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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
summarization | transformers |
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
`t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by [huseinzol05](https://huggingface.co/huseinzol05), finetuned using [id_liputan6](https://huggingface... | {"language": "id", "tags": ["pipeline:summarization", "summarization", "t5"], "datasets": ["id_liputan6"]} | cahya/t5-base-indonesian-summarization-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"pipeline:summarization",
"summarization",
"id",
"dataset:id_liputan6",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-id_liputan6 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using id_liputan6 dataset.
## Load Finetuned Model
## Code Sample
Outp... | [
"# Indonesian T5 Summarization Base Model\n\nFinetuned T5 base summarization model for Indonesian.",
"## Finetuning Corpus\n\n't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Co... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-id_liputan6 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Summarization Base Model\n\nFinetuned T5 base summarization model for Indonesian.... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned
[cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial)
model on [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, ... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Base Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Rec... | cahya/wav2vec2-base-turkish-artificial-cv | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned
cahya/wav2vec2-base-turkish-artificial
model on Turkish Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a languag... | [
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-base-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (wi... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned \nc... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned [ceyda/wav2vec2-base-760](https://huggingface.co/ceyda/wav2vec2-base-760)
on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Base Turkish with Artificial Voices by Cahya", "results": [{"task": {"type": "automatic-speech-recognitio... | cahya/wav2vec2-base-turkish-artificial | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned ceyda/wav2vec2-base-760
on the Turkish Artificial Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as ... | [
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned ceyda/wav2vec2-base-760\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned ceyda/wav2vec2-base-760\non the Turkish Artificial Common Voice da... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turki... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | cahya/wav2vec2-base-turkish-cv7 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2893
* Wer: 0.2713
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [./checkpoint-1000](https://huggingface.co/./checkpoint-1000) on the MOZILLA-FOUNDATION/C... | {"language": ["tr"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | cahya/wav2vec2-base-turkish-cv8 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us
|
This model is a fine-tuned version of ./checkpoint-1000 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3282
* Wer: 0.2836
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 192\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0... |
automatic-speech-recognition | transformers |
#
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
| | Dataset | WER | CER |
|---|-... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "tr"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "Wav2Vec2 Base Turkish by Cahya", "results": [{"task": {"type... | cahya/wav2vec2-base-turkish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-06\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe follow... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Basque
This is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Basque Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your sp... | {"language": "eu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Basque by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Reco... | cahya/wav2vec2-large-xlsr-basque | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"eu",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-Basque
This is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned
facebook/wav2vec2-large-xlsr-53
model on the Basque Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as fol... | [
"# Wav2Vec2-Large-XLSR-Basque\n\nThis is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Basque Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-Basque\n\nThis is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tun... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Breton
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Breton Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be ... | {"language": "br", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Breton by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Reco... | cahya/wav2vec2-large-xlsr-breton | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"br"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Breton
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Breton Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
The above code leads to the following prediction fo... | [
"# Wav2Vec2-Large-XLSR-Breton\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Breton Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nThe above code leads to the followi... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Breton\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Breton Common Voice ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage... | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian with Artificial Voice by Cahya", "results": [{"task": {"type": "automatic-speech-recognit... | cahya/wav2vec2-large-xlsr-indonesian-artificial | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Artificial Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be... | [
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Artificial Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Artif... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice) and synthetic voices
generated using [Artificial Common Voicer](https://github.com/cahya-wirawan/... | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian Mix by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spe... | cahya/wav2vec2-large-xlsr-indonesian-mix | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset and synthetic voices
generated using Artificial Common Voicer, which
again based on Google Text To Speech.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | [
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset and synthetic voices\ngenerated using Artificial Common Voicer, which\nagain based on Google Text To Speech.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## U... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | {"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian by cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech ... | cahya/wav2vec2-large-xlsr-indonesian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated ... | [
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Javanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [OpenSLR High quality TTS data for Javanese](https://openslr.org/41/).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used... | {"language": "jv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Javanese by cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recogni... | cahya/wav2vec2-large-xlsr-javanese | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"jv",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"jv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #jv #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-Javanese
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the OpenSLR High quality TTS data for Javanese.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be e... | [
"# Wav2Vec2-Large-XLSR-Javanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Javanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nT... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #jv #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-Javanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Sundanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be us... | {"language": "su", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Sundanese by cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recogn... | cahya/wav2vec2-large-xlsr-sundanese | null | [
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"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"su",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"su"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #su #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Sundanese
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the OpenSLR High quality TTS data for Sundanese.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be... | [
"# Wav2Vec2-Large-XLSR-Sundanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Sundanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #su #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Sundanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality T... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned
[cahya/wav2vec2-large-xlsr-turkish-artificial](https://huggingface.co/cahya/wav2vec2-large-xlsr-turkish-artificial)
model on [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice).
When ... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Rec... | cahya/wav2vec2-large-xlsr-turkish-artificial-cv | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned
cahya/wav2vec2-large-xlsr-turkish-artificial
model on Turkish Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (witho... | [
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-large-xlsr-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fin... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish with Artificial Voices by Cahya", "results": [{"task": {"type": "automatic-speech-recognitio... | cahya/wav2vec2-large-xlsr-turkish-artificial | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Turkish Artificial Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe m... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Turkish Artificial Co... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Rec... | cahya/wav2vec2-large-xlsr-turkish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned
facebook/wav2vec2-large-xlsr-53
model on the Turkish Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as ... | [
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Turkish Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a langu... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned \nface... |
automatic-speech-recognition | transformers |
# Automatic Speech Recognition for Luganda
This is the model built for the
[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xl... | {"language": "lg", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "common_voice", "hf-asr-leaderboard", "lg", "robust-speech-event", "speech"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Luganda by Indonesian-NLP", "results": [... | cahya/wav2vec2-luganda | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"common_voice",
"hf-asr-leaderboard",
"lg",
"robust-speech-event",
"speech",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lg"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #common_voice #hf-asr-leaderboard #lg #robust-speech-event #speech #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Automatic Speech Recognition for Luganda
This is the model built for the
Mozilla Luganda Automatic Speech Recognition competition.
It is a fine-tuned facebook/wav2vec2-large-xlsr-53
model on the Luganda Common Voice dataset version 7.0.
We also provide a live demo to test the model.
When using this model, make s... | [
"# Automatic Speech Recognition for Luganda\n\nThis is the model built for the \nMozilla Luganda Automatic Speech Recognition competition.\nIt is a fine-tuned facebook/wav2vec2-large-xlsr-53\nmodel on the Luganda Common Voice dataset version 7.0.\n\nWe also provide a live demo to test the model.\n\nWhen using this ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #common_voice #hf-asr-leaderboard #lg #robust-speech-event #speech #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Automatic Speech Recognition for Luganda\n\nThis is... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATI... | {"language": ["ab"], "tags": ["ab", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "", "results": []}]} | cahya/xls-r-ab-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"ab",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #ab #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 135.4675
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Train... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 135.4675\n- Wer: 1.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informati... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #ab #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-md
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-md", "results": []}]} | caioamb/bert-base-uncased-finetuned-md | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-md
==============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3329
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | caioamb/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7647
* Matthews Correlation: 0.5167
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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. -->
# distilgpt2-finetuned-wikitexts
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitexts", "results": []}]} | calebcsjm/distilgpt2-finetuned-wikitexts | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitexts
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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-en-vi-finetuned-eng-to-vie
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Hel... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "opus-mt-en-vi-finetuned-eng-to-vie", "results": []}]} | callmeJ/opus-mt-en-vi-finetuned-eng-to-vie | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-vi-finetuned-eng-to-vie
==================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training a... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
feature-extraction | transformers |
# BioRedditBERT
## Model description
BioRedditBERT is a BERT model initialised from BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) and further pre-trained on health-related Reddit posts. Please view our paper [COMETA: A Corpus for Medical Entity Linking in the Social Media](https://arxiv.org/pdf/2010.03295.pd... | {"language": ["en"], "tags": ["BioNLP", "social_media"]} | cambridgeltl/BioRedditBERT-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"BioNLP",
"social_media",
"en",
"arxiv:2010.03295",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.03295"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #BioNLP #social_media #en #arxiv-2010.03295 #endpoints_compatible #has_space #region-us
| BioRedditBERT
=============
Model description
-----------------
BioRedditBERT is a BERT model initialised from BioBERT ('BioBERT-Base v1.0 + PubMed 200K + PMC 270K') and further pre-trained on health-related Reddit posts. Please view our paper COMETA: A Corpus for Medical Entity Linking in the Social Media (EMNLP 2... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #BioNLP #social_media #en #arxiv-2010.03295 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
feature-extraction | transformers | ---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-XLMR
SapBERT [(Liu et... | {} | cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2010.11784",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11784"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us
| ---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-XLMR
SapBERT (Liu et al. 2021) traine... | [
"### SapBERT-XLMR\nSapBERT (Liu et al. 2021) trained with UMLS 2020AB, using xlm-roberta-large as the base model. Please use [CLS] as the representation of the input.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us \n",
"### SapBERT-XLMR\nSapBERT (Liu et al. 2021) trained with UMLS 2020AB, using xlm-roberta-large as the base model. Please use [CLS] as the representation of the input.",
"#### Extracting embeddi... |
feature-extraction | transformers | ---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-XLMR
SapBERT [(Liu et... | {} | cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:2010.11784",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11784"
] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us
| ---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-XLMR
SapBERT (Liu et al. 2020) traine... | [
"### SapBERT-XLMR\nSapBERT (Liu et al. 2020) trained with UMLS 2020AB, using xlm-roberta-base as the base model. Please use [CLS] as the representation of the input.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details ... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us \n",
"### SapBERT-XLMR\nSapBERT (Liu et al. 2020) trained with UMLS 2020AB, using xlm-roberta-base as the base model. Please use [CLS] as the representation of the input.",
"#### Extrac... |
feature-extraction | transformers | ---
language: en
tags:
- biomedical
- lexical-semantics
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-PubMedBERT
SapBERT by [Liu et al. (2020)](https:... | {} | cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2010.11784",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11784"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #arxiv-2010.11784 #endpoints_compatible #has_space #region-us
| ---
language: en
tags:
- biomedical
- lexical-semantics
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-PubMedBERT
SapBERT by Liu et al. (2020). Trained with UMLS 2020A... | [
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. Please use the mean-pooling of the output as the representation.",
"#### Extracting embeddings from SapBERT\n\nThe following script... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #arxiv-2010.11784 #endpoints_compatible #has_space #region-us \n",
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the ba... |
feature-extraction | transformers | ---
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-PubMedBERT
SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS... | {"language": ["en"], "license": "apache-2.0", "tags": ["biomedical", "lexical semantics", "bionlp", "biology", "science", "embedding", "entity linking"]} | cambridgeltl/SapBERT-from-PubMedBERT-fulltext | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"biomedical",
"lexical semantics",
"bionlp",
"biology",
"science",
"embedding",
"entity linking",
"en",
"arxiv:2010.11784",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us... | null | 2022-03-02T23:29:05+00:00 | [
"2010.11784"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #biomedical #lexical semantics #bionlp #biology #science #embedding #entity linking #en #arxiv-2010.11784 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| ---
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-PubMedBERT
SapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedB... | [
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model.",
"### Expected input and output\nThe input should be a string of biomedical entity names, e.g., \"covid infection\" or \"Hydroxych... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #biomedical #lexical semantics #bionlp #biology #science #embedding #entity linking #en #arxiv-2010.11784 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Traine... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence-drophead
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf), using [drophead](https://aclanthology.org/2020.findings-emnlp.178.pdf) instead of dropo... | {} | cambridgeltl/mirror-bert-base-uncased-sentence-drophead | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08027"
] | [] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence-drophead
An unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using bert-base-uncased... | [
"### cambridgeltl/mirror-bert-base-uncased-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* a... | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-bert-base-uncased-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). Trained with unlabelled raw sentences, using [bert-base-uncased](https://huggingface.co/bert-bas... | {} | cambridgeltl/mirror-bert-base-uncased-sentence | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08027"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence
An unsupervised sentence encoder proposed by Liu et al. (2021). Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* (including p... | [
"### cambridgeltl/mirror-bert-base-uncased-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* (including padded ones) as the representation of the input.\n\nNote the mod... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-bert-base-uncased-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Ple... |
feature-extraction | transformers | ---
language: en
tags:
- word-embeddings
- word-similarity
### mirror-bert-base-uncased-word
An unsupervised word encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). Trained with a set of unlabelled words, using [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the base model... | {} | cambridgeltl/mirror-bert-base-uncased-word | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08027"
] | [] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
| ---
language: en
tags:
- word-embeddings
- word-similarity
### mirror-bert-base-uncased-word
An unsupervised word encoder proposed by Liu et al. (2021). Trained with a set of unlabelled words, using bert-base-uncased as the base model. Please use '[CLS]' as the representation of the input.
| [
"### mirror-bert-base-uncased-word\nAn unsupervised word encoder proposed by Liu et al. (2021). Trained with a set of unlabelled words, using bert-base-uncased as the base model. Please use '[CLS]' as the representation of the input."
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### mirror-bert-base-uncased-word\nAn unsupervised word encoder proposed by Liu et al. (2021). Trained with a set of unlabelled words, using bert-base-uncased as the base model. Please use... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence-drophead
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf), using [drophead](https://aclanthology.org/2020.findings-emnlp.178.pdf) instead of dropout as... | {} | cambridgeltl/mirror-roberta-base-sentence-drophead | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08027"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence-drophead
An unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. The model is trained with unlabelled raw sentences, using roberta-b... | [
"### cambridgeltl/mirror-roberta-base-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as th... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-roberta-base-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. ... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). The model is trained with unlabelled raw sentences, using [roberta-base](https://huggingface.co/rober... | {} | cambridgeltl/mirror-roberta-base-sentence | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08027"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence
An unsupervised sentence encoder proposed by Liu et al. (2021). The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the represent... | [
"### cambridgeltl/mirror-roberta-base-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.\n\nNote the model does not replicate the ex... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-roberta-base-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). The model is trained with unlabelled raw sentences, using roberta-base as the base mode... |
text-generation | transformers | This model provides a GPT-2 language model trained with SimCTG on the English Wikipedia based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our [project repo](https://github.com/yxua... | {} | cambridgeltl/simctg_english_wikipedia | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2202.06417",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.06417"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model provides a GPT-2 language model trained with SimCTG on the English Wikipedia based on our paper _A Contrastive Framework for Neural Text Generation_.
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustrate a brief tutorial on how to u... | [
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a s... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n... |
text-generation | transformers | This model provides a Chinese GPT-2 language model trained with SimCTG on the LCCC benchmark [(Wang et al., 2020)](https://arxiv.org/pdf/2008.03946v2.pdf) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG a... | {} | cambridgeltl/simctg_lccc_dialogue | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2008.03946",
"arxiv:2202.06417",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.03946",
"2202.06417"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2008.03946 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model provides a Chinese GPT-2 language model trained with SimCTG on the LCCC benchmark (Wang et al., 2020) based on our paper _A Contrastive Framework for Neural Text Generation_.
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustrate a b... | [
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a s... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2008.03946 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contr... |
text-generation | transformers | This model provides a GPT-2 language model trained with SimCTG on the Wikitext-103 benchmark [(Merity et al., 2016)](https://arxiv.org/abs/1609.07843) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG and C... | {} | cambridgeltl/simctg_wikitext103 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:1609.07843",
"arxiv:2202.06417",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1609.07843",
"2202.06417"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-1609.07843 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model provides a GPT-2 language model trained with SimCTG on the Wikitext-103 benchmark (Merity et al., 2016) based on our paper _A Contrastive Framework for Neural Text Generation_.
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustrate a... | [
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a s... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-1609.07843 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-base
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from STS... | {} | cambridgeltl/trans-encoder-bi-simcse-bert-base | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.13059"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-base
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using prin... | [
"### cambridgeltl/trans-encoder-bi-simcse-bert-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-base-uncased as the base model. Please use '[CLS]'... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-bert-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-large
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from ST... | {} | cambridgeltl/trans-encoder-bi-simcse-bert-large | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.13059"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-large
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using pri... | [
"### cambridgeltl/trans-encoder-bi-simcse-bert-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-large-uncased as the base model. Please use '[CLS... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-bert-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS201... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-base
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from ... | {} | cambridgeltl/trans-encoder-bi-simcse-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.13059"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-base
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using p... | [
"### cambridgeltl/trans-encoder-bi-simcse-roberta-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-base as the base model. Please use '[CLS]' (... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-roberta-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from S... |
feature-extraction | transformers | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-large
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from... | {} | cambridgeltl/trans-encoder-bi-simcse-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.13059"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
| ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-large
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using ... | [
"### cambridgeltl/trans-encoder-bi-simcse-roberta-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-large as the base model. Please use '[CLS]'... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-roberta-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from ... |
null | transformers |
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretrain... | {"language": "fr"} | almanach/camembert-base-ccnet-4gb | null | [
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
| CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining dat... | [
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\... | [
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features fr... |
null | transformers |
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretrain... | {"language": "fr"} | almanach/camembert-base-ccnet | null | [
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
| CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining dat... | [
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\... | [
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features fr... |
null | transformers |
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretrain... | {"language": "fr"} | almanach/camembert-base-oscar-4gb | null | [
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
| CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining dat... | [
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\... | [
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features fr... |
null | transformers |
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretrain... | {"language": "fr"} | almanach/camembert-base-wikipedia-4gb | null | [
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
| CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining dat... | [
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\... | [
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features fr... |
fill-mask | transformers |
> 🚨 **Update:** This checkpoint is deprecated, please use https://huggingface.co/almanach/camembert-base instead 🚨
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now avail... | {"language": "fr"} | almanach/camembert-base-legacy | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"fr",
"arxiv:1911.03894",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fill-mask #fr #arxiv-1911.03894 #autotrain_compatible #endpoints_compatible #region-us
|
>
> Update: This checkpoint is deprecated, please use URL instead
>
>
>
CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 ... | [
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\... | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #fr #arxiv-1911.03894 #autotrain_compatible #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract... |
null | transformers |
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretrain... | {"language": "fr"} | almanach/camembert-large | null | [
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
| CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining dat... | [
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\... | [
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features fr... |
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. -->
# bart-large-cnn-finetuned-weaksup-1000-earlystop
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-1000-earlystop", "results": []}]} | cammy/bart-large-cnn-finetuned-weaksup-1000-earlystop | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-weaksup-1000-earlystop
===============================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9095
* Rouge1: 27.9262
* Rouge2: 11.895
* Rougel: 21.4029
* Rougelsu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #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: 1\n* eval\\_... |
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. -->
# bart-large-cnn-finetuned-weaksup-1000-pad
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-1000-pad", "results": []}]} | cammy/bart-large-cnn-finetuned-weaksup-1000-pad | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-weaksup-1000-pad
=========================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4168
* Rouge1: 26.2506
* Rouge2: 10.7802
* Rougel: 19.2236
* Rougelsum: 22.6883
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #bart #text2text-generation #generated_from_trainer #license-mit #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:... |
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. -->
# bart-large-cnn-finetuned-weaksup-1000
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/fac... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-1000", "results": []}]} | cammy/bart-large-cnn-finetuned-weaksup-1000 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-weaksup-1000
=====================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6325
* Rouge1: 26.1954
* Rouge2: 10.7128
* Rougel: 19.3873
* Rougelsum: 22.785
* Gen Len... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #bart #text2text-generation #generated_from_trainer #license-mit #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: 1\n* eval\\_... |
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. -->
# bart-large-cnn-finetuned-weaksup-10000-pad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-10000-pad-early", "results": []}]} | cammy/bart-large-cnn-finetuned-weaksup-10000-pad-early | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-large-cnn-finetuned-weaksup-10000-pad-early
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_g... | [
"# bart-large-cnn-finetuned-weaksup-10000-pad-early\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3541\n- eval_rouge1: 27.8229\n- eval_rouge2: 12.9484\n- eval_rougeL: 21.4909\n- eval_rougeLsum: 24.773... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-large-cnn-finetuned-weaksup-10000-pad-early\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the fo... |
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. -->
# bart-large-cnn-finetuned-weaksup-10000
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/fa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-10000", "results": []}]} | cammy/bart-large-cnn-finetuned-weaksup-10000 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-weaksup-10000
======================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6031
* Rouge1: 28.3912
* Rouge2: 13.655
* Rougel: 22.287
* Rougelsum: 25.4794
* Gen Le... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #bart #text2text-generation #generated_from_trainer #license-mit #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: 1\n* eval\\_... |
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. -->
# distilbart-cnn-12-6-finetuned-weaksup-1000
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-12-6-finetuned-weaksup-1000", "results": []}]} | cammy/distilbart-cnn-12-6-finetuned-weaksup-1000 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbart-cnn-12-6-finetuned-weaksup-1000
==========================================
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6818
* Rouge1: 25.9199
* Rouge2: 11.2697
* Rougel: 20.3598
* Rougelsum: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-finetuned-weaksup-1000-pegasus
This model is a fine-tuned version of [google/pegasus-multi_news](https://hugg... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-multi_news-finetuned-weaksup-1000-pegasus", "results": []}]} | cammy/pegasus-multi_news-finetuned-weaksup-1000-pegasus | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| pegasus-multi\_news-finetuned-weaksup-1000-pegasus
==================================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1309
* Rouge1: 23.342
* Rouge2: 8.67
* Rougel: 17.2865
* Ro... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #pegasus #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_si... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-weaksup-1000
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
##... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned-weaksup-1000", "results": []}]} | cammy/roberta-base-finetuned-weaksup-1000 | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-finetuned-weaksup-1000
This model is a fine-tuned version of [](URL on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# roberta-base-finetuned-weaksup-1000\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-finetuned-weaksup-1000\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore info... |
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-base-finetuned-weaksup-1000
This model is a fine-tuned version of [cammy/t5-base-finetuned-weaksup-1000](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-weaksup-1000", "results": []}]} | cammy/t5-base-finetuned-weaksup-1000 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-weaksup-1000
==============================
This model is a fine-tuned version of cammy/t5-base-finetuned-weaksup-1000 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6699
* Rouge1: 22.2079
* Rouge2: 9.54
* Rougel: 19.9593
* Rougelsum: 20.2524
* Gen Len: 1... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
text-generation | transformers | news generator dummy | {} | candra/gpt2-newgen-test | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| news generator dummy | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | small gpt2 headline | {} | candra/headline-small-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| small gpt2 headline | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
audio-to-audio | asteroid | ## Asteroid model `cankeles/ConvTasNet_WHAMR_enhsingle_16k`
Description:
This model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.
The initial model was taken from here: https://huggingface.co/JorisCos/ConvTasNet_Lib... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]} | cankeles/ConvTasNet_WHAMR_enhsingle_16k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us
| ## Asteroid model 'cankeles/ConvTasNet_WHAMR_enhsingle_16k'
Description:
This model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.
The initial model was taken from here: URL
This model was trained by M. Can Keles us... | [
"## Asteroid model 'cankeles/ConvTasNet_WHAMR_enhsingle_16k'\n\nDescription:\n\nThis model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.\n\nThe initial model was taken from here: URL\n\nThis model was trained by M.... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'cankeles/ConvTasNet_WHAMR_enhsingle_16k'\n\nDescription:\n\nThis model was fine tuned on a modified version of WHAMR! where the speakers were tak... |
audio-to-audio | asteroid | ## Asteroid model `cankeles/DPTNet_WHAMR_enhsignle_16k`
Description:
This model was trained by M. Can Keleş using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
mode: min
nondefault_ns... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "DPTNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]} | cankeles/DPTNet_WHAMR_enhsingle_16k | null | [
"asteroid",
"pytorch",
"audio",
"DPTNet",
"audio-to-audio",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us
| ## Asteroid model 'cankeles/DPTNet_WHAMR_enhsignle_16k'
Description:
This model was trained by M. Can Keleş using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On custom min test set :
| [
"## Asteroid model 'cankeles/DPTNet_WHAMR_enhsignle_16k'\n\nDescription:\n\nThis model was trained by M. Can Keleş using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn custom min test set :"
] | [
"TAGS\n#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'cankeles/DPTNet_WHAMR_enhsignle_16k'\n\nDescription:\n\nThis model was trained by M. Can Keleş using the librimix recipe in Asteroid.\nIt was trained on the 'en... |
feature-extraction | transformers |
# BERT-of-Theseus
See our paper ["BERT-of-Theseus: Compressing BERT by Progressive Module Replacing"](http://arxiv.org/abs/2002.02925).
BERT-of-Theseus is a new compressed BERT by progressively replacing the components of the original BERT.
![BERT of Theseus](https://github.com/JetRunner/BERT-of-Theseus/blob/master/... | {"datasets": ["multi_nli"], "thumbnail": "https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png"} | canwenxu/BERT-of-Theseus-MNLI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"dataset:multi_nli",
"arxiv:2002.02925",
"arxiv:2005.00628",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.02925",
"2005.00628"
] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #dataset-multi_nli #arxiv-2002.02925 #arxiv-2005.00628 #endpoints_compatible #region-us
| BERT-of-Theseus
===============
See our paper "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing".
BERT-of-Theseus is a new compressed BERT by progressively replacing the components of the original BERT.
!BERT of Theseus
Load Pretrained Model on MNLI
-----------------------------
We provide a ... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #dataset-multi_nli #arxiv-2002.02925 #arxiv-2005.00628 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
#Chris DialoGPT Model | {"tags": ["conversational"]} | caps1994/DialoGPT-small-chrisbot-caps1994 | 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
|
#Chris DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Chris DialoGPT Model | {"tags": ["conversational"]} | caps1994/DialoGPT-small-chrisbot | 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
|
#Chris DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | caps1994/DialoGPT-small-harrypotter-caps1994 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
fill-mask | transformers |
# Twitter 2021 90M (RoBERTa-base)
This is a RoBERTa-base model trained on 90M tweets until the end of 2019.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interfac... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-2019-90m | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter 2021 90M (RoBERTa-base)
This is a RoBERTa-base model trained on 90M tweets until the end of 2019.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictio... | [
"# Twitter 2021 90M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 90M tweets until the end of 2019.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter 2021 90M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 90M tweets until the end of 2019.\nMore d... |
fill-mask | transformers |
# Twitter 2021 124M (RoBERTa-base)
This is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another int... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-2021-124m | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter 2021 124M (RoBERTa-base)
This is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing pred... | [
"# Twitter 2021 124M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to compa... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter 2021 124M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.\nMore details... |
fill-mask | transformers |
# Twitter December 2020 (RoBERTa-base, 107M)
This is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interfa... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-dec2020 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter December 2020 (RoBERTa-base, 107M)
This is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suite... | [
"# Twitter December 2020 (RoBERTa-base, 107M)\n\nThis is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface m... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter December 2020 (RoBERTa-base, 107M)\n\nThis is a RoBERTa-base model trained on 107.06M tweets until the end of December ... |
fill-mask | transformers |
# Twitter December 2021 (RoBERTa-base, 124M)
This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interfa... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-dec2021 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter December 2021 (RoBERTa-base, 124M)
This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suite... | [
"# Twitter December 2021 (RoBERTa-base, 124M)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface m... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter December 2021 (RoBERTa-base, 124M)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of December ... |
text-classification | transformers | # Twitter-roBERTa-base for Emoji prediction
This is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.... | {} | cardiffnlp/twitter-roberta-base-emoji | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12421"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Twitter-roBERTa-base for Emoji prediction
This is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
## Example of classification
Output:
| [
"# Twitter-roBERTa-base for Emoji prediction\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutpu... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Emoji prediction\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval be... |
text-classification | transformers | # Twitter-roBERTa-base for Emotion Recognition
This is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://g... | {} | cardiffnlp/twitter-roberta-base-emotion | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12421"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Twitter-roBERTa-base for Emotion Recognition
This is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
<b>New!</b> We just released a new emotion recogn... | [
"# Twitter-roBERTa-base for Emotion Recognition\n\nThis is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.\n\n<b>New!</b> We just released a new em... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Emotion Recognition\n\nThis is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetE... |
text-classification | transformers | # Twitter-roBERTa-base for Hate Speech Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark.
This model is specialized to detect hate speech against women and immigrants.
**NEW!** We have made available a more recent and robust hate speech... | {} | cardiffnlp/twitter-roberta-base-hate | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12421"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Twitter-roBERTa-base for Hate Speech Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark.
This model is specialized to detect hate speech against women and immigrants.
NEW! We have made available a more recent and robust hate speech det... | [
"# Twitter-roBERTa-base for Hate Speech Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark. \nThis model is specialized to detect hate speech against women and immigrants.\n\nNEW! We have made available a more recent and robust hate ... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Hate Speech Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the Tw... |
text-classification | transformers | # Twitter-roBERTa-base for Irony Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
This model has integrated into the [TweetNLP Python library](https://github.com/cardiffnlp/tweetnlp/).
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020... | {"language": ["en"], "datasets": ["tweet_eval"]} | cardiffnlp/twitter-roberta-base-irony | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12421"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-tweet_eval #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Twitter-roBERTa-base for Irony Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
This model has integrated into the TweetNLP Python library.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repositor... | [
"# Twitter-roBERTa-base for Irony Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. \nThis model has integrated into the TweetNLP Python library.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval officia... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-tweet_eval #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Irony Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection... |
fill-mask | transformers |
# Twitter June 2020 (RoBERTa-base, 99M)
This is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For an... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-jun2020 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter June 2020 (RoBERTa-base, 99M)
This is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to compa... | [
"# Twitter June 2020 (RoBERTa-base, 99M)\n\nThis is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter June 2020 (RoBERTa-base, 99M)\n\nThis is a RoBERTa-base model trained on 98.66M tweets until the end of June... |
fill-mask | transformers |
# Twitter June 2021 (RoBERTa-base, 115M)
This is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For ... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-jun2021 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter June 2021 (RoBERTa-base, 115M)
This is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to com... | [
"# Twitter June 2021 (RoBERTa-base, 115M)\n\nThis is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suit... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter June 2021 (RoBERTa-base, 115M)\n\nThis is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.\nM... |
fill-mask | transformers |
# Twitter March 2020 (RoBERTa-base, 94M)
This is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For ... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-mar2020 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter March 2020 (RoBERTa-base, 94M)
This is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to com... | [
"# Twitter March 2020 (RoBERTa-base, 94M)\n\nThis is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suit... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter March 2020 (RoBERTa-base, 94M)\n\nThis is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.\nM... |
fill-mask | transformers |
# Twitter March 2021 (RoBERTa-base, 111M)
This is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. Fo... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-mar2021 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter March 2021 (RoBERTa-base, 111M)
This is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to c... | [
"# Twitter March 2021 (RoBERTa-base, 111M)\n\nThis is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more su... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter March 2021 (RoBERTa-base, 111M)\n\nThis is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.\... |
text-classification | transformers | # Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval of... | {} | cardiffnlp/twitter-roberta-base-offensive | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12421"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
## Example of classif... | [
"# Twitter-roBERTa-base for Offensive Language Identification\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Exam... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Offensive Language Identification\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language id... |
text-classification | transformers | # Twitter-roBERTa-base for Sentiment Analysis
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)).
... | {"language": ["en"], "datasets": ["tweet_eval"]} | cardiffnlp/twitter-roberta-base-sentiment | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12421"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-tweet_eval #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Twitter-roBERTa-base for Sentiment Analysis
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see XLM-T).
- Reference Paper: _TweetEval_ (Findings of EMNLP 2020).
- Git Repo: T... | [
"# Twitter-roBERTa-base for Sentiment Analysis\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see XLM-T).\n\n- Reference Paper: _TweetEval_ (Findings of EMNLP 2020). \n- G... | [
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"# Twitter-roBERTa-base for Sentiment Analysis\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment an... |
fill-mask | transformers |
# Twitter September 2020 (RoBERTa-base, 103M)
This is a RoBERTa-base model trained on 102.86M tweets until the end of September 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers inter... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-sep2020 | null | [
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"fill-mask",
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"twitter",
"en",
"dataset:twitter-api",
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter September 2020 (RoBERTa-base, 103M)
This is a RoBERTa-base model trained on 102.86M tweets until the end of September 2020.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more sui... | [
"# Twitter September 2020 (RoBERTa-base, 103M)\n\nThis is a RoBERTa-base model trained on 102.86M tweets until the end of September 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter September 2020 (RoBERTa-base, 103M)\n\nThis is a RoBERTa-base model trained on 102.86M tweets until the end of Septembe... |
fill-mask | transformers |
# Twitter September 2021 (RoBERTa-base, 120M)
This is a RoBERTa-base model trained on 119.66M tweets until the end of September 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers inter... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-sep2021 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter September 2021 (RoBERTa-base, 120M)
This is a RoBERTa-base model trained on 119.66M tweets until the end of September 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more sui... | [
"# Twitter September 2021 (RoBERTa-base, 120M)\n\nThis is a RoBERTa-base model trained on 119.66M tweets until the end of September 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter September 2021 (RoBERTa-base, 120M)\n\nThis is a RoBERTa-base model trained on 119.66M tweets until the end of Septembe... |
fill-mask | transformers | # Twitter-roBERTa-base
This is a RoBERTa-base model trained on ~58M tweets on top of the original RoBERTa-base checkpoint, as described and evaluated in the [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
To evaluate this and other LMs on Twitter-specific data, please refer to ... | {} | cardiffnlp/twitter-roberta-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12421"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Twitter-roBERTa-base
This is a RoBERTa-base model trained on ~58M tweets on top of the original RoBERTa-base checkpoint, as described and evaluated in the _TweetEval_ benchmark (Findings of EMNLP 2020).
To evaluate this and other LMs on Twitter-specific data, please refer to the Tweeteval official repository.
## P... | [
"# Twitter-roBERTa-base\n\nThis is a RoBERTa-base model trained on ~58M tweets on top of the original RoBERTa-base checkpoint, as described and evaluated in the _TweetEval_ benchmark (Findings of EMNLP 2020). \nTo evaluate this and other LMs on Twitter-specific data, please refer to the Tweeteval official repositor... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base\n\nThis is a RoBERTa-base model trained on ~58M tweets on top of the original RoBERTa-base checkpoint, as described and evaluated in the _Twee... |
text-classification | transformers |
# twitter-XLM-roBERTa-base for Sentiment Analysis
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).
- Paper: [XL... | {"language": "multilingual", "widget": [{"text": "\ud83e\udd17"}, {"text": "T'estimo! \u2764\ufe0f"}, {"text": "I love you!"}, {"text": "I hate you \ud83e\udd2e"}, {"text": "Mahal kita!"}, {"text": "\uc0ac\ub791\ud574!"}, {"text": "\ub09c \ub108\uac00 \uc2eb\uc5b4"}, {"text": "\ud83d\ude0d\ud83d\ude0d\ud83d\ude0d"}]} | cardiffnlp/twitter-xlm-roberta-base-sentiment | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"multilingual",
"arxiv:2104.12250",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.12250"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #multilingual #arxiv-2104.12250 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# twitter-XLM-roBERTa-base for Sentiment Analysis
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).
- Paper: XLM... | [
"# twitter-XLM-roBERTa-base for Sentiment Analysis\n\nThis is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).\n\n- Pap... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #text-classification #multilingual #arxiv-2104.12250 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# twitter-XLM-roBERTa-base for Sentiment Analysis\n\nThis is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for s... |
fill-mask | transformers |
# Twitter-XLM-Roberta-base
This is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the [reference paper](https://arxiv.org/abs/2104.12250). To evaluate this and other LMs on Twitter-specific data, please refer to the [main repository](https://github.com/cardiffnlp/xlm-t). A u... | {"language": "multilingual", "widget": [{"text": "\ud83e\udd17\ud83e\udd17\ud83e\udd17<mask>"}, {"text": "\ud83d\udd25The goal of life is <mask> . \ud83d\udd25"}, {"text": "Il segreto della vita \u00e8 l\u2019<mask> . \u2764\ufe0f"}, {"text": "Hasta <mask> \ud83d\udc4b!"}]} | cardiffnlp/twitter-xlm-roberta-base | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"multilingual",
"arxiv:2104.12250",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.12250"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #multilingual #arxiv-2104.12250 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter-XLM-Roberta-base
This is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the reference paper. To evaluate this and other LMs on Twitter-specific data, please refer to the main repository. A usage example is provided below.
## Computing tweet similarity
### Bib... | [
"# Twitter-XLM-Roberta-base\nThis is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the reference paper. To evaluate this and other LMs on Twitter-specific data, please refer to the main repository. A usage example is provided below.",
"## Computing tweet similarity",
... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #multilingual #arxiv-2104.12250 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-XLM-Roberta-base\nThis is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the reference paper. To ev... |
token-classification | transformers | Med Labs Cariai
| {} | cariai/medslabs | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Med Labs Cariai
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 | # TODO: Fill this model card
| {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | carlosaguayo/Simonini-ppo-LunarLander-v2 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # TODO: Fill this model card
| [
"# TODO: Fill this model card"
] | [
"TAGS\n#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"# TODO: Fill this model card"
] |
image-classification | keras |
# Classify Cats and Dogs
VGG16 fine tuned to classify cats and dogs
Notebook
https://www.kaggle.com/carlosaguayo/cats-vs-dogs-transfer-learning-pre-trained-vgg16
### How to use
Here is how to use this model to classify an image as a cat or dog:
```python
from skimage import io
import cv2
import matplotlib.pyplot... | {"tags": ["image-classification"], "widget": [{"src": "https://upload.wikimedia.org/wikipedia/commons/0/0c/About_The_Dog.jpg", "example_title": "Dog-1"}, {"src": "https://yt3.ggpht.com/ytc/AKedOLRvxGYSdEHqu0X4EYcJ2kq7BttRKBNpfwdHJf3FSg=s900-c-k-c0x00ffffff-no-rj", "example_title": "Dog-2"}, {"src": "https://upload.wiki... | carlosaguayo/cats_vs_dogs | null | [
"keras",
"image-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-classification #has_space #region-us
|
# Classify Cats and Dogs
VGG16 fine tuned to classify cats and dogs
Notebook
URL
### How to use
Here is how to use this model to classify an image as a cat or dog:
| [
"# Classify Cats and Dogs\n\nVGG16 fine tuned to classify cats and dogs\n\nNotebook\n\nURL",
"### How to use\n\nHere is how to use this model to classify an image as a cat or dog:"
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"# Classify Cats and Dogs\n\nVGG16 fine tuned to classify cats and dogs\n\nNotebook\n\nURL",
"### How to use\n\nHere is how to use this model to classify an image as a cat or dog:"
] |
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... | carlosaguayo/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1689
* Accuracy: 0.9295
* F1: 0.9300
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 #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | carlosaguayo/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4842
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
text-generation | transformers |
# Harry Potter Bot | {"tags": ["conversational"]} | cartyparty/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
|
# Harry Potter Bot | [
"# Harry Potter Bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter Bot"
] |
text-generation | transformers |
# Iteration 1 | {"tags": ["conversational"]} | cartyparty/DialoGPT-small-iteration1 | 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
|
# Iteration 1 | [
"# Iteration 1"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Iteration 1"
] |
text-generation | transformers |
# inspired by greentext | {"tags": ["conversational"]} | cartyparty/DialoGPT-small-nerdherd | 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
|
# inspired by greentext | [
"# inspired by greentext"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# inspired by greentext"
] |
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. -->
# camembert-ner-tcp-ca
This model is a fine-tuned version of [cassandra-themis/camembert-base-juri](https://huggingface.co/cassand... | {"tags": ["generated_from_trainer"], "datasets": ["cassandra-themis/ner-tcp-ca"], "widget": [{"text": "R\u00c9PUBLIQUE FRANCAISE\n\nAU NOM DU PEUPLE FRANCAIS\n\n\n\nCOUR D'APPEL D'AIX EN PROVENCE\n\n\n\n10e Chambre\n\n\n\nARR\u00caT MIXTE\n\nDU 14 JUIN 2006\n\n\n\nNo/2006\n\n\n\n\n\nR\u00f4le No 99/09967\n\n\n\n\n\nJoh... | cassandra-themis/test_tcp_ca | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"dataset:cassandra-themis/ner-tcp-ca",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #dataset-cassandra-themis/ner-tcp-ca #autotrain_compatible #endpoints_compatible #region-us
|
# camembert-ner-tcp-ca
This model is a fine-tuned version of cassandra-themis/camembert-base-juri on the cassandra-themis/ner-tcp-ca full dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tra... | [
"# camembert-ner-tcp-ca\n\nThis model is a fine-tuned version of cassandra-themis/camembert-base-juri on the cassandra-themis/ner-tcp-ca full dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore info... | [
"TAGS\n#transformers #pytorch #camembert #token-classification #generated_from_trainer #dataset-cassandra-themis/ner-tcp-ca #autotrain_compatible #endpoints_compatible #region-us \n",
"# camembert-ner-tcp-ca\n\nThis model is a fine-tuned version of cassandra-themis/camembert-base-juri on the cassandra-themis/ner-... |
fill-mask | transformers | Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
---
# afriberta_base
## Model description
AfriBERTa base is a pretrained multilingual language model with around 111 million parameters.
The model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed fo... | {} | castorini/afriberta_base | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
---
# afriberta_base
## Model description
AfriBERTa base is a pretrained multilingual language model with around 111 million parameters.
The model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed fo... | [
"# afriberta_base",
"## Model description\nAfriBERTa base is a pretrained multilingual language model with around 111 million parameters.\nThe model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed forward size.\nThe model was pretrained on 11 African languages namely - Afaan Oromoo (also called Or... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# afriberta_base",
"## Model description\nAfriBERTa base is a pretrained multilingual language model with around 111 million parameters.\nThe model has 8 layers, 6 attention heads, 768 hidden u... |
fill-mask | transformers |
# afriberta_large
## Model description
AfriBERTa large is a pretrained multilingual language model with around 126 million parameters.
The model has 10 layers, 6 attention heads, 768 hidden units and 3072 feed forward size.
The model was pretrained on 11 African languages namely - Afaan Oromoo (also called Oromo), Amh... | {"language": ["om", "am", "rw", "rn", "ha", "ig", "so", "sw", "ti", "yo", "pcm", "multilingual"], "license": "mit", "datasets": ["castorini/afriberta-corpus"]} | castorini/afriberta_large | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"om",
"am",
"rw",
"rn",
"ha",
"ig",
"so",
"sw",
"ti",
"yo",
"pcm",
"multilingual",
"dataset:castorini/afriberta-corpus",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"om",
"am",
"rw",
"rn",
"ha",
"ig",
"so",
"sw",
"ti",
"yo",
"pcm",
"multilingual"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #om #am #rw #rn #ha #ig #so #sw #ti #yo #pcm #multilingual #dataset-castorini/afriberta-corpus #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# afriberta_large
## Model description
AfriBERTa large is a pretrained multilingual language model with around 126 million parameters.
The model has 10 layers, 6 attention heads, 768 hidden units and 3072 feed forward size.
The model was pretrained on 11 African languages namely - Afaan Oromoo (also called Oromo), Amh... | [
"# afriberta_large",
"## Model description\nAfriBERTa large is a pretrained multilingual language model with around 126 million parameters.\nThe model has 10 layers, 6 attention heads, 768 hidden units and 3072 feed forward size.\nThe model was pretrained on 11 African languages namely - Afaan Oromoo (also called... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #om #am #rw #rn #ha #ig #so #sw #ti #yo #pcm #multilingual #dataset-castorini/afriberta-corpus #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# afriberta_large",
"## Model description\nAfriBERTa large is a pretrained multilingu... |
fill-mask | transformers | Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
---
# afriberta_small
## Model description
AfriBERTa small is a pretrained multilingual language model with around 97 million parameters.
The model has 4 layers, 6 attention heads, 768 hidden units and 3072 feed f... | {} | castorini/afriberta_small | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
---
# afriberta_small
## Model description
AfriBERTa small is a pretrained multilingual language model with around 97 million parameters.
The model has 4 layers, 6 attention heads, 768 hidden units and 3072 feed f... | [
"# afriberta_small",
"## Model description\nAfriBERTa small is a pretrained multilingual language model with around 97 million parameters.\nThe model has 4 layers, 6 attention heads, 768 hidden units and 3072 feed forward size.\nThe model was pretrained on 11 African languages namely - Afaan Oromoo (also called O... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# afriberta_small",
"## Model description\nAfriBERTa small is a pretrained multilingual language model with around 97 million parameters.\nThe model has 4 layers, 6 attention heads, 768 hidden ... |
null | transformers | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://ar... | {} | castorini/ance-dpr-context-multi | null | [
"transformers",
"pytorch",
"dpr",
"arxiv:2007.00808",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00808"
] | [] | TAGS
#transformers #pytorch #dpr #arxiv-2007.00808 #endpoints_compatible #region-us
| This model is converted from the original ANCE repo and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
For more details on how to use it, check our exp... | [] | [
"TAGS\n#transformers #pytorch #dpr #arxiv-2007.00808 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arx... | {} | castorini/ance-dpr-question-multi | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"arxiv:2007.00808",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00808"
] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #arxiv-2007.00808 #endpoints_compatible #has_space #region-us
| This model is converted from the original ANCE repo and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
For more details on how to use it, check our expe... | [] | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #arxiv-2007.00808 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arx... | {} | castorini/ance-msmarco-doc-firstp | null | [
"transformers",
"pytorch",
"roberta",
"arxiv:2007.00808",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00808"
] | [] | TAGS
#transformers #pytorch #roberta #arxiv-2007.00808 #endpoints_compatible #region-us
| This model is converted from the original ANCE repo and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
For more details on how to use it, check our expe... | [] | [
"TAGS\n#transformers #pytorch #roberta #arxiv-2007.00808 #endpoints_compatible #region-us \n"
] |
null | transformers | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arx... | {} | castorini/ance-msmarco-doc-maxp | null | [
"transformers",
"pytorch",
"roberta",
"arxiv:2007.00808",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00808"
] | [] | TAGS
#transformers #pytorch #roberta #arxiv-2007.00808 #endpoints_compatible #has_space #region-us
| This model is converted from the original ANCE repo and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
For more details on how to use it, check our expe... | [] | [
"TAGS\n#transformers #pytorch #roberta #arxiv-2007.00808 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | # Model Card for ance-msmarco-passage
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
# Model Details
## Model Description
Pyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-st... | {"language": ["en"]} | castorini/ance-msmarco-passage | null | [
"transformers",
"pytorch",
"roberta",
"en",
"arxiv:1910.09700",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #en #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
| # Model Card for ance-msmarco-passage
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
# Model Details
## Model Description
Pyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-st... | [
"# Model Card for ance-msmarco-passage\n \n \nPyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.",
"# Model Details",
"## Model Description\n \nPyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieva... | [
"TAGS\n#transformers #pytorch #roberta #en #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n",
"# Model Card for ance-msmarco-passage\n \n \nPyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.",
"# Model Details",
"## Model Descrip... |
fill-mask | transformers |
## About
Here we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using [pya0](https://github.com/approach0/pya0), which adds very limited new tokens for latex markup (total vocabulary is just 31,061).
This model is trained on 4 x 2 Tesla V100 with a tota... | {"language": "en", "license": "mit", "tags": ["azbert", "pretraining", "fill-mask"], "widget": [{"text": "$f$ $($ $x$ [MASK] $y$ $)$", "example_title": "mathy"}, {"text": "$x$ [MASK] $x$ $equal$ $2$ $x$", "example_title": "mathy"}, {"text": "Proof by [MASK] that $n$ $fact$ $gt$ $3$ $n$ for $n$ $gt$ $6$", "example_title... | castorini/azbert-base | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"pretraining",
"azbert",
"fill-mask",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #pretraining #azbert #fill-mask #en #license-mit #endpoints_compatible #region-us
|
## About
Here we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using pya0, which adds very limited new tokens for latex markup (total vocabulary is just 31,061).
This model is trained on 4 x 2 Tesla V100 with a total batch size of 64, using Math StackE... | [
"## About\nHere we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using pya0, which adds very limited new tokens for latex markup (total vocabulary is just 31,061).\n\nThis model is trained on 4 x 2 Tesla V100 with a total batch size of 64, using Math... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #pretraining #azbert #fill-mask #en #license-mit #endpoints_compatible #region-us \n",
"## About\nHere we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using pya0, which adds very limited new tokens ... |
null | transformers | This model is converted from the original BPR [repo](https://github.com/studio-ousia/bpr) and fitted into Pyserini:
> Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. arXiv:2106.00882. | {} | castorini/bpr-nq-ctx-encoder | null | [
"transformers",
"pytorch",
"dpr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #endpoints_compatible #region-us
| This model is converted from the original BPR repo and fitted into Pyserini:
> Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. arXiv:2106.00882. | [] | [
"TAGS\n#transformers #pytorch #dpr #endpoints_compatible #region-us \n"
] |
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