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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-300m-te", "results": [{"task": {"type": "automatic-speech-... | chmanoj/xls-r-300m-te | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"te"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OPENSLR\_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2680
* Wer: 0.3467
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpar... |
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": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | chmanoj/xls-r-demo-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #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: 156.8786
- Wer: 1.3460
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# \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: 156.8786\n- Wer: 1.3460",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB datase... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)
| {"language": "th", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning"], "datasets": ["common_voice"]} | chompk/wav2vec2-large-xlsr-thai-tokenized | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning",
"th",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #th #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)
| [
"# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #th #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)"
] |
text2text-generation | transformers | Test English-Dhivehi/Dhivehi-English NMT
Would need a lot more data to get accurate translations. | {} | chopey/testmntdv | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Test English-Dhivehi/Dhivehi-English NMT
Would need a lot more data to get accurate translations. | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | These models were made for my course project in NLP and AI special course at the University of Latvia during my first semester of study. | {} | chrisAS12/specseminars | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| These models were made for my course project in NLP and AI special course at the University of Latvia during my first semester of study. | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Fon
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data).
When using this model, make sure that your sp... | {"language": "fon", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["fon_dataset"], "metrics": ["wer"], "model-index": [{"name": "Fon XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "... | chrisjay/fonxlsr | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"fon",
"dataset:fon_dataset",
"arxiv:2103.07762",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.07762"
] | [
"fon"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #fon #dataset-fon_dataset #arxiv-2103.07762 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Fon
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Fon (or Fongbe) using the Fon 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-53-Fon\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Fon (or Fongbe) using the Fon Dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\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 #hf-asr-leaderboard #fon #dataset-fon_dataset #arxiv-2103.07762 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Fon\n\nFine-tuned facebook/wav2vec2-larg... |
null | null | # Interacting with the Masakhane Benchmark Models
I created this demo for very easy interaction with the [benchmark models on Masakhane](https://github.com/masakhane-io/masakhane-mt/tree/master/benchmarks) which were trained with [JoeyNMT](https://github.com/chrisemezue/joeynmt)(my forked version).
To access the spac... | {"language": "african-languages", "license": "apache-2.0", "tags": ["african-languages", "machine-translation", "text"]} | chrisjay/masakhane_benchmarks | null | [
"african-languages",
"machine-translation",
"text",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"african-languages"
] | TAGS
#african-languages #machine-translation #text #license-apache-2.0 #has_space #region-us
| Interacting with the Masakhane Benchmark Models
===============================================
I created this demo for very easy interaction with the benchmark models on Masakhane which were trained with JoeyNMT(my forked version).
To access the space click here.
To include your language, all you need to do is:
... | [] | [
"TAGS\n#african-languages #machine-translation #text #license-apache-2.0 #has_space #region-us \n"
] |
text-classification | spacy | Text statistics including readability and formality.
| Feature | Description |
| --- | --- |
| **Name** | `en_statistics` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `syllables`, `formality`, `readability` |
| **C... | {"language": ["en"], "license": "mit", "tags": ["spacy", "text-classification"], "model-index": [{"name": "en_statistics", "results": []}]} | chrisknowles/en_statistics | null | [
"spacy",
"text-classification",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #text-classification #en #license-mit #region-us
| Text statistics including readability and formality.
### Label Scheme
View label scheme (96 labels for 3 components)
| [
"### Label Scheme\n\n\n\nView label scheme (96 labels for 3 components)"
] | [
"TAGS\n#spacy #text-classification #en #license-mit #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (96 labels for 3 components)"
] |
token-classification | spacy | Check style on English text (currently passive text).
| Feature | Description |
| --- | --- |
| **Name** | `en_stylecheck` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` |
| **Components** | `tok2... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"], "model-index": [{"name": "en_stylecheck", "results": []}]} | chrisknowles/en_stylecheck | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #region-us
| Check style on English text (currently passive text).
### Label Scheme
View label scheme (115 labels for 5 components)
| [
"### Label Scheme\n\n\n\nView label scheme (115 labels for 5 components)"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (115 labels for 5 components)"
] |
text-generation | transformers | [DistilGPT2](https://huggingface.co/distilgpt2) English language model fine-tuned on mathematical proofs extracted from [arXiv.org](https://arxiv.org) LaTeX sources from 1992 to 2020.
Proofs have been cleaned up a bit. In particular, they use
* `CITE` for any citation
* `REF` for any reference
* `MATH` for any La... | {"widget": [{"text": "Let MATH be given."}, {"text": "If MATH is a nonempty"}, {"text": "By the inductive hypothesis,"}]} | christopherastone/distilgpt2-proofs | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DistilGPT2 English language model fine-tuned on mathematical proofs extracted from URL LaTeX sources from 1992 to 2020.
Proofs have been cleaned up a bit. In particular, they use
* 'CITE' for any citation
* 'REF' for any reference
* 'MATH' for any LaTeX mathematical formula
* 'CASE:' for any '\item' or labeled s... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-cola
This model is a fine-tuned version of [bert-base-multilingual-cased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": [{"name": "bert-base-multilingual-cased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9755}}]}]} | chrommium/bert-base-multilingual-cased-finetuned-news-headlines | 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-multilingual-cased-finetuned-cola
===========================================
This model is a fine-tuned version of bert-base-multilingual-cased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1729
* Accuracy: 0.9755
Model description
-----------------
More inf... | [
"### 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 #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: 2e-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. -->
# rubert-base-cased-sentence-finetuned-headlines_X
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](h... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | chrommium/rubert-base-cased-sentence-finetuned-headlines_X | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us
| rubert-base-cased-sentence-finetuned-headlines\_X
=================================================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased-sentence on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2535
* Accuracy: 0.952
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# rubert-base-cased-sentence-finetuned-sent_in_news_sents
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sent... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"]} | chrommium/rubert-base-cased-sentence-finetuned-sent_in_news_sents | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us
| rubert-base-cased-sentence-finetuned-sent\_in\_news\_sents
==========================================================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased-sentence on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9506
* Accuracy: 0.7224
* F1: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
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. -->
# rubert-base-cased-sentence-finetuned-sent_in_ru
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](ht... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "rubert-base-cased-sentence-finetuned-sent_in_ru", "results": []}]} | chrommium/rubert-base-cased-sentence-finetuned-sent_in_ru | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| rubert-base-cased-sentence-finetuned-sent\_in\_ru
=================================================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased-sentence on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3503
* Accuracy: 0.6884
* F1: 0.6875
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #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: 5e-05\n* train\\_batch\\_size: 15\n* eval\\_... |
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. -->
# sbert_large-finetuned-sent_in_news_sents
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingf... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sbert_large-finetuned-sent_in_news_sents", "results": []}]} | chrommium/sbert_large-finetuned-sent_in_news_sents | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| sbert\_large-finetuned-sent\_in\_news\_sents
============================================
This model is a fine-tuned version of sberbank-ai/sbert\_large\_nlu\_ru on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7056
* Accuracy: 0.7301
* F1: 0.5210
Model examples
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #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: 6\n* eval\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sbert_large-finetuned-sent_in_news_sents_3lab
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://hug... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sbert_large-finetuned-sent_in_news_sents_3lab", "results": []}]} | chrommium/sbert_large-finetuned-sent_in_news_sents_3lab | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| sbert\_large-finetuned-sent\_in\_news\_sents\_3lab
==================================================
This model is a fine-tuned version of sberbank-ai/sbert\_large\_nlu\_ru on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9443
* Accuracy: 0.8580
* F1: 0.6199
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 17",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #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: 4\n* eval\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-sent_in_news
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-ro... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "xlm-roberta-large-finetuned-sent_in_news", "results": []}]} | chrommium/xlm-roberta-large-finetuned-sent_in_news | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-large-finetuned-sent\_in\_news
==========================================
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8872
* Accuracy: 0.7273
* F1: 0.5125
Model description
-----------------
Модель ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #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: 3e-05\n* train\\_batch\\... |
text-generation | transformers |
[blenderbot-400M-distill](https://huggingface.co/facebook/blenderbot-400M-distill) fine-tuned on the [ESConv dataset](https://github.com/thu-coai/Emotional-Support-Conversation). Usage example:
```python
import torch
from transformers import AutoTokenizer
from transformers.models.blenderbot import BlenderbotTokenizer... | {"language": ["en"], "tags": ["pytorch", "coai"], "pipeline_tag": "conversational"} | thu-coai/blenderbot-400M-esconv | null | [
"transformers",
"pytorch",
"safetensors",
"blenderbot",
"text2text-generation",
"coai",
"conversational",
"en",
"arxiv:2106.01144",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.01144"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #blenderbot #text2text-generation #coai #conversational #en #arxiv-2106.01144 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
blenderbot-400M-distill fine-tuned on the ESConv dataset. Usage example:
Please kindly cite the original paper if you use this model:
| [] | [
"TAGS\n#transformers #pytorch #safetensors #blenderbot #text2text-generation #coai #conversational #en #arxiv-2106.01144 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
## EnDR-BERT
EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization and... | {"language": ["ru", "en"], "tags": ["bio", "med", "biomedical"]} | cimm-kzn/endr-bert | null | [
"transformers",
"pytorch",
"bio",
"med",
"biomedical",
"ru",
"en",
"arxiv:2004.03659",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.03659"
] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us
|
## EnDR-BERT
EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Mult... | [
"## EnDR-BERT\n\n EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as i... | [
"TAGS\n#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us \n",
"## EnDR-BERT\n\n EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code ... |
null | transformers |
## EnRuDR-BERT
EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particu... | {"language": ["ru", "en"], "tags": ["bio", "med", "biomedical"]} | cimm-kzn/enrudr-bert | null | [
"transformers",
"pytorch",
"bio",
"med",
"biomedical",
"ru",
"en",
"arxiv:2004.03659",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.03659"
] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us
|
## EnRuDR-BERT
EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initializa... | [
"## EnRuDR-BERT\n\nEnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for init... | [
"TAGS\n#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us \n",
"## EnRuDR-BERT\n\nEnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]... |
null | transformers | ## RuDR-BERT
RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens ... | {"language": ["ru"], "tags": ["bio", "med", "biomedical"]} | cimm-kzn/rudr-bert | null | [
"transformers",
"pytorch",
"bio",
"med",
"biomedical",
"ru",
"arxiv:2004.03659",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.03659"
] | [
"ru"
] | TAGS
#transformers #pytorch #bio #med #biomedical #ru #arxiv-2004.03659 #endpoints_compatible #region-us
| ## RuDR-BERT
RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BER... | [
"## RuDR-BERT\n\nRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Mu... | [
"TAGS\n#transformers #pytorch #bio #med #biomedical #ru #arxiv-2004.03659 #endpoints_compatible #region-us \n",
"## RuDR-BERT\n\nRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular... |
null | null | End-2-End with english | {} | cjcu/End2End-asr | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| End-2-End with english | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afriberta_base-finetuned-tydiqa
This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorin... | {"language": ["sw"], "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "afriberta_base-finetuned-tydiqa", "results": []}]} | cjrowe/afriberta_base-finetuned-tydiqa | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"sw",
"dataset:tydiqa",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sw"
] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #sw #dataset-tydiqa #endpoints_compatible #region-us
| afriberta\_base-finetuned-tydiqa
================================
This model is a fine-tuned version of castorini/afriberta\_base on the tydiqa dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3728
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #sw #dataset-tydiqa #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eva... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nlu_sherlock_model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlu_sherlock_model", "results": []}]} | ckenlam/nlu_sherlock_model | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# nlu_sherlock_model
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
... | [
"# nlu_sherlock_model\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMor... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# nlu_sherlock_model\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nlu_sherlock_model_20220220
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown da... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlu_sherlock_model_20220220", "results": []}]} | ckenlam/nlu_sherlock_model_20220220 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# nlu_sherlock_model_20220220
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information... | [
"# nlu_sherlock_model_20220220\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation da... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# nlu_sherlock_model_20220220\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:... |
token-classification | transformers |
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-base-chinese-ner | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-... |
token-classification | transformers |
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-base-chinese-pos | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-... |
token-classification | transformers |
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-base-chinese-ws | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-... |
fill-mask | transformers |
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-base-chinese | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of... |
token-classification | transformers |
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-tiny-chinese-ner | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-... |
token-classification | transformers |
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-tiny-chinese-pos | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-... |
token-classification | transformers |
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-tiny-chinese-ws | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmenta... |
fill-mask | transformers |
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gi... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/albert-tiny-chinese | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Co... | [
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentati... |
token-classification | transformers |
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gith... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/bert-base-chinese-ner | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Cont... | [
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n\n... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segment... |
token-classification | transformers |
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gith... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/bert-base-chinese-pos | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Cont... | [
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n\n... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part... |
token-classification | transformers |
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gith... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/bert-base-chinese-ws | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Cont... | [
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n\n... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part... |
fill-mask | transformers |
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gith... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/bert-base-chinese | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Cont... | [
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n\n... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentat... |
text-generation | transformers |
# CKIP GPT2 Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://gith... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "gpt2", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/gpt2-base-chinese | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# CKIP GPT2 Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- URL
## Cont... | [
"# CKIP GPT2 Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。",
"## Homepage\n\n... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# CKIP GPT2 Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NL... |
fill-mask | transformers |
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary ... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]} | tohoku-nlp/bert-base-japanese-char-v2 | null | [
"transformers",
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"fill-mask",
"ja",
"dataset:wikipedia",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followe... | [
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package),... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese... |
fill-mask | transformers |
# BERT base Japanese (character tokenization, whole word masking enabled)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u4ed9\u53f0\u306f\u300c[MASK]\u306e\u90fd\u300d\u3068\u547c\u3070\u308c\u3066\u3044\u308b\u3002"}]} | tohoku-nlp/bert-base-japanese-char-whole-word-masking | null | [
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"fill-mask",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base Japanese (character tokenization, whole word masking enabled)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.
Additionally, the model is t... | [
"# BERT base Japanese (character tokenization, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\nAdditionally, the m... | [
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"# BERT base Japanese (character tokenization, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis ... |
fill-mask | transformers |
# BERT base Japanese (character tokenization)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.
The codes fo... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u4ed9\u53f0\u306f\u300c[MASK]\u306e\u90fd\u300d\u3068\u547c\u3070\u308c\u3066\u3044\u308b\u3002"}]} | tohoku-nlp/bert-base-japanese-char | null | [
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"dataset:wikipedia",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base Japanese (character tokenization)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.
The codes for the pretraining are available at cl-tohok... | [
"# BERT base Japanese (character tokenization)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\n\nThe codes for the pretraining are available a... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base Japanese (character tokenization)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model process... |
fill-mask | transformers |
# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [un... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]} | tohoku-nlp/bert-base-japanese-v2 | null | [
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"dataset:wikipedia",
"license:cc-by-sa-4.0",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiec... | [
"# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese langu... |
fill-mask | transformers |
# BERT base Japanese (IPA dictionary, whole word masking enabled)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword t... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]} | tohoku-nlp/bert-base-japanese-whole-word-masking | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base Japanese (IPA dictionary, whole word masking enabled)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.
Additionally, the model is tra... | [
"# BERT base Japanese (IPA dictionary, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\nAdditionally, the mod... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base Japanese (IPA dictionary, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version ... |
fill-mask | transformers |
# BERT base Japanese (IPA dictionary)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.
The codes for ... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]} | tohoku-nlp/bert-base-japanese | null | [
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"tf",
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"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base Japanese (IPA dictionary)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.
The codes for the pretraining are available at cl-tohoku/... | [
"# BERT base Japanese (IPA dictionary)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\n\nThe codes for the pretraining are available at ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base Japanese (IPA dictionary)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input... |
fill-mask | transformers |
# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]} | tohoku-nlp/bert-large-japanese-char | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), follow... | [
"# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package)... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanes... |
fill-mask | transformers |
# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [u... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]} | tohoku-nlp/bert-large-japanese | null | [
"transformers",
"pytorch",
"tf",
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"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)
This is a BERT model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPie... | [
"# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nTh... |
text-generation | transformers |
# A somewhat positive chatbot | {"tags": ["conversational"]} | clairesb/kindness_bot | 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
|
# A somewhat positive chatbot | [
"# A somewhat positive chatbot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# A somewhat positive chatbot"
] |
text-generation | transformers |
# Affirmation Bot | {"tags": ["conversational"]} | clairesb/kindness_bot_repo | 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
|
# Affirmation Bot | [
"# Affirmation Bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Affirmation Bot"
] |
text-classification | transformers |
# Multi-lingual sentiment prediction trained from COVID19-related tweets
Repository: [https://github.com/clampert/multilingual-sentiment-analysis/](https://github.com/clampert/multilingual-sentiment-analysis/)
Model trained on a large-scale (18437530 examples) dataset of
multi-lingual tweets that was collected betw... | {"language": "multilingual", "license": "apache-2.0", "tags": ["sentiment-analysis", "multilingual"], "pipeline_tag": "text-classification", "widget": [{"text": "I am very happy.", "example_title": "English"}, {"text": "Heute bin ich schlecht drauf.", "example_title": "Deutsch"}, {"text": "Quel cauchemard!", "example_t... | clampert/multilingual-sentiment-covid19 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"sentiment-analysis",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Multi-lingual sentiment prediction trained from COVID19-related tweets
Repository: URL
Model trained on a large-scale (18437530 examples) dataset of
multi-lingual tweets that was collected between March 2020
and November 2021 using Twitter’s Streaming API with varying
COVID19-related keywords. Labels were auto-g... | [
"# Multi-lingual sentiment prediction trained from COVID19-related tweets\n\nRepository: URL\n\nModel trained on a large-scale (18437530 examples) dataset of \nmulti-lingual tweets that was collected between March 2020 \nand November 2021 using Twitter’s Streaming API with varying\nCOVID19-related keywords. Labels ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Multi-lingual sentiment prediction trained from COVID19-related tweets\n\nRepository: URL\n\nModel trained on a large-scale (18437530... |
null | null |
# KGR10 FastText Polish word embeddings
Distributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):
- dimension: 100, 300
- method: skipgram, cbow
- tool: FastText, Magnit... | {"language": "pl", "tags": ["fastText"], "datasets": ["kgr10"]} | clarin-pl/fastText-kgr10 | null | [
"fastText",
"pl",
"dataset:kgr10",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#fastText #pl #dataset-kgr10 #region-us
|
# KGR10 FastText Polish word embeddings
Distributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):
- dimension: 100, 300
- method: skipgram, cbow
- tool: FastText, Magnit... | [
"# KGR10 FastText Polish word embeddings\n\nDistributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):\n- dimension: 100, 300\n- method: skipgram, cbow\n- tool: FastTex... | [
"TAGS\n#fastText #pl #dataset-kgr10 #region-us \n",
"# KGR10 FastText Polish word embeddings\n\nDistributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):\n- dimensio... |
fill-mask | transformers | # Work in Progress Polish RoBERTa
The model has been trained for about 5% time of the target. We will publish new increments as they will be trained.
The model pre-trained on KGR10 corpora.
More about model at [CLARIN-dspace](https://huggingface.co/clarin/roberta-polish-v1)
## Usage
## Huggingface model hub
## ... | {} | clarin-pl/roberta-polish-kgr10 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Work in Progress Polish RoBERTa
The model has been trained for about 5% time of the target. We will publish new increments as they will be trained.
The model pre-trained on KGR10 corpora.
More about model at CLARIN-dspace
## Usage
## Huggingface model hub
## Acknowledgments
CLARIN-PL and CLARIN-BIZ project | [
"# Work in Progress Polish RoBERTa \n\nThe model has been trained for about 5% time of the target. We will publish new increments as they will be trained. \n\nThe model pre-trained on KGR10 corpora.\n\nMore about model at CLARIN-dspace",
"## Usage",
"## Huggingface model hub",
"## Acknowledgments\n\nCLARIN-PL... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Work in Progress Polish RoBERTa \n\nThe model has been trained for about 5% time of the target. We will publish new increments as they will be trained. \n\nThe model pre-trained on KGR1... |
null | null |
# KGR10 word2vec Polish word embeddings
Distributional language models for Polish trained on the KGR10 corpora.
## Models
In the repository you can find two selected models, that were selected after evaluation (see table below).
A model that performed the best is the default model/config (see `default_config.json`... | {"language": "pl", "tags": ["word2vec"], "datasets": ["KGR10"]} | clarin-pl/word2vec-kgr10 | null | [
"word2vec",
"pl",
"dataset:KGR10",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#word2vec #pl #dataset-KGR10 #has_space #region-us
| KGR10 word2vec Polish word embeddings
=====================================
Distributional language models for Polish trained on the KGR10 corpora.
Models
------
In the repository you can find two selected models, that were selected after evaluation (see table below).
A model that performed the best is the defaul... | [
"### Utilising the default model (the easiest way)\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):",
"### Customisable way\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):\n\n\nor"
] | [
"TAGS\n#word2vec #pl #dataset-KGR10 #has_space #region-us \n",
"### Utilising the default model (the easiest way)\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):",
"### Customisable way\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):\n\n\nor"
] |
text-classification | transformers | # bcms-bertic-frenk-hate
Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fi... | {"language": "hr", "license": "cc-by-sa-4.0", "tags": ["text-classification", "hate-speech"], "widget": [{"text": "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'."}]} | classla/bcms-bertic-frenk-hate | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"hate-speech",
"hr",
"arxiv:1906.02045",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.02045"
] | [
"hr"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| bcms-bertic-frenk-hate
======================
Text classification model based on 'classla/bcms-bertic' and fine-tuned on the FRENK dataset comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian
* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames ... | {"language": ["hr", "bs", "sr", "cnr", "hbs"], "license": "apache-2.0", "tags": ["masked-lm"], "widget": [{"text": "Zovem se Marko i radim u [MASK]."}]} | classla/bcms-bertic-generator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"masked-lm",
"hr",
"bs",
"sr",
"cnr",
"hbs",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr",
"bs",
"sr",
"cnr",
"hbs"
] | TAGS
#transformers #pytorch #electra #pretraining #masked-lm #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #region-us
|
# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian
* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames ... | [
"# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian\n\n* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most sur... | [
"TAGS\n#transformers #pytorch #electra #pretraining #masked-lm #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian\n\n* The name should resemble the facts (1) that the ... |
token-classification | transformers |
# The [BERTić](https://huggingface.co/classla/bcms-bertic)* [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS)
* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -i... | {"language": ["hr", "bs", "sr", "cnr", "hbs"], "license": "apache-2.0", "widget": [{"text": "Zovem se Marko i \u017eivim u Zagrebu. Studirao sam u Beogradu na Filozofskom fakultetu. Obo\u017eavam album Moanin."}]} | classla/bcms-bertic-ner | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"hr",
"bs",
"sr",
"cnr",
"hbs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr",
"bs",
"sr",
"cnr",
"hbs"
] | TAGS
#transformers #pytorch #safetensors #electra #token-classification #hr #bs #sr #cnr #hbs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# The BERTić* [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS)
* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very... | [
"# The BERTić* [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS)\n\n* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) ar... | [
"TAGS\n#transformers #pytorch #safetensors #electra #token-classification #hr #bs #sr #cnr #hbs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# The BERTić* [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montene... |
null | transformers |
# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian
* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames ... | {"language": ["hr", "bs", "sr", "cnr", "hbs"], "license": "apache-2.0"} | classla/bcms-bertic | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"hr",
"bs",
"sr",
"cnr",
"hbs",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr",
"bs",
"sr",
"cnr",
"hbs"
] | TAGS
#transformers #pytorch #electra #pretraining #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #has_space #region-us
| BERTić\* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian
===========================================================================================================
\* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminut... | [
"### Part-of-speech tagging\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001... | [
"TAGS\n#transformers #pytorch #electra #pretraining #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Part-of-speech tagging\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is c... |
text-classification | transformers |
# roberta-base-frenk-hate
Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning ... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["text-classification", "hate-speech"], "widget": [{"text": "Gay is okay."}]} | classla/roberta-base-frenk-hate | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"hate-speech",
"en",
"arxiv:1907.11692",
"arxiv:1906.02045",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"1906.02045"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #hate-speech #en #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| roberta-base-frenk-hate
=======================
Text classification model based on 'roberta-base' and fine-tuned on the FRENK dataset comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or accep... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #hate-speech #en #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
Text classification model based on `EMBEDDIA/sloberta` and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the slovenian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (... | {"language": "sl", "license": "cc-by-sa-4.0", "tags": ["text-classification", "hate-speech"], "widget": [{"text": "Silva, ti si grda in neprijazna"}]} | classla/sloberta-frenk-hate | null | [
"transformers",
"pytorch",
"safetensors",
"camembert",
"text-classification",
"hate-speech",
"sl",
"arxiv:1907.11692",
"arxiv:1906.02045",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"1906.02045"
] | [
"sl"
] | TAGS
#transformers #pytorch #safetensors #camembert #text-classification #hate-speech #sl #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Text classification model based on 'EMBEDDIA/sloberta' and fine-tuned on the FRENK dataset comprising of LGBT and migrant hatespeech. Only the slovenian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).
Fine-tuning hyperparameters
-----... | [] | [
"TAGS\n#transformers #pytorch #safetensors #camembert #text-classification #hate-speech #sl #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-parlaspeech-hr
This model for Croatian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpeech-HR v1.0](http://hdl.handle.net/113... | {"language": "hr", "tags": ["audio", "automatic-speech-recognition", "parlaspeech"], "datasets": ["parlaspeech-hr"], "widget": [{"example_title": "example 1", "src": "https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a"}, {"example_title": "example 2", "src": "https://huggingface.co/classla/w... | classla/wav2vec2-xls-r-parlaspeech-hr | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"parlaspeech",
"hr",
"dataset:parlaspeech-hr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us
| wav2vec2-xls-r-parlaspeech-hr
=============================
This model for Croatian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset ParlaSpeech-HR v1.0.
If you use this model, please cite the following pa... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# hiccupBot medium GPT | {"tags": ["conversational"]} | clayfox/DialoGPT-medium-Hiccup | 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
|
# hiccupBot medium GPT | [
"# hiccupBot medium GPT"
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"# hiccupBot medium GPT"
] |
text-generation | transformers |
# HiccupBot DialoGPT Model | {"tags": ["conversational"]} | clayfox/DialoGPT-small-Hiccup | 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
|
# HiccupBot DialoGPT Model | [
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"# HiccupBot DialoGPT Model"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101779
## Validation Metrics
- Loss: 0.282466858625412
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H... | {"language": "en", "tags": "autonlp", "datasets": ["clem/autonlp-data-test3"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | clem/autonlp-test3-2101779 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:clem/autonlp-data-test3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101779
## Validation Metrics
- Loss: 0.282466858625412
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101779",
"## Validation Metrics\n\n- Loss: 0.282466858625412\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
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"## Validation Metrics\n\n- Loss: 0.282466858625412\n-... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101782
## Validation Metrics
- Loss: 0.015991805121302605
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY"... | {"language": "en", "tags": "autonlp", "datasets": ["clem/autonlp-data-test3"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | clem/autonlp-test3-2101782 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:clem/autonlp-data-test3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2101782
## Validation Metrics
- Loss: 0.015991805121302605
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101782",
"## Validation Metrics\n\n- Loss: 0.015991805121302605\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
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"## Validation Metrics\n\n- Loss: 0.015991805121302605... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification Urgent/Not Urgent
## Validation Metrics
- Loss: 0.08956164121627808
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H... | {"language": "en", "tags": "autonlp", "datasets": ["clem/autonlp-data-test3"], "widget": [{"text": "this can wait"}]} | clem/autonlp-test3-2101787 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:clem/autonlp-data-test3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification Urgent/Not Urgent
## Validation Metrics
- Loss: 0.08956164121627808
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
Or Python API:
| [
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"## Validation Metrics\n\n- Loss: 0.08956164121627808\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
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"## Validation Metrics\n\n- Loss: 0.089561641216278... |
text-classification | transformers |
# Model Card for distilroberta-base-climate-commitment
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and action... | {"language": ["en"], "license": "apache-2.0", "datasets": ["climatebert/climate_commitments_actions"], "metrics": ["accuracy"]} | climatebert/distilroberta-base-climate-commitment | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:climatebert/climate_commitments_actions",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_commitments_actions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for distilroberta-base-climate-commitment
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and action... | [
"# Model Card for distilroberta-base-climate-commitment",
"## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments ... | [
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"# Model Card for distilroberta-base-climate-commitment",
"## Model Description\n\nThis is the fine-tuned ... |
fill-mask | transformers |
# Model Card for distilroberta-base-climate-d-s
## Model Description
This is the ClimateBERT language model based on the DIV-SELECT and SIM-SELECT sample selection strategy.
*Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) lan... | {"language": "en", "license": "apache-2.0", "tags": ["climate"]} | climatebert/distilroberta-base-climate-d-s | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"climate",
"en",
"arxiv:2110.12010",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.12010"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model Card for distilroberta-base-climate-d-s
=============================================
Model Description
-----------------
This is the ClimateBERT language model based on the DIV-SELECT and SIM-SELECT sample selection strategy.
*Note: We generally recommend choosing the distilroberta-base-climate-f language ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Model Card for distilroberta-base-climate-d
## Model Description
This is the ClimateBERT language model based on the DIV-SELECT sample selection strategy.
*Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over ... | {"language": "en", "license": "apache-2.0"} | climatebert/distilroberta-base-climate-d | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"arxiv:2110.12010",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.12010"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model Card for distilroberta-base-climate-d
===========================================
Model Description
-----------------
This is the ClimateBERT language model based on the DIV-SELECT sample selection strategy.
*Note: We generally recommend choosing the distilroberta-base-climate-f language model over this lan... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Card for distilroberta-base-climate-detector
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.
Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) langu... | {"language": ["en"], "license": "apache-2.0", "datasets": ["climatebert/climate_detection"], "metrics": ["accuracy"]} | climatebert/distilroberta-base-climate-detector | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:climatebert/climate_detection",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_detection #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Card for distilroberta-base-climate-detector
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.
Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-detecto... | [
"# Model Card for distilroberta-base-climate-detector",
"## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-cli... | [
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"# Model Card for distilroberta-base-climate-detector",
"## Model Description\n\nThis is the fine-tuned C... |
fill-mask | transformers |
# Model Card for distilroberta-base-climate-f
## Model Description
This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy.
*Note: We generally recommend choosing this language model over those based on the other sample selection strategies (unless you have good reasons not to). T... | {"language": "en", "license": "apache-2.0", "tags": ["climate"]} | climatebert/distilroberta-base-climate-f | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"climate",
"en",
"arxiv:2110.12010",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.12010"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Card for distilroberta-base-climate-f
===========================================
Model Description
-----------------
This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy.
*Note: We generally recommend choosing this language model over those based on the other sample sel... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# Model Card for distilroberta-base-climate-s
## Model Description
This is the ClimateBERT language model based on the SIM-SELECT sample selection strategy.
*Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over ... | {"language": "en", "license": "apache-2.0"} | climatebert/distilroberta-base-climate-s | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"en",
"arxiv:2110.12010",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.12010"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model Card for distilroberta-base-climate-s
===========================================
Model Description
-----------------
This is the ClimateBERT language model based on the SIM-SELECT sample selection strategy.
*Note: We generally recommend choosing the distilroberta-base-climate-f language model over this lan... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Card for distilroberta-base-climate-sentiment
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk.
Using the [climatebert/distilroberta-base-clima... | {"language": ["en"], "license": "apache-2.0", "datasets": ["climatebert/climate_sentiment"], "metrics": ["accuracy"]} | climatebert/distilroberta-base-climate-sentiment | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:climatebert/climate_sentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_sentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for distilroberta-base-climate-sentiment
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk.
Using the climatebert/distilroberta-base-climat... | [
"# Model Card for distilroberta-base-climate-sentiment",
"## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk.\n\nUsing the climatebert/distilroberta... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_sentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for distilroberta-base-climate-sentiment",
"## Model Description\n\nThis is the fine-tuned ClimateBERT... |
text-classification | transformers |
# Model Card for distilroberta-base-climate-specificity
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs.
Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/... | {"language": ["en"], "license": "apache-2.0", "tags": ["climate"], "datasets": ["climatebert/climate_specificity"], "metrics": ["accuracy"]} | climatebert/distilroberta-base-climate-specificity | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"climate",
"en",
"dataset:climatebert/climate_specificity",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/climate_specificity #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Card for distilroberta-base-climate-specificity
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs.
Using the climatebert/distilroberta-base-climate-f language model as startin... | [
"# Model Card for distilroberta-base-climate-specificity",
"## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language mode... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/climate_specificity #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Card for distilroberta-base-climate-specificity",
"## Model Description\n\nThis is th... |
text-classification | transformers |
# Model Card for distilroberta-base-climate-tcfd
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories ([fsb-tcfd.org](https://www.fsb-tcfd.org)).
Using the [climatebert/distilroberta-... | {"language": ["en"], "license": "apache-2.0", "tags": ["climate"], "datasets": ["climatebert/tcfd_recommendations"], "metrics": ["accuracy"]} | climatebert/distilroberta-base-climate-tcfd | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"climate",
"en",
"dataset:climatebert/tcfd_recommendations",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/tcfd_recommendations #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for distilroberta-base-climate-tcfd
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories (URL).
Using the climatebert/distilroberta-base-climate-f language model as start... | [
"# Model Card for distilroberta-base-climate-tcfd",
"## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories (URL).\n\nUsing the climatebert/distilroberta-base-climate-f language mo... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/tcfd_recommendations #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for distilroberta-base-climate-tcfd",
"## Model Description\n\nThis is the fine-tuned Clim... |
null | transformers |
# CLIP-Italian
CLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision.
We fine-tuned a competitive Italian CLIP model with only ~1.4 million Itali... | {"language": "it", "tags": ["italian", "bert", "vit", "vision"], "datasets": ["wit", "ctl/conceptualCaptions", "mscoco-it"]} | clip-italian/clip-italian-final | null | [
"transformers",
"jax",
"hybrid-clip",
"italian",
"bert",
"vit",
"vision",
"it",
"dataset:wit",
"dataset:ctl/conceptualCaptions",
"dataset:mscoco-it",
"arxiv:2103.00020",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.00020"
] | [
"it"
] | TAGS
#transformers #jax #hybrid-clip #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2103.00020 #endpoints_compatible #region-us
|
# CLIP-Italian
CLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision.
We fine-tuned a competitive Italian CLIP model with only ~1.4 million Itali... | [
"# CLIP-Italian\nCLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision. \n\nWe fine-tuned a competitive Italian CLIP model with only ~1.4 millio... | [
"TAGS\n#transformers #jax #hybrid-clip #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2103.00020 #endpoints_compatible #region-us \n",
"# CLIP-Italian\nCLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) wa... |
feature-extraction | transformers |
# Italian CLIP
Paper: [Contrastive Language-Image Pre-training for the Italian Language](https://arxiv.org/abs/2108.08688)
With a few tricks, we have been able to fine-tune a competitive Italian CLIP model with **only 1.4 million** training samples. Our Italian CLIP model is built upon the [Italian BERT](https://hug... | {"language": "it", "license": "gpl-3.0", "tags": ["italian", "bert", "vit", "vision"], "datasets": ["wit", "ctl/conceptualCaptions", "mscoco-it"]} | clip-italian/clip-italian | null | [
"transformers",
"pytorch",
"jax",
"vision-text-dual-encoder",
"feature-extraction",
"italian",
"bert",
"vit",
"vision",
"it",
"dataset:wit",
"dataset:ctl/conceptualCaptions",
"dataset:mscoco-it",
"arxiv:2108.08688",
"arxiv:2103.01913",
"arxiv:2103.00020",
"license:gpl-3.0",
"endpoi... | null | 2022-03-02T23:29:05+00:00 | [
"2108.08688",
"2103.01913",
"2103.00020"
] | [
"it"
] | TAGS
#transformers #pytorch #jax #vision-text-dual-encoder #feature-extraction #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2108.08688 #arxiv-2103.01913 #arxiv-2103.00020 #license-gpl-3.0 #endpoints_compatible #has_space #region-us
| Italian CLIP
============
Paper: Contrastive Language-Image Pre-training for the Italian Language
With a few tricks, we have been able to fine-tune a competitive Italian CLIP model with only 1.4 million training samples. Our Italian CLIP model is built upon the Italian BERT model provided by dbmdz and the OpenAI vi... | [
"### mCLIP\n\n\nThe multilingual CLIP (henceforth, mCLIP), is a model introduced by Nils Reimers in his\nsentence-transformer library. mCLIP is based on a multilingual encoder\nthat was created through multilingual knowledge distillation (see Reimers et al., 2020).",
"### Tasks\n\n\nWe selected two different task... | [
"TAGS\n#transformers #pytorch #jax #vision-text-dual-encoder #feature-extraction #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2108.08688 #arxiv-2103.01913 #arxiv-2103.00020 #license-gpl-3.0 #endpoints_compatible #has_space #region-us \n",
"### mCLIP\n\n\nT... |
feature-extraction | transformers | # CoNTACT
### Model description
<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or **CoNTACT** is a Dutch RobBERT model (```pdelobelle/robbert-v2-dutch-base```) adapted to the domain of COVID-19 tweets. The model was developed at [CLiPS](https://www.uantwerpen.be/en/... | {} | clips/contact | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.07362",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.07362"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2203.07362 #endpoints_compatible #region-us
| # CoNTACT
### Model description
<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or CoNTACT is a Dutch RobBERT model () adapted to the domain of COVID-19 tweets. The model was developed at CLiPS by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelemans. A full... | [
"# CoNTACT",
"### Model description\n\n<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or CoNTACT is a Dutch RobBERT model () adapted to the domain of COVID-19 tweets. The model was developed at CLiPS by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelem... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2203.07362 #endpoints_compatible #region-us \n",
"# CoNTACT",
"### Model description\n\n<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or CoNTACT is a Dutch RobBERT model () adapted to the domai... |
sentence-similarity | sentence-transformers |
# MFAQ
We present a multilingual FAQ retrieval model trained on the [MFAQ dataset](https://huggingface.co/datasets/clips/mfaq), it ranks candidate answers according to a given question.
## Installation
```
pip install sentence-transformers transformers
```
## Usage
You can use MFAQ with sentence-transformers or di... | {"language": ["cs", "da", "de", "en", "es", "fi", "fr", "he", "hr", "hu", "id", "it", "nl", "no", "pl", "pt", "ro", "ru", "sv", "tr", "vi"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["clips/mfaq"], "pipeline_tag": "sentence-simi... | clips/mfaq | null | [
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"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"cs",
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"nl",
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"pl",
"pt",
"ro",
"ru",
"sv",
"tr",
"vi",
"dataset:clips/m... | null | 2022-03-02T23:29:05+00:00 | [
"2109.12870"
] | [
"cs",
"da",
"de",
"en",
"es",
"fi",
"fr",
"he",
"hr",
"hu",
"id",
"it",
"nl",
"no",
"pl",
"pt",
"ro",
"ru",
"sv",
"tr",
"vi"
] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #cs #da #de #en #es #fi #fr #he #hr #hu #id #it #nl #no #pl #pt #ro #ru #sv #tr #vi #dataset-clips/mfaq #arxiv-2109.12870 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# MFAQ
We present a multilingual FAQ retrieval model trained on the MFAQ dataset, it ranks candidate answers according to a given question.
## Installation
## Usage
You can use MFAQ with sentence-transformers or directly with a HuggingFace model.
In both cases, questions need to be prepended with '<Q>', and answ... | [
"# MFAQ\n\nWe present a multilingual FAQ retrieval model trained on the MFAQ dataset, it ranks candidate answers according to a given question.",
"## Installation",
"## Usage\nYou can use MFAQ with sentence-transformers or directly with a HuggingFace model. \nIn both cases, questions need to be prepended with '... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #cs #da #de #en #es #fi #fr #he #hr #hu #id #it #nl #no #pl #pt #ro #ru #sv #tr #vi #dataset-clips/mfaq #arxiv-2109.12870 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# MFAQ\n\nW... |
null | transformers |
## albert_chinese_small
### Overview
**Language model:** albert-small
**Model size:** 18.5M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks li... | {"language": "zh"} | clue/albert_chinese_small | null | [
"transformers",
"pytorch",
"albert",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #zh #endpoints_compatible #region-us
|
## albert_chinese_small
### Overview
Language model: albert-small
Model size: 18.5M
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE:Since sentencepiece is not used in 'al... | [
"## albert_chinese_small",
"### Overview\n\nLanguage model: albert-small\nModel size: 18.5M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE:Since senten... | [
"TAGS\n#transformers #pytorch #albert #zh #endpoints_compatible #region-us \n",
"## albert_chinese_small",
"### Overview\n\nLanguage model: albert-small\nModel size: 18.5M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text cl... |
null | transformers |
## albert_chinese_tiny
### Overview
**Language model:** albert-tiny
**Model size:** 16M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like t... | {"language": "zh"} | clue/albert_chinese_tiny | null | [
"transformers",
"pytorch",
"albert",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #albert #zh #endpoints_compatible #region-us
|
## albert_chinese_tiny
### Overview
Language model: albert-tiny
Model size: 16M
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE:Since sentencepiece is not used in 'albert... | [
"## albert_chinese_tiny",
"### Overview\n\nLanguage model: albert-tiny\nModel size: 16M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE:Since sentencepi... | [
"TAGS\n#transformers #pytorch #albert #zh #endpoints_compatible #region-us \n",
"## albert_chinese_tiny",
"### Overview\n\nLanguage model: albert-tiny\nModel size: 16M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classi... |
null | transformers |
# Introduction
This model was trained on TPU and the details are as follows:
## Model
##
| Model_name | params | size | Training_corpus | Vocab |
| :------------------------------------------ | :----- | :------- | :----------------- | :-----------: |
| **`Ro... | {"language": "zh"} | clue/roberta_chinese_3L312_clue_tiny | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"arxiv:2003.01355",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.01355"
] | [
"zh"
] | TAGS
#transformers #pytorch #jax #roberta #zh #arxiv-2003.01355 #endpoints_compatible #region-us
| Introduction
============
This model was trained on TPU and the details are as follows:
Model
-----
### Usage
With the help ofHuggingface-Transformers 2.5.1, you could use these model as follows
'MODEL\_NAME'：
Details
-------
Please read <a href='URL/URL
Please visit our repository: URL
| [
"### Usage\n\n\nWith the help ofHuggingface-Transformers 2.5.1, you could use these model as follows\n\n\n'MODEL\\_NAME'：\n\n\n\nDetails\n-------\n\n\nPlease read <a href='URL/URL\n\n\nPlease visit our repository: URL"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #zh #arxiv-2003.01355 #endpoints_compatible #region-us \n",
"### Usage\n\n\nWith the help ofHuggingface-Transformers 2.5.1, you could use these model as follows\n\n\n'MODEL\\_NAME'：\n\n\n\nDetails\n-------\n\n\nPlease read <a href='URL/URL\n\n\nPlease visit our reposito... |
null | transformers |
## roberta_chinese_base
### Overview
**Language model:** roberta-base
**Model size:** 392M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks lik... | {"language": "zh"} | clue/roberta_chinese_base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us
|
## roberta_chinese_base
### Overview
Language model: roberta-base
Model size: 392M
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE: You have to call BertTokenizer instead... | [
"## roberta_chinese_base",
"### Overview\n\nLanguage model: roberta-base\nModel size: 392M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE: You have to ... | [
"TAGS\n#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us \n",
"## roberta_chinese_base",
"### Overview\n\nLanguage model: roberta-base\nModel size: 392M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like te... |
null | transformers |
## roberta_chinese_large
### Overview
**Language model:** roberta-large
**Model size:** 1.2G
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks l... | {"language": "zh"} | clue/roberta_chinese_large | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us
|
## roberta_chinese_large
### Overview
Language model: roberta-large
Model size: 1.2G
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE: You have to call BertTokenizer inste... | [
"## roberta_chinese_large",
"### Overview\n\nLanguage model: roberta-large\nModel size: 1.2G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE: You have t... | [
"TAGS\n#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us \n",
"## roberta_chinese_large",
"### Overview\n\nLanguage model: roberta-large\nModel size: 1.2G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like ... |
null | transformers |
## xlnet_chinese_large
### Overview
**Language model:** xlnet-large
**Model size:** 1.3G
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like ... | {"language": "zh"} | clue/xlnet_chinese_large | null | [
"transformers",
"pytorch",
"xlnet",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #xlnet #zh #endpoints_compatible #region-us
|
## xlnet_chinese_large
### Overview
Language model: xlnet-large
Model size: 1.3G
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
### About CLUE benchmark
Organization of La... | [
"## xlnet_chinese_large",
"### Overview\n\nLanguage model: xlnet-large\nModel size: 1.3G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage",
"### About CLUE ben... | [
"TAGS\n#transformers #pytorch #xlnet #zh #endpoints_compatible #region-us \n",
"## xlnet_chinese_large",
"### Overview\n\nLanguage model: xlnet-large\nModel size: 1.3G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classi... |
token-classification | transformers | DistilCamemBERT-NER
===================
We present DistilCamemBERT-NER, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Bapti... | {"language": "fr", "license": "mit", "datasets": ["Jean-Baptiste/wikiner_fr"], "widget": [{"text": "Boulanger, habitant \u00e0 Boulanger et travaillant dans le magasin Boulanger situ\u00e9 dans la ville de Boulanger. Boulanger a \u00e9crit le livre \u00e9ponyme Boulanger \u00e9dit\u00e9 par la maison d'\u00e9dition Bou... | cmarkea/distilcamembert-base-ner | null | [
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilCamemBERT-NER
===================
We present DistilCamemBERT-NER, which is DistilCamemBERT fine-tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by Jean-Baptiste/camembert-ner based on the CamemBERT model. The problem of the modelizations based on CamemBERT is at t... | [
"### Optimum + ONNX\n\n\nCitation\n--------"
] | [
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
zero-shot-classification | transformers |
DistilCamemBERT-NLI
===================
We present DistilCamemBERT-NLI, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the Natural Language Inference (NLI) task for the french language, also known as recognizing textual entailment (RTE). This model is constructed on the... | {"language": "fr", "license": "mit", "tags": ["zero-shot-classification", "sentence-similarity", "nli"], "datasets": ["flue"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Selon certains physiciens, un univers parall\u00e8le, miroir du n\u00f4tre ou relevant de ce que l'on appelle la th\u00e9orie de... | cmarkea/distilcamembert-base-nli | null | [
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"text-classification",
"zero-shot-classification",
"sentence-similarity",
"nli",
"fr",
"dataset:flue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #zero-shot-classification #sentence-similarity #nli #fr #dataset-flue #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DistilCamemBERT-NLI
===================
We present DistilCamemBERT-NLI, which is DistilCamemBERT fine-tuned for the Natural Language Inference (NLI) task for the french language, also known as recognizing textual entailment (RTE). This model is constructed on the XNLI dataset, which determines whether a premise entai... | [
"### Optimum + ONNX\n\n\nCitation\n--------"
] | [
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #zero-shot-classification #sentence-similarity #nli #fr #dataset-flue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
question-answering | transformers |
DistilCamemBERT-QA
==================
We present DistilCamemBERT-QA, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the Question-Answering task for the french language. This model is built using two datasets, FQuAD v1.0 and Piaf, composed of contexts and questions with ... | {"language": "fr", "license": "cc-by-nc-sa-3.0", "datasets": ["fquad", "piaf"], "widget": [{"text": "Quand et o\u00f9 est sorti Toy Story ?", "context": "Pixar Animation Studios, ou simplement Pixar dans le langage courant, est une soci\u00e9t\u00e9 am\u00e9ricaine de production de films en images tridimensionnelles de... | cmarkea/distilcamembert-base-qa | null | [
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"question-answering",
"fr",
"dataset:fquad",
"dataset:piaf",
"license:cc-by-nc-sa-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #question-answering #fr #dataset-fquad #dataset-piaf #license-cc-by-nc-sa-3.0 #endpoints_compatible #region-us
| DistilCamemBERT-QA
==================
We present DistilCamemBERT-QA, which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language. This model is built using two datasets, FQuAD v1.0 and Piaf, composed of contexts and questions with their answers inside the context.
This modelization i... | [
"### Optimum + ONNX\n\n\nCitation\n--------"
] | [
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #question-answering #fr #dataset-fquad #dataset-piaf #license-cc-by-nc-sa-3.0 #endpoints_compatible #region-us \n",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
text-classification | transformers |
DistilCamemBERT-Sentiment
=========================
We present DistilCamemBERT-Sentiment, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: [Amazon Reviews](https://huggingface.co/... | {"language": "fr", "license": "mit", "datasets": ["amazon_reviews_multi", "allocine"], "widget": [{"text": "Je pensais lire un livre nul, mais finalement je l'ai trouv\u00e9 super !"}, {"text": "Cette banque est tr\u00e8s bien, mais elle n'offre pas les services de paiements sans contact."}, {"text": "Cette banque est ... | cmarkea/distilcamembert-base-sentiment | null | [
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"text-classification",
"fr",
"dataset:amazon_reviews_multi",
"dataset:allocine",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #fr #dataset-amazon_reviews_multi #dataset-allocine #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilCamemBERT-Sentiment
=========================
We present DistilCamemBERT-Sentiment, which is DistilCamemBERT fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: Amazon Reviews and Allociné.fr to minimize the bias. Indeed, Amazon reviews are similar in mess... | [
"#### bert-base-multilingual-uncased-sentiment\n\n\nnlptown/bert-base-multilingual-uncased-sentiment is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targets and their definitions are the same.",
"#### tf-allociné... | [
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #fr #dataset-amazon_reviews_multi #dataset-allocine #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### bert-base-multilingual-uncased-sentiment\n\n\nnlptown/bert-base-multilingual-uncased-se... |
fill-mask | transformers |
DistilCamemBERT
===============
We present a distillation version of the well named [CamemBERT](https://huggingface.co/camembert-base), a RoBERTa French model version, alias DistilCamemBERT. The aim of distillation is to drastically reduce the complexity of the model while preserving the performances. The proof of co... | {"language": "fr", "license": "mit", "datasets": ["oscar"], "widget": [{"text": "J'aime lire les <mask> de SF."}]} | cmarkea/distilcamembert-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1910.01108",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"fr"
] | TAGS
#transformers #pytorch #tf #safetensors #camembert #fill-mask #fr #dataset-oscar #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DistilCamemBERT
===============
We present a distillation version of the well named CamemBERT, a RoBERTa French model version, alias DistilCamemBERT. The aim of distillation is to drastically reduce the complexity of the model while preserving the performances. The proof of concept is shown in the DistilBERT paper an... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #camembert #fill-mask #fr #dataset-oscar #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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... | cnu/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"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 #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: 0.8651
* Matthews Correlation: 0.5475
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 #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\\_rate: 2e-0... |
fill-mask | transformers |
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council,... | {"language": "zh", "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "\u4e0a\u8ff0\u4e8b\u5b9e\uff0c\u88ab\u544a\u4eba\u5728\u5ead\u5ba1\u8fc7\u7a0b\u4e2d\u4ea6\u65e0\u5f02\u8bae\uff0c\u4e14\u6709<mask>\u7684\u9648\u8ff0\uff0c\u73b0\u573a\u8fa8\u8ba4\u7b14\u5f5... | coastalcph/fairlex-cail-minilm | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"legal",
"fairlex",
"zh",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #zh #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #zh #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council,... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "The applicant submitted that her husband was subjected to treatment amounting to <mask> whilst in the custody of Adana Security Directorate"}]} | coastalcph/fairlex-ecthr-minilm | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"legal",
"fairlex",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council... | {"language": ["de", "fr", "it"], "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "Aus seinem damaligen strafbaren Verhalten resultierte eine Forderung der Nachlassverwaltung eines <mask>, wor\u00fcber eine aussergerichtliche Vereinbarung \u00fcber Fr. 500'000... | coastalcph/fairlex-fscs-minilm | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"legal",
"fairlex",
"de",
"fr",
"it",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de",
"fr",
"it"
] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #de #fr #it #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #de #fr #it #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council,... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "Because the Court granted <mask> before judgment, the Court effectively stands in the shoes of the Court of Appeals and reviews the defendants\u2019 appeals."}]} | coastalcph/fairlex-scotus-minilm | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"legal",
"fairlex",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Kohaku DialoGPT Model | {"tags": ["conversational"]} | cocoaclef/DialoGPT-small-kohaku | 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
|
# Kohaku DialoGPT Model | [
"# Kohaku DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kohaku DialoGPT Model"
] |
text-generation | transformers |
# Rick Morty DialoGPT Model | {"tags": ["conversational"]} | codealtgeek/DiabloGPT-medium-rickmorty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Morty DialoGPT Model | [
"# Rick Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Morty DialoGPT Model"
] |
automatic-speech-recognition | transformers | HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was... | {} | codeceejay/HIYACCENT_Wav2Vec2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was... | [
"# Preprocessing the datasets.",
"# We need to read the audio files as arrays\ndef speech_file_to_array_fn(batch):\n speech_array, sampling_rate = URL(batch[\"path\"], sr=16_000)\n batch[\"speech\"] = speech_array\n batch[\"sentence\"] = batch[\"sentence\"].upper()\n return batch\n\ntest_dataset = tes... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# Preprocessing the datasets.",
"# We need to read the audio files as arrays\ndef speech_file_to_array_fn(batch):\n speech_array, sampling_rate = URL(batch[\"path\"], sr=16_000)\n batc... |
null | transformers |
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version and `base-uncased` (the one you're looking at) as well, and was pretrained on the [OSCAR dataset](https://... | {"language": "ca", "license": "mit", "tags": ["masked-lm", "catalan", "exbert"]} | codegram/calbert-base-uncased | null | [
"transformers",
"pytorch",
"albert",
"masked-lm",
"catalan",
"exbert",
"ca",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us
| Calbert: a Catalan Language Model
=================================
Introduction
------------
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its 'tiny-uncased' version and 'base-uncased' (the one you're looking at) as well, and wa... | [
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%... | [
"TAGS\n#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us \n",
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained a... |
null | transformers |
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version (the one you're looking at) and `base-uncased` as well, and was pretrained on the [OSCAR dataset](https://... | {"language": "ca", "license": "mit", "tags": ["masked-lm", "catalan", "exbert"]} | codegram/calbert-tiny-uncased | null | [
"transformers",
"pytorch",
"albert",
"masked-lm",
"catalan",
"exbert",
"ca",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us
| Calbert: a Catalan Language Model
=================================
Introduction
------------
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its 'tiny-uncased' version (the one you're looking at) and 'base-uncased' as well, and wa... | [
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%... | [
"TAGS\n#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us \n",
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained a... |
text2text-generation | transformers | This model is a paraphraser designed for the Adversarial Paraphrasing Task described and used in this paper: https://aclanthology.org/2021.acl-long.552/.
Please refer to `nap_generation.py` on the github repository for ways to better utilize this model using concepts of top-k sampling and top-p sampling. The demo on hu... | {} | AMHR/T5-for-Adversarial-Paraphrasing | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model is a paraphraser designed for the Adversarial Paraphrasing Task described and used in this paper: URL
Please refer to 'nap_generation.py' on the github repository for ways to better utilize this model using concepts of top-k sampling and top-p sampling. The demo on huggingface will output only one sentence w... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | This model is a paraphrase detector trained on the Adversarial Paraphrasing datasets described and used in this paper: https://aclanthology.org/2021.acl-long.552/.
Github repository: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git
Please cite the following if you use this model:
```bib
@inproceedings{... | {} | AMHR/adversarial-paraphrasing-detector | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a paraphrase detector trained on the Adversarial Paraphrasing datasets described and used in this paper: URL
Github repository: URL
Please cite the following if you use this model:
| [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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