\n\n\nASR config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-bert-cased-first512-Conflict-SEP
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy", "precision", "recall"], "model-index": [{"name": "bert-bert-cased-first512-Conflict-SEP", "results": []}]} | Raychanan/bert-bert-cased-first512-Conflict-SEP | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T17:44:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-bert-cased-first512-Conflict-SEP
=====================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6806
* F1: 0.6088
* Accuracy: 0.5914
* Precision: 0.5839
* Recall: 0.6360
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
token-classification | transformers |
# BERT BASE (cased) finetuned on Bulgarian part-of-speech data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is ca... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/bert-base-pos-theseus-bg | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1810.04805",
"arxiv:2002.02925",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-16T17:58:51+00:00 | [
"1810.04805",
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #bert #token-classification #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us
|
# BERT BASE (cased) finetuned on Bulgarian part-of-speech data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it does make a difference
between bulgarian and Bulgarian. The training da... | [
"# BERT BASE (cased) finetuned on Bulgarian part-of-speech data\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it does make a difference\nbetween bulgarian and Bulgarian. The tr... | [
"TAGS\n#transformers #pytorch #bert #token-classification #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us \n",
"# BERT BASE (cased) finetuned on Bulgarian part-of-speech data\n\nPretrained model on Bulgarian language ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mordred501/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "mordred501/bert-finetuned-ner", "results": []}]} | mordred501/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T17:59:08+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mordred501/bert-finetuned-ner
=============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0250
* Validation Loss: 0.0601
* Epoch: 2
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': '... |
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. -->
# DSPFirst-Finetuning-3
This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](https://huggingface... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "DSPFirst-Finetuning-3", "results": []}]} | ptran74/DSPFirst-Finetuning-3 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T18:12:37+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
| DSPFirst-Finetuning-3
=====================
This model is a fine-tuned version of ahotrod/electra\_large\_discriminator\_squad2\_512 on a generated Questions and Answers dataset from the DSPFirst textbook based on the SQuAD 2.0 format.
It achieves the following results on the evaluation set:
* Loss: 0.9996
* Exact:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* gradient\\_accumulation\\_steps: 86\n* total\\_train\\_batch\\_size: 516\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #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: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* gra... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jiaxin97/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jiaxin97/bert-finetuned-ner", "results": []}]} | jiaxin97/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T18:14:44+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jiaxin97/bert-finetuned-ner
===========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0243
* Validation Loss: 0.0595
* Epoch: 2
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
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