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text-generation | transformers |
# DioloGPT KaeyaBot model | {"tags": ["conversational"]} | felinecity/DioloGPT-small-KaeyaBot | 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
|
# DioloGPT KaeyaBot model | [
"# DioloGPT KaeyaBot model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT KaeyaBot model"
] |
text-generation | transformers |
# DioloGPT KaeyaBot model | {"tags": ["conversational"]} | felinecity/DioloGPT-small-KaeyaBot2 | 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
|
# DioloGPT KaeyaBot model | [
"# DioloGPT KaeyaBot model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT KaeyaBot model"
] |
text-generation | transformers |
# DioloGPT LisaBot model | {"tags": ["conversational"]} | felinecity/DioloGPT-small-LisaBot | 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
|
# DioloGPT LisaBot model | [
"# DioloGPT LisaBot model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT LisaBot model"
] |
text-generation | transformers |
# DioloGPT KaeyaBot model | {"tags": ["conversational"]} | felinecity/ScaraBot | 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
|
# DioloGPT KaeyaBot model | [
"# DioloGPT KaeyaBot model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT KaeyaBot model"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-de-en-finetuned-de-to-en-second
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-de-en-finetuned-de-to-en-second", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt... | felipetanios/opus-mt-de-en-finetuned-de-to-en-second | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"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 #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-de-en-finetuned-de-to-en-second
=======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-de-en on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2282
* Bleu: 37.9762
* Gen Len: 25.3696
Model description
-----------------
Mo... | [
"### 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 #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #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\\... |
text2text-generation | transformers | # mbart for 9-3
| {} | felixai/distilmbart-9-3 | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # mbart for 9-3
| [
"# mbart for 9-3"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart for 9-3"
] |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | ferdinand/rare-puppers | 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
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
text-classification | transformers |
# FinBERT fine-tuned with the FinnSentiment dataset
This is a FinBERT model fine-tuned with the [FinnSentiment dataset](https://arxiv.org/pdf/2012.02613.pdf). 90% of sentences were used for training and 10% for evaluation.
## Evaluation results
|Metric|Score|
|--|--|
|Accuracy|0.8639028475711893|
|F1-score|0.864302... | {"language": "fi", "license": "cc-by-4.0"} | fergusq/finbert-finnsentiment | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"fi",
"arxiv:2012.02613",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.02613"
] | [
"fi"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #fi #arxiv-2012.02613 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| FinBERT fine-tuned with the FinnSentiment dataset
=================================================
This is a FinBERT model fine-tuned with the FinnSentiment dataset. 90% of sentences were used for training and 10% for evaluation.
Evaluation results
------------------
!URL
License
-------
FinBERT-FinnSentime... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #fi #arxiv-2012.02613 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | <br />
<p align="center">
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer">
<img src="https://huggingface.co/fernandoperlar/preprocessing_image/resolve/main/duck.png" alt="Logo" width="100" height="146">
</a>
<h3 align="center">Image Preprocessing Model</h3>
<p align="center">
... | {} | fernandoperlar/preprocessing_image | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| <br />
<p align="center">
<a href="URL
<img src="URL alt="Logo" width="100" height="146">
</a>
<h3 align="center">Image Preprocessing Model</h3>
<p align="center">
Image preprocessing in a convolutional model
<br />
<a href="URL more about the model »</strong></a>
<br />
<br />
<a ... | [
"## Preprocessing\nIn this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:\n* Warm zones: red and white colors are obtained.\n* Warm zones: The green color is obtained.\n* Cold zones: The color blue is obtained.\n\nWithin the code you can find the decl... | [
"TAGS\n#region-us \n",
"## Preprocessing\nIn this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:\n* Warm zones: red and white colors are obtained.\n* Warm zones: The green color is obtained.\n* Cold zones: The color blue is obtained.\n\nWithin the c... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | ffalcao/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2108
* Accuracy: 0.9265
* F1: 0.9265
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [patric... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": []}]} | ffsouza/t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training an... | [
"# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-0.0002-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [patric... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-0.0002-weight_decay-0.01-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "da... | ffsouza/t5-tiny-random-length-96-learning_rate-0.0002-weight_decay-0.01-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-tiny-random-length-96-learning\_rate-0.0002-weight\_decay-0.01-finetuned-en-to-ro
====================================================================================
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following result... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [patrick... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": []}]} | ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and... | [
"# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.02-finetuned-en-to-ro
This model is a fine-tuned version of [patrick... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.02-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dat... | ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.02-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-tiny-random-length-96-learning\_rate-2e-05-weight\_decay-0.02-finetuned-en-to-ro
===================================================================================
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results ... | [
"### 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: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/tiny-mbart](https://huggingface.co/sshleifer/tiny... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type":... | ffsouza/tiny-mbart-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-mbart-finetuned-en-to-ro
=============================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation set:
* Loss: 8.4792
* Bleu: 0.0
* Gen Len: 20.0
Model description
-----------------
More inform... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "datase... | ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-mbart-length-128-learning\_rate-2e-05-weight\_decay-0.01-finetuned-en-to-ro
================================================================================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "datase... | ffsouza/tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-mbart-length-96-learning\_rate-2e-05-weight\_decay-0.005-finetuned-en-to-ro
================================================================================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/t... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset... | ffsouza/tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-mbart-length-96-learning\_rate-2e-05-weight\_decay-0.01-finetuned-en-to-ro
===============================================================================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation s... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers |
T5-small for QA
---
[Google's T5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) pre-trained on the [C4](https://huggingface.co/datasets/c4) dataset, fine-tuned for Question-Answering on [SQuAD v2](https://huggingface.co/datasets/squad_v2) with the following hyperparameters:
```
... | {"language": ["en"], "license": "apache-2.0", "tags": ["text2text-generation"], "datasets": ["c4", "squad"], "widget": [{"text": "question: What is the atomic number for oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8."}, {"text": "question: What is the chemical symbol of Oxygen? context... | fgaim/t5-small-squad-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:squad",
"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 #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
T5-small for QA
---
Google's T5-small pre-trained on the C4 dataset, fine-tuned for Question-Answering on SQuAD v2 with the following hyperparameters:
Usage
---
The input [context and question] has to be prepared in a specific way as follows:
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# BERT Base for Tigrinya Language
We pre-train a BERT base-uncased model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
This repo contains the original pre-trained Flax model that was trained on a TPU v3.8 and its corresponding PyTorch version.
## Hyperparameters
The hyperparameters correspo... | {"language": "ti", "widget": [{"text": "\u12d3\u1255\u121a \u12f0\u1242\u12a3\u1295\u1235\u1275\u12ee [MASK] \u1265\u130d\u1265\u122a \u1270\u122b\u12a5\u12e9"}]} | fgaim/tibert-base | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ti",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ti"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERT Base for Tigrinya Language
===============================
We pre-train a BERT base-uncased model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
This repo contains the original pre-trained Flax model that was trained on a TPU v3.8 and its corresponding PyTorch version.
Hyperparameters
... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
# Tigrinya POS tagging with TiELECTRA
This model is a fine-tuned version of [TiELECTRA](https://huggingface.co/fgaim/tielectra-small) on the NTC-v1 dataset (Tedla et al. 2016).
## Basic usage
```python
from transformers import pipeline
ti_pos = pipeline("token-classification", model="fgaim/tielectra-small-pos")
... | {"language": "ti", "datasets": ["TLMD", "NTC"], "metrics": ["f1", "precision", "recall", "accuracy"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u1... | fgaim/tielectra-small-pos | null | [
"transformers",
"pytorch",
"electra",
"token-classification",
"ti",
"dataset:TLMD",
"dataset:NTC",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ti"
] | TAGS
#transformers #pytorch #electra #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Tigrinya POS tagging with TiELECTRA
This model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).
## Basic usage
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 4... | [
"# Tigrinya POS tagging with TiELECTRA\n\nThis model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_... | [
"TAGS\n#transformers #pytorch #electra #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Tigrinya POS tagging with TiELECTRA\n\nThis model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).",
"... |
text-classification | transformers |
# Sentiment Analysis for Tigrinya with TiELECTRA small
This model is a fine-tuned version of [TiELECTRA small](https://huggingface.co/fgaim/tielectra-small) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
```python
from transformers import pipeline
ti_sent = pipel... | {"language": "ti", "metrics": ["f1", "precision", "recall", "accuracy"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u12ed\u1290\u1265\u122d"}]} | fgaim/tielectra-small-sentiment | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"ti",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ti"
] | TAGS
#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Analysis for Tigrinya with TiELECTRA small
This model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rat... | [
"# Sentiment Analysis for Tigrinya with TiELECTRA small\n\nThis model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:... | [
"TAGS\n#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Analysis for Tigrinya with TiELECTRA small\n\nThis model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for ... |
fill-mask | transformers |
# Pre-trained ELECTRA small for Tigrinya Language
We pre-train ELECTRA small on the [TLMD](https://zenodo.org/record/5139094) dataset, with over 40 million tokens.
Contained are trained Flax and PyTorch models.
## Hyperparameters
The hyperparameters corresponding to model sizes mentioned above are as follows:
| ... | {"language": "ti", "widget": [{"text": "\u12d3\u1255\u121a \u1218\u1295\u12a5\u1230\u12ed \u12a4\u122d\u1275\u122b [MASK] \u1270\u122b\u12a5\u12e9"}]} | fgaim/tielectra-small | null | [
"transformers",
"pytorch",
"jax",
"electra",
"fill-mask",
"ti",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ti"
] | TAGS
#transformers #pytorch #jax #electra #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us
| Pre-trained ELECTRA small for Tigrinya Language
===============================================
We pre-train ELECTRA small on the TLMD dataset, with over 40 million tokens.
Contained are trained Flax and PyTorch models.
Hyperparameters
---------------
The hyperparameters corresponding to model sizes mentioned a... | [
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] | [
"TAGS\n#transformers #pytorch #jax #electra #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please ci... |
fill-mask | transformers |
# TiRoBERTa: RoBERTa Pretrained for the Tigrinya Language
We pretrain a RoBERTa base model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
Contained in this repo is the original pretrained Flax model that was trained on a TPU v3.8 and it's corresponding PyTorch version.
## Hyperparameters
Th... | {"language": "ti", "widget": [{"text": "\u12d3\u1255\u121a \u1218\u1295\u12a5\u1230\u12ed \u12a4\u122d\u1275\u122b <mask> \u1270\u122b\u12a5\u12e9"}]} | fgaim/tiroberta-base | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"ti",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ti"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us
| TiRoBERTa: RoBERTa Pretrained for the Tigrinya Language
=======================================================
We pretrain a RoBERTa base model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
Contained in this repo is the original pretrained Flax model that was trained on a TPU v3.8 and it's ... | [
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or resear... |
token-classification | transformers |
# Tigrinya POS tagging with TiRoBERTa
This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/tiroberta) on the NTC-v1 dataset (Tedla et al. 2016).
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_ba... | {"language": "ti", "datasets": ["TLMD", "NTC"], "metrics": ["f1", "precision", "recall", "accuracy"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u1... | fgaim/tiroberta-pos | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"ti",
"dataset:TLMD",
"dataset:NTC",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ti"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Tigrinya POS tagging with TiRoBERTa
This model is a fine-tuned version of TiRoBERTa on the NTC-v1 dataset (Tedla et al. 2016).
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam ... | [
"# Tigrinya POS tagging with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on the NTC-v1 dataset (Tedla et al. 2016).",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Tigrinya POS tagging with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on the NTC-v1 dataset (Tedla et al.... |
text-classification | transformers |
# Sentiment Analysis for Tigrinya with TiRoBERTa
This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/roberta-base-tigrinya) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
```python
from transformers import pipeline
ti_sent = pipeline("s... | {"language": "ti", "datasets": ["TLMD"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u12ed\u12... | fgaim/tiroberta-sentiment | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"ti",
"dataset:TLMD",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ti"
] | TAGS
#transformers #pytorch #roberta #text-classification #ti #dataset-TLMD #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Analysis for Tigrinya with TiRoBERTa
This model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- t... | [
"# Sentiment Analysis for Tigrinya with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #ti #dataset-TLMD #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Analysis for Tigrinya with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset fo... |
text-classification | transformers |
# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles
## Important: [use our PyPI package](https://pypi.org/project/NewsSentiment/) instead of this model on the Hub
The Huggingface Hub architecture currently [does not support](https://github.com/huggingface/transformer... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "sentiment-analysis", "sentiment-classification", "targeted-sentiment-classification", "target-depentent-sentiment-classification"], "datasets": "fhamborg/news_sentiment_newsmtsc"} | fhamborg/roberta-targeted-sentiment-classification-newsarticles | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"sentiment-analysis",
"sentiment-classification",
"targeted-sentiment-classification",
"target-depentent-sentiment-classification",
"en",
"dataset:fhamborg/news_sentiment_newsmtsc",
"license:apache-2.0",
"endpoints_compatible",
"re... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us
|
# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles
## Important: use our PyPI package instead of this model on the Hub
The Huggingface Hub architecture currently does not support target-dependent sentiment classification since you cannot provide the required inputs,... | [
"# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles",
"## Important: use our PyPI package instead of this model on the Hub\nThe Huggingface Hub architecture currently does not support target-dependent sentiment classification since you cannot provide the require... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us \n",
"# NewsSentiment: easy-to-... |
token-classification | transformers | # BERT-DE-NER
## What is it?
This is a German BERT model fine-tuned for named entity recognition.
## Base model & training
This model is based on [bert-base-german-dbmdz-cased](https://huggingface.co/bert-base-german-dbmdz-cased) and has been fine-tuned
for NER on the training data from [GermEval2014](https://sites.g... | {"language": "de", "license": "cc-by-sa-4.0", "tags": ["German", "de", "NER"], "datasets": ["germeval_14"]} | fhswf/bert_de_ner | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"token-classification",
"German",
"de",
"NER",
"dataset:germeval_14",
"doi:10.57967/hf/0655",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #token-classification #German #de #NER #dataset-germeval_14 #doi-10.57967/hf/0655 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT-DE-NER
===========
What is it?
-----------
This is a German BERT model fine-tuned for named entity recognition.
Base model & training
---------------------
This model is based on bert-base-german-dbmdz-cased and has been fine-tuned
for NER on the training data from GermEval2014.
Model results
-----------... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #German #de #NER #dataset-germeval_14 #doi-10.57967/hf/0655 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Fibruh Bot Model | {"tags": ["conversational"]} | fibruh/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fibruh Bot Model | [
"# Fibruh Bot Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fibruh Bot 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. -->
# biobert_v1.1_pubmed-finetuned-ner-finetuned-ner
This model is a fine-tuned version of [fidukm34/biobert_v1.1_pubmed-finetuned-ne... | {"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "biobert_v1.1_pubmed-finetuned-ner-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "ncbi_disease", ... | fidukm34/biobert_v1.1_pubmed-finetuned-ner-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us
| biobert\_v1.1\_pubmed-finetuned-ner-finetuned-ner
=================================================
This model is a fine-tuned version of fidukm34/biobert\_v1.1\_pubmed-finetuned-ner on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0715
* Precision: 0.8464
* Recall: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #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\... |
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. -->
# biobert_v1.1_pubmed-finetuned-ner
This model is a fine-tuned version of [monologg/biobert_v1.1_pubmed](https://huggingface.co/mo... | {"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "biobert_v1.1_pubmed-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "ncbi_disease", "type": "ncbi_... | fidukm34/biobert_v1.1_pubmed-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us
| biobert\_v1.1\_pubmed-finetuned-ner
===================================
This model is a fine-tuned version of monologg/biobert\_v1.1\_pubmed on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0657
* Precision: 0.8338
* Recall: 0.8933
* F1: 0.8625
* Accuracy: 0.9827
Mo... | [
"### 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 #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #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\... |
null | transformers | This model can measure semantic similarity between pairs of texts containing figurative language. As far as we know,
this model works slightly better than sup-simCSE-roberta-base. For example :
**sentence 1**: I have been in seventh heaven since Harry entered my life .
**sentence 2**: I have been in very happy sin... | {} | figurative-nlp/se4fig-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #endpoints_compatible #region-us
| This model can measure semantic similarity between pairs of texts containing figurative language. As far as we know,
this model works slightly better than sup-simCSE-roberta-base. For example :
sentence 1: I have been in seventh heaven since Harry entered my life .
sentence 2: I have been in very happy since Harry... | [] | [
"TAGS\n#transformers #pytorch #roberta #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | This model can convert the literal expression to figurative/metaphorical expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-generation")
model = AutoModelForSeq2SeqL... | {} | figurative-nlp/t5-figurative-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model can convert the literal expression to figurative/metaphorical expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-generation")
model = AutoModelForSeq2SeqL... | [
"# Batch size 1\n outputs = model.generate(input_ids,beam_search = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : research is a tough nut to crack for me.\n\n\n\nFor example (the <m> and </m> is the mark that inform the model which literal expression we want to conver... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Batch size 1\n outputs = model.generate(input_ids,beam_search = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : research is a tough n... |
text2text-generation | transformers | This model can convert the figurative/metaphorical expression to the literal expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-paraphrase")
model = AutoModelForSeq2SeqLM.fr... | {} | figurative-nlp/t5-figurative-paraphrase | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model can convert the figurative/metaphorical expression to the literal expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-paraphrase")
model = AutoModelForSeq2SeqLM.fr... | [
"# Batch size 1\n outputs = model.generate(input_ids,num_beams = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : i will talk this story to you from beginning to end..\n \n\n\n\nFor example:\n\n Input: He is always bang on when he makes a speech.\n \n Output: He is always presic... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Batch size 1\n outputs = model.generate(input_ids,num_beams = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : i will talk this story to you... |
null | null | import requests
API_URL = "https://api-inference.huggingface.co/models/huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad"
headers = {"Authorization": "Bearer api_UXqrzQBiZKXaWxstVwEKcYvHQpGSGiQGbr"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.jso... | {} | fihtrotuld/123 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| import requests
API_URL = "URL
headers = {"Authorization": "Bearer api_UXqrzQBiZKXaWxstVwEKcYvHQpGSGiQGbr"}
def query(payload):
response = URL(API_URL, headers=headers, json=payload)
return URL()
output = query({
"inputs": {
"question": "What's my name?",
"context": "My name is Clara and I live in Berkeley... | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | # GPT2 base style transfer paraphraser
This is the trained base-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by th... | {} | filco306/gpt2-base-style-paraphraser | null | [
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05700"
] | [] | TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
| # GPT2 base style transfer paraphraser
This is the trained base-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found this ... | [
"# GPT2 base style transfer paraphraser\n\nThis is the trained base-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you f... | [
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 base style transfer paraphraser\n\nThis is the trained base-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I ... |
text-generation | transformers | # GPT2 Bible style transfer paraphraser
This is the trained Bible model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by ... | {} | filco306/gpt2-bible-paraphraser | null | [
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05700"
] | [] | TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
| # GPT2 Bible style transfer paraphraser
This is the trained Bible model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found thi... | [
"# GPT2 Bible style transfer paraphraser\n\nThis is the trained Bible model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you... | [
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Bible style transfer paraphraser\n\nThis is the trained Bible model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that ... |
text-generation | transformers | # GPT2 Romantic poetry style transfer paraphraser
This is the trained Romantic poetry-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggi... | {} | filco306/gpt2-romantic-poetry-paraphraser | null | [
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05700"
] | [] | TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
| # GPT2 Romantic poetry style transfer paraphraser
This is the trained Romantic poetry-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.... | [
"# GPT2 Romantic poetry style transfer paraphraser\n\nThis is the trained Romantic poetry-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main ... | [
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Romantic poetry style transfer paraphraser\n\nThis is the trained Romantic poetry-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna ... |
text-generation | transformers | # GPT2 Shakespeare style transfer paraphraser
This is the trained Shakespeare-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface w... | {} | filco306/gpt2-shakespeare-paraphraser | null | [
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05700"
] | [] | TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # GPT2 Shakespeare style transfer paraphraser
This is the trained Shakespeare-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If y... | [
"# GPT2 Shakespeare style transfer paraphraser\n\nThis is the trained Shakespeare-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. ... | [
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT2 Shakespeare style transfer paraphraser\n\nThis is the trained Shakespeare-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krish... |
text-generation | transformers | # GPT2 Switchboard style transfer paraphraser
This is the trained Switchboard-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface w... | {} | filco306/gpt2-switchboard-paraphraser | null | [
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05700"
] | [] | TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
| # GPT2 Switchboard style transfer paraphraser
This is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If y... | [
"# GPT2 Switchboard style transfer paraphraser\n\nThis is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. ... | [
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Switchboard style transfer paraphraser\n\nThis is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al... |
text-generation | transformers | # GPT2 Tweet style transfer paraphraser
This is the trained Tweet-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by ... | {} | filco306/gpt2-tweet-paraphraser | null | [
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05700"
] | [] | TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
| # GPT2 Tweet style transfer paraphraser
This is the trained Tweet-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found thi... | [
"# GPT2 Tweet style transfer paraphraser\n\nThis is the trained Tweet-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you... | [
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Tweet style transfer paraphraser\n\nThis is the trained Tweet-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that ... |
image-classification | transformers |
# beer_vs_wine
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | filipafcastro/beer_vs_wine | 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
|
# beer_vs_wine
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
#### beer
!beer
#### wine
!wine | [
"# beer_vs_wine\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",
"#### beer\n\n!beer",
"#### wine\n\n!wine"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# beer_vs_wine\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
text-classification | transformers | # Emotion Analysis in English
## bertweet-base-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with EmoEvent corpus for Emotion detection in English. Base model is [BerTweet](https://huggingface.co/vinai/bertweet-base).
... | {"language": ["en"], "tags": ["emotion-analysis"]} | finiteautomata/bertweet-base-emotion-analysis | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
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"emotion-analysis",
"en",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #emotion-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Emotion Analysis in English
## bertweet-base-emotion-analysis
Repository: URL
Model trained with EmoEvent corpus for Emotion detection in English. Base model is BerTweet.
## License
'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that model... | [
"# Emotion Analysis in English",
"## bertweet-base-emotion-analysis\n\nRepository: URL\n\n\nModel trained with EmoEvent corpus for Emotion detection in English. Base model is BerTweet.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #emotion-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Emotion Analysis in English",
"## bertweet-base-emotion-analysis\n\nRepository: URL\n\n\nModel trained with EmoEvent corpus f... |
text-classification | transformers | # Sentiment Analysis in English
## bertweet-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is [BERTweet](https://github.com/VinAIResearch/BERTweet), a RoBERTa m... | {"language": ["en"], "tags": ["sentiment-analysis"]} | finiteautomata/bertweet-base-sentiment-analysis | null | [
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"tf",
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"en",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
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"en"
] | TAGS
#transformers #pytorch #tf #roberta #text-classification #sentiment-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Sentiment Analysis in English
## bertweet-sentiment-analysis
Repository: URL
Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.
Uses 'POS', 'NEG', 'NEU' labels.
## License
'pysentimiento' is an open-source library for non-commercial us... | [
"# Sentiment Analysis in English",
"## bertweet-sentiment-analysis\n\nRepository: URL\n\n\nModel trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.\n\nUses 'POS', 'NEG', 'NEU' labels.",
"## License\n\n'pysentimiento' is an open-source librar... | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #sentiment-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Analysis in English",
"## bertweet-sentiment-analysis\n\nRepository: URL\n\n\nModel trained with SemEval 2017 corpus (arou... |
text-classification | transformers |
# Emotion Analysis in Spanish
## beto-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model t... | {"language": ["es"], "tags": ["emotion-analysis"]} | finiteautomata/beto-emotion-analysis | null | [
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"safetensors",
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"es",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #emotion-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Emotion Analysis in Spanish
## beto-emotion-analysis
Repository: URL
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is BETO, a BERT model trained in Spanish.
## License
'pysentimiento' is an open-source library for non-commercial use and scientific research purposes onl... | [
"# Emotion Analysis in Spanish",
"## beto-emotion-analysis\n\nRepository: URL\n\n\nModel trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is BETO, a BERT model trained in Spanish.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific re... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #emotion-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Emotion Analysis in Spanish",
"## beto-emotion-analysis\n\nRepository: URL\n\n\nModel trained with TASS 2020 Task 2 corpus for E... |
text-classification | transformers | # Targeted Sentiment Analysis in News Headlines
BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.
(details to be published)
## Examples
Input is as follows
`Headline [SEP] Target`
where headline is the news title and target is an entity present in the headline.
Try
`Alberto ... | {} | finiteautomata/beto-headlines-sentiment-analysis | null | [
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"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Targeted Sentiment Analysis in News Headlines
BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.
(details to be published)
## Examples
Input is as follows
'Headline [SEP] Target'
where headline is the news title and target is an entity present in the headline.
Try
'Alberto ... | [
"# Targeted Sentiment Analysis in News Headlines\n\nBERT classifier fine-tuned in a news headlines dataset annotated for target polarity.\n\n(details to be published)",
"## Examples\n\nInput is as follows\n\n'Headline [SEP] Target'\n\nwhere headline is the news title and target is an entity present in the headlin... | [
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"# Targeted Sentiment Analysis in News Headlines\n\nBERT classifier fine-tuned in a news headlines dataset annotated for target polarity.\n\n(details to be published)",
"#... |
text-classification | transformers |
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
**NOTE: this model will be removed soon -- use [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) instead**
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/pysen... | {"language": ["es"], "tags": ["sentiment-analysis"]} | finiteautomata/beto-sentiment-analysis | null | [
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"sentiment-analysis",
"es",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
NOTE: this model will be removed soon -- use pysentimiento/robertuito-sentiment-analysis instead
Repository: URL
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.
U... | [
"# Sentiment Analysis in Spanish",
"## beto-sentiment-analysis\n\nNOTE: this model will be removed soon -- use pysentimiento/robertuito-sentiment-analysis instead\n\nRepository: URL\n\n\nModel trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model traine... | [
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"# Sentiment Analysis in Spanish",
"## beto-sentiment-analysis\n\nNOTE: this model will be removed soon -- use pysentimiento/robertuito-s... |
image-classification | transformers |
# llama_or_what
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/huggin... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | firebolt/llama_or_what | null | [
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"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
|
# llama_or_what
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
#### alpaca
!alpaca
#### guanaco
!guanaco
#### llama
!llama
#### vicuna
!vicuna | [
"# llama_or_what\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",
"#### alpaca\n\n!alpaca",
"#### guanaco\n\n!guanaco",
"#### llama\n\n!llama",
"#### vicuna... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# llama_or_what\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... |
image-classification | transformers |
# llama_or_what2
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/huggi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | firebolt/llama_or_what2 | 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
|
# llama_or_what2
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
#### alpaca
!alpaca
#### guanaco
!guanaco
#### llama
!llama
#### vicuna
!vicuna | [
"# llama_or_what2\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",
"#### alpaca\n\n!alpaca",
"#### guanaco\n\n!guanaco",
"#### llama\n\n!llama",
"#### vicun... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# llama_or_what2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issue... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 310939
## Validation Metrics
- Loss: 0.027471264824271202
- Accuracy: 0.9931118314424635
- Precision: 0.946236559139785
- Recall: 0.88
- AUC: 0.9952871621621622
- F1: 0.911917098445596
## Usage
You can use cURL to access this model:
... | {"language": "en", "tags": ["autonlp"], "datasets": ["fjarrett/autonlp-data-giveaway_detection_05"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | popsmash-admin/autonlp-giveaway_detection_05-310939 | null | [
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"autonlp",
"en",
"dataset:fjarrett/autonlp-data-giveaway_detection_05",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-fjarrett/autonlp-data-giveaway_detection_05 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 310939
## Validation Metrics
- Loss: 0.027471264824271202
- Accuracy: 0.9931118314424635
- Precision: 0.946236559139785
- Recall: 0.88
- AUC: 0.9952871621621622
- F1: 0.911917098445596
## Usage
You can use cURL to access this model:
... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 310939",
"## Validation Metrics\n\n- Loss: 0.027471264824271202\n- Accuracy: 0.9931118314424635\n- Precision: 0.946236559139785\n- Recall: 0.88\n- AUC: 0.9952871621621622\n- F1: 0.911917098445596",
"## Usage\n\nYou can use cURL... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-fjarrett/autonlp-data-giveaway_detection_05 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 310939",
"## Validation Metrics\n\n- L... |
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... | fjluque/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
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"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: 0.2157
* Accuracy: 0.9173
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\... |
automatic-speech-recognition | transformers | this is my model card
| {} | fkHug/modelFromWav2vec | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| this is my model card
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
token-classification | flair |
## English Chunking in Flair (fast model)
This is the fast phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **96,22** (CoNLL-2000)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| ADJP | a... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2000"], "widget": [{"text": "The happy man has been eating at the diner"}]} | flair/chunk-english-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2000",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #region-us
| English Chunking in Flair (fast model)
--------------------------------------
This is the fast phrase chunking model for English that ships with Flair.
F1-Score: 96,22 (CoNLL-2000)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install f... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at ... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) an... |
token-classification | flair |
## English Chunking in Flair (default model)
This is the standard phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **96,48** (CoNLL-2000)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| ADJP ... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2000"], "widget": [{"text": "The happy man has been eating at the diner"}]} | flair/chunk-english | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2000",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #has_space #region-us
| English Chunking in Flair (default model)
-----------------------------------------
This is the standard phrase chunking model for English that ships with Flair.
F1-Score: 96,48 (CoNLL-2000)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at ... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phra... |
token-classification | flair |
## English Verb Disambiguation in Flair (fast model)
This is the fast verb disambiguation model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **88,27** (Ontonotes) - predicts [Proposition Bank verb frames](http://verbs.colorado.edu/propbank/framesets-english-aliases/).
Based on ... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "George returned to Berlin to return his hat."}]} | flair/frame-english-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us
|
## English Verb Disambiguation in Flair (fast model)
This is the fast verb disambiguation model for English that ships with Flair.
F1-Score: 88,27 (Ontonotes) - predicts Proposition Bank verb frames.
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
... | [
"## English Verb Disambiguation in Flair (fast model)\n\nThis is the fast verb disambiguation model for English that ships with Flair.\n\nF1-Score: 88,27 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---",
"### Demo: How to use in Flair\n\nRequires: Flair ('pip... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n",
"## English Verb Disambiguation in Flair (fast model)\n\nThis is the fast verb disambiguation model for English that ships with Flair.\n\nF1-Score: 88,27 (Ontonotes) - predicts Proposition Bank verb frames.\... |
token-classification | flair |
## English Verb Disambiguation in Flair (default model)
This is the standard verb disambiguation model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **89,34** (Ontonotes) - predicts [Proposition Bank verb frames](http://verbs.colorado.edu/propbank/framesets-english-aliases/).
Ba... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "George returned to Berlin to return his hat."}]} | flair/frame-english | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us
|
## English Verb Disambiguation in Flair (default model)
This is the standard verb disambiguation model for English that ships with Flair.
F1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb frames.
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install fl... | [
"## English Verb Disambiguation in Flair (default model)\n\nThis is the standard verb disambiguation model for English that ships with Flair.\n\nF1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---",
"### Demo: How to use in Flair\n\nRequires: Flai... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n",
"## English Verb Disambiguation in Flair (default model)\n\nThis is the standard verb disambiguation model for English that ships with Flair.\n\nF1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb f... |
token-classification | flair |
# Danish NER in Flair (default model)
This is the standard 4-class NER model for Danish that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **81.78** (DaNER)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person n... | {"language": "da", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["DaNE"], "widget": [{"text": "Jens Peter Hansen kommer fra Danmark"}]} | flair/ner-danish | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"da",
"dataset:DaNE",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #da #dataset-DaNE #region-us
| Danish NER in Flair (default model)
===================================
This is the standard 4-class NER model for Danish that ships with Flair.
F1-Score: 81.78 (DaNER)
Predicts 4 tags:
Based on Transformer embeddings and LSTM-CRF.
---
Demo: How to use in Flair
=========================
Requires: Flair... | [
"### Training: Script to train this model\n\n\nThe model was trained by the DaNLP project using the DaNE corpus. Check their repo for more information.\n\n\nThe following Flair script may be used to train such a model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following papers when using this model.\n\n\nAnd ... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #da #dataset-DaNE #region-us \n",
"### Training: Script to train this model\n\n\nThe model was trained by the DaNLP project using the DaNE corpus. Check their repo for more information.\n\n\nThe following Flair script may be used to train such a ... |
token-classification | flair |
## Dutch NER in Flair (large model)
This is the large 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **95,25** (CoNLL-03 Dutch)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | perso... | {"language": "nl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging naar Washington"}]} | flair/ner-dutch-large | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"nl",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.06993"
] | [
"nl"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
| Dutch NER in Flair (large model)
--------------------------------
This is the large 4-class NER model for Dutch that ships with Flair.
F1-Score: 95,25 (CoNLL-03 Dutch)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip install fl... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging naar Washington*\".\n\n\n\n\n---",
"... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a perso... |
token-classification | flair |
# Dutch NER in Flair (default model)
This is the standard 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,58** (CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person ... | {"language": "nl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging naar Washington."}]} | flair/ner-dutch | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"nl",
"dataset:conll2003",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #region-us
| Dutch NER in Flair (default model)
==================================
This is the standard 4-class NER model for Dutch that ships with Flair.
F1-Score: 92,58 (CoNLL-03)
Predicts 4 tags:
Based on Transformer embeddings and LSTM-CRF.
---
Demo: How to use in Flair
=========================
Requires: Flair... | [
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #region-us \n",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
... |
token-classification | flair |
## English NER in Flair (fast model)
This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,92** (corrected CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER |... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]} | flair/ner-english-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us
| English NER in Flair (fast model)
---------------------------------
This is the fast 4-class NER model for English that ships with Flair.
F1-Score: 92,92 (corrected CoNLL-03)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"##... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washingt... |
token-classification | flair |
## English NER in Flair (large model)
This is the large 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **94,36** (corrected CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER ... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]} | flair/ner-english-large | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.06993"
] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
| English NER in Flair (large model)
----------------------------------
This is the large 4-class NER model for English that ships with Flair.
F1-Score: 94,36 (corrected CoNLL-03)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip ... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"##... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a perso... |
token-classification | flair |
## English NER in Flair (Ontonotes fast model)
This is the fast version of the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **89.3** (Ontonotes)
Predicts 18 tags:
| **tag** | **meaning** |
|---------------------------------|-----------... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "On September 1st George Washington won 1 dollar."}]} | flair/ner-english-ontonotes-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us
| English NER in Flair (Ontonotes fast model)
-------------------------------------------
This is the fast version of the 18-class NER model for English that ships with Flair.
F1-Score: 89.3 (Ontonotes)
Predicts 18 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: ... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George W... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington... |
token-classification | flair |
## English NER in Flair (Ontonotes large model)
This is the large 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **90.93** (Ontonotes)
Predicts 18 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| CARDINAL... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "On September 1st George won 1 dollar while watching Game of Thrones."}]} | flair/ner-english-ontonotes-large | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"arxiv:2011.06993",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.06993"
] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #arxiv-2011.06993 #has_space #region-us
| English NER in Flair (Ontonotes large model)
--------------------------------------------
This is the large 18-class NER model for English that ships with Flair.
F1-Score: 90.93 (Ontonotes)
Predicts 18 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires:... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George*\" (labeled as a person), \"*1 dollar*\" (labeled as a money) and \"Game of Thrones\" (labeled as a work of art) are found in the ... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"... |
token-classification | flair |
## English NER in Flair (Ontonotes default model)
This is the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **89.27** (Ontonotes)
Predicts 18 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| CARDINAL ... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "On September 1st George Washington won 1 dollar."}]} | flair/ner-english-ontonotes | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us
| English NER in Flair (Ontonotes default model)
----------------------------------------------
This is the 18-class NER model for English that ships with Flair.
F1-Score: 89.27 (Ontonotes)
Predicts 18 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip i... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George W... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington... |
token-classification | flair |
## English NER in Flair (default model)
This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **93,06** (corrected CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER ... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]} | flair/ner-english | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us
| English NER in Flair (default model)
------------------------------------
This is the standard 4-class NER model for English that ships with Flair.
F1-Score: 93,06 (corrected CoNLL-03)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip insta... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"##... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washingt... |
token-classification | flair |
## French NER in Flair (default model)
This is the standard 4-class NER model for French that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **90,61** (WikiNER)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | perso... | {"language": "fr", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington est all\u00e9 \u00e0 Washington"}]} | flair/ner-french | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fr",
"dataset:conll2003",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-conll2003 #has_space #region-us
| French NER in Flair (default model)
-----------------------------------
This is the standard 4-class NER model for French that ships with Flair.
F1-Score: 90,61 (WikiNER)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
T... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington est allé à Washington*\".\n\n\n\n\n---",
... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washingt... |
token-classification | flair |
## German NER in Flair (large model)
This is the large 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,31** (CoNLL-03 German revised)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER ... | {"language": "de", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]} | flair/ner-german-large | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.06993"
] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
| German NER in Flair (large model)
---------------------------------
This is the large 4-class NER model for German that ships with Flair.
F1-Score: 92,31 (CoNLL-03 German revised)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pi... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a perso... |
token-classification | flair |
## NER for German Legal Text in Flair (default model)
This is the legal NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **96,35** (LER German dataset)
Predicts 19 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| AN ... | {"language": "de", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["legal"], "widget": [{"text": "Herr W. verstie\u00df gegen \u00a7 36 Abs. 7 IfSG."}]} | flair/ner-german-legal | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:legal",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-legal #region-us
| NER for German Legal Text in Flair (default model)
--------------------------------------------------
This is the legal NER model for German that ships with Flair.
F1-Score: 96,35 (LER German dataset)
Predicts 19 tags:
Based on Flair embeddings and LSTM-CRF.
More details on the Legal NER dataset here
---... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*W.*\" (labeled as a person) and \"*§ 36 Abs. 7 IfSG*\" (labeled as a Gesetz) are found in the sentence \"*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*\".\n\n\n\n\n---",
"### Train... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-legal #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*W.*\" (labeled as a person) and \"*§ 36 Abs. 7 IfSG*\" (labeled as a Gese... |
token-classification | flair |
## German NER in Flair (default model)
This is the standard 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **87,94** (CoNLL-03 German revised)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER... | {"language": "de", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]} | flair/ner-german | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:conll2003",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #has_space #region-us
| German NER in Flair (default model)
-----------------------------------
This is the standard 4-class NER model for German that ships with Flair.
F1-Score: 87,94 (CoNLL-03 German revised)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip ins... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washingt... |
token-classification | flair |
## 4-Language NER in Flair (English, German, Dutch and Spanish)
This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
F1-Score: **91,51** (CoNLL-03 English), **85,72** (CoNLL-03 German revised), *... | {"language": ["en", "de", "nl", "es"], "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]} | flair/ner-multi-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"nl",
"es",
"dataset:conll2003",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de",
"nl",
"es"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #dataset-conll2003 #has_space #region-us
| 4-Language NER in Flair (English, German, Dutch and Spanish)
------------------------------------------------------------
This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with Flair. Also kind of works for related languages like French.
F1-Score: 91,51 (CoNLL-03 English), 85,72 (CoNLL-03 Germa... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and... |
token-classification | flair |
## 4-Language NER in Flair (English, German, Dutch and Spanish)
This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
F1-Score: **92,16** (CoNLL-03 English), **87,33** (CoNLL-03 German revised... | {"language": ["en", "de", "nl", "es", "multilingual"], "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]} | flair/ner-multi | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"nl",
"es",
"multilingual",
"dataset:conll2003",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de",
"nl",
"es",
"multilingual"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #multilingual #dataset-conll2003 #region-us
| 4-Language NER in Flair (English, German, Dutch and Spanish)
------------------------------------------------------------
This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with Flair. Also kind of works for related languages like French.
F1-Score: 92,16 (CoNLL-03 English), 87,33 (CoNLL-03 G... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #multilingual #dataset-conll2003 #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) ... |
token-classification | flair |
## Spanish NER in Flair (large model)
This is the large 4-class NER model for Spanish that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **90,54** (CoNLL-03 Spanish)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER |... | {"language": "es", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington fue a Washington"}]} | flair/ner-spanish-large | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"es",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.06993"
] | [
"es"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #es #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
| Spanish NER in Flair (large model)
----------------------------------
This is the large 4-class NER model for Spanish that ships with Flair.
F1-Score: 90,54 (CoNLL-03 Spanish)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip in... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington fue a Washington*\".\n\n\n\n\n---",
"### ... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #es #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a perso... |
token-classification | flair |
## English Part-of-Speech Tagging in Flair (fast model)
This is the fast part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **98,10** (Ontonotes)
Predicts fine-grained POS tags:
| **tag** | **meaning** |
|--------------------------... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "I love Berlin."}]} | flair/pos-english-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us
| English Part-of-Speech Tagging in Flair (fast model)
----------------------------------------------------
This is the fast part-of-speech tagging model for English that ships with Flair.
F1-Score: 98,10 (Ontonotes)
Predicts fine-grained POS tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How ... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRP), \"*love*\" is labeled as a verb (VBP) and \"*Berlin*\" is labeled as a proper noun (NNP) in the sentence \"*I love Berlin*\".\n\n\n\n\n---",
"##... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRP), \"*love*\" is labeled as a... |
token-classification | flair |
## English Part-of-Speech Tagging in Flair (default model)
This is the standard part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **98,19** (Ontonotes)
Predicts fine-grained POS tags:
| **tag** | **meaning** |
|-------------------... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "inference": false} | flair/pos-english | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us
| English Part-of-Speech Tagging in Flair (default model)
-------------------------------------------------------
This is the standard part-of-speech tagging model for English that ships with Flair.
F1-Score: 98,19 (Ontonotes)
Predicts fine-grained POS tags:
Based on Flair embeddings and LSTM-CRF.
---
### ... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRP), \"*love*\" is labeled as a verb (VBP) and \"*Berlin*\" is labeled as a proper noun (NNP) in the sentence \"*I love Berlin*\".\n\n\n\n\n---",
"##... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRP), \"*love*\" is labeled as a... |
token-classification | flair |
## English Universal Part-of-Speech Tagging in Flair (fast model)
This is the fast universal part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **98,47** (Ontonotes)
Predicts universal POS tags:
| **tag** | **meaning** |
|---------... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "I love Berlin."}]} | flair/upos-english-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us
| English Universal Part-of-Speech Tagging in Flair (fast model)
--------------------------------------------------------------
This is the fast universal part-of-speech tagging model for English that ships with Flair.
F1-Score: 98,47 (Ontonotes)
Predicts universal POS tags:
Based on Flair embeddings and LSTM-CR... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRON), \"*love*\" is labeled as a verb (VERB) and \"*Berlin*\" is labeled as a proper noun (PROPN) in the sentence \"*I love Berlin*\".\n\n\n\n\n---",
... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRON), \"*love*\" is labeled as a verb (VER... |
token-classification | flair |
## English Universal Part-of-Speech Tagging in Flair (default model)
This is the standard universal part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **98,6** (Ontonotes)
Predicts universal POS tags:
| **tag** | **meaning** |
|---... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "I love Berlin."}]} | flair/upos-english | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us
| English Universal Part-of-Speech Tagging in Flair (default model)
-----------------------------------------------------------------
This is the standard universal part-of-speech tagging model for English that ships with Flair.
F1-Score: 98,6 (Ontonotes)
Predicts universal POS tags:
Based on Flair embeddings an... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRON), \"*love*\" is labeled as a verb (VERB) and \"*Berlin*\" is labeled as a proper noun (PROPN) in the sentence \"*I love Berlin*\".\n\n\n\n\n---",
... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the word \"*I*\" is labeled as a pronoun (PRON), \"*love*\" is labeled as a verb (VER... |
token-classification | flair |
## Multilingual Universal Part-of-Speech Tagging in Flair (fast model)
This is the fast multilingual universal part-of-speech tagging model that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,88** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Dan... | {"language": ["en", "de", "fr", "it", "nl", "pl", "es", "sv", "da", false, "fi", "cs"], "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "Ich liebe Berlin, as they say."}]} | flair/upos-multi-fast | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"fr",
"it",
"nl",
"pl",
"es",
"sv",
"da",
"no",
"fi",
"cs",
"dataset:ontonotes",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de",
"fr",
"it",
"nl",
"pl",
"es",
"sv",
"da",
"no",
"fi",
"cs"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #de #fr #it #nl #pl #es #sv #da #no #fi #cs #dataset-ontonotes #region-us
| Multilingual Universal Part-of-Speech Tagging in Flair (fast model)
-------------------------------------------------------------------
This is the fast multilingual universal part-of-speech tagging model that ships with Flair.
F1-Score: 92,88 (12 UD Treebanks covering English, German, French, Italian, Dutch, Polis... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the words \"*Ich*\" and \"*they*\" are labeled as pronouns (PRON), while \"*liebe*\" and \"*say*\" are labeled as verbs (VERB) in the multilingual sentence \"*Ich liebe Berlin, as they say*\".\n... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #fr #it #nl #pl #es #sv #da #no #fi #cs #dataset-ontonotes #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the words \"*Ich*\" and \"*they*\" are la... |
token-classification | flair |
## Multilingual Universal Part-of-Speech Tagging in Flair (default model)
This is the default multilingual universal part-of-speech tagging model that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **98,47** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedis... | {"language": ["en", "de", "fr", "it", "nl", "pl", "es", "sv", "da", false, "fi", "cs"], "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "Ich liebe Berlin, as they say"}]} | flair/upos-multi | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"fr",
"it",
"nl",
"pl",
"es",
"sv",
"da",
"no",
"fi",
"cs",
"dataset:ontonotes",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de",
"fr",
"it",
"nl",
"pl",
"es",
"sv",
"da",
"no",
"fi",
"cs"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #de #fr #it #nl #pl #es #sv #da #no #fi #cs #dataset-ontonotes #region-us
| Multilingual Universal Part-of-Speech Tagging in Flair (default model)
----------------------------------------------------------------------
This is the default multilingual universal part-of-speech tagging model that ships with Flair.
F1-Score: 98,47 (12 UD Treebanks covering English, German, French, Italian, Dut... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the words \"*Ich*\" and \"*they*\" are labeled as pronouns (PRON), while \"*liebe*\" and \"*say*\" are labeled as verbs (VERB) in the multilingual sentence \"*Ich liebe Berlin, as they say*\".\n... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #fr #it #nl #pl #es #sv #da #no #fi #cs #dataset-ontonotes #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the words \"*Ich*\" and \"*they*\" are la... |
token-classification | flair | ## Test model README
Some test README description | {"tags": ["flair", "token-classification"], "widget": [{"text": "does this work"}]} | flairbook/flairmodel | null | [
"flair",
"pytorch",
"token-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#flair #pytorch #token-classification #region-us
| ## Test model README
Some test README description | [
"## Test model README\nSome test README description"
] | [
"TAGS\n#flair #pytorch #token-classification #region-us \n",
"## Test model README\nSome test README description"
] |
token-classification | flair | ## Test model README
Some test README description | {"tags": ["flair", "token-classification"], "widget": [{"text": "does this work"}]} | flairbook2/flairmodel | null | [
"flair",
"pytorch",
"token-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#flair #pytorch #token-classification #region-us
| ## Test model README
Some test README description | [
"## Test model README\nSome test README description"
] | [
"TAGS\n#flair #pytorch #token-classification #region-us \n",
"## Test model README\nSome test README description"
] |
text-generation | transformers |
# Marty DialoGPT Model | {"tags": ["conversational"]} | flakje/DialoGPT-small-Marty | 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
|
# Marty DialoGPT Model | [
"# Marty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Marty DialoGPT Model"
] |
fill-mask | transformers |
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/ ) supercomputer.... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "flue", "french", "bert-base", "flaubert-base", "cased"], "datasets": ["flaubert"], "metrics": ["flue"]} | flaubert/flaubert_base_cased | null | [
"transformers",
"pytorch",
"flaubert",
"fill-mask",
"bert",
"language-model",
"flue",
"french",
"bert-base",
"flaubert-base",
"cased",
"fr",
"dataset:flaubert",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #bert-base #flaubert-base #cased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| FlauBERT: Unsupervised Language Model Pre-training for French
=============================================================
FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean ... | [] | [
"TAGS\n#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #bert-base #flaubert-base #cased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/ ) supercomputer.... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "flue", "french", "flaubert-base", "uncased"], "datasets": ["flaubert"], "metrics": ["flue"]} | flaubert/flaubert_base_uncased | null | [
"transformers",
"pytorch",
"flaubert",
"fill-mask",
"bert",
"language-model",
"flue",
"french",
"flaubert-base",
"uncased",
"fr",
"dataset:flaubert",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #flaubert-base #uncased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| FlauBERT: Unsupervised Language Model Pre-training for French
=============================================================
FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean ... | [] | [
"TAGS\n#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #flaubert-base #uncased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/ ) supercomputer.... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "flue", "french", "bert-large", "flaubert-large", "cased"], "datasets": ["flaubert"], "metrics": ["flue"]} | flaubert/flaubert_large_cased | null | [
"transformers",
"pytorch",
"flaubert",
"fill-mask",
"bert",
"language-model",
"flue",
"french",
"bert-large",
"flaubert-large",
"cased",
"fr",
"dataset:flaubert",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #bert-large #flaubert-large #cased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| FlauBERT: Unsupervised Language Model Pre-training for French
=============================================================
FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean ... | [] | [
"TAGS\n#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #bert-large #flaubert-large #cased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/ ) supercomputer.... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "flue", "french", "flaubert-small", "cased"], "datasets": ["flaubert"], "metrics": ["flue"]} | flaubert/flaubert_small_cased | null | [
"transformers",
"pytorch",
"flaubert",
"fill-mask",
"bert",
"language-model",
"flue",
"french",
"flaubert-small",
"cased",
"fr",
"dataset:flaubert",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #flaubert-small #cased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| FlauBERT: Unsupervised Language Model Pre-training for French
=============================================================
FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean ... | [] | [
"TAGS\n#transformers #pytorch #flaubert #fill-mask #bert #language-model #flue #french #flaubert-small #cased #fr #dataset-flaubert #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | MLM fine-tuned from Bertimbau-Base model on the Brazilian Federal Official Gazette (200k instances)
| {} | flavio-nakasato/berdou_200k | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| MLM fine-tuned from Bertimbau-Base model on the Brazilian Federal Official Gazette (200k instances)
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | MLM fine-tuned from Bertimbau-Base model on the Brazilian Federal Official Gazette (500k instances)
| {} | flavio-nakasato/berdou_500k | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| MLM fine-tuned from Bertimbau-Base model on the Brazilian Federal Official Gazette (500k instances)
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | RoBERTa model pretrained on the Brazilian Federal Official Gazette (200k instances).
| {} | flavio-nakasato/deeppolicytracker_200k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa model pretrained on the Brazilian Federal Official Gazette (200k instances).
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | RoBERTa model pretrained on the Brazilian Federal Official Gazette (500k instances).
| {} | flavio-nakasato/deeppolicytracker_500k | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa model pretrained on the Brazilian Federal Official Gazette (500k instances).
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | MLM fine-tuned from BR-BERTo model on the Brazilian Federal Official Gazette (100k instances)
| {} | flavio-nakasato/roberdou_100k | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| MLM fine-tuned from BR-BERTo model on the Brazilian Federal Official Gazette (100k instances)
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# Image-captioning-Indonesia
This is an encoder-decoder image captioning model using [CLIP](https://huggingface.co/transformers/model_doc/clip.html) as the visual encoder and [Marian](https://huggingface.co/transformers/model_doc/marian.html) as the textual decoder on datasets with Indonesian captions.
This model wa... | {"language": "id"} | flax-community/Image-captioning-Indonesia | null | [
"transformers",
"jax",
"clip-vision-marian",
"text2text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #jax #clip-vision-marian #text2text-generation #id #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Image-captioning-Indonesia
This is an encoder-decoder image captioning model using CLIP as the visual encoder and Marian as the textual decoder on datasets with Indonesian captions.
This model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community Week organized by HuggingFace. All tr... | [
"# Image-captioning-Indonesia\n\nThis is an encoder-decoder image captioning model using CLIP as the visual encoder and Marian as the textual decoder on datasets with Indonesian captions.\n\nThis model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community Week organized by HuggingFace... | [
"TAGS\n#transformers #jax #clip-vision-marian #text2text-generation #id #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Image-captioning-Indonesia\n\nThis is an encoder-decoder image captioning model using CLIP as the visual encoder and Marian as the textual decoder on datasets with Indo... |
null | null | # Neural ODE with Flax
This is the result of project ["Reproduce Neural ODE and SDE"][projectlink] in [HuggingFace Flax/JAX community week][comweeklink].
<code>main.py</code> will execute training of ResNet or OdeNet for MNIST dataset.
[projectlink]: https://discuss.huggingface.co/t/reproduce-neural-ode-and-neural-sd... | {} | flax-community/NeuralODE_SDE | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Neural ODE with Flax
This is the result of project ["Reproduce Neural ODE and SDE"][projectlink] in [HuggingFace Flax/JAX community week][comweeklink].
<code>URL</code> will execute training of ResNet or OdeNet for MNIST dataset.
[projectlink]: URL
[comweeklink]: URL
## Dependency
### JAX and Flax
For JAX insta... | [
"# Neural ODE with Flax\nThis is the result of project [\"Reproduce Neural ODE and SDE\"][projectlink] in [HuggingFace Flax/JAX community week][comweeklink].\n\n<code>URL</code> will execute training of ResNet or OdeNet for MNIST dataset.\n\n[projectlink]: URL\n\n[comweeklink]: URL",
"## Dependency",
"### JAX a... | [
"TAGS\n#region-us \n",
"# Neural ODE with Flax\nThis is the result of project [\"Reproduce Neural ODE and SDE\"][projectlink] in [HuggingFace Flax/JAX community week][comweeklink].\n\n<code>URL</code> will execute training of ResNet or OdeNet for MNIST dataset.\n\n[projectlink]: URL\n\n[comweeklink]: URL",
"## ... |
fill-mask | transformers |
# NOTE: We have trained newer and better Finnish RoBERTa large model which can be found from different repository: [https://huggingface.co/Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish). Our future Finnish models will be available at the [Finnish-NLP](https://huggingface.c... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "roberta"], "datasets": ["mc4"], "widget": [{"text": "Moikka olen <mask> kielimalli."}]} | flax-community/RoBERTa-large-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"finnish",
"fi",
"dataset:mc4",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"fi"
] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #finnish #fi #dataset-mc4 #arxiv-1907.11692 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| NOTE: We have trained newer and better Finnish RoBERTa large model which can be found from different repository: URL Our future Finnish models will be available at the Finnish-NLP Hugging Face organization
==================================================================================================================... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content ... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #finnish #fi #dataset-mc4 #arxiv-1907.11692 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use... |
text-generation | transformers | # Sinhala GPT2 trained on MC4 (manually cleaned)
### Overview
This is a smaller GPT2 model trained on [MC4](https://github.com/allenai/allennlp/discussions/5056) Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to star... | {"language": "si", "tags": ["Sinhala", "text-generation", "gpt2"], "datasets": ["mc4"]} | flax-community/Sinhala-gpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"gpt2",
"feature-extraction",
"Sinhala",
"text-generation",
"si",
"dataset:mc4",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #gpt2 #feature-extraction #Sinhala #text-generation #si #dataset-mc4 #endpoints_compatible #has_space #text-generation-inference #region-us
| # Sinhala GPT2 trained on MC4 (manually cleaned)
### Overview
This is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
This model uses a... | [
"# Sinhala GPT2 trained on MC4 (manually cleaned)",
"### Overview\n\nThis is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.\n\nThis... | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #gpt2 #feature-extraction #Sinhala #text-generation #si #dataset-mc4 #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Sinhala GPT2 trained on MC4 (manually cleaned)",
"### Overview\n\nThis is a smaller GPT2 model trained on MC4 Si... |
fill-mask | transformers | ## Sinhala Roberta model trained on MC4 Sinhala dataset (manually cleaned) | {"language": "si", "tags": ["fill-mask", "sinhala", "roberta"]} | flax-community/Sinhala-roberta | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"feature-extraction",
"fill-mask",
"sinhala",
"si",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si"
] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #feature-extraction #fill-mask #sinhala #si #endpoints_compatible #region-us
| ## Sinhala Roberta model trained on MC4 Sinhala dataset (manually cleaned) | [
"## Sinhala Roberta model trained on MC4 Sinhala dataset (manually cleaned)"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #feature-extraction #fill-mask #sinhala #si #endpoints_compatible #region-us \n",
"## Sinhala Roberta model trained on MC4 Sinhala dataset (manually cleaned)"
] |
fill-mask | transformers |
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Update:</b>
This model has been moved to <a href="h... | {"language": "es", "license": "cc-by-4.0", "tags": ["multilingual", "bert"], "pipeline_tag": "fill-mask", "widget": [{"text": "\u00bfQu\u00e9 es la vida? Un [MASK]."}]} | flax-community/alberti-bert-base-multilingual-cased | null | [
"transformers",
"pytorch",
"jax",
"joblib",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #joblib #safetensors #bert #fill-mask #multilingual #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Update:</b>
This model has been moved to <a href="U... | [
"# ALBERTI\n\nALBERTI is a set of two BERT-based multilingual model for poetry. One for verses and another one for stanzas. This model has been further trained with the PULPO corpus for verses using Flax, including training scripts.\n\nThis is part of the\nFlax/Jax Community Week, organised by HuggingFace and TPU u... | [
"TAGS\n#transformers #pytorch #jax #joblib #safetensors #bert #fill-mask #multilingual #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ALBERTI\n\nALBERTI is a set of two BERT-based multilingual model for poetry. One for verses and another one for stanzas. This mode... |
text2text-generation | transformers |
# arabic-t5-small
This is a T5v1.1 (small) trained on the concatenation of the Arabic Billion Words corpus and the Arabic subsets of the mC4 and Oscar datasets.
The model could only be trained for about `10%` of the whole dataset due to time limitations. This is equivalent to `22'000` steps or about `4.3` Billion to... | {"language": ["ar"], "datasets": ["mc4", "oscar", "arabic_billion_words"]} | flax-community/arabic-t5-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"ar",
"dataset:mc4",
"dataset:oscar",
"dataset:arabic_billion_words",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #safetensors #t5 #text2text-generation #ar #dataset-mc4 #dataset-oscar #dataset-arabic_billion_words #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| arabic-t5-small
===============
This is a T5v1.1 (small) trained on the concatenation of the Arabic Billion Words corpus and the Arabic subsets of the mC4 and Oscar datasets.
The model could only be trained for about '10%' of the whole dataset due to time limitations. This is equivalent to '22'000' steps or about '... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #safetensors #t5 #text2text-generation #ar #dataset-mc4 #dataset-oscar #dataset-arabic_billion_words #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | # bengali-t5-base
**bengali-t5-base** is a model trained on the Bengali portion of MT5 dataset. We used the `T5-base` model for this model.
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by [HuggingFace](https://huggingface.co... | {} | flax-community/bengali-t5-base | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #mt5 #text2text-generation #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # bengali-t5-base
bengali-t5-base is a model trained on the Bengali portion of MT5 dataset. We used the 'T5-base' model for this model.
Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.
The model is trained on around ~11B tokens (64 size batch, 512 tokens, 350k steps).
## load to... | [
"# bengali-t5-base\n\nbengali-t5-base is a model trained on the Bengali portion of MT5 dataset. We used the 'T5-base' model for this model.\n\nFlax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.\n\nThe model is trained on around ~11B tokens (64 size batch, 512 tokens, 350k steps).",... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #mt5 #text2text-generation #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# bengali-t5-base\n\nbengali-t5-base is a model trained on the Bengali portion of MT5 dataset. We used the 'T... |
fill-mask | transformers |
## BERT base-uncased for in Swahili
This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was done on ... | {"language": "sw", "datasets": ["flax-community/swahili-safi"], "widget": [{"text": "Si kila mwenye makucha [MASK] simba."}]} | flax-community/bert-base-uncased-swahili | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"sw",
"dataset:flax-community/swahili-safi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sw"
] | TAGS
#transformers #pytorch #jax #tensorboard #bert #fill-mask #sw #dataset-flax-community/swahili-safi #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## BERT base-uncased for in Swahili
This model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community Week organized by HuggingFace. All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
## How to use
#### Training Data:
This model was trained on Swahili Safi
##... | [
"## BERT base-uncased for in Swahili\n\nThis model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community Week organized by HuggingFace. All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.",
"## How to use",
"#### Training Data:\nThis model was trained on Swah... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #bert #fill-mask #sw #dataset-flax-community/swahili-safi #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## BERT base-uncased for in Swahili\n\nThis model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community W... |
text-classification | transformers |
## Swahili News Classification with BERT
This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was don... | {"language": "sw", "datasets": ["flax-community/swahili-safi"], "widget": [{"text": "Idris ameandika kwenye ukurasa wake wa Instagram akimkumbusha Diamond kutekeleza ahadi yake kumpigia Zari magoti kumuomba msamaha kama alivyowahi kueleza awali.Idris ameandika;"}]} | flax-community/bert-swahili-news-classification | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"sw",
"dataset:flax-community/swahili-safi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sw"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #bert #text-classification #sw #dataset-flax-community/swahili-safi #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Swahili News Classification with BERT
This model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community Week organized by HuggingFace. All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
This model was used as the base and fine-tuned for this task.
## How to us... | [
"## Swahili News Classification with BERT\n\nThis model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community Week organized by HuggingFace. All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.\n\nThis model was used as the base and fine-tuned for this task.",
"... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #bert #text-classification #sw #dataset-flax-community/swahili-safi #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Swahili News Classification with BERT\n\nThis model was trained using HuggingFace's Flax framework and is part... |
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