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text-generation | transformers |
# Peter Griffin DialoGPT Model | {"tags": ["conversational"]} | person123/DialoGPT-small-petergriffin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peter Griffin DialoGPT Model | [
"# Peter Griffin DialoGPT Model"
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter Griffin DialoGPT Model"
] |
feature-extraction | transformers | # Mang Bert
## Model description
Fine-Tuned Roberta Model using RobertaForMaskedLM
Tagalog Dataset from OSCAR tl
## Training data
458206 text dataset from OSCAR
| {"language": ["Tagalog"], "license": "apache-2.0", "tags": ["Tagalog", "Mang Bert"], "datasets": ["OSCAR tl"]} | petabyte/unang_mang_bert | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"Tagalog",
"Mang Bert",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Tagalog"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #Tagalog #Mang Bert #license-apache-2.0 #endpoints_compatible #region-us
| # Mang Bert
## Model description
Fine-Tuned Roberta Model using RobertaForMaskedLM
Tagalog Dataset from OSCAR tl
## Training data
458206 text dataset from OSCAR
| [
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"## Model description\n\nFine-Tuned Roberta Model using RobertaForMaskedLM \nTagalog Dataset from OSCAR tl",
"## Training data\n\n458206 text dataset from OSC... |
text-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `textcat` |
| **Components** | `textcat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]()... | {"language": ["en"], "tags": ["spacy", "text-classification"], "model-index": [{"name": "en_pipeline", "results": []}]} | peter-explosion-ai/en_pipeline | null | [
"spacy",
"text-classification",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #text-classification #en #region-us
|
### Label Scheme
View label scheme (2 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (2 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #text-classification #en #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2 labels for 1 components)",
"### Accuracy"
] |
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. -->
# xlm-roberta-base-finetuned-ecoicop
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-ecoicop", "results": []}]} | peter2000/xlm-roberta-base-finetuned-ecoicop | null | [
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"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-ecoicop
==================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1685
* Acc: 0.9659
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* trai... |
image-classification | transformers |
# Visual_transformer_chihuahua_cookies
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://gi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | peterbonnesoeur/Visual_transformer_chihuahua_cookies | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Visual_transformer_chihuahua_cookies
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
#### chihuahua
!chihuahua
#### cookies
!cookies
#### corgi
!corgi
#### samoyed
!sa... | [
"# Visual_transformer_chihuahua_cookies\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",
"#### chihuahua\n\n!chihuahua",
"#### cookies\n\n!cookies",
"#### cor... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Visual_transformer_chihuahua_cookies\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Cola... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | peterhsu/bert-finetuned-ner | null | [
"transformers",
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"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0607
* Precision: 0.9350
* Recall: 0.9495
* F1: 0.9422
* Accuracy: 0.9866
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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": []}]} | peterhsu/distilbert-base-uncased-finetuned-imdb | null | [
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"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+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: 2.4718
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... |
translation | transformers | # marian-finetuned-kde4-en-to-zh_TW-accelerate
## Model description
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Bleu: 40.70
More information needed
#... | {"license": "apache-2.0", "tags": ["translation"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-zh_TW-accelerate", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "arg... | peterhsu/marian-finetuned-kde4-en-to-zh_TW-accelerate | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # marian-finetuned-kde4-en-to-zh_TW-accelerate
## Model description
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the kde4 dataset.
It achieves the following results on the evaluation set:
- Bleu: 40.70
More information needed
## Intended uses & limitations
More information nee... | [
"# marian-finetuned-kde4-en-to-zh_TW-accelerate",
"## Model description\r\n\r\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the kde4 dataset.\r\nIt achieves the following results on the evaluation set:\r\n- Bleu: 40.70\r\n\r\nMore information needed",
"## Intended uses & limitations\r\n\r... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-zh_TW-accelerate",
"## Model description\r\n\r\nThis model is a fine-tuned version of Helsinki-NLP/op... |
translation | 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. -->
# marian-finetuned-kde4-en-to-zh_TW
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Hels... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-zh_TW", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type... | peterhsu/marian-finetuned-kde4-en-to-zh_TW | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-zh_TW
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0047
- Bleu: 39.0863
## Model description
More information needed
## Intended uses & limitations
More information needed
#... | [
"# marian-finetuned-kde4-en-to-zh_TW\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0047\n- Bleu: 39.0863",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore in... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-zh_TW\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-m... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | peterhsu/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0255
* Rouge1: 17.5202
* Rouge2: 8.4634
* Rougel: 17.0175
* Rougelsum: 17.0528
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* l... |
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. -->
# peterhsu/tf-distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "peterhsu/tf-distilbert-base-uncased-finetuned-imdb", "results": []}]} | peterhsu/tf-distilbert-base-uncased-finetuned-imdb | null | [
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"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| peterhsu/tf-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: 2.5691
* Validation Loss: 2.4661
* Epoch: 2
Model descri... | [
"### 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 #tensorboard #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', 'lea... |
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. -->
# tf-dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-dummy-model", "results": []}]} | peterhsu/tf-dummy-model | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# tf-dummy-model
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
##... | [
"# tf-dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore ... | [
"TAGS\n#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# tf-dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## ... |
translation | 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. -->
# tf-marian-finetuned-kde4-en-to-zh_TW
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_keras_callback"], "model-index": [{"name": "tf-marian-finetuned-kde4-en-to-zh_TW", "results": []}]} | peterhsu/tf-marian-finetuned-kde4-en-to-zh_TW | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"translation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #translation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| tf-marian-finetuned-kde4-en-to-zh\_TW
=====================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7752
* Validation Loss: 0.9022
* Epoch: 2
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': 5e-05, 'decay\\_steps': 11973, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #marian #text2text-generation #translation #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... |
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. -->
# tf_bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf_bert-finetuned-ner", "results": []}]} | peterhsu/tf_bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| tf\_bert-finetuned-ner
======================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0272
* Validation Loss: 0.0522
* Epoch: 2
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | pewriebontal/DialoGPT-medium-Pewpewbon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
null | null | AIShell MMI CER is 4.94%
| {} | pfluo/icefall_aishell_mmi_model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| AIShell MMI CER is 4.94%
| [] | [
"TAGS\n#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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_review... | pgperrone/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2259
* Accuracy: 0.9313
Model description
--... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #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\... |
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-yahd-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-yahd-2", "results": []}]} | phailyoor/distilbert-base-uncased-finetuned-yahd-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-yahd-2
========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3850
* Accuracy: 0.2652
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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-yahd-twval-hptune
This model is a fine-tuned version of [distilbert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-yahd-twval-hptune", "results": []}]} | phailyoor/distilbert-base-uncased-finetuned-yahd-twval-hptune | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-yahd-twval-hptune
===================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.3727
* Accuracy: 0.2039
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_b... |
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-yahd-twval
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-yahd-twval", "results": []}]} | phailyoor/distilbert-base-uncased-finetuned-yahd-twval | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-yahd-twval
============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2540
* Accuracy: 0.2664
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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-yahd
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-yahd", "results": []}]} | phailyoor/distilbert-base-uncased-finetuned-yahd | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-yahd
======================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.7685
* Accuracy: 0.4010
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-generation | transformers | #Rick Style dialoGPT Model | {"tags": ["conversational"]} | phantom-deluxe/dialoGPT-RickBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #Rick Style dialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | #Harry Style dialoGPT Model | {"tags": ["conversational"]} | phantom-deluxe/dialoGPT-harry | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #Harry Style dialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# XLS-R-300m-Arabic | {"language": ["ar"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["WER"], "thumbnail": "wav2vec2-large-xls-r-300m-arabic fine-tuned for Modern Standard Arabic", "model-index": [{"name": "w... | phantomcoder1996/wav2vec2-large-xls-r-300m-arabic | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ar",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# XLS-R-300m-Arabic | [
"# XLS-R-300m-Arabic"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# XLS-R-300m-Arabic"
] |
text-generation | transformers |
# Try out in the Hosted inference API
In the right panel, you can try the model (although it only handles a short sequence length).
Feel free to try Example 1, and modify it to inspect model ability.
# Model Loading
The model can be loaded in the following way:
```
from transformers import AutoTokenizer, AutoModelF... | {"language": ["en"], "license": "apache-2.0", "tags": ["simplification"], "datasets": ["cnn_dailymail"], "widget": [{"text": "A capsule containing asteroid soil samples landed in the Australian Outback. The precision required to carry out the mission thrilled many.<|endoftext|>", "example_title": "Example 1"}]} | philippelaban/keep_it_simple | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"simplification",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #simplification #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Try out in the Hosted inference API
In the right panel, you can try the model (although it only handles a short sequence length).
Feel free to try Example 1, and modify it to inspect model ability.
# Model Loading
The model can be loaded in the following way:
# Example use
And then used by first inputting a pa... | [
"# Try out in the Hosted inference API\nIn the right panel, you can try the model (although it only handles a short sequence length).\nFeel free to try Example 1, and modify it to inspect model ability.",
"# Model Loading\n\nThe model can be loaded in the following way:",
"# Example use\n\nAnd then used by firs... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #simplification #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Try out in the Hosted inference API\nIn the right panel, you can try the model (although it onl... |
summarization | transformers |
# Try out in the Hosted inference API
In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.
# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
```
fro... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"]} | philippelaban/summary_loop10 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Try out in the Hosted inference API
In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.
# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
# Ex... | [
"# Try out in the Hosted inference API\r\n\r\nIn the right panel, you can try to the model (although it only handles a short sequence length).\r\nEnter the document you want to summarize in the panel on the right.",
"# Model Loading\r\nThe model (based on a GPT2 base architecture) can be loaded in the following w... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Try out in the Hosted inference API\r\n\r\nIn the right panel, you can try to the model (although it only han... |
summarization | transformers | # Try out in the Hosted inference API
In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.
# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
```
from ... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"]} | philippelaban/summary_loop24 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Try out in the Hosted inference API
In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.
# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
# Exam... | [
"# Try out in the Hosted inference API\r\n\r\nIn the right panel, you can try to the model (although it only handles a short sequence length).\r\nEnter the document you want to summarize in the panel on the right.",
"# Model Loading\r\nThe model (based on a GPT2 base architecture) can be loaded in the following w... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Try out in the Hosted inference API\r\n\r\nIn the right panel, you can try to the model (although it only han... |
summarization | transformers |
# Try out in the Hosted inference API
In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.
# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
```
from transfo... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"]} | philippelaban/summary_loop46 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Try out in the Hosted inference API
In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.
# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
# Example Use
... | [
"# Try out in the Hosted inference API\n\nIn the right panel, you can try to the model (although it only handles a short sequence length).\nEnter the document you want to summarize in the panel on the right.",
"# Model Loading\nThe model (based on a GPT2 base architecture) can be loaded in the following way:",
... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Try out in the Hosted inference API\n\nIn the right panel, you can try to the model (although it only handles... |
text-classification | transformers | # `BERT-tweet-eval-emotion` trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.5408923625946045
- Accuracy: 0.8099929627023223
- Macro F1: 0.7737195387641751
- Micro F1: 0.8099929627023222
- Weighted F1: 0.8063100677512649
- Macro Precision: 0.8083955817268176
- Micro Pre... | {"language": "en", "tags": "autonlp", "datasets": ["tweet_eval"], "widget": [{"text": "Worry is a down payment on a problem you may never have'. Joyce Meyer. #motivation #leadership #worry"}], "model-index": [{"name": "BERT-tweet-eval-emotion", "results": [{"task": {"type": "sentiment-analysis", "name": "Sentiment Ana... | philschmid/BERT-tweet-eval-emotion | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #region-us
| # 'BERT-tweet-eval-emotion' trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.5408923625946045
- Accuracy: 0.8099929627023223
- Macro F1: 0.7737195387641751
- Micro F1: 0.8099929627023222
- Weighted F1: 0.8063100677512649
- Macro Precision: 0.8083955817268176
- Micro Pre... | [
"# 'BERT-tweet-eval-emotion' trained using autoNLP\n- Problem type: Multi-class Classification",
"## Validation Metrics\n\n- Loss: 0.5408923625946045\n- Accuracy: 0.8099929627023223\n- Macro F1: 0.7737195387641751\n- Micro F1: 0.8099929627023222\n- Weighted F1: 0.8063100677512649\n- Macro Precision: 0.80839558172... | [
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"# 'BERT-tweet-eval-emotion' trained using autoNLP\n- Problem type: Multi-class Classification",
"## Validation Metrics\n\n- Loss: 0.5408923625946045... |
text-classification | transformers | # `DistilBERT-tweet-eval-emotion` trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.5564454197883606
- Accuracy: 0.8057705840957072
- Macro F1: 0.7536021792986777
- Micro F1: 0.8057705840957073
- Weighted F1: 0.8011390170248318
- Macro Precision: 0.7817458823222652
- Mic... | {"language": "en", "tags": "autonlp", "datasets": ["tweet_eval"], "widget": [{"text": "Worry is a down payment on a problem you may never have'. Joyce Meyer. #motivation #leadership #worry"}], "model-index": [{"name": "DistilBERT-tweet-eval-emotion", "results": [{"task": {"type": "sentiment-analysis", "name": "Sentime... | philschmid/DistilBERT-tweet-eval-emotion | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #region-us
| # 'DistilBERT-tweet-eval-emotion' trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.5564454197883606
- Accuracy: 0.8057705840957072
- Macro F1: 0.7536021792986777
- Micro F1: 0.8057705840957073
- Weighted F1: 0.8011390170248318
- Macro Precision: 0.7817458823222652
- Mic... | [
"# 'DistilBERT-tweet-eval-emotion' trained using autoNLP\n- Problem type: Multi-class Classification",
"## Validation Metrics\n\n- Loss: 0.5564454197883606\n- Accuracy: 0.8057705840957072\n- Macro F1: 0.7536021792986777\n- Micro F1: 0.8057705840957073\n- Weighted F1: 0.8011390170248318\n- Macro Precision: 0.78174... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# 'DistilBERT-tweet-eval-emotion' trained using autoNLP\n- Problem type: Multi-class Classification",
"## Validation Metrics\n\n- Loss: 0.5564... |
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. -->
# MiniLMv2-L12-H384-emotion
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https:... | {"tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "ac... | philschmid/MiniLMv2-L12-H384-emotion | null | [
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"text-classification",
"generated_from_trainer",
"dataset:emotion",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L12-H384-emotion
=========================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2069
* Accuracy: 0.925
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\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 #roberta #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLMv2-L6-H384-emotion
This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://... | {"tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L6-H384-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "acc... | philschmid/MiniLMv2-L6-H384-emotion | null | [
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"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L6-H384-emotion
========================
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2140
* Accuracy: 0.9215
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* 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 #roberta #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\... |
text-classification | transformers | # `RoBERTa-Banking77` trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision:... | {"language": "en", "tags": "autonlp", "datasets": ["banking77"], "widget": [{"text": "I am still waiting on my card?"}], "model-index": [{"name": "RoBERTa-Banking77", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "BANKING77", "type": "banking77"}, "metrics": [{... | philschmid/RoBERTa-Banking77 | null | [
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"text-classification",
"autonlp",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # 'RoBERTa-Banking77' trained using autoNLP
- Problem type: Multi-class Classification
## Validation Metrics
- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision:... | [
"# 'RoBERTa-Banking77' trained using autoNLP\n\n\n- Problem type: Multi-class Classification",
"## Validation Metrics\n\n- Loss: 0.27382662892341614\n- Accuracy: 0.935064935064935\n- Macro F1: 0.934939412967268\n- Micro F1: 0.935064935064935\n- Weighted F1: 0.934939412967268\n- Macro Precision: 0.9372295644352715... | [
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"# 'RoBERTa-Banking77' trained using autoNLP\n\n\n- Problem type: Multi-class Classification",
"## Validation Metrics\n\n- Loss: 0.2738266289234161... |
summarization | transformers |
## `bart-base-samsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
You can find the notebook [here]() and the referring blog post [here]().
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemake... | {"language": "en", "license": "apache-2.0", "tags": ["sagemaker", "bart", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Jeff: Can I train a \ud83e\udd17 Transformers model on Amazon SageMaker? \nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff: ok.\nJeff: and how can I get... | philschmid/bart-base-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"sagemaker",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| 'bart-base-samsum'
------------------
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
You can find the notebook here and the referring blog post here.
For more information look at:
* Transformers Documentation: Amazon SageMaker
* Example Notebooks
* Amazon SageMak... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
## `bart-large-cnn-samsum`
> If you want to use the model you should try a newer fine-tuned FLAN-T5 version [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) out socring the BART version with `+6` on `ROGUE1` achieving `47.24`.
# TRY [philschmid/flan-t5-base-samsum](https://hugg... | {"language": "en", "license": "mit", "tags": ["sagemaker", "bart", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Jeff: Can I train a \ud83e\udd17 Transformers model on Amazon SageMaker? \nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff: ok.\nJeff: and how can I get starte... | philschmid/bart-large-cnn-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"sagemaker",
"summarization",
"en",
"dataset:samsum",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| 'bart-large-cnn-samsum'
-----------------------
>
> If you want to use the model you should try a newer fine-tuned FLAN-T5 version philschmid/flan-t5-base-samsum out socring the BART version with '+6' on 'ROGUE1' achieving '47.24'.
>
>
>
TRY philschmid/flan-t5-base-samsum
==================================
T... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-mit #model-index #autotrain_compatible #endpoints_compatible #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. -->
# bert-mini-sst2-distilled
This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-mini-sst2-distilled", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [... | philschmid/bert-mini-sst2-distilled | null | [
"transformers",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-mini-sst2-distilled
========================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-256\_A-4 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1792
* Accuracy: 0.8567
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00021185586235152412\n* train\\_batch\\_size: 1024\n* eval\\_batch\\_size: 1024\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epoc... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00021185... |
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. -->
# deberta-v3-xsmall-emotion
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/d... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-xsmall-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metri... | philschmid/deberta-v3-xsmall-emotion | null | [
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"deberta-v2",
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"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #dataset-emotion #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-xsmall-emotion
=========================
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1877
* Accuracy: 0.932
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* 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 #deberta-v2 #text-classification #generated_from_trainer #dataset-emotion #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\\_rate: 3e-05\n*... |
summarization | transformers |
## `distilbart-cnn-12-6-samsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/no... | {"language": "en", "license": "apache-2.0", "tags": ["sagemaker", "bart", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Jeff: Can I train a \ud83e\udd17 Transformers model on Amazon SageMaker? \nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff: ok.\nJeff: and how can I get... | philschmid/distilbart-cnn-12-6-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"sagemaker",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| 'distilbart-cnn-12-6-samsum'
----------------------------
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
* Transformers Documentation: Amazon SageMaker
* Example Notebooks
* Amazon SageMaker documentation for Hugging Face
* Python SDK ... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #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-multilingual-cased-sentiment-2
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-multilingual-cased-sentiment-2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_r... | philschmid/distilbert-base-multilingual-cased-sentiment-2 | null | [
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"tensorboard",
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"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-sentiment-2
==============================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6067
* Accuracy: 0.7476
* F1: 0.7476
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00024\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-sentiment
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-multilingual-cased-sentiment", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_rev... | philschmid/distilbert-base-multilingual-cased-sentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-sentiment
============================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5842
* Accuracy: 0.7648
* F1: 0.7648
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:... |
question-answering | transformers |
# ONNX Conversion of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad)
# DistilBERT base cased distilled SQuAD
This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/distilbert-base-cased), fine-tuned using (a second step of) knowled... | {"language": "en", "license": "apache-2.0", "datasets": ["squad"], "metrics": ["squad"]} | philschmid/distilbert-onnx | null | [
"transformers",
"onnx",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #onnx #distilbert #question-answering #en #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# ONNX Conversion of distilbert-base-cased-distilled-squad
# DistilBERT base cased distilled SQuAD
This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.
This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-b... | [
"# ONNX Conversion of distilbert-base-cased-distilled-squad",
"# DistilBERT base cased distilled SQuAD\n\nThis model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.\nThis model reaches a F1 score of 87.1 on the dev set (for comparison, ... | [
"TAGS\n#transformers #onnx #distilbert #question-answering #en #dataset-squad #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# ONNX Conversion of distilbert-base-cased-distilled-squad",
"# DistilBERT base cased distilled SQuAD\n\nThis model is a fine-tune checkpoint of DistilBERT-base-cas... |
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. -->
# distilroberta-base-ner-conll2003
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta... | {"license": "apache-2.0", "tags": ["token-classification"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilroberta-base-ner-conll2003", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003"... | philschmid/distilroberta-base-ner-conll2003 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-ner-conll2003
This model is a fine-tuned version of distilroberta-base on the conll2003 dataset.
eval F1-Score: 95,29 (CoNLL-03)
test F1-Score: 90,74 (CoNLL-03)
eval F1-Score: 95,29 (CoNLL++ / CoNLL-03 corrected)
test F1-Score: 92,23 (CoNLL++ / CoNLL-03 corrected)
## Model Usage
... | [
"# distilroberta-base-ner-conll2003\n\nThis model is a fine-tuned version of distilroberta-base on the conll2003 dataset.\n\neval F1-Score: 95,29 (CoNLL-03) \ntest F1-Score: 90,74 (CoNLL-03) \n\neval F1-Score: 95,29 (CoNLL++ / CoNLL-03 corrected) \ntest F1-Score: 92,23 (CoNLL++ / CoNLL-03 corrected)",
"## Mod... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilroberta-base-ner-conll2003\n\nThis model is a fine-tuned version of distilroberta-base on the conll2003 dataset.\n\neval F1-Score: 9... |
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. -->
# distilroberta-base-ner-wikiann-conll2003-3-class
This model is a fine-tuned version of [distilroberta-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["token-classification"], "datasets": ["wikiann-conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilroberta-base-ner-wikiann-conll2003-3-class", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "datase... | philschmid/distilroberta-base-ner-wikiann-conll2003-3-class | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:wikiann-conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-ner-wikiann-conll2003-3-class
This model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of wikiann.
O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).
eval F1-Score: 96,25 (merged dataset)
test F1-Sco... | [
"# distilroberta-base-ner-wikiann-conll2003-3-class\n\nThis model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. \n\nO (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).\n\neval F1-Score: 96,25 (merged dataset) \nte... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-wikiann-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilroberta-base-ner-wikiann-conll2003-3-class\n\nThis model is a fine-tuned version of distilroberta-base on the wikiann and co... |
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. -->
# distilroberta-base-ner-wikiann-conll2003-4-class
This model is a fine-tuned version of [distilroberta-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["token-classification"], "datasets": ["wikiann-conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilroberta-base-ner-wikiann-conll2003-4-class", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "datase... | philschmid/distilroberta-base-ner-wikiann-conll2003-4-class | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:wikiann-conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-ner-wikiann-conll2003-4-class
This model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of conll2003.
O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6) B-MISC (7), I-MISC (8).
eval F1-Score: 95,39 (merge... | [
"# distilroberta-base-ner-wikiann-conll2003-4-class\n\nThis model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of conll2003. \n\nO (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6) B-MISC (7), I-MISC (8).\n\neval F1-Score: 95,3... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-wikiann-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilroberta-base-ner-wikiann-conll2003-4-class\n\nThis model is a fine-tuned version of distilroberta-base on the wikiann and co... |
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. -->
# distilroberta-base-ner-wikiann
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-b... | {"license": "apache-2.0", "tags": ["token-classification"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilroberta-base-ner-wikiann", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "typ... | philschmid/distilroberta-base-ner-wikiann | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-ner-wikiann
This model is a fine-tuned version of distilroberta-base on the wikiann dataset.
eval F1-Score: 83,78
test F1-Score: 83,76
## Model Usage
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.90869035... | [
"# distilroberta-base-ner-wikiann\n\nThis model is a fine-tuned version of distilroberta-base on the wikiann dataset.\n\n\neval F1-Score: 83,78 \ntest F1-Score: 83,76",
"## Model Usage",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learn... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilroberta-base-ner-wikiann\n\nThis model is a fine-tuned version of distilroberta-base on the wikiann dataset.\n\n\neval F1-Score: 83,78... |
fill-mask | transformers |
# This repository is a fork of [yiyanghkust/finbert-pretrain](https://huggingface.co/yiyanghkust/finbert-pretrain)
> All credits to [@yiyanghkust](https://huggingface.co/yiyanghkust).
I added the TensorFlow model and a proper `tokenizer.json`
---
`FinBERT` is a BERT model pre-trained on financial communication tex... | {"language": "en", "pipeline_tag": "fill-mask"} | philschmid/finbert-pretrain-yiyanghkust | null | [
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"en",
"arxiv:2006.08097",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.08097"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #fill-mask #en #arxiv-2006.08097 #autotrain_compatible #endpoints_compatible #region-us
|
# This repository is a fork of yiyanghkust/finbert-pretrain
> All credits to @yiyanghkust.
I added the TensorFlow model and a proper 'URL'
---
'FinBERT' is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three f... | [
"# This repository is a fork of yiyanghkust/finbert-pretrain\n\n> All credits to @yiyanghkust.\n\nI added the TensorFlow model and a proper 'URL'\n\n---\n\n'FinBERT' is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the follo... | [
"TAGS\n#transformers #pytorch #tf #bert #fill-mask #en #arxiv-2006.08097 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This repository is a fork of yiyanghkust/finbert-pretrain\n\n> All credits to @yiyanghkust.\n\nI added the TensorFlow model and a proper 'URL'\n\n---\n\n'FinBERT' is a BERT 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. -->
# gbert-base-germaner
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the... | {"language": ["de"], "license": "mit", "datasets": ["germaner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Philipp ist 26 Jahre alt und lebt in N\u00fcrnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um k\u00fcnstliche Intelligenz du... | philschmid/gbert-base-germaner | null | [
"transformers",
"tf",
"tensorboard",
"bert",
"token-classification",
"de",
"dataset:germaner",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #tf #tensorboard #bert #token-classification #de #dataset-germaner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# gbert-base-germaner
This model is a fine-tuned version of deepset/gbert-base on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8521
- recall: 0.8754
- f1: 0.8636
- accuracy: 0.9761
If you want to learn how to fine-tune BERT yourself using Keras and Tensorflow check ... | [
"# gbert-base-germaner\n\nThis model is a fine-tuned version of deepset/gbert-base on the germaner dataset.\nIt achieves the following results on the evaluation set:\n- precision: 0.8521\n- recall: 0.8754\n- f1: 0.8636\n- accuracy: 0.9761\n\nIf you want to learn how to fine-tune BERT yourself using Keras and Tensor... | [
"TAGS\n#transformers #tf #tensorboard #bert #token-classification #de #dataset-germaner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# gbert-base-germaner\n\nThis model is a fine-tuned version of deepset/gbert-base on the germaner dataset.\nIt achieves the following resul... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-prompted-germanquad-1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small... | {"license": "apache-2.0", "tags": ["summarization"], "datasets": ["philschmid/prompted-germanquad"], "metrics": ["rouge"], "widget": [{"text": "Philipp ist 26 Jahre alt und lebt in N\u00fcrnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um k\u00fcnstliche Intelligen... | philschmid/mt5-small-prompted-germanquad-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"dataset:philschmid/prompted-germanquad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #dataset-philschmid/prompted-germanquad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-prompted-germanquad-1
===============================
This model is a fine-tuned version of google/mt5-small on an philschmid/prompted-germanquad dataset. A prompt datasets using the BigScience PromptSource library. The dataset is a copy of germanquad with applying the 'squad' template and translated it to ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #dataset-philschmid/prompted-germanquad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
text-classification | transformers | # Pytorch Fork of [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine)
A french sentiment analysis model, based on [CamemBERT](https://camembert-model.fr/), and finetuned on a large-scale dataset scraped from [Allociné.fr](http://www.allocine.fr/) user reviews.
## Results
| Validation Accuracy | Validation ... | {"language": "fr"} | philschmid/pt-tblard-tf-allocine | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #fr #autotrain_compatible #endpoints_compatible #region-us
| Pytorch Fork of tblard/tf-allocine
==================================
A french sentiment analysis model, based on CamemBERT, and finetuned on a large-scale dataset scraped from Allociné.fr user reviews.
Results
-------
Usage
-----
Author
------
Théophile Blard – :email: URL@URL
If you use this work (code, mo... | [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | 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. -->
# philschmid/tf-distilbart-cnn-12-6-tradetheevent
This model is a fine-tuned version of [philschmid/tf-distilbart-cnn-12-6](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "philschmid/tf-distilbart-cnn-12-6-tradetheevent", "results": []}]} | philschmid/tf-distilbart-cnn-12-6-tradetheevent | null | [
"transformers",
"tf",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bart #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| philschmid/tf-distilbart-cnn-12-6-tradetheevent
===============================================
This model is a fine-tuned version of philschmid/tf-distilbart-cnn-12-6 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6894
* Validation Loss: 1.7245
* Epoch: 4
Model de... | [
"### 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': 5.6e-05, 'decay... | [
"TAGS\n#transformers #tf #tensorboard #bart #text2text-generation #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': {'cla... |
summarization | transformers |
# This is an Tensorflow fork of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6)
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForCondit... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | philschmid/tf-distilbart-cnn-12-6 | null | [
"transformers",
"tf",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This is an Tensorflow fork of sshleifer/distilbart-cnn-12-6
===========================================================
### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Metrics for DistilBART models
| [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Metrics for DistilBART models"
] | [
"TAGS\n#transformers #tf #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for... |
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. -->
# tiny-bert-sst2-distilled
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-distilled", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [... | philschmid/tiny-bert-sst2-distilled | null | [
"transformers",
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"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| tiny-bert-sst2-distilled
========================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7305
* Accuracy: 0.8326
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0007199555649276667\n* train\\_batch\\_size: 1024\n* eval\\_batch\\_size: 1024\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epoch... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-classification | transformers | # Test model
> ## This model is used to run tests for the Hugging Face DLCs | {} | philschmid/tiny-distilbert-classification | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Test model
> ## This model is used to run tests for the Hugging Face DLCs | [
"# Test model\n\n> ## This model is used to run tests for the Hugging Face DLCs"
] | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Test model\n\n> ## This model is used to run tests for the Hugging Face DLCs"
] |
image-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. -->
# philschmid/vit-base-patch16-224-in21k-euroSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "metrics": ["accuracy"], "widget": [{"src": "https://cdn.prod.www.spiegel.de/images/6b1135cd-0001-0004-0000-000000867699_w996_r1.778_fpx50_fpy47.38.jpg"}], "model-index": [{"name": "philschmid/vit-base-patch16-224-in21k-euroSat", "results": [{"task": ... | philschmid/vit-base-patch16-224-in21k-euroSat | null | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| philschmid/vit-base-patch16-224-in21k-euroSat
=============================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0218
* Train Accuracy: 0.9990
* Train Top-3-accuracy: 1.... | [
"### 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': 3e-05, 'decay\\... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optim... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-image-classification-sagemaker
This model is a fine-tuned version of [vit-base-patch16-224-in21k](htt... | {"tags": ["image-classification"], "metrics": ["accuracy"]} | philschmid/vit-base-patch16-224-in21k-image-classification-sagemaker | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #autotrain_compatible #endpoints_compatible #region-us
| vit-base-patch16-224-in21k-image-classification-sagemaker
=========================================================
This model is a fine-tuned version of vit-base-patch16-224-in21k on the cifar10 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3033
* Accuracy: 0.972
Model description
-... | [
"### 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: 64\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 #vit #image-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* optim... |
question-answering | transformers |
# roberta-large-finetuned-squad2
## Model description
This model is based on [facebook/bart-large](https://huggingface.co/facebook/bart-large) and was finetuned on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/). The corresponding papers you can found [here (model)](https://arxiv.org/pdf/1910.13461.pdf) and ... | {"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2"], "metrics": ["exact", "f1"], "widget": [{"text": "What discipline did Winkelmann create?", "context": "Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the dif... | phiyodr/bart-large-finetuned-squad2 | null | [
"transformers",
"pytorch",
"bart",
"question-answering",
"en",
"dataset:squad2",
"arxiv:1910.13461",
"arxiv:1806.03822",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461",
"1806.03822"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #question-answering #en #dataset-squad2 #arxiv-1910.13461 #arxiv-1806.03822 #endpoints_compatible #region-us
|
# roberta-large-finetuned-squad2
## Model description
This model is based on facebook/bart-large and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).
## How to use
## Training procedure
## Eval results
- Data: dev-v2.0.json
- Script: evaluate-v2.0.py (origina... | [
"# roberta-large-finetuned-squad2",
"## Model description\n\nThis model is based on facebook/bart-large and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).",
"## How to use",
"## Training procedure",
"## Eval results\n\n- Data: dev-v2.0.json\n- Script: evaluat... | [
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"# roberta-large-finetuned-squad2",
"## Model description\n\nThis model is based on facebook/bart-large and was finetuned on SQuAD2.0. The corresponding papers yo... |
question-answering | transformers |
# bert-base-finetuned-squad2
## Model description
This model is based on **[bert-base-uncased](https://huggingface.co/bert-base-uncased)** and was finetuned on **[SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/)**. The corresponding papers you can found [here (model)](https://arxiv.org/abs/1810.04805) and [her... | {"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2"], "metrics": ["exact", "f1"], "widget": [{"text": "What discipline did Winkelmann create?", "context": "Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the dif... | phiyodr/bert-base-finetuned-squad2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad2",
"arxiv:1810.04805",
"arxiv:1806.03822",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805",
"1806.03822"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #en #dataset-squad2 #arxiv-1810.04805 #arxiv-1806.03822 #endpoints_compatible #has_space #region-us
|
# bert-base-finetuned-squad2
## Model description
This model is based on bert-base-uncased and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).
## How to use
## Training procedure
## Eval results
- Data: dev-v2.0.json
- Script: evaluate-v2.0.py (original scri... | [
"# bert-base-finetuned-squad2",
"## Model description\n\nThis model is based on bert-base-uncased and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).",
"## How to use",
"## Training procedure",
"## Eval results\n\n- Data: dev-v2.0.json\n- Script: evaluate-v2.0... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #en #dataset-squad2 #arxiv-1810.04805 #arxiv-1806.03822 #endpoints_compatible #has_space #region-us \n",
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"## Model description\n\nThis model is based on bert-base-uncased and was finetuned on SQuAD2.0. The corresponding... |
question-answering | transformers |
# bert-large-finetuned-squad2
## Model description
This model is based on **[bert-large-uncased](https://huggingface.co/bert-large-uncased)** and was finetuned on **[SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/)**. The corresponding papers you can found [here (model)](https://arxiv.org/abs/1810.04805) and [... | {"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2"], "metrics": ["exact", "f1"], "widget": [{"text": "What discipline did Winkelmann create?", "context": "Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the dif... | phiyodr/bert-large-finetuned-squad2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad2",
"arxiv:1810.04805",
"arxiv:1806.03822",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805",
"1806.03822"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #en #dataset-squad2 #arxiv-1810.04805 #arxiv-1806.03822 #endpoints_compatible #region-us
|
# bert-large-finetuned-squad2
## Model description
This model is based on bert-large-uncased and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).
## How to use
## Training procedure
## Eval results
- Data: dev-v2.0.json
- Script: evaluate-v2.0.py (original sc... | [
"# bert-large-finetuned-squad2",
"## Model description\n\nThis model is based on bert-large-uncased and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).",
"## How to use",
"## Training procedure",
"## Eval results\n\n- Data: dev-v2.0.json\n- Script: evaluate-v2... | [
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"## Model description\n\nThis model is based on bert-large-uncased and was finetuned on SQuAD2.0. The corresponding papers y... |
question-answering | transformers |
# roberta-large-finetuned-squad2
## Model description
This model is based on [roberta-large](https://huggingface.co/roberta-large) and was finetuned on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/). The corresponding papers you can found [here (model)](https://arxiv.org/abs/1907.11692) and [here (data)](ht... | {"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2"], "metrics": ["exact", "f1"], "widget": [{"text": "What discipline did Winkelmann create?", "context": "Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the dif... | phiyodr/roberta-large-finetuned-squad2 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"dataset:squad2",
"arxiv:1907.11692",
"arxiv:1806.03822",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"1806.03822"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #question-answering #en #dataset-squad2 #arxiv-1907.11692 #arxiv-1806.03822 #endpoints_compatible #region-us
|
# roberta-large-finetuned-squad2
## Model description
This model is based on roberta-large and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).
## How to use
## Training procedure
## Eval results
- Data: dev-v2.0.json
- Script: evaluate-v2.0.py (original scri... | [
"# roberta-large-finetuned-squad2",
"## Model description\n\nThis model is based on roberta-large and was finetuned on SQuAD2.0. The corresponding papers you can found here (model) and here (data).",
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"## Training procedure",
"## Eval results\n\n- Data: dev-v2.0.json\n- Script: evaluate-v2.0... | [
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"## Model description\n\nThis model is based on roberta-large and was finetuned on SQuAD2.0. The corresponding papers ... |
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. -->
# fb-vindata-vi-large
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "phongdtd/VinDataVLSP", "generated_from_trainer"], "model-index": [{"name": "fb-vindata-vi-large", "results": []}]} | phongdtd/fb-vindata-vi-large | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"phongdtd/VinDataVLSP",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# fb-vindata-vi-large
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the PHONGDTD/VINDATAVLSP - NA dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedu... | [
"# fb-vindata-vi-large\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the PHONGDTD/VINDATAVLSP - NA dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information neede... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# fb-vindata-vi-large\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the PHONGDTD/VINDATAVLSP - 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. -->
# fb-youtube-vi-large
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "phongdtd/youtube_casual_audio", "generated_from_trainer"], "model-index": [{"name": "fb-youtube-vi-large", "results": []}]} | phongdtd/fb-youtube-vi-large | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"phongdtd/youtube_casual_audio",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #phongdtd/youtube_casual_audio #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# fb-youtube-vi-large
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the PHONGDTD/YOUTUBE_CASUAL_AUDIO - NA dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Trainin... | [
"# fb-youtube-vi-large\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the PHONGDTD/YOUTUBE_CASUAL_AUDIO - NA dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informat... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #phongdtd/youtube_casual_audio #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# fb-youtube-vi-large\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the PHON... |
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. -->
# wavLM-VLSP-vi-base
This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base... | {"tags": ["automatic-speech-recognition", "phongdtd/VinDataVLSP", "generated_from_trainer"], "model-index": [{"name": "wavLM-VLSP-vi-base", "results": []}]} | phongdtd/wavLM-VLSP-vi-base | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"phongdtd/VinDataVLSP",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #endpoints_compatible #region-us
|
# wavLM-VLSP-vi-base
This model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0390
- Wer: 0.9995
- Cer: 0.9414
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# wavLM-VLSP-vi-base\n\nThis model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.0390\n- Wer: 0.9995\n- Cer: 0.9414",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #endpoints_compatible #region-us \n",
"# wavLM-VLSP-vi-base\n\nThis model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.\nIt achieves the fo... |
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. -->
# wavLM-VLSP-vi
This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus... | {"tags": ["automatic-speech-recognition", "phongdtd/VinDataVLSP", "generated_from_trainer"], "model-index": [{"name": "wavLM-VLSP-vi", "results": []}]} | phongdtd/wavLM-VLSP-vi | null | [
"transformers",
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"tensorboard",
"wavlm",
"automatic-speech-recognition",
"phongdtd/VinDataVLSP",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #endpoints_compatible #region-us
| wavLM-VLSP-vi
=============
This model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 45.8892
* Wer: 0.9999
* Cer: 0.9973
Model description
-----------------
More information needed
Intended uses &... | [
"### 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: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 8\n* total\\_eval\\_batch\\_size: 16\n* op... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\... |
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. -->
# wavlm-vindata-demo-dist
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base... | {"tags": ["automatic-speech-recognition", "phongdtd/VinDataVLSP", "generated_from_trainer"], "datasets": ["vin_data_vlsp"], "model-index": [{"name": "wavlm-vindata-demo-dist", "results": []}]} | phongdtd/wavlm-vindata-demo-dist | null | [
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"automatic-speech-recognition",
"phongdtd/VinDataVLSP",
"generated_from_trainer",
"dataset:vin_data_vlsp",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #dataset-vin_data_vlsp #endpoints_compatible #region-us
| wavlm-vindata-demo-dist
=======================
This model is a fine-tuned version of microsoft/wavlm-base on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4439
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 2\n* total\\_eval\\_batch\\_size: 16\n* op... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | phozon/harry-potter-medium | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
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"# My Awesome Model"
] |
fill-mask | transformers |
## BabyBERTA
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.
The three provided models are randoml... | {"language": "en", "tags": ["BabyBERTa"], "datasets": ["CHILDES"], "widget": [{"text": "Look here. What is that <mask> ?"}, {"text": "Do you like your <mask> ?"}]} | phueb/BabyBERTa-1 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #BabyBERTa #en #dataset-CHILDES #autotrain_compatible #endpoints_compatible #region-us
| BabyBERTA
---------
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.
The three provided models are... | [
"### Overview\n\n\nBabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.\nIt is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.\n\n\nThe three provided models are randomly ... | [
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"### Overview\n\n\nBabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.\nIt is intended for language acquisition res... |
fill-mask | transformers |
## BabyBERTA
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.
The three provided models are randoml... | {"language": "en", "tags": ["BabyBERTa"], "datasets": ["CHILDES"], "widget": [{"text": "Look here. What is that <mask> ?"}, {"text": "Do you like your <mask> ?"}]} | phueb/BabyBERTa-2 | null | [
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"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #BabyBERTa #en #dataset-CHILDES #autotrain_compatible #endpoints_compatible #region-us
| BabyBERTA
---------
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.
The three provided models are... | [
"### Overview\n\n\nBabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.\nIt is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.\n\n\nThe three provided models are randomly ... | [
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"### Overview\n\n\nBabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.\nIt is intended for language acquisition res... |
fill-mask | transformers |
## BabyBERTA
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.
The three provided models are randoml... | {"language": "en", "license": "mit", "tags": ["BabyBERTa"], "datasets": ["CHILDES"], "widget": [{"text": "Look here. What is that <mask> ?"}, {"text": "Do you like your <mask> ?"}]} | phueb/BabyBERTa-3 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #BabyBERTa #en #dataset-CHILDES #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BabyBERTA
---------
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.
The three provided models are... | [
"### Overview\n\n\nBabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.\nIt is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.\n\n\nThe three provided models are randomly ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #BabyBERTa #en #dataset-CHILDES #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\nBabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.\nIt is intended for language ac... |
question-answering | transformers |
# BraQuAD BERT
## Model description
This is a question-answering model trained in BraQuAD 2.0, a version of SQuAD 2.0 translated to PT-BR using Google Cloud Translation API.
### Context
Edith Ranzini (São Paulo,[1] 1946) é uma engenheira brasileira formada pela USP, professora doutora da Pontifícia Universidade Cat... | {"language": ["pt-br"], "license": "apache-2.0", "tags": ["question-answering"], "metrics": ["em", "f1"], "pipeline_tag": "question-answering"} | piEsposito/braquad-bert-qna | null | [
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"jax",
"safetensors",
"bert",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt-br"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us
| BraQuAD BERT
============
Model description
-----------------
This is a question-answering model trained in BraQuAD 2.0, a version of SQuAD 2.0 translated to PT-BR using Google Cloud Translation API.
### Context
Edith Ranzini (São Paulo,[1] 1946) é uma engenheira brasileira formada pela USP, professora doutora ... | [
"### Context\n\n\nEdith Ranzini (São Paulo,[1] 1946) é uma engenheira brasileira formada pela USP, professora doutora da Pontifícia Universidade Católica de São Paulo[2] e professora sênior da Escola Politécnica da Universidade de São Paulo (Poli).[3] Ela compôs a equipe responsável pela criação do primeiro computa... | [
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"### Context\n\n\nEdith Ranzini (São Paulo,[1] 1946) é uma engenheira brasileira formada pela USP, professora doutora da Pontifícia Universidade Católica de São Paulo[2] e professora... |
null | null | This is a test of the system | {} | picheny/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is a test of the system | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 3471039
## Validation Metrics
- Loss: 0.6704344749450684
- Accuracy: 0.59375
- Macro F1: 0.37254901960784315
- Micro F1: 0.59375
- Weighted F1: 0.4424019607843137
- Macro Precision: 0.296875
- Micro Precision: 0.59375
- Weighted Pr... | {"language": "fr", "tags": "autonlp", "datasets": ["pierreant-p/autonlp-data-jcvd-or-linkedin"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | pierreant-p/autonlp-jcvd-or-linkedin-3471039 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 3471039
## Validation Metrics
- Loss: 0.6704344749450684
- Accuracy: 0.59375
- Macro F1: 0.37254901960784315
- Micro F1: 0.59375
- Weighted F1: 0.4424019607843137
- Macro Precision: 0.296875
- Micro Precision: 0.59375
- Weighted Pr... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 3471039",
"## Validation Metrics\n\n- Loss: 0.6704344749450684\n- Accuracy: 0.59375\n- Macro F1: 0.37254901960784315\n- Micro F1: 0.59375\n- Weighted F1: 0.4424019607843137\n- Macro Precision: 0.296875\n- Micro Precision: 0.... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 3471039",
"#... |
fill-mask | transformers |
## (BERT base) Language modeling in the legal domain in Portuguese (LeNER-Br)
**bert-base-cased-pt-lenerbr** is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the datas... | {"language": ["pt"], "tags": ["generated_from_trainer"], "datasets": ["pierreguillou/lener_br_finetuning_language_model"], "widget": [{"text": "Com efeito, se tal fosse poss\u00edvel, o Poder [MASK] \u2013 que n\u00e3o disp\u00f5e de fun\u00e7\u00e3o legislativa \u2013 passaria a desempenhar atribui\u00e7\u00e3o que lh... | pierreguillou/bert-base-cased-pt-lenerbr | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
## (BERT base) Language modeling in the legal domain in Portuguese (LeNER-Br)
bert-base-cased-pt-lenerbr is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model BERTimbau base on the dataset LeNER-Br language modeling by using a MASK objective.
You can ch... | [
"## (BERT base) Language modeling in the legal domain in Portuguese (LeNER-Br)\n\nbert-base-cased-pt-lenerbr is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model BERTimbau base on the dataset LeNER-Br language modeling by using a MASK objective.\n\nYo... | [
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"## (BERT base) Language modeling in the legal domain in Portuguese (LeNER-Br)\n\nbert-base-cased-pt-lenerbr... |
question-answering | transformers |
# Portuguese BERT base cased QA (Question Answering), finetuned on SQUAD v1.1
## Introduction
The model was trained on the dataset SQUAD v1.1 in po... | {"language": "pt", "license": "mit", "tags": ["question-answering", "bert", "bert-base", "pytorch"], "datasets": ["brWaC", "squad", "squad_v1_pt"], "metrics": ["squad"], "widget": [{"text": "Quando come\u00e7ou a pandemia de Covid-19 no mundo?", "context": "A pandemia de COVID-19, tamb\u00e9m conhecida como pandemia de... | pierreguillou/bert-base-cased-squad-v1.1-portuguese | null | [
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"dataset:squad",
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"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #bert-base #pt #dataset-brWaC #dataset-squad #dataset-squad_v1_pt #license-mit #endpoints_compatible #has_space #region-us
|
# Portuguese BERT base cased QA (Question Answering), finetuned on SQUAD v1.1
!Exemple of what can do the Portuguese BERT base cased QA (Question Answering), finetuned on SQUAD v1.1
## Introduction
The model was trained on the dataset SQUAD v1.1 in portuguese from the Deep Learning Brasil group on Google Colab.
T... | [
"# Portuguese BERT base cased QA (Question Answering), finetuned on SQUAD v1.1\n\n!Exemple of what can do the Portuguese BERT base cased QA (Question Answering), finetuned on SQUAD v1.1",
"## Introduction\n\nThe model was trained on the dataset SQUAD v1.1 in portuguese from the Deep Learning Brasil group on Googl... | [
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"# Portuguese BERT base cased QA (Question Answering), finetuned on SQUAD v1.1\n\n!Exemple of what can do the Portuguese ... |
fill-mask | transformers |
## (BERT large) Language modeling in the legal domain in Portuguese (LeNER-Br)
**bert-large-cased-pt-lenerbr** is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [BERTimbau large](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the d... | {"language": ["pt"], "tags": ["generated_from_trainer"], "datasets": ["pierreguillou/lener_br_finetuning_language_model"], "widget": [{"text": "Com efeito, se tal fosse poss\u00edvel, o Poder [MASK] \u2013 que n\u00e3o disp\u00f5e de fun\u00e7\u00e3o legislativa \u2013 passaria a desempenhar atribui\u00e7\u00e3o que lh... | pierreguillou/bert-large-cased-pt-lenerbr | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
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|
## (BERT large) Language modeling in the legal domain in Portuguese (LeNER-Br)
bert-large-cased-pt-lenerbr is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model BERTimbau large on the dataset LeNER-Br language modeling by using a MASK objective.
You can... | [
"## (BERT large) Language modeling in the legal domain in Portuguese (LeNER-Br)\n\nbert-large-cased-pt-lenerbr is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model BERTimbau large on the dataset LeNER-Br language modeling by using a MASK objective.\n\... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #pt #dataset-pierreguillou/lener_br_finetuning_language_model #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"## (BERT large) Language modeling in the legal domain in Portuguese (LeNER-Br)\n\nbert-large-cased-pt-lener... |
question-answering | transformers |
# Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1
## Introduction
The model was trained on the dataset SQUAD v1.1 in ... | {"language": "pt", "license": "mit", "tags": ["question-answering", "bert", "bert-large", "pytorch"], "datasets": ["brWaC", "squad", "squad_v1_pt"], "metrics": ["squad"], "widget": [{"text": "Quando come\u00e7ou a pandemia de Covid-19 no mundo?", "context": "A pandemia de COVID-19, tamb\u00e9m conhecida como pandemia d... | pierreguillou/bert-large-cased-squad-v1.1-portuguese | null | [
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"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #bert-large #pt #dataset-brWaC #dataset-squad #dataset-squad_v1_pt #license-mit #endpoints_compatible #has_space #region-us
|
# Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1
!Exemple of what can do the Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1
## Introduction
The model was trained on the dataset SQUAD v1.1 in portuguese from the Deep Learning Brasil group.
The language mo... | [
"# Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1\n\n!Exemple of what can do the Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1",
"## Introduction\n\nThe model was trained on the dataset SQUAD v1.1 in portuguese from the Deep Learning Brasil group. \n\nT... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #bert-large #pt #dataset-brWaC #dataset-squad #dataset-squad_v1_pt #license-mit #endpoints_compatible #has_space #region-us \n",
"# Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1\n\n!Exemple of what can do the Portuguese BER... |
text2text-generation | transformers |
# ByT5 small finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese
Check our other QA models in Portuguese finetuned on SQUAD v1.1:
- [Portuguese... | {"language": "pt", "license": "apache-2.0", "tags": ["text2text-generation", "byt5", "pytorch", "qa"], "datasets": "squad", "metrics": "squad", "widget": [{"text": "question: \"Quando come\u00e7ou a pandemia de Covid-19 no mundo?\" context: \"A pandemia de COVID-19, tamb\u00e9m conhecida como pandemia de coronav\u00edr... | pierreguillou/byt5-small-qa-squad-v1.1-portuguese | null | [
"transformers",
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"t5",
"text2text-generation",
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"arxiv:2105.13626",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06292",
"2105.13626"
] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #byt5 #qa #pt #dataset-squad #arxiv-1907.06292 #arxiv-2105.13626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5 small finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese
!Exemple of what can do the Portuguese ByT5 small QA (Question Answering), finetuned on SQUAD v1.1
Check our other QA models in Portuguese finetuned on SQUAD v1.1:
- Portuguese BERT base cased QA
- Portuguese BERT large cased QA
- Portuguese ... | [
"# ByT5 small finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese\n!Exemple of what can do the Portuguese ByT5 small QA (Question Answering), finetuned on SQUAD v1.1\n\nCheck our other QA models in Portuguese finetuned on SQUAD v1.1:\n- Portuguese BERT base cased QA\n- Portuguese BERT large cased QA\n- P... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #byt5 #qa #pt #dataset-squad #arxiv-1907.06292 #arxiv-2105.13626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5 small finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese\n!Exemple... |
text-generation | transformers |
# GPorTuguese-2: a Language Model for Portuguese text generation (and more NLP tasks...)
## Introduction
GPorTuguese-2 (Portuguese GPT-2 small) is a state-of-the-art language model for Portuguese based on the GPT-2 small model.
It was trained on Portuguese Wikipedia using **Transfer Learning and Fine-tuning techni... | {"language": "pt", "license": "mit", "datasets": ["wikipedia"], "widget": [{"text": "Quem era Jim Henson? Jim Henson era um"}, {"text": "Em um achado chocante, o cientista descobriu um"}, {"text": "Barack Hussein Obama II, nascido em 4 de agosto de 1961, \u00e9"}, {"text": "Corrida por vacina contra Covid-19 j\u00e1 te... | pierreguillou/gpt2-small-portuguese | null | [
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"jax",
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"text-generation",
"pt",
"dataset:wikipedia",
"license:mit",
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"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #pt #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPorTuguese-2: a Language Model for Portuguese text generation (and more NLP tasks...)
======================================================================================
Introduction
------------
GPorTuguese-2 (Portuguese GPT-2 small) is a state-of-the-art language model for Portuguese based on the GPT-2 small ... | [
"### Load GPorTuguese-2 and its sub-word tokenizer (Byte-level BPE)",
"### Generate one word",
"### Generate one full sequence\n\n\nHow to use GPorTuguese-2 with HuggingFace (TensorFlow)\n------------------------------------------------------\n\n\nThe following code use TensorFlow. To use PyTorch, check the abo... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #pt #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Load GPorTuguese-2 and its sub-word tokenizer (Byte-level BPE)",
"### Generate one word",
"### Generate one f... |
token-classification | transformers |
## (BERT base) NER model in the legal domain in Portuguese (LeNER-Br)
**ner-bert-base-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pie... | {"language": ["pt"], "tags": ["generated_from_trainer"], "datasets": ["lener_br"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Ao Instituto M\u00e9dico Legal da jurisdi\u00e7\u00e3o do acidente ou da resid\u00eancia cumpre fornecer, no prazo de 90 dias, laudo \u00e0 v\u00edtima (art. 5, \... | pierreguillou/ner-bert-base-cased-pt-lenerbr | null | [
"transformers",
"pytorch",
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"model-index",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## (BERT base) NER model in the legal domain in Portuguese (LeNER-Br)
ner-bert-base-portuguese-cased-lenerbr is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model pierreguillou/bert-base-cased-pt-lenerbr on the dataset LeNER_br by using... | [
"## (BERT base) NER model in the legal domain in Portuguese (LeNER-Br)\n\nner-bert-base-portuguese-cased-lenerbr is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model pierreguillou/bert-base-cased-pt-lenerbr on the dataset LeNER_br by... | [
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"## (BERT base) NER model in the legal domain in Portuguese (LeNER-Br)\n\nner-bert-base-portuguese-cased-lenerbr is a NER mode... |
token-classification | transformers |
## (BERT large) NER model in the legal domain in Portuguese (LeNER-Br)
**ner-bert-large-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [pierreguillou/bert-large-cased-pt-lenerbr](https://huggingface.co/... | {"language": ["pt"], "tags": ["generated_from_trainer"], "datasets": ["lener_br"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Ao Instituto M\u00e9dico Legal da jurisdi\u00e7\u00e3o do acidente ou da resid\u00eancia cumpre fornecer, no prazo de 90 dias, laudo \u00e0 v\u00edtima (art. 5, \... | pierreguillou/ner-bert-large-cased-pt-lenerbr | null | [
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## (BERT large) NER model in the legal domain in Portuguese (LeNER-Br)
ner-bert-large-portuguese-cased-lenerbr is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model pierreguillou/bert-large-cased-pt-lenerbr on the dataset LeNER_br by us... | [
"## (BERT large) NER model in the legal domain in Portuguese (LeNER-Br)\n\nner-bert-large-portuguese-cased-lenerbr is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model pierreguillou/bert-large-cased-pt-lenerbr on the dataset LeNER_br... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## (BERT large) NER model in the legal domain in Portuguese (LeNER-Br)\n\nner-bert-large-portuguese-cased-lenerbr is a NER mo... |
text2text-generation | transformers |
# T5 base finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese
## Introduction
**t5-base-qa-squad-v1.1-portuguese** is a QA model... | {"language": ["pt"], "tags": ["text2text-generation", "t5", "pytorch", "qa"], "datasets": ["squad", "squad_v1_pt"], "metrics": ["precision", "recall", "f1", "accuracy", "squad"], "widget": [{"text": "question: Quando come\u00e7ou a pandemia de Covid-19 no mundo? context: A pandemia de COVID-19, tamb\u00e9m conhecida co... | pierreguillou/t5-base-qa-squad-v1.1-portuguese | null | [
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #qa #pt #dataset-squad #dataset-squad_v1_pt #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5 base finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese
!Exemple of what can do with a T5 model (for example: Question Answering finetuned on SQUAD v1.1 in Portuguese)
## Introduction
t5-base-qa-squad-v1.1-portuguese is a QA model (Question Answering) in Portuguese that was finetuned on 27/01/2022 ... | [
"# T5 base finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese\n\n!Exemple of what can do with a T5 model (for example: Question Answering finetuned on SQUAD v1.1 in Portuguese)",
"## Introduction\n\nt5-base-qa-squad-v1.1-portuguese is a QA model (Question Answering) in Portuguese that was finetuned on... | [
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"# T5 base finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese\n\n!Exemple of what can do wi... |
question-answering | transformers |
Txt | {"language": "en", "license": "apache-2.0", "tags": ["html"], "datasets": ["squadv2"], "inference": {"parameters": {"handle_impossible_answer": true}}} | pierrerappolt/cart | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"html",
"en",
"dataset:squadv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #html #en #dataset-squadv2 #license-apache-2.0 #endpoints_compatible #region-us
|
Txt | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #html #en #dataset-squadv2 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
token-classification | transformers | . | {"inference": {"parameters": {"aggregation_strategy": "first"}}} | pierrerappolt-okta/app | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| . | [] | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2131817
## Validation Metrics
- Loss: 0.24430708587169647
- Accuracy: 0.9452
- Precision: 0.9303944315545244
- Recall: 0.9624
- AUC: 0.9793824287999999
- F1: 0.946126622099882
## Usage
You can use cURL to access this model:
```
$ cur... | {"language": "en", "tags": "autonlp", "datasets": ["pierric/autonlp-data-my-own-imdb-sentiment-analysis"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | pierric/autonlp-my-own-imdb-sentiment-analysis-2131817 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:pierric/autonlp-data-my-own-imdb-sentiment-analysis",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-pierric/autonlp-data-my-own-imdb-sentiment-analysis #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2131817
## Validation Metrics
- Loss: 0.24430708587169647
- Accuracy: 0.9452
- Precision: 0.9303944315545244
- Recall: 0.9624
- AUC: 0.9793824287999999
- F1: 0.946126622099882
## Usage
You can use cURL to access this model:
Or Pyth... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2131817",
"## Validation Metrics\n\n- Loss: 0.24430708587169647\n- Accuracy: 0.9452\n- Precision: 0.9303944315545244\n- Recall: 0.9624\n- AUC: 0.9793824287999999\n- F1: 0.946126622099882",
"## Usage\n\nYou can use cURL to acces... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2131817",
"## Validation Metrics\... |
image-classification | transformers |
# ny-cr-fr
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/huggingpics... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | pierric/ny-cr-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# ny-cr-fr
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
#### new york
!new york
#### playas del coco, costa rica
!playas del coco, costa rica
#### toulouse
!toulouse | [
"# ny-cr-fr\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",
"#### new york\n\n!new york",
"#### playas del coco, costa rica\n\n!playas del coco, costa rica",
... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# ny-cr-fr\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... |
fill-mask | transformers |
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
**Companion model to blog post https://huggingface.co/blog/how-to-train** 🔥
### Training Details
- current checkpoint: 566000
- machine name: `galinette`
| {"language": "eo", "thumbnail": "https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png"} | pierric/test-EsperBERTo-small | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"eo",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #eo #autotrain_compatible #endpoints_compatible #region-us
|
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
Companion model to blog post URL
### Training Details
- current checkpoint: 566000
- machine name: 'galinette'
| [
"## EsperBERTo: RoBERTa-like Language model trained on Esperanto\n\nCompanion model to blog post URL",
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"### Training Details\n\n- current checkpoint: 566000\n- machine name: 'galinette'"
] |
null | null |
Test
[](https://huggingface.co/spaces/akhaliq/anything-v3.... | {"license": "other", "inference": false, "extra_gated_prompt": "One more step before getting this model\n ", "extra_gated_fields": {"I have read the License and agree with its terms": "checkbox"}} | pierric/tetsetestset | null | [
"license:other",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#license-other #region-us
|
Test
![Open In Spaces](URL
# My H1 with _italic_
## My H2 with _italic_
And my text | [
"# My H1 with _italic_",
"## My H2 with _italic_\n\nAnd my text"
] | [
"TAGS\n#license-other #region-us \n",
"# My H1 with _italic_",
"## My H2 with _italic_\n\nAnd my text"
] |
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. -->
# hate_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "hate_trained", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "hate"}, "metrics": [... | pietrotrope/hate_trained | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| hate\_trained
=============
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9661
* F1: 0.7730
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.303025140957233e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-classification | transformers | # Danish BERT fine-tuned for Detecting 'Analytical'
This model detects if a Danish text is 'subjective' or 'objective'.
It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](ht... | {"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "sentiment", "analytical"], "widget": [{"text": "Jeg synes, det er en elendig film"}]} | pin/analytical | null | [
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"bert",
"text-classification",
"danish",
"sentiment",
"analytical",
"da",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #danish #sentiment #analytical #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # Danish BERT fine-tuned for Detecting 'Analytical'
This model detects if a Danish text is 'subjective' or 'objective'.
It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by Alexandra Institute. The model is trained with the 'senda' package.
Here is an example of how to l... | [
"# Danish BERT fine-tuned for Detecting 'Analytical'\n\nThis model detects if a Danish text is 'subjective' or 'objective'.\n\nIt is trained and tested on Tweets and texts transcribed from the European Parliament annotated by Alexandra Institute. The model is trained with the 'senda' package.\n\nHere is an example ... | [
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"# Danish BERT fine-tuned for Detecting 'Analytical'\n\nThis model detects if a Danish text is 'subjective' or 'objective'.\n\nIt is train... |
text-classification | transformers | # Danish BERT fine-tuned for Sentiment Analysis with `senda`
This model detects polarity ('positive', 'neutral', 'negative') of Danish texts.
It is trained and tested on Tweets annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/s... | {"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "sentiment", "polarity"], "widget": [{"text": "Sikke en dejlig dag det er i dag"}]} | pin/senda | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"danish",
"sentiment",
"polarity",
"da",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #danish #sentiment #polarity #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # Danish BERT fine-tuned for Sentiment Analysis with 'senda'
This model detects polarity ('positive', 'neutral', 'negative') of Danish texts.
It is trained and tested on Tweets annotated by Alexandra Institute. The model is trained with the 'senda' package.
Here is an example of how to load the model in PyTorch usi... | [
"# Danish BERT fine-tuned for Sentiment Analysis with 'senda'\n\nThis model detects polarity ('positive', 'neutral', 'negative') of Danish texts.\n\nIt is trained and tested on Tweets annotated by Alexandra Institute. The model is trained with the 'senda' package.\n\nHere is an example of how to load the model in ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #danish #sentiment #polarity #da #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish BERT fine-tuned for Sentiment Analysis with 'senda'\n\nThis model detects polarity ('positive', 'neutral', 'negative') of Dan... |
text-classification | transformers | # Med-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | {} | pinecone/bert-medqp-cross-encoder | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Med-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course. | [
"# Med-QP Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Med-QP Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] |
text-classification | transformers | # MRPC Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | {} | pinecone/bert-mrpc-cross-encoder | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # MRPC Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course. | [
"# MRPC Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# MRPC Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] |
null | null | # Quora-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | {} | pinecone/bert-qqp-cross-encoder | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Quora-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course. | [
"# Quora-QP Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] | [
"TAGS\n#region-us \n",
"# Quora-QP Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pinecone/bert-retriever-squad2 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
null | null | # RTE Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | {} | pinecone/bert-rte-cross-encoder | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # RTE Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course. | [
"# RTE Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] | [
"TAGS\n#region-us \n",
"# RTE Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] |
null | null | # STSb Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). | {} | pinecone/bert-stsb-cross-encoder | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # STSb Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course. | [
"# STSb Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] | [
"TAGS\n#region-us \n",
"# STSb Cross Encoder\n\nDemo model for use as part of Augmented SBERT chapters of the NLP for Semantic Search course."
] |
sentence-similarity | sentence-transformers |
# MPNet Retriever (Discourse)
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used as a retriever model in open-domain question-answering tasks.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Usin... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "question-answering"], "pipeline_tag": "sentence-similarity"} | pinecone/mpnet-retriever-discourse | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #question-answering #endpoints_compatible #region-us
|
# MPNet Retriever (Discourse)
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used as a retriever model in open-domain question-answering tasks.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# MPNet Retriever (Discourse)\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used as a retriever model in open-domain question-answering tasks.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #question-answering #endpoints_compatible #region-us \n",
"# MPNet Retriever (Discourse)\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be u... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pinecone/mpnet-retriever-squad2 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clus... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | piotr-rybak/poleval2021-task4-herbert-large-encoder | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.... |
text2text-generation | transformers |
This is a T5 small model finetuned on CoNLL-2003 dataset for named entity recognition (NER).
Example Input and Output:
“Recognize all the named entities in this sequence (replace named entities with one of [PER], [ORG], [LOC], [MISC]): When Alice visited New York” → “When PER visited LOC LOC"
Evaluation Result:
% o... | {"language": ["en"], "license": "apache-2.0", "datasets": ["CoNLL-2003"], "metrics": ["F1"]} | pitehu/T5_NER_CONLL_ENTITYREPLACE | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:CoNLL-2003",
"arxiv:2111.10952",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.10952",
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-CoNLL-2003 #arxiv-2111.10952 #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a T5 small model finetuned on CoNLL-2003 dataset for named entity recognition (NER).
Example Input and Output:
“Recognize all the named entities in this sequence (replace named entities with one of [PER], [ORG], [LOC], [MISC]): When Alice visited New York” → “When PER visited LOC LOC"
Evaluation Result:
%... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-CoNLL-2003 #arxiv-2111.10952 #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | pixyz/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1586
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
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