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null | null | transformers | @Piglin Talks Harry Potter | {"tags": ["conversational"]} | text-generation | CracklesCreeper/Piglin-Talks-Harry-Potter | [
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# A model.
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null | null | sentence-transformers |
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is a clone of the original model, with `pipeline_tag` metadata chan... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | feature-extraction | Craig/paraphrase-MiniLM-L6-v2 | [
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|
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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null | null | transformers |
# Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
## Validation Metrics
- Loss: 0.4422711133956909
- Accuracy: 0.8615328555811976
- Macro F1: 0.8642434650461513
- Micro F1: 0.8615328555811976
- Weighted F1: 0.8617743626671308
- Macro Precision: 0.8649112225076049
-... | {"language": "en", "tags": "autonlp", "datasets": ["Crasher222/autonlp-data-kaggle-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 60.744727079482495} | text-classification | Crasher222/kaggle-comp-test | [
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# Model Finetuned from BERT-base for
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## Validation Metrics
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null | null | transformers | hello
| {} | text-generation | CrisLeaf/generador-de-historias-de-tolkien | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | text-classification | Crives/distilbert-base-uncased-finetuned-emotion | [
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"generated_from_trainer",
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2175
* Accuracy: 0.9215
* F1: 0.9216
Model description
-----------------
Mo... | [
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null | null | transformers | #rick DialoGPT Model | {"tags": ["conversational"]} | text-generation | Cryptikdw/DialoGPT-small-rick | [
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| #rick DialoGPT Model | [] | [
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null | null | transformers |
# Paladin Danse DialoGPT Model | {"tags": ["conversational"]} | text-generation | Cthyllax/DialoGPT-medium-PaladinDanse | [
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"conversational",
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | 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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "typ... | token-classification | Culmenus/IceBERT-finetuned-ner | [
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0807
* Precision: 0.8927
* Recall: 0.8632
* F1: 0.8777
* Accuracy: 0.9850
Model description
-----------------
More ... | [
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null | null | 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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "... | token-classification | Culmenus/XLMR-ENIS-finetuned-ner | [
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0891
* Precision: 0.8804
* Recall: 0.8517
* F1: 0.8658
* Accuracy: 0.9837
Model description
-----------------
... | [
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null | null | transformers |
# Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [Infore_25h dataset](https://files.huylenguyen.com/25hours.zip) (Password: BroughtToYouByInfoR... | {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice, infore_25h"], "metrics": ["wer"], "model-index": [{"name": "Cuong-Cong XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | automatic-speech-recognition | CuongLD/wav2vec2-large-xlsr-vietnamese | [
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|
# Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe)
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)... | [
"# Wav2Vec2-Large-XLSR-53-Vietnamese \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, Infore_25h dataset (Password: BroughtToYouByInfoRe)\n \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
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# Sora DialoGPT Model | {"tags": ["conversational"]} | text-generation | CurtisBowser/DialoGPT-medium-sora-two | [
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"conversational",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#pytorch #conversational #region-us
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null | null | transformers |
# Sora DialoGPT Model
| {"tags": ["conversational"]} | text-generation | CurtisBowser/DialoGPT-medium-sora | [
"transformers",
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
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null | null | transformers |
# Sora DialoGPT Model | {"tags": ["conversational"]} | text-generation | CurtisBowser/DialoGPT-small-sora | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
# Chandler Bot DialoGPT model | {"tags": ["conversational"]} | text-generation | CyberMuffin/DialoGPT-small-ChandlerBot | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | 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. -->
# electra-base-discriminator-finetuned-cola
This model is a fine-tuned version of [google/electra-base-discriminator](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "electra-base-discriminator-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", ... | text-classification | D3xter1922/electra-base-discriminator-finetuned-cola | [
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"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-base-discriminator-finetuned-cola
=========================================
This model is a fine-tuned version of google/electra-base-discriminator on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6367
* Matthews Correlation: 0.6824
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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null | null | transformers |
# Anakin Skywalker DialoGPT Model | {"tags": ["conversational"]} | text-generation | DARKVIP3R/DialoGPT-medium-Anakin | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
# bert-base-irish-cased-v1
[gaBERT](https://aclanthology.org/2022.lrec-1.511/) is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.
## Model description
Encoder-based Transformer to be used to obtain features f... | {"tags": ["generated_from_keras_callback"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}], "model-index": [{"name": "bert-base-irish-cased-v1", "results": []}]} | fill-mask | DCU-NLP/bert-base-irish-cased-v1 | [
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-irish-cased-v1
gaBERT is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.
## Model description
Encoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish.
... | [
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null | null | transformers |
# gaELECTRA
[gaELECTRA](https://aclanthology.org/2022.lrec-1.511/) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use t... | {"language": ["ga"], "license": "apache-2.0", "tags": ["irish", "electra"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}]} | null | DCU-NLP/electra-base-irish-cased-discriminator-v1 | [
"transformers",
"pytorch",
"electra",
"pretraining",
"irish",
"ga",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #electra #pretraining #irish #ga #license-apache-2.0 #endpoints_compatible #region-us
|
# gaELECTRA
gaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
### Limitations and ... | [
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null | null | transformers |
# gaELECTRA
[gaELECTRA](https://aclanthology.org/2022.lrec-1.511/) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use t... | {"language": ["ga"], "license": "apache-2.0", "tags": ["irish", "electra"], "widget": [{"text": "Ceolt\u00f3ir [MASK] ab ea Johnny Cash."}]} | fill-mask | DCU-NLP/electra-base-irish-cased-generator-v1 | [
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"electra",
"fill-mask",
"irish",
"ga",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #electra #fill-mask #irish #ga #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# gaELECTRA
gaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
### Limitations and ... | [
"# gaELECTRA\ngaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.",
"### Limitat... | [
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null | null | transformers |
# Danish BERT (uncased) model
[BotXO.ai](https://www.botxo.ai/) developed this model. For data and training details see their [GitHub repository](https://github.com/botxo/nordic_bert).
The original model was trained in TensorFlow then I converted it to Pytorch using [transformers-cli](https://huggingface.co/trans... | {"language": "da", "license": "cc-by-4.0", "tags": ["bert", "masked-lm"], "datasets": ["common_crawl", "wikipedia"], "pipeline_tag": "fill-mask", "widget": [{"text": "K\u00f8benhavn er [MASK] i Danmark."}]} | fill-mask | DJSammy/bert-base-danish-uncased_BotXO-ai | [
"transformers",
"pytorch",
"jax",
"bert",
"masked-lm",
"fill-mask",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #jax #bert #masked-lm #fill-mask #da #dataset-common_crawl #dataset-wikipedia #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Danish BERT (uncased) model
URL developed this model. For data and training details see their GitHub repository.
The original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli.
For TensorFlow version download here: URL
## Architecture
## Example Pipeline
| [
"# Danish BERT (uncased) model \n\nURL developed this model. For data and training details see their GitHub repository. \n\nThe original model was trained in TensorFlow then I converted it to Pytorch using transformers-cli.\n\nFor TensorFlow version download here: URL",
"## Architecture",
"## Example Pipeline"... | [
"TAGS\n#transformers #pytorch #jax #bert #masked-lm #fill-mask #da #dataset-common_crawl #dataset-wikipedia #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Danish BERT (uncased) model \n\nURL developed this model. For data and training details see their GitHub repository. \n\nThe original model was t... | [
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null | null | transformers | ** Human-Directed Sentiment Analysis in Arabic
A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion. | {} | text-classification | DSI/human-directed-sentiment | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Human-Directed Sentiment Analysis in Arabic
A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers |
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
[Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947)
This model... | {"language": ["multilingual", "nl", "fr", "en"], "tags": ["Tweets", "Sentiment analysis"], "widget": [{"text": "I really wish I could leave my house after midnight, this makes no sense!"}]} | text-classification | DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"Tweets",
"Sentiment analysis",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.09947"
] | [
"multilingual",
"nl",
"fr",
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #Tweets #Sentiment analysis #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us
|
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
Blog post » · paper »
This model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English.
We categorized severa... | [
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nThis model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English. \n\nWe categoriz... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #Tweets #Sentiment analysis #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n... | [
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null | null | transformers |
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
[Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947)
We categor... | {"language": ["multilingual", "nl", "fr", "en"], "tags": ["Dutch", "French", "English", "Tweets", "Topic classification"], "widget": [{"text": "I really can't wait for this lockdown to be over and go back to waking up early."}]} | text-classification | DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"Dutch",
"French",
"English",
"Tweets",
"Topic classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.09947"
] | [
"multilingual",
"nl",
"fr",
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #Dutch #French #English #Tweets #Topic classification #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us
|
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
Blog post » · paper »
We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the ... | [
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\nBlog post » · paper »\n\nWe categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) a... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #Dutch #French #English #Tweets #Topic classification #multilingual #nl #fr #en #arxiv-2104.09947 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT\... | [
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null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robb... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, een gedistilleer... | fill-mask | DTAI-KULeuven/robbertje-1-gb-bort | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints... | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch t... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
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0.065855361... |
null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robb... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, ee... | fill-mask | DTAI-KULeuven/robbertje-1-gb-merged | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch t... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robb... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png", "widget": [{"text": "Hallo, ik ben RobBERTje,... | fill-mask | DTAI-KULeuven/robbertje-1-gb-non-shuffled | [
"transformers",
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"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
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"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints... | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch t... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
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0.065855361... |
null | null | transformers |
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robb... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERTje, ee... | fill-mask | DTAI-KULeuven/robbertje-1-gb-shuffled | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2101.05716"
] | [
"nl"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
About RobBERTje
===============
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch t... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #RobBERTje #nl #arxiv-2101.05716 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
81
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null | null | transformers |
# Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not.
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data.
See the [DaNLP documentation](https://danlp-alexandra.... | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Der er et tr\u00e6 i haven."}]} | text-classification | alexandrainst/da-binary-emotion-classification-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not.
It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
... | [
"# Danish BERT for emotion detection\n\nThe BERT Emotion model detects whether a Danish text is emotional or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## T... | [
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null | null | transformers |
# Danish BERT for emotion classification
The BERT Emotion model classifies a Danish text in one of the following class:
* Glæde/Sindsro
* Tillid/Accept
* Forventning/Interrese
* Overasket/Målløs
* Vrede/Irritation
* Foragt/Modvilje
* Sorg/trist
* Frygt/Bekymret
It is based on the pretrained [Danish BERT](https://git... | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Jeg ejer en r\u00f8d bil og det er en god bil."}]} | text-classification | alexandrainst/da-emotion-classification-base | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Danish BERT for emotion classification
The BERT Emotion model classifies a Danish text in one of the following class:
* Glæde/Sindsro
* Tillid/Accept
* Forventning/Interrese
* Overasket/Målløs
* Vrede/Irritation
* Foragt/Modvilje
* Sorg/trist
* Frygt/Bekymret
It is based on the pretrained Danish BERT model by BotX... | [
"# Danish BERT for emotion classification\n\nThe BERT Emotion model classifies a Danish text in one of the following class:\n* Glæde/Sindsro\n* Tillid/Accept\n* Forventning/Interrese\n* Overasket/Målløs\n* Vrede/Irritation\n* Foragt/Modvilje\n* Sorg/trist\n* Frygt/Bekymret\n\nIt is based on the pretrained Danish BE... | [
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null | null | transformers |
# Danish BERT for hate speech classification
The BERT HateSpeech model classifies offensive Danish text into 4 categories:
* `Særlig opmærksomhed` (special attention, e.g. threat)
* `Personangreb` (personal attack)
* `Sprogbrug` (offensive language)
* `Spam & indhold` (spam)
This model is intended to be used af... | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]} | text-classification | alexandrainst/da-hatespeech-classification-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT for hate speech classification
The BERT HateSpeech model classifies offensive Danish text into 4 categories:
* 'Særlig opmærksomhed' (special attention, e.g. threat)
* 'Personangreb' (personal attack)
* 'Sprogbrug' (offensive language)
* 'Spam & indhold' (spam)
This model is intended to be used af... | [
"# Danish BERT for hate speech classification\n\nThe BERT HateSpeech model classifies offensive Danish text into 4 categories: \n * 'Særlig opmærksomhed' (special attention, e.g. threat)\n * 'Personangreb' (personal attack) \n * 'Sprogbrug' (offensive language)\n * 'Spam & indhold' (spam)\nThis model is intended to... | [
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null | null | transformers |
# Danish BERT for hate speech (offensive language) detection
The BERT HateSpeech model detects whether a Danish text is offensive or not.
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data.
See the [DaNLP documentati... | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]} | text-classification | alexandrainst/da-hatespeech-detection-base | [
"transformers",
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"tf",
"safetensors",
"bert",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT for hate speech (offensive language) detection
The BERT HateSpeech model detects whether a Danish text is offensive or not.
It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
See the DaNLP documentation for more details.
Here is how to use the ... | [
"# Danish BERT for hate speech (offensive language) detection\n\nThe BERT HateSpeech model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how... | [
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null | null | transformers |
# BERT fine-tuned for Named Entity Recognition in Danish
The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].
The pretrained language model used for fine-tuning is the [Danish BERT](https://github.com/certainlyio/nordic_bert) by BotXO.
See the [DaNLP documentation](... | {"language": ["da"], "license": "apache-2.0", "datasets": ["dane"], "widget": [{"text": "Jens Peter Hansen kommer fra Danmark"}]} | token-classification | alexandrainst/da-ner-base | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"da",
"dataset:dane",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #da #dataset-dane #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT fine-tuned for Named Entity Recognition in Danish
The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].
The pretrained language model used for fine-tuning is the Danish BERT by BotXO.
See the DaNLP documentation for more details.
Here is how to use the model:... | [
"# BERT fine-tuned for Named Entity Recognition in Danish\n\nThe model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC].\nThe pretrained language model used for fine-tuning is the Danish BERT by BotXO. \n\nSee the DaNLP documentation for more details.\n\nHere is how to use... | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #da #dataset-dane #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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null | null | transformers |
# Model Card for Danish BERT
Danish BERT Tone for sentiment polarity detection
# Model Details
## Model Description
The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.
- **Developed by:** DaNLP
... | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Det er super godt"}]} | text-classification | alexandrainst/da-sentiment-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1910.09700"
] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Danish BERT
Danish BERT Tone for sentiment polarity detection
# Model Details
## Model Description
The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.
- Developed by: DaNLP
- Sh... | [
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"# Model Details",
"## Model Description\n \nThe BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.\n \n- Developed by:... | [
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null | null | transformers |
# Danish BERT Tone for the detection of subjectivity/objectivity
The BERT Tone model detects whether a text (in Danish) is subjective or objective.
The model is based on the finetuning of the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO.
See the [DaNLP documentation](https://... | {"language": ["da"], "license": "apache-2.0", "datasets": ["DDSC/twitter-sent", "DDSC/europarl"], "widget": [{"text": "Jeg tror alligvel, det bliver godt"}]} | text-classification | alexandrainst/da-subjectivivity-classification-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #da #dataset-DDSC/twitter-sent #dataset-DDSC/europarl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish BERT Tone for the detection of subjectivity/objectivity
The BERT Tone model detects whether a text (in Danish) is subjective or objective.
The model is based on the finetuning of the pretrained Danish BERT model by BotXO.
See the DaNLP documentation for more details.
Here is how to use the model:
##... | [
"# Danish BERT Tone for the detection of subjectivity/objectivity\n\nThe BERT Tone model detects whether a text (in Danish) is subjective or objective. \nThe model is based on the finetuning of the pretrained Danish BERT model by BotXO. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the ... | [
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null | null | transformers |
# Danish ELECTRA for hate speech (offensive language) detection
The ELECTRA Offensive model detects whether a Danish text is offensive or not.
It is based on the pretrained [Danish Ælæctra](Maltehb/aelaectra-danish-electra-small-cased) model.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/... | {"language": ["da"], "license": "apache-2.0", "widget": [{"text": "Senile gamle idiot"}]} | text-classification | alexandrainst/da-hatespeech-detection-small | [
"transformers",
"pytorch",
"electra",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #electra #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish ELECTRA for hate speech (offensive language) detection
The ELECTRA Offensive model detects whether a Danish text is offensive or not.
It is based on the pretrained Danish Ælæctra model.
See the DaNLP documentation for more details.
Here is how to use the model:
## Training data
The data used for tr... | [
"# Danish ELECTRA for hate speech (offensive language) detection\n\nThe ELECTRA Offensive model detects whether a Danish text is offensive or not. \nIt is based on the pretrained Danish Ælæctra model. \n\nSee the DaNLP documentation for more details. \n\n\nHere is how to use the model:",
"## Training data\n\nThe ... | [
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null | null | transformers |
# XLM-Roberta fine-tuned for Named Entity Disambiguation
Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification).
The base language model used is the [xlm-roberta-base](https://huggin... | {"language": ["da"], "license": "apache-2.0"} | text-classification | alexandrainst/da-ned-base | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"text-classification",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #text-classification #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-Roberta fine-tuned for Named Entity Disambiguation
Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification).
The base language model used is the xlm-roberta-base.
Here is how t... | [
"# XLM-Roberta fine-tuned for Named Entity Disambiguation\n\nGiven a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification). \nThe base language model used is the xlm-roberta-base. \n\nHere... | [
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null | null | transformers |
#Saitama DialoGPT model | {"tags": ["conversational"]} | text-generation | Daivakai/DialoGPT-small-saitama | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Saitama DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# scientific-challenges-and-directions
We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ a... | {"language": ["en"], "tags": ["generated_from_trainer", "text-classification"], "datasets": ["DanL/scientific-challenges-and-directions-dataset"], "metrics": ["precision", "recall", "f1"], "widget": [{"text": "severe atypical cases of pneumonia emerged and quickly spread worldwide.", "example_title": "challenge"}, {"te... | text-classification | DanL/scientific-challenges-and-directions | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:DanL/scientific-challenges-and-directions-dataset",
"arxiv:2108.13751",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2108.13751"
] | [
"en"
] | TAGS
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|
# scientific-challenges-and-directions
We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challenges_ a... | [
"# scientific-challenges-and-directions\n\nWe present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. At a high level, the _challen... | [
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null | null | transformers | Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking
# CovidBERT-MedNLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary ... | {} | text-classification | Darkrider/covidbert_medmarco | [
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"arxiv:2010.05987",
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| Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking
# CovidBERT-MedNLI
This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI d... | [
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null | null | transformers | # CovidBERT-MedNLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanfo... | {} | null | Darkrider/covidbert_mednli | [
"transformers",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
| # CovidBERT-MedNLI
This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce uni... | [
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null | null | transformers |
# Marathi DistilBERT
## Model description
This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.
```
DISCLAIMER
This model has not been thoroughly tested and may contain biased o... | {"language": ["mr"], "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["Oscar Corpus, News, Stories"], "widget": [{"text": "\u0939\u093e \u0916\u0930\u094b\u0916\u0930 \u091a\u093e\u0902\u0917\u0932\u093e [MASK] \u0906\u0939\u0947."}]} | fill-mask | DarshanDeshpande/marathi-distilbert | [
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"fill-mask",
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"arxiv:1910.01108",
"license:apache-2.0",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [
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"mr"
] | TAGS
#transformers #pytorch #tf #distilbert #fill-mask #mr #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Marathi DistilBERT
## Model description
This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences.
## Training data
The training data has been extracted from a variety of sources,... | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-retrained_ru_covid
This model is a fine-tuned version of [blinoff/roberta-base-russian-v0](https://huggingface.co/blinof... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-retrained_ru_covid", "results": []}]} | fill-mask | Daryaflp/roberta-retrained_ru_covid | [
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"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-retrained_ru_covid
This model is a fine-tuned version of blinoff/roberta-base-russian-v0 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and e... | [
"# roberta-retrained_ru_covid\n\nThis model is a fine-tuned version of blinoff/roberta-base-russian-v0 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8518",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed"... | [
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null | null | transformers | TinyBERT: Distilling BERT for Natural Language Understanding
========
**This model is a copy of [this model repository](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) from Huawei Noah at the specific commit `34707a33cd59a94ecde241ac209bf35103691b43`.**
TinyBERT is 7.5x smaller and 9.4x faster on infere... | {} | null | DataikuNLP/TinyBERT_General_4L_312D | [
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"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1909.10351"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #region-us
| TinyBERT: Distilling BERT for Natural Language Understanding
========
This model is a copy of this model repository from Huawei Noah at the specific commit '34707a33cd59a94ecde241ac209bf35103691b43'.
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tas... | [] | [
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null | null | sentence-transformers |
# average_word_embeddings_glove.6B.300d
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d) from sentence-transformers at the specific commit `5d2b7d1c127036ae98b9d487eca4d48744edc709`.**
This is a [sentence-transformers](https://www.S... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/average_word_embeddings_glove.6B.300d | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# average_word_embeddings_glove.6B.300d
This model is a copy of this model repository from sentence-transformers at the specific commit '5d2b7d1c127036ae98b9d487eca4d48744edc709'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks l... | [
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null | null | transformers |
# CamemBERT: a Tasty French Language Model
**This model is a copy of [this model repository](https://huggingface.co/camembert-base) at the specific commit `482393b6198924f9da270b1aaf37d238aafca99b`.**
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based... | {"language": "fr", "license": "mit", "datasets": ["oscar"]} | fill-mask | DataikuNLP/camembert-base | [
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"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #tf #camembert #fill-mask #fr #dataset-oscar #arxiv-1911.03894 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| CamemBERT: a Tasty French Language Model
========================================
This model is a copy of this model repository at the specific commit '482393b6198924f9da270b1aaf37d238aafca99b'.
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is... | [
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"##### Filling masks using pipeline",
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null | null | sentence-transformers |
# DataikuNLP/distiluse-base-multilingual-cased-v1
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) from sentence-transformers at the specific commit `3a706e4d65c04f868c4684adfd4da74141be8732`.**
This is a [sentence-transformers](http... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/distiluse-base-multilingual-cased-v1 | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# DataikuNLP/distiluse-base-multilingual-cased-v1
This model is a copy of this model repository from sentence-transformers at the specific commit '3a706e4d65c04f868c4684adfd4da74141be8732'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used f... | [
"# DataikuNLP/distiluse-base-multilingual-cased-v1\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '3a706e4d65c04f868c4684adfd4da74141be8732'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can b... | [
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null | null | sentence-transformers |
# DataikuNLP/paraphrase-MiniLM-L6-v2
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2/) from sentence-transformers at the specific commit `c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e`.**
This is a [sentence-transformers](https://www.SBERT.net) model:... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/paraphrase-MiniLM-L6-v2 | [
"sentence-transformers",
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"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
] | [] | TAGS
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|
# DataikuNLP/paraphrase-MiniLM-L6-v2
This model is a copy of this model repository from sentence-transformers at the specific commit 'c4dfcde8a3e3e17e85cd4f0ec1925a266187f48e'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like... | [
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null | null | sentence-transformers |
# DataikuNLP/paraphrase-albert-small-v2
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2/) from sentence-transformers at the specific commit `1eb1996223dd90a4c25be2fc52f6f336419a0d52`.**
This is a [sentence-transformers](https://www.SBERT.net) ... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/paraphrase-albert-small-v2 | [
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"feature-extraction",
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] | 2022-03-02T23:29:04+00:00 | [
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|
# DataikuNLP/paraphrase-albert-small-v2
This model is a copy of this model repository from sentence-transformers at the specific commit '1eb1996223dd90a4c25be2fc52f6f336419a0d52'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks l... | [
"# DataikuNLP/paraphrase-albert-small-v2\n\nThis model is a copy of this model repository from sentence-transformers at the specific commit '1eb1996223dd90a4c25be2fc52f6f336419a0d52'.\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for... | [
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null | null | sentence-transformers |
# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) from sentence-transformers at the specific commit `d66eff4d8a8598f264f166af8db67f7797164651`.**
This is a [sentence-transformers](ht... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
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"feature-extraction",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1908.10084"
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|
# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
This model is a copy of this model repository from sentence-transformers at the specific commit 'd66eff4d8a8598f264f166af8db67f7797164651'.
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used ... | [
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null | null | transformers | Hugging Face's logo
---
language: am
datasets:
---
# bert-base-multilingual-cased-finetuned-amharic
## Model description
**bert-base-multilingual-cased-finetuned-amharic** is a **Amharic BERT** model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning **... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-amharic | [
"transformers",
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"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language: am
datasets:
---
bert-base-multilingual-cased-finetuned-amharic
==============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-amharic is a Amharic BERT model obtained by replacing mBERT vocabulary b... | [
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null | null | transformers | Hugging Face's logo
---
language: ha
datasets:
---
# bert-base-multilingual-cased-finetuned-hausa
## Model description
**bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa language texts. It provides **better performance** t... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-hausa | [
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"tf",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ha
datasets:
---
bert-base-multilingual-cased-finetuned-hausa
============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-hausa is a Hausa BERT model obtained by fine-tuning bert-base-multilingual-c... | [
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null | null | transformers | Hugging Face's logo
---
language: ig
datasets:
---
# bert-base-multilingual-cased-finetuned-igbo
## Model description
**bert-base-multilingual-cased-finetuned-igbo** is a **Igbo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Igbo language texts. It provides **better performance** than ... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"transformers",
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"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ig
datasets:
---
bert-base-multilingual-cased-finetuned-igbo
===========================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-igbo is a Igbo BERT model obtained by fine-tuning bert-base-multilingual-cased... | [
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null | null | transformers | Hugging Face's logo
---
language: rw
datasets:
---
# bert-base-multilingual-cased-finetuned-kinyarwanda
## Model description
**bert-base-multilingual-cased-finetuned-kinyarwanda** is a **Kinyarwanda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Kinyarwanda language texts. It provides ... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: rw
datasets:
---
bert-base-multilingual-cased-finetuned-kinyarwanda
==================================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-kinyarwanda is a Kinyarwanda BERT model obtained by fine-tuning ... | [
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null | null | transformers | Hugging Face's logo
---
language: lg
datasets:
---
# bert-base-multilingual-cased-finetuned-luganda
## Model description
**bert-base-multilingual-cased-finetuned-luganda** is a **Luganda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luganda language texts. It provides **better perform... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-luganda | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: lg
datasets:
---
bert-base-multilingual-cased-finetuned-luganda
==============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-luganda is a Luganda BERT model obtained by fine-tuning bert-base-multil... | [
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"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
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null | null | transformers | Hugging Face's logo
---
language: luo
datasets:
---
# bert-base-multilingual-cased-finetuned-luo
## Model description
**bert-base-multilingual-cased-finetuned-luo** is a **Luo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luo language texts. It provides **better performance** than the... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-luo | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: luo
datasets:
---
bert-base-multilingual-cased-finetuned-luo
==========================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-luo is a Luo BERT model obtained by fine-tuning bert-base-multilingual-cased mo... | [
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null | null | transformers | Hugging Face's logo
---
language: pcm
datasets:
---
# bert-base-multilingual-cased-finetuned-naija
## Model description
**bert-base-multilingual-cased-finetuned-naija** is a **Nigerian-Pidgin BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Nigerian-Pidgin language texts. It provides **b... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-naija | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: pcm
datasets:
---
bert-base-multilingual-cased-finetuned-naija
============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-naija is a Nigerian-Pidgin BERT model obtained by fine-tuning bert-base-mul... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
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"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated ne... | [
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null | null | transformers | Hugging Face's logo
---
language: ha
datasets:
---
# bert-base-multilingual-cased-finetuned-swahili
## Model description
**bert-base-multilingual-cased-finetuned-swahili** is a **Swahili BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Swahili language texts. It provides **better perform... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-swahili | [
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"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ha
datasets:
---
bert-base-multilingual-cased-finetuned-swahili
==============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-swahili is a Swahili BERT model obtained by fine-tuning bert-base-multil... | [
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null | null | transformers | Hugging Face's logo
---
language: wo
datasets:
---
# bert-base-multilingual-cased-finetuned-wolof
## Model description
**bert-base-multilingual-cased-finetuned-wolof** is a **Wolof BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Wolof language texts. It provides **better performance** t... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-wolof | [
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"bert",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: wo
datasets:
---
bert-base-multilingual-cased-finetuned-wolof
============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-wolof is a Wolof BERT model obtained by fine-tuning bert-base-multilingual-c... | [
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null | null | transformers | Hugging Face's logo
---
language: yo
datasets:
---
# bert-base-multilingual-cased-finetuned-yoruba
## Model description
**bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts. It provides **better performance... | {} | fill-mask | Davlan/bert-base-multilingual-cased-finetuned-yoruba | [
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"tf",
"jax",
"bert",
"fill-mask",
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: yo
datasets:
---
bert-base-multilingual-cased-finetuned-yoruba
=============================================
Model description
-----------------
bert-base-multilingual-cased-finetuned-yoruba is a Yoruba BERT model obtained by fine-tuning bert-base-multilingu... | [
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null | null | transformers | Hugging Face's logo
---
language:
- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# bert-base-multilingual-cased-masakhaner
## Model description
**bert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, Igbo, K... | {} | token-classification | Davlan/bert-base-multilingual-cased-masakhaner | [
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"pytorch",
"tf",
"bert",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ha
* ig
* rw
* lg
* luo
* pcm
* sw
* wo
* yo
* multilingual
datasets:
* masakhaner
---
bert-base-multilingual-cased-masakhaner
=======================================
Model description
-----------------
bert-base-multilingual-cased-masakhaner is the ... | [
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# bert-base-multilingual-cased-ner-hrl
## Model description
**bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French,... | {"license": "afl-3.0"} | token-classification | Davlan/bert-base-multilingual-cased-ner-hrl | [
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"tf",
"onnx",
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"token-classification",
"license:afl-3.0",
"autotrain_compatible",
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"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #onnx #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
bert-base-multilingual-cased-ner-hrl
====================================
Model description
-----------------
bert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for ... | [
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# byt5-base-eng-yor-mt
## Model description
**byt5-base-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned byt5-base model. It esta... | {} | text2text-generation | Davlan/byt5-base-eng-yor-mt | [
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"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
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"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
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] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# byt5-base-eng-yor-mt
## Model description
byt5-base-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating... | [
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# byt5-base-yor-eng-mt
## Model description
**byt5-base-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned byt5-base model. It esta... | {} | text2text-generation | Davlan/byt5-base-yor-eng-mt | [
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"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# byt5-base-yor-eng-mt
## Model description
byt5-base-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating... | [
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null | null | transformers | Hugging Face's logo
---
language:
- ha
- ig
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- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# bert-base-multilingual-cased-masakhaner
## Model description
**distilbert-base-multilingual-cased-masakhaner** is the first **Named Entity Recognition** model for 9 African languages (Hausa, I... | {} | token-classification | Davlan/distilbert-base-multilingual-cased-masakhaner | [
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"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #tf #distilbert #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ha
* ig
* rw
* lg
* luo
* pcm
* sw
* wo
* yo
* multilingual
datasets:
* masakhaner
---
bert-base-multilingual-cased-masakhaner
=======================================
Model description
-----------------
distilbert-base-multilingual-cased-masakhaner i... | [
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null | null | transformers | Hugging Face's logo
---
language:
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- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# distilbert-base-multilingual-cased-ner-hrl
## Model description
**distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Span... | {"license": "afl-3.0"} | token-classification | Davlan/distilbert-base-multilingual-cased-ner-hrl | [
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"pytorch",
"tf",
"safetensors",
"distilbert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #distilbert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
distilbert-base-multilingual-cased-ner-hrl
==========================================
Model description
-----------------
distilbert-base-multilingual-cased-ner-hrl is a Named Entity Reco... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# m2m100_418M-eng-yor-mt
## Model description
**m2m100_418M-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M ... | {} | text2text-generation | Davlan/m2m100_418M-eng-yor-mt | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# m2m100_418M-eng-yor-mt
## Model description
m2m100_418M-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatica... | [
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# m2m100_418M-eng-yor-mt
## Model description
**m2m100_418M-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M ... | {} | text2text-generation | Davlan/m2m100_418M-yor-eng-mt | [
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"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# m2m100_418M-eng-yor-mt
## Model description
m2m100_418M-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a strong baseline for automatica... | [
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null | null | transformers | Hugging Face's logo
---
language: yo
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_yoruba_adr
## Model description
**mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the **state-of... | {} | text2text-generation | Davlan/mT5_base_yoruba_adr | [
"transformers",
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"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2003.10564",
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language: yo
datasets:
- JW300 + Menyo-20k
---
# mT5_base_yoruba_adr
## Model description
mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the state-of-the-art performance for adding the correct diacritics or... | [
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mbart50-large-eng-yor-mt
## Model description
**mbart50-large-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned facebook/mbart-la... | {} | text2text-generation | Davlan/mbart50-large-eng-yor-mt | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mbart50-large-eng-yor-mt
## Model description
mbart50-large-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for aut... | [
"# mbart50-large-eng-yor-mt",
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mbart50-large-yor-eng-mt
## Model description
**mbart50-large-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/mbart-la... | {} | text2text-generation | Davlan/mbart50-large-yor-eng-mt | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mbart50-large-yor-eng-mt
## Model description
mbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for aut... | [
"# mbart50-large-yor-eng-mt",
"## Model description\nmbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifical... | [
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_eng_yor_mt
## Model description
**mT5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establi... | {} | text2text-generation | Davlan/mt5_base_eng_yor_mt | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mT5_base_eng_yor_mt
## Model description
mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating te... | [
"# mT5_base_eng_yor_mt",
"## Model description\nmT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. \n\nSpecifically, this model is a *mT5_... | [
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null | null | transformers | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_yor_eng_mt
## Model description
**mT5_base_yor_eng_mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establi... | {} | text2text-generation | Davlan/mt5_base_yor_eng_mt | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.08647"
] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mT5_base_yor_eng_mt
## Model description
mT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating te... | [
"# mT5_base_yor_eng_mt",
"## Model description\nmT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English. \n\nSpecifically, this model is a *mT5_... | [
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null | null | transformers | Hugging Face's logo
---
language:
- hau
- ibo
- pcm
- yor
- multilingual
---
# naija-twitter-sentiment-afriberta-large
## Model description
**naija-twitter-sentiment-afriberta-large** is the first multilingual twitter **sentiment classification** model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, an... | {} | text-classification | Davlan/naija-twitter-sentiment-afriberta-large | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.08277",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2201.08277"
] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #arxiv-2201.08277 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Hugging Face's logo
-------------------
language:
* hau
* ibo
* pcm
* yor
* multilingual
---
naija-twitter-sentiment-afriberta-large
=======================================
Model description
-----------------
naija-twitter-sentiment-afriberta-large is the first multilingual twitter sentiment classificatio... | [
"#### How to use\n\n\nYou can use this model with Transformers for Sentiment Classification.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset and domain i.e Twitter. This may not generalize well for all use cases in different domains.\n\n\nTraining procedure\n------------------\n\n\... | [
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-0.0025473914574831724,
-0.005726978648453951,
0.11812916398048401,
0.08928731828927994,
0.04521244391798973,
0.186834... |
null | null | transformers | Hugging Face's logo
---
language: am
datasets:
---
# xlm-roberta-base-finetuned-amharic
## Model description
**xlm-roberta-base-finetuned-amharic** is a **Amharic RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Amharic language texts. It provides **better performance** than the XLM-RoBERTa on na... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-amharic | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: am
datasets:
---
xlm-roberta-base-finetuned-amharic
==================================
Model description
-----------------
xlm-roberta-base-finetuned-amharic is a Amharic RoBERTa model obtained by fine-tuning xlm-roberta-base model on Amharic language texts.... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
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"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
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0.0939646065235138,
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0.1457824558019638,
0.051333680748939514,
0.04049794748425484,
-0.0011500997934490442,
0.19015361368656158,
0.05433284863829613,
0.044023144990205765,
0.198979... |
null | null | transformers | Hugging Face's logo
---
language: ha
datasets:
---
# xlm-roberta-base-finetuned-hausa
## Model description
**xlm-roberta-base-finetuned-hausa** is a **Hausa RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on text class... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-hausa | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ha
datasets:
---
xlm-roberta-base-finetuned-hausa
================================
Model description
-----------------
xlm-roberta-base-finetuned-hausa is a Hausa RoBERTa model obtained by fine-tuning xlm-roberta-base model on Hausa language texts. It provid... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
40,
27,
150,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
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0.03143756836652756,
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0.08975314348936081,
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0.16706782579421997,
0.08699733763933182,
0.027623191475868225,
0.004583436530083418,
0.15468306839466095,
0.03477529436349869,
0.09418293833732605,
0.220404461... |
null | null | transformers | Hugging Face's logo
---
language: ig
datasets:
---
# xlm-roberta-base-finetuned-igbo
## Model description
**xlm-roberta-base-finetuned-igbo** is a **Igbo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on named entity ... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-igbo | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: ig
datasets:
---
xlm-roberta-base-finetuned-igbo
===============================
Model description
-----------------
xlm-roberta-base-finetuned-igbo is a Igbo RoBERTa model obtained by fine-tuning xlm-roberta-base model on Hausa language texts. It provides b... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
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147,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
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0.07569832354784012,
0.0007428711396642029,
0.09445839375257492,
0.04536716639995575,
0.041106827557086945,
0.1716591715812683,
0.09555742889642715,
0.04865835979580879,
0.0066335429437458515,
0.1303367018699646,
0.02158549800515175,
0.10520320385694504,
0.195604041218... |
null | null | transformers | Hugging Face's logo
---
language: rw
datasets:
---
# xlm-roberta-base-finetuned-kinyarwanda
## Model description
**xlm-roberta-base-finetuned-kinyarwanda** is a **Kinyarwanda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Kinyarwanda language texts. It provides **better performance** than the X... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: rw
datasets:
---
xlm-roberta-base-finetuned-kinyarwanda
======================================
Model description
-----------------
xlm-roberta-base-finetuned-kinyarwanda is a Kinyarwanda RoBERTa model obtained by fine-tuning xlm-roberta-base model on Kinyarw... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
40,
27,
136,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
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0.01883709244430065,
0.0001438557665096596,
0.1022925078868866,
0.10043568164110184,
0.01867111772298813,
0.16167327761650085,
0.07119225710630417,
0.062482938170433044,
0.00027807860169559717,
0.17216533422470093,
0.057655707001686096,
0.05463198944926262,
0.184828147... |
null | null | transformers | Hugging Face's logo
---
language: lg
datasets:
---
# xlm-roberta-base-finetuned-luganda
## Model description
**xlm-roberta-base-finetuned-luganda** is a **Luganda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luganda language texts. It provides **better performance** than the XLM-RoBERTa on na... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-luganda | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: lg
datasets:
---
xlm-roberta-base-finetuned-luganda
==================================
Model description
-----------------
xlm-roberta-base-finetuned-luganda is a Luganda RoBERTa model obtained by fine-tuning xlm-roberta-base model on Luganda language texts.... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
40,
27,
137,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
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0.04132223501801491,
-0.00020352214050944895,
0.11515142023563385,
0.10617215931415558,
0.014161425642669201,
0.15840421617031097,
0.066668801009655,
0.03706981986761093,
0.004021158441901207,
0.1654333621263504,
0.05512116476893425,
0.053944166749715805,
0.1806514114... |
null | null | transformers | Hugging Face's logo
---
language: luo
datasets:
---
# xlm-roberta-base-finetuned-luo
## Model description
**xlm-roberta-base-finetuned-luo** is a **Luo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luo language texts. It provides **better performance** than the XLM-RoBERTa on named entity reco... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-luo | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: luo
datasets:
---
xlm-roberta-base-finetuned-luo
==============================
Model description
-----------------
xlm-roberta-base-finetuned-luo is a Luo RoBERTa model obtained by fine-tuning xlm-roberta-base model on Luo language texts. It provides better... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
40,
27,
126,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
-0.12206385284662247,
0.016796544194221497,
0.0000379915873054415,
0.10884106904268265,
0.10949715971946716,
0.008525412529706955,
0.1406143456697464,
0.07649845629930496,
0.038105688989162445,
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0.18291646242141724,
0.07796947658061981,
0.042411766946315765,
0.2087911... |
null | null | transformers | Hugging Face's logo
---
language: pcm
datasets:
---
# xlm-roberta-base-finetuned-naija
## Model description
**xlm-roberta-base-finetuned-naija** is a **Nigerian Pidgin RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Nigerian Pidgin language texts. It provides **better performance** than the XLM-... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-naija | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: pcm
datasets:
---
xlm-roberta-base-finetuned-naija
================================
Model description
-----------------
xlm-roberta-base-finetuned-naija is a Nigerian Pidgin RoBERTa model obtained by fine-tuning xlm-roberta-base model on Nigerian Pidgin lang... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
40,
27,
131,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
-0.14059534668922424,
0.015713529661297798,
0.0006167374085634947,
0.10801149159669876,
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0.15818744897842407,
0.07030486315488815,
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0.010791257955133915,
0.19390563666820526,
0.06676238030195236,
0.0567355677485466,
0.220384612... |
null | null | transformers | Hugging Face's logo
---
language: sw
datasets:
---
# xlm-roberta-base-finetuned-swahili
## Model description
**xlm-roberta-base-finetuned-swahili** is a **Swahili RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Swahili language texts. It provides **better performance** than the XLM-RoBERTa on te... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-swahili | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: sw
datasets:
---
xlm-roberta-base-finetuned-swahili
==================================
Model description
-----------------
xlm-roberta-base-finetuned-swahili is a Swahili RoBERTa model obtained by fine-tuning xlm-roberta-base model on Swahili language texts.... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of... | [
45,
27,
130,
15
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of en... | [
-0.1464148908853531,
0.01461686659604311,
0.0007122665410861373,
0.09132449328899384,
0.11916112154722214,
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0.17418889701366425,
0.04166771098971367,
0.06633538752794266,
0.193188... |
null | null | transformers | Hugging Face's logo
---
language: wo
datasets:
---
# xlm-roberta-base-finetuned-wolof
## Model description
**xlm-roberta-base-finetuned-luganda** is a **Wolof RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Wolof language texts. It provides **better performance** than the XLM-RoBERTa on named en... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-wolof | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: wo
datasets:
---
xlm-roberta-base-finetuned-wolof
================================
Model description
-----------------
xlm-roberta-base-finetuned-luganda is a Wolof RoBERTa model obtained by fine-tuning xlm-roberta-base model on Wolof language texts. It prov... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annot... | [
40,
27,
147,
15
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotate... | [
-0.12370675802230835,
0.04303447902202606,
0.0009075227426365018,
0.10750071704387665,
0.08168624341487885,
0.02529282122850418,
0.18673363327980042,
0.06790195405483246,
0.027953721582889557,
-0.0023719940800219774,
0.12623091042041779,
0.009315557777881622,
0.10746962577104568,
0.1745232... |
null | null | transformers | Hugging Face's logo
---
language: yo
datasets:
---
# xlm-roberta-base-finetuned-yoruba
## Model description
**xlm-roberta-base-finetuned-yoruba** is a **Yoruba RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Yorùbá language texts. It provides **better performance** than the XLM-RoBERTa on text c... | {} | fill-mask | Davlan/xlm-roberta-base-finetuned-yoruba | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language: yo
datasets:
---
xlm-roberta-base-finetuned-yoruba
=================================
Model description
-----------------
xlm-roberta-base-finetuned-yoruba is a Yoruba RoBERTa model obtained by fine-tuning xlm-roberta-base model on Yorùbá language texts. It p... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for masked token prediction.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains... | [
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null | null | transformers | Hugging Face's logo
---
language:
- am
- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# xlm-roberta-base-masakhaner
## Model description
**xlm-roberta-base-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwand... | {} | token-classification | Davlan/xlm-roberta-base-masakhaner | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* am
* ha
* ig
* rw
* lg
* luo
* pcm
* sw
* wo
* yo
* multilingual
datasets:
* masakhaner
---
xlm-roberta-base-masakhaner
===========================
Model description
-----------------
xlm-roberta-base-masakhaner is the first Named Entity Recognition ... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data... | [
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# xlm-roberta-base-ner-hrl
## Model description
**xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch... | {"license": "afl-3.0"} | token-classification | Davlan/xlm-roberta-base-ner-hrl | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
xlm-roberta-base-ner-hrl
========================
Model description
-----------------
xlm-roberta-base-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic,... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.\n\n\nTraining data... | [
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null | null | transformers | Hugging Face's logo
---
language:
- af
- nr
- nso
- ss
- st
- tn
- ts
- ve
- xh
- zu
- multilingual
datasets:
- masakhaner
---
# xlm-roberta-base-sadilar-ner
## Model description
**xlm-roberta-base-sadilar-ner** is the first **Named Entity Recognition** model for 10 South African languages (Afri... | {} | token-classification | Davlan/xlm-roberta-base-sadilar-ner | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* af
* nr
* nso
* ss
* st
* tn
* ts
* ve
* xh
* zu
* multilingual
datasets:
* masakhaner
---
xlm-roberta-base-sadilar-ner
============================
Model description
-----------------
xlm-roberta-base-sadilar-ner is the first Named Entity Recognitio... | [
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null | null | transformers | Hugging Face's logo
---
language:
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- es
- eu
- fr
- gu
- hi
- id
- ig
- mr
- pa
- pt
- sw
- ur
- vi
- yo
- zh
- multilingual
datasets:
- wikiann
---
# xlm-roberta-base-wikiann-ner
## Model description
**xlm-roberta-base-wikiann-ner** is the first **Named Entity ... | {} | token-classification | Davlan/xlm-roberta-base-wikiann-ner | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ar
* as
* bn
* ca
* en
* es
* eu
* fr
* gu
* hi
* id
* ig
* mr
* pa
* pt
* sw
* ur
* vi
* yo
* zh
* multilingual
datasets:
* wikiann
---
xlm-roberta-base-wikiann-ner
============================
Model description
-----------------
xlm-roberta-base-wi... | [
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null | null | transformers | Hugging Face's logo
---
language:
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- kin
- lug
- luo
- pcm
- swa
- wol
- yor
- multilingual
datasets:
- masakhaner
---
# xlm-roberta-large-masakhaner
## Model description
**xlm-roberta-large-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, ... | {} | token-classification | Davlan/xlm-roberta-large-masakhaner | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.11811"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* amh
* hau
* ibo
* kin
* lug
* luo
* pcm
* swa
* wol
* yor
* multilingual
datasets:
* masakhaner
---
xlm-roberta-large-masakhaner
============================
Model description
-----------------
xlm-roberta-large-masakhaner is the first Named Entity R... | [
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null | null | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# xlm-roberta-large-ner-hrl
## Model description
**xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dut... | {"license": "afl-3.0"} | token-classification | Davlan/xlm-roberta-large-ner-hrl | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
-------------------
language:
* ar
* de
* en
* es
* fr
* it
* lv
* nl
* pt
* zh
* multilingual
---
xlm-roberta-large-ner-hrl
=========================
Model description
-----------------
xlm-roberta-large-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arab... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
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null | null | transformers |
# Iron Man DialoGPT Model | {"tags": ["conversational"]} | text-generation | Dawit/DialogGPT-small-ironman | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Iron Man DialoGPT Model | [
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null | null | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | text-generation | Daymarebait/Discord_BOT_RICK | [
"transformers",
"conversational",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #conversational #endpoints_compatible #region-us
|
# My Awesome Model
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null | null | 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. -->
# emoBERTTamil
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tamilmix... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tamilmixsentiment"], "metrics": ["accuracy"], "model_index": [{"name": "emoBERTTamil", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "tamilmixsentiment", "type": "tamilmixsentiment", "a... | text-classification | DeadBeast/emoBERTTamil | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:tamilmixsentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| emoBERTTamil
============
This model is a fine-tuned version of bert-base-uncased on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9666
* Accuracy: 0.671
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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0.16496677696704865,
0.013906530104577541,
0.115841560... |
null | null | transformers |
# **Korean-mBERT**
This model is a fine-tune checkpoint of mBERT-base-cased over **Hugging Face Kore_Scm** dataset for Text classification.
### **How to use?**
**Task**: binary-classification
- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)
- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not conta... | {"language": "korean", "license": "apache-2.0", "datasets": ["Korean-Sarcasm"]} | text-classification | DeadBeast/korscm-mBERT | [
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:Korean-Sarcasm",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"korean"
] | TAGS
#transformers #pytorch #bert #text-classification #dataset-Korean-Sarcasm #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Korean-mBERT
This model is a fine-tune checkpoint of mBERT-base-cased over Hugging Face Kore_Scm dataset for Text classification.
### How to use?
Task: binary-classification
- LABEL_1: Sarcasm (*Sarcasm means tweets contains sarcasm*)
- LABEL_0: Not Sarcasm (*Not Sarcasm means tweets do not contain sarcasm*)
Cl... | [
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null | null | transformers |
# **mBERT-base-cased-finetuned-bengali-fakenews**
This model is a fine-tune checkpoint of mBERT-base-cased over **[Bengali-fake-news Dataset](https://www.kaggle.com/cryptexcode/banfakenews)** for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.
### **How to use?**
... | {"language": "bengali", "license": "apache-2.0", "datasets": ["BanFakeNews"]} | text-classification | DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:BanFakeNews",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"bengali"
] | TAGS
#transformers #pytorch #bert #text-classification #dataset-BanFakeNews #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBERT-base-cased-finetuned-bengali-fakenews
This model is a fine-tune checkpoint of mBERT-base-cased over Bengali-fake-news Dataset for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.
### How to use?
Task: binary-classification
- LABEL_1: Authentic (*Authentic... | [
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"### How to use?\n\nTask: binary-classification\n\n- LABEL_1: Authent... | [
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null | null | null | ---
Summarisation model summarsiation | {} | null | Dean/summarsiation | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| ---
Summarisation model summarsiation | [] | [
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null | null | transformers |
#Scaramouche DialoGPT Model | {"tags": ["conversational"]} | text-generation | DecafNosebleed/DialoGPT-small-ScaraBot | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Scaramouche DialoGPT Model | [] | [
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null | null | transformers |
# Model Card for ChemBERTa-10M-MTR
# Model Details
## Model Description
More information needed
- **Developed by:** DeepChem
- **Shared by [Optional]:** DeepChem
- **Model type:** Token Classification
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:** ... | {"tags": ["roberta"]} | null | DeepChem/ChemBERTa-10M-MTR | [
"transformers",
"pytorch",
"roberta",
"arxiv:1910.09700",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #pytorch #roberta #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
|
# Model Card for ChemBERTa-10M-MTR
# Model Details
## Model Description
More information needed
- Developed by: DeepChem
- Shared by [Optional]: DeepChem
- Model type: Token Classification
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Model: RoBERTa
- Resources for ... | [
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null | null | transformers | RoBERTa model trained on 1M SMILES from PubChem 77M set in MoleculeNet. Uses Smiles-Tokenizer | {} | feature-extraction | DeepChem/SmilesTokenizer_PubChem_1M | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
| RoBERTa model trained on 1M SMILES from PubChem 77M set in MoleculeNet. Uses Smiles-Tokenizer | [] | [
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null | null | transformers | # GPT2-Spanish
GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.
## Corpus
This model was trained with a... | {"language": "es", "license": "mit", "tags": ["GPT-2", "Spanish", "ebooks", "nlg"], "datasets": ["ebooks"], "widget": [{"text": "Quisiera saber que va a suceder"}]} | text-generation | DeepESP/gpt2-spanish-medium | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"es",
"dataset:ebooks",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #GPT-2 #Spanish #ebooks #nlg #es #dataset-ebooks #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT2-Spanish
GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.
## Corpus
This model was trained with a... | [
"# GPT2-Spanish\nGPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.",
"## Corpus\nThis model was tra... | [
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