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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | codingJacob/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0611
* Precision: 0.9272
* Recall: 0.9382
* F1: 0.9327
* Accuracy: 0.9843
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | cogito233/distilbert-base-uncased-finetuned-ner | null | [
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"tensorboard",
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"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0605
* Precision: 0.9251
* Recall: 0.9357
* F1: 0.9304
* Accuracy: 0.9837
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
feature-extraction | transformers | # LaBSE for English and Russian
This is a truncated version of [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is, in turn, a port of [LaBSE](https://tfhub.dev/google/LaBSE/1) by Google.
The current model has only English and Russian tokens left in the vocabulary.
Thus, the voc... | {"language": ["ru", "en"], "tags": ["feature-extraction", "embeddings", "sentence-similarity"]} | cointegrated/LaBSE-en-ru | null | [
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"en",
"arxiv:2007.01852",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.01852"
] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #pretraining #feature-extraction #embeddings #sentence-similarity #ru #en #arxiv-2007.01852 #endpoints_compatible #has_space #region-us
| # LaBSE for English and Russian
This is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google.
The current model has only English and Russian tokens left in the vocabulary.
Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the ... | [
"# LaBSE for English and Russian\nThis is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google.\n\nThe current model has only English and Russian tokens left in the vocabulary.\nThus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27... | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #pretraining #feature-extraction #embeddings #sentence-similarity #ru #en #arxiv-2007.01852 #endpoints_compatible #has_space #region-us \n",
"# LaBSE for English and Russian\nThis is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port ... |
text-classification | transformers | This is a RoBERTa-large classifier trained on the CoLA corpus [Warstadt et al., 2019](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00290),
which contains sentences paired with grammatical acceptability judgments. The model can be used to evaluate fluency of machine-generated English sentences, e.g. for eval... | {} | cointegrated/roberta-large-cola-krishna2020 | null | [
"transformers",
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"safetensors",
"roberta",
"text-classification",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05700"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
| This is a RoBERTa-large classifier trained on the CoLA corpus Warstadt et al., 2019,
which contains sentences paired with grammatical acceptability judgments. The model can be used to evaluate fluency of machine-generated English sentences, e.g. for evaluation of text style transfer.
The model was trained in the pape... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
This is a version of paraphrase detector by DeepPavlov ([details in the documentation](http://docs.deeppavlov.ai/en/master/features/overview.html#ranking-model-docs)) ported to the `Transformers` format.
All credit goes to the authors of DeepPavlov.
The model has been trained on the dataset from http://paraphraser.... | {"language": ["ru"], "tags": ["sentence-similarity", "text-classification"], "datasets": ["merionum/ru_paraphraser"]} | cointegrated/rubert-base-cased-dp-paraphrase-detection | null | [
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"ru",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #ru #dataset-merionum/ru_paraphraser #autotrain_compatible #endpoints_compatible #region-us
|
This is a version of paraphrase detector by DeepPavlov (details in the documentation) ported to the 'Transformers' format.
All credit goes to the authors of DeepPavlov.
The model has been trained on the dataset from URL
It classifies texts as paraphrases (class 1) or non-paraphrases (class 0).
P.S. In the Deep... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #ru #dataset-merionum/ru_paraphraser #autotrain_compatible #endpoints_compatible #region-us \n"
] |
zero-shot-classification | transformers | # RuBERT for NLI (natural language inference)
This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.
## Usage
How to run the model for NLI:
```python
# !pip install t... | {"language": "ru", "tags": ["rubert", "russian", "nli", "rte", "zero-shot-classification"], "datasets": ["cointegrated/nli-rus-translated-v2021"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u042f \u0445\u043e\u0447\u0443 \u043f\u043e\u0435\u0445\u0430\u0442\u044c \u0432 \u0410\u0432\u0441\u0442\u... | cointegrated/rubert-base-cased-nli-threeway | null | [
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"ru",
"dataset:cointegrated/nli-rus-translated-v2021",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #has_space #region-us
| RuBERT for NLI (natural language inference)
===========================================
This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.
Usage
-----
How to run the model for NLI:
You can also use this model fo... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
zero-shot-classification | transformers | # RuBERT for NLI (natural language inference)
This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a similar model: https://huggingface.co... | {"language": "ru", "tags": ["rubert", "russian", "nli", "rte", "zero-shot-classification"], "datasets": ["cointegrated/nli-rus-translated-v2021"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u042f \u0445\u043e\u0447\u0443 \u043f\u043e\u0435\u0445\u0430\u0442\u044c \u0432 \u0410\u0432\u0441\u0442\u... | cointegrated/rubert-base-cased-nli-twoway | null | [
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"ru",
"dataset:cointegrated/nli-rus-translated-v2021",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
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| # RuBERT for NLI (natural language inference)
This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a similar model: URL | [
"# RuBERT for NLI (natural language inference)\n\nThis is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a similar model: URL"
] | [
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"# RuBERT for NLI (natural language inference)\n\nThis is the DeepPavlov/rubert-... |
token-classification | transformers | The model for https://github.com/Lesha17/Punctuation; all credits go to the owner of this repository. | {} | cointegrated/rubert-base-lesha17-punctuation | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| The model for URL all credits go to the owner of this repository. | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
zero-shot-classification | transformers | # RuBERT-tiny for NLI (natural language inference)
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a related model: https://huggingface... | {"language": "ru", "tags": ["rubert", "russian", "nli", "rte", "zero-shot-classification"], "datasets": ["cointegrated/nli-rus-translated-v2021"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u0421\u0435\u0440\u0432\u0438\u0441 \u043e\u0442\u0441\u0442\u043e\u0439\u043d\u044b\u0439, \u043a\u043e\u0... | cointegrated/rubert-tiny-bilingual-nli | null | [
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"ru",
"dataset:cointegrated/nli-rus-translated-v2021",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
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| # RuBERT-tiny for NLI (natural language inference)
This is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a related model: URL
| [
"# RuBERT-tiny for NLI (natural language inference)\n\nThis is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a related model: URL"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RuBERT-tiny for NLI (natural language inference)\n\nThis is the cointegrated/... |
text-classification | transformers | This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of sentiment for short Russian texts.
The problem is formulated as multiclass classification: `negative` vs `neutral` vs `positive`.
## Usage
The function below estimates the sentiment of the ... | {"language": ["ru"], "tags": ["russian", "classification", "sentiment", "multiclass"], "widget": [{"text": "\u041a\u0430\u043a\u0430\u044f \u0433\u0430\u0434\u043e\u0441\u0442\u044c \u044d\u0442\u0430 \u0432\u0430\u0448\u0430 \u0437\u0430\u043b\u0438\u0432\u043d\u0430\u044f \u0440\u044b\u0431\u0430!"}]} | cointegrated/rubert-tiny-sentiment-balanced | null | [
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"sentiment",
"multiclass",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us
| This is the cointegrated/rubert-tiny model fine-tuned for classification of sentiment for short Russian texts.
The problem is formulated as multiclass classification: 'negative' vs 'neutral' vs 'positive'.
Usage
-----
The function below estimates the sentiment of the given text:
Training
--------
We trained t... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks.
The problem is formulated as multilabel classification with the following classes:
- `non... | {"language": ["ru"], "tags": ["russian", "classification", "toxicity", "multilabel"], "widget": [{"text": "\u0418\u0434\u0438 \u0442\u044b \u043d\u0430\u0444\u0438\u0433!"}]} | cointegrated/rubert-tiny-toxicity | null | [
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"toxicity",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.05345"
] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #toxicity #multilabel #ru #arxiv-2103.05345 #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is the cointegrated/rubert-tiny model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks.
The problem is formulated as multilabel classification with the following classes:
- 'non-toxic': the text does NOT contain insults, obsceni... | [
"## Usage\n\nThe function below estimates the probability that the text is either toxic OR dangerous:",
"## Training\n\nThe model has been trained on the joint dataset of OK ML Cup and Babakov URL. with 'Adam' optimizer, the learning rate of '1e-5', and batch size of '64' for '15' epochs. A text was considered in... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #toxicity #multilabel #ru #arxiv-2103.05345 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Usage\n\nThe function below estimates the probability that the text is either toxic OR dangerous:",
... |
fill-mask | transformers | This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English (45 MB, 12M parameters). There is also an **updated version of this model**, [rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2), with a larger voc... | {"language": ["ru", "en"], "license": "mit", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm", "tiny", "feature-extraction", "sentence-similarity"], "widget": [{"text": "\u041c\u0438\u043d\u0438\u0430\u0442\u044e\u0440\u043d\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f [M... | cointegrated/rubert-tiny | null | [
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"sentence-similarity",
"ru",
"en",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #ru #en #license-mit #endpoints_compatible #has_space #region-us
| This is a very small distilled version of the bert-base-multilingual-cased model for Russian and English (45 MB, 12M parameters). There is also an updated version of this model, rubert-tiny2, with a larger vocabulary and better quality on practically all Russian NLU tasks.
This model is useful if you want to fine-tune... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #ru #en #license-mit #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | This is the [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for classification of emotions in Russian sentences. The task is multilabel classification, because one sentence can contain multiple emotions.
The model on the [CEDR dataset](https://huggingface.co/datasets/cedr... | {"language": ["ru"], "tags": ["russian", "classification", "sentiment", "emotion-classification", "multiclass"], "datasets": ["cedr"], "widget": [{"text": "\u0411\u0435\u0441\u0438\u0448\u044c \u043c\u0435\u043d\u044f, \u043f\u0430\u0434\u043b\u0430"}, {"text": "\u041a\u0430\u043a \u0437\u0434\u043e\u0440\u043e\u0432\u... | cointegrated/rubert-tiny2-cedr-emotion-detection | null | [
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"ru",
"dataset:cedr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is the cointegrated/rubert-tiny2 model fine-tuned for classification of emotions in Russian sentences. The task is multilabel classification, because one sentence can contain multiple emotions.
The model on the CEDR dataset described in the paper "Data-Driven Model for Emotion Detection in Russian Texts" by Sboe... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers | This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny): a small Russian BERT-based encoder with high-quality sentence embeddings. This [post in Russian](https://habr.com/ru/post/669674/) gives more details.
The differences from the previous version include:
- a larger... | {"language": ["ru"], "license": "mit", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm", "tiny", "feature-extraction", "sentence-similarity", "sentence-transformers", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"text": "\u041c\u0438\u043d\u0438\u0430\u0442\u044e\u0440\u0... | cointegrated/rubert-tiny2 | null | [
"sentence-transformers",
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"feature-extraction",
"sentence-similarity",
"transformers",
"ru",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#sentence-transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #transformers #ru #license-mit #endpoints_compatible #has_space #region-us
| This is an updated version of cointegrated/rubert-tiny: a small Russian BERT-based encoder with high-quality sentence embeddings. This post in Russian gives more details.
The differences from the previous version include:
- a larger vocabulary: 83828 tokens instead of 29564;
- larger supported sequences: 2048 instead ... | [] | [
"TAGS\n#sentence-transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #transformers #ru #license-mit #endpoints_compatible #has_space #region-us \n"
] |
summarization | transformers | This is a model for abstractive Russian summarization, based on [cointegrated/rut5-base-multitask](https://huggingface.co/cointegrated/rut5-base-multitask) and fine-tuned on 4 datasets.
It can be used as follows:
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'co... | {"language": ["ru"], "license": "mit", "tags": ["russian", "summarization"], "datasets": ["IlyaGusev/gazeta", "csebuetnlp/xlsum", "mlsum", "wiki_lingua"], "widget": [{"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 324 \u043c\u043... | cointegrated/rut5-base-absum | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"russian",
"summarization",
"ru",
"dataset:IlyaGusev/gazeta",
"dataset:csebuetnlp/xlsum",
"dataset:mlsum",
"dataset:wiki_lingua",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #russian #summarization #ru #dataset-IlyaGusev/gazeta #dataset-csebuetnlp/xlsum #dataset-mlsum #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This is a model for abstractive Russian summarization, based on cointegrated/rut5-base-multitask and fine-tuned on 4 datasets.
It can be used as follows:
| [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #russian #summarization #ru #dataset-IlyaGusev/gazeta #dataset-csebuetnlp/xlsum #dataset-mlsum #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) with only some Rusian and English embeddings left.
More details are given in a Russian post: https://habr.com/ru/post/581932/
The model has been fine-tuned for several tasks with sentences or short paragraphs:
* translation (`... | {"language": ["ru", "en"], "license": "mit", "tags": ["russian"], "widget": [{"text": "fill | \u041f\u043e\u0447\u0435\u043c\u0443 \u043e\u043d\u0438 \u043d\u0435 ___ \u043d\u0430 \u043c\u0435\u043d\u044f?"}]} | cointegrated/rut5-base-multitask | null | [
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"text2text-generation",
"russian",
"ru",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This is a smaller version of the google/mt5-base with only some Rusian and English embeddings left.
More details are given in a Russian post: URL
The model has been fine-tuned for several tasks with sentences or short paragraphs:
* translation ('translate ru-en' and 'translate en-ru')
* Paraphrasing ('paraphrase')
*... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
This is a paraphraser for Russian sentences described [in this Habr post](https://habr.com/ru/post/564916/).
It is recommended to use the model with the `encoder_no_repeat_ngram_size` argument:
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'cointegrated/rut5-base-paraphraser'
mode... | {"language": ["ru"], "license": "mit", "tags": ["russian", "paraphrasing", "paraphraser", "paraphrase"], "datasets": ["cointegrated/ru-paraphrase-NMT-Leipzig"], "widget": [{"text": "\u041a\u0430\u0436\u0434\u044b\u0439 \u043e\u0445\u043e\u0442\u043d\u0438\u043a \u0436\u0435\u043b\u0430\u0435\u0442 \u0437\u043d\u0430\u0... | cointegrated/rut5-base-paraphraser | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"russian",
"paraphrasing",
"paraphraser",
"paraphrase",
"ru",
"dataset:cointegrated/ru-paraphrase-NMT-Leipzig",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #russian #paraphrasing #paraphraser #paraphrase #ru #dataset-cointegrated/ru-paraphrase-NMT-Leipzig #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This is a paraphraser for Russian sentences described in this Habr post.
It is recommended to use the model with the 'encoder_no_repeat_ngram_size' argument:
| [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #russian #paraphrasing #paraphraser #paraphrase #ru #dataset-cointegrated/ru-paraphrase-NMT-Leipzig #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Russian and some English embeddings left.
* The original model has 582M parameters, with 384M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 30K (top 10... | {"language": ["ru", "en", "multilingual"], "license": "mit", "tags": ["russian"]} | cointegrated/rut5-base | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"russian",
"ru",
"en",
"multilingual",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a smaller version of the google/mt5-base model with only Russian and some English embeddings left.
* The original model has 582M parameters, with 384M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 30K (top 10K English and top 20K Russian tokens) the ... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
This is a version of the [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small) model fine-tuned on some Russian dialogue data. It is not very smart and creative, but it is small and fast, and can serve as a fallback response generator for some chatbot or can be fine-tuned to imitate the style of so... | {"language": "ru", "license": "mit", "tags": ["dialogue", "russian"]} | cointegrated/rut5-small-chitchat | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"dialogue",
"russian",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #dialogue #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a version of the cointegrated/rut5-small model fine-tuned on some Russian dialogue data. It is not very smart and creative, but it is small and fast, and can serve as a fallback response generator for some chatbot or can be fine-tuned to imitate the style of someone.
The input of the model is the previous dia... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #dialogue #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | A version of https://huggingface.co/cointegrated/rut5-small-chitchat which is more dull but less toxic. | {} | cointegrated/rut5-small-chitchat2 | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A version of URL which is more dull but less toxic. | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences.
This model was produced by fine-tuning the [rut5-small](https://huggingface.co/cointegrated/rut5-small) model on the task of reconstructing a sentence:
* restoring word positions (after slightly shuffling them)
* restoring... | {"language": "ru", "license": "mit", "tags": ["normalization", "denoising autoencoder", "russian"], "widget": [{"text": "\u043c\u0435\u043d\u044f \u0442\u043e\u0431\u043e\u0439 \u043d\u0435 \u043f\u043e\u043d\u0438\u043c\u0430\u0442\u044c"}]} | cointegrated/rut5-small-normalizer | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"normalization",
"denoising autoencoder",
"russian",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #normalization #denoising autoencoder #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences.
This model was produced by fine-tuning the rut5-small model on the task of reconstructing a sentence:
* restoring word positions (after slightly shuffling them)
* restoring dropped words and punctuation marks (after droppi... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #normalization #denoising autoencoder #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
This is a small Russian paraphraser based on the [google/mt5-small](https://huggingface.co/google/mt5-small) model.
It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks.
This model was created by taking the [alenusch/mt5small-ruparaphraser](https://huggingface.co/alenusch/mt5sma... | {"language": "ru", "license": "mit", "tags": ["paraphrasing", "russian"]} | cointegrated/rut5-small | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"mt5",
"text2text-generation",
"paraphrasing",
"russian",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #safetensors #mt5 #text2text-generation #paraphrasing #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a small Russian paraphraser based on the google/mt5-small model.
It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks.
This model was created by taking the alenusch/mt5small-ruparaphraser model and stripping 96% of its vocabulary which is unrelated to the Russian languag... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #mt5 #text2text-generation #paraphrasing #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-address-ner
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-rober... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "chinese-address-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9758259... | jiaqianjing/chinese-address-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-address-ner
===================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1080
* Precision: 0.9664
* Recall: 0.9774
* F1: 0.9719
* Accuracy: 0.9758
Model description
-----------------
输入一串地址中... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 50\n* eval\\_batch\\_size: 50\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 50\n*... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | coldfir3/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2500
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\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: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | coldfir3/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #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: 0.922
* F1: 0.9222
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | coldfir3/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1759
* F1: 0.8527
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | coldfir3/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1667
* F1: 0.8582
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | coldfir3/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3925
* F1: 0.7075
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": ... | coldfir3/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2651
* F1: 0.8355
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": ... | coldfir3/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2323
* F1: 0.8228
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | colochoplay/DialoGTP-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
fill-mask | transformers |
# BERT base Japanese model
This repository contains a BERT base model trained on Japanese Wikipedia dataset.
## Training data
[Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset as of June 20, 2021 which is released under [Creative Commons Attribution-ShareAlike 3.0](https://creativec... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": "wikipedia", "pipeline_tag": "fill-mask", "widget": [{"text": "\u5f97\u610f\u306a\u79d1\u76ee\u306f[MASK]\u3067\u3059\u3002"}]} | colorfulscoop/bert-base-ja | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# BERT base Japanese model
This repository contains a BERT base model trained on Japanese Wikipedia dataset.
## Training data
Japanese Wikipedia dataset as of June 20, 2021 which is released under Creative Commons Attribution-ShareAlike 3.0 is used for training.
The dataset is splitted into three subsets - train, v... | [
"# BERT base Japanese model\n\nThis repository contains a BERT base model trained on Japanese Wikipedia dataset.",
"## Training data\n\nJapanese Wikipedia dataset as of June 20, 2021 which is released under Creative Commons Attribution-ShareAlike 3.0 is used for training.\nThe dataset is splitted into three subse... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# BERT base Japanese model\n\nThis repository contains a BERT base model trained on Japanese Wikipedia dataset.",
"## Training data\n\nJapanese Wikipedia dataset as... |
text-generation | transformers |
# GPT-2 small Japanese model
This repository contains a GPT2-small model trained on Japanese Wikipedia dataset.
## Training data
[Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset as of Aug20, 2021 released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.o... | {"language": "ja", "license": "cc", "datasets": "wikipedia", "widget": [{"text": "\u7d71\u8a08\u7684\u6a5f\u68b0\u5b66\u7fd2\u3067\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af"}]} | colorfulscoop/gpt2-small-ja | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #ja #dataset-wikipedia #license-cc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT-2 small Japanese model
==========================
This repository contains a GPT2-small model trained on Japanese Wikipedia dataset.
Training data
-------------
Japanese Wikipedia dataset as of Aug20, 2021 released under Creative Commons Attribution-ShareAlike 3.0 is used for both tokenizer and GPT-2 model.
... | [] | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #ja #dataset-wikipedia #license-cc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# Sentence BERT base Japanese model
This repository contains a Sentence BERT base model for Japanese.
## Pretrained model
This model utilizes a Japanese BERT model [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) v1.0 released under [Creative Commons Attribution-ShareAlike 3.0](https:... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity", "widget": {"source_sentence": "\u8d70\u308b\u306e\u304c\u8da3\u5473\u3067\u3059", "sentences": ["\u5916\u3092\u30e9\u30f3\u30cb\u30f3\u30b0\u3059\u308b\u30... | colorfulscoop/sbert-base-ja | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"ja",
"arxiv:1908.10084",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"ja"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #ja #arxiv-1908.10084 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
|
# Sentence BERT base Japanese model
This repository contains a Sentence BERT base model for Japanese.
## Pretrained model
This model utilizes a Japanese BERT model colorfulscoop/bert-base-ja v1.0 released under Creative Commons Attribution-ShareAlike 3.0 as a pretrained model.
## Training data
Japanese SNLI datas... | [
"# Sentence BERT base Japanese model\n\nThis repository contains a Sentence BERT base model for Japanese.",
"## Pretrained model\n\nThis model utilizes a Japanese BERT model colorfulscoop/bert-base-ja v1.0 released under Creative Commons Attribution-ShareAlike 3.0 as a pretrained model.",
"## Training data\n\nJ... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #ja #arxiv-1908.10084 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# Sentence BERT base Japanese model\n\nThis repository contains a Sentence BERT base model for Japanese.",
"## Pretrained model\n\nT... |
automatic-speech-recognition | transformers |
# Czech wav2vec2-xls-r-300m-cs-250
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset as well as other datasets listed below.
It achieves the following results on the evaluation set:
- Loss: 0.1271
- Wer: 0.1475
- ... | {"language": ["cs"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week"], "datasets": ["mozilla-foundation/common_voice_8_0", "ovm", "pscr", "vystadial2016"], "base_model"... | comodoro/wav2vec2-xls-r-300m-cs-250 | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"cs",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:ovm",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-mozilla-foundation/common_voice_8_0 #dataset-ovm #dataset-pscr #dataset-vystadial2016 #base_model-fac... | Czech wav2vec2-xls-r-300m-cs-250
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice 8.0 dataset as well as other datasets listed below.
It achieves the following results on the evaluation set:
* Loss: 0.1271
* Wer: 0.1475
* Cer: 0.0329
The 'U... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-mozilla-foundation/common_voice_8_0 #dataset-ovm #dataset-pscr #dataset-vystadial2016 #base_mod... |
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-cs-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/w... | {"language": ["cs"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Czech comodoro Wav... | comodoro/wav2vec2-xls-r-300m-cs-cv8 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"cs",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-inde... | null | 2022-03-02T23:29:05+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #cs #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-cs-cv8
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice 8.0 dataset.
It achieves the following results on the evaluation set while training:
* Loss: 0.2327
* Wer: 0.1608
* Cer: 0.0376
The 'URL' script results using a LM are:
WER... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during first stage of training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 640\n* optimizer: Adam with betas=(0.9,0.... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #cs #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Czech
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model ... | {"language": ["cs"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "Czech comodoro Wav2Vec2 XLSR 300M CV6.1", "results": [{"task": {... | comodoro/wav2vec2-xls-r-300m-cs | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"xlsr-fine-tuning-week",
"cs",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Czech
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated ... | [
"# Wav2Vec2-Large-XLSR-53-Czech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Czech\n\nFine-tuned... |
automatic-speech-recognition | transformers |
# Upper Sorbian wav2vec2-xls-r-300m-hsb-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Wer: 0.5037
- Cer: 0.1278
## Evaluation
The mod... | {"language": ["hsb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "Upper Sorbian comodoro Wav2Vec2 XLSR 300... | comodoro/wav2vec2-xls-r-300m-hsb-cv8 | null | [
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"endpoints_compa... | null | 2022-03-02T23:29:05+00:00 | [] | [
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| Upper Sorbian wav2vec2-xls-r-300m-hsb-cv8
=========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9643
* Wer: 0.5037
* Cer: 0.1278
Evaluation
----------
The model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperpa... |
automatic-speech-recognition | transformers | # wav2vec2-xls-r-300m-pl-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.1716
- Wer: 0.1697
- Cer: 0.0385
The `eval.py` scrip... | {"language": ["pl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "Polish comodoro Wav2Vec2 XLSR 300M CV8", "results": [{"task": {"typ... | comodoro/wav2vec2-xls-r-300m-pl-cv8 | null | [
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"hf-asr-leaderboard",
"pl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
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| # wav2vec2-xls-r-300m-pl-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.1716
- Wer: 0.1697
- Cer: 0.0385
The 'URL' script results are:
WER: 0.16970531733661967
CER: 0.038391354165... | [
"# wav2vec2-xls-r-300m-pl-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\nIt achieves the following results on the evaluation set while training:\n- Loss: 0.1716\n- Wer: 0.1697\n- Cer: 0.0385\n\nThe 'URL' script results are:\nWER: 0.16970531733661967\nCER:... | [
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"# wav2vec2-xls-r-300m-pl-cv8\n\nThis model is... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-cs-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset.
It achieves the following results on the evaluation set:
- WER: 0.49575384615384616
- CER: 0.13333333333333333
## Usage
... | {"language": ["sk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "Slovak comodoro Wav2Vec2 XLSR 300M CV8", "results": [{"task": {"typ... | comodoro/wav2vec2-xls-r-300m-sk-cv8 | null | [
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"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sk"
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|
# wav2vec2-xls-r-300m-cs-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.
It achieves the following results on the evaluation set:
- WER: 0.49575384615384616
- CER: 0.13333333333333333
## Usage
The model can be used directly (without a language mod... | [
"# wav2vec2-xls-r-300m-cs-cv8\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\r\nIt achieves the following results on the evaluation set:\r\n\r\n- WER: 0.49575384615384616\r\n- CER: 0.13333333333333333",
"## Usage\r\n\r\nThe model can be used directly (wi... | [
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"# wav2vec2-xls-r-300m-cs-cv8\r\n\r\nThis mode... |
automatic-speech-recognition | transformers |
# Serbian wav2vec2-xls-r-300m-sr-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7302
- Wer: 0.4825
- Cer: 0.1847
Evaluation on mozilla-foundat... | {"language": ["sr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0", {"name": "Serbian comodoro Wav2Vec2 XLSR 300M... | comodoro/wav2vec2-xls-r-300m-sr-cv8 | null | [
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"model-index",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sr"
] | TAGS
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| Serbian wav2vec2-xls-r-300m-sr-cv8
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7302
* Wer: 0.4825
* Cer: 0.1847
Evaluation on mozilla-foundation/common\_voice\... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe follo... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-west-slavic-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets... | {"language": ["cs", "hsb", "pl", "sk", "sl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"n... | comodoro/wav2vec2-xls-r-300m-west-slavic-cv8 | null | [
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"pl",
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"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"l... | null | 2022-03-02T23:29:05+00:00 | [] | [
"cs",
"hsb",
"pl",
"sk",
"sl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #hsb #pl #sk #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #regio... |
# wav2vec2-xls-r-300m-west-slavic-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.
Evaluation set used f... | [
"# wav2vec2-xls-r-300m-west-slavic-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.\n\nEvaluation se... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #hsb #pl #sk #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-toxic
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilrober... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-toxic", "results": []}]} | conjuring92/distilroberta-base-finetuned-toxic | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-toxic
==================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2768
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-generation | transformers |
# Snape DialoGPT Model
| {"tags": ["conversational"]} | conniezyj/DialoGPT-small-snape | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Snape DialoGPT Model
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"# Snape DialoGPT Model"
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] |
token-classification | transformers | Named-entity recognition model trained on the I2B2 training data set for PHI.
| {} | connorboyle/bert-ner-i2b2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Named-entity recognition model trained on the I2B2 training data set for PHI.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | hello
| {} | conversify/response-score | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | # LIMIT-BERT
Code and model for the *EMNLP 2020 Findings* paper:
[LIMIT-BERT: Linguistic Informed Multi-task BERT](https://arxiv.org/abs/1910.14296))
## Contents
1. [Requirements](#Requirements)
2. [Training](#Training)
## Requirements
* Python 3.6 or higher.
* Cython 0.25.2 or any compatible version.
* [PyTorc... | {} | cooelf/limitbert | null | [
"transformers",
"pytorch",
"arxiv:1910.14296",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.14296"
] | [] | TAGS
#transformers #pytorch #arxiv-1910.14296 #endpoints_compatible #region-us
| # LIMIT-BERT
Code and model for the *EMNLP 2020 Findings* paper:
LIMIT-BERT: Linguistic Informed Multi-task BERT)
## Contents
1. Requirements
2. Training
## Requirements
* Python 3.6 or higher.
* Cython 0.25.2 or any compatible version.
* PyTorch 1.0.0+.
* EVALB. Before starting, run 'make' inside the 'EVALB/'... | [
"# LIMIT-BERT\n\nCode and model for the *EMNLP 2020 Findings* paper: \n\nLIMIT-BERT: Linguistic Informed Multi-task BERT)",
"## Contents\n\n1. Requirements\n2. Training",
"## Requirements\n\n* Python 3.6 or higher.\n* Cython 0.25.2 or any compatible version.\n* PyTorch 1.0.0+. \n* EVALB. Before starting, run 'm... | [
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"## Contents\n\n1. Requirements\n2. Training",
"## Requirements\n\n* Python 3.6 or higher.\n* Cython... |
fill-mask | transformers |
# Cicero-Similis
## Model description
A Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
## Intended u... | {"language": ["la"], "license": "apache-2.0", "tags": ["language model"], "datasets": ["Tesserae", "Phi5", "Thomas Aquinas", "Patrologia Latina"]} | cook/cicero-similis | null | [
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"tf",
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"bert",
"fill-mask",
"language model",
"la",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"la"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #language model #la #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cicero-Similis
## Model description
A Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
## Intended u... | [
"# Cicero-Similis",
"## Model description\n\nA Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2.",
... | [
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"# Cicero-Similis",
"## Model description\n\nA Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the p... |
text-generation | transformers |
# Joreyar DialoGPT Model | {"tags": ["conversational"]} | cookirei/DialoGPT-medium-Joreyar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Joreyar DialoGPT Model | [
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"# Joreyar DialoGPT Model"
] |
feature-extraction | transformers | This is the SciBERT pretrained language model further fine-tuned on masked language modeling and cite-worthiness detection on the [CiteWorth](https://github.com/copenlu/cite-worth) dataset. Note that this model should be used for further fine-tuning on downstream scientific document understanding tasks. | {} | copenlu/citebert | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| This is the SciBERT pretrained language model further fine-tuned on masked language modeling and cite-worthiness detection on the CiteWorth dataset. Note that this model should be used for further fine-tuning on downstream scientific document understanding tasks. | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Uzbek news category classifier (based on UzBERT)
UzBERT fine-tuned to classify news articles into one of the following
categories:
- дунё
- жамият
- жиноят
- иқтисодиёт
- маданият
- реклама
- саломатлик
- сиёсат
- спорт
- фан ва техника
- шоу-бизнес
## How to use
```python
>>> from transformers import pipeline
>... | {"language": "uz", "license": "mit", "tags": ["uzbek", "cyrillic", "news category classifier"], "datasets": ["webcrawl"]} | coppercitylabs/uzbek-news-category-classifier | null | [
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"safetensors",
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"text-classification",
"uzbek",
"cyrillic",
"news category classifier",
"uz",
"dataset:webcrawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uz"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #uzbek #cyrillic #news category classifier #uz #dataset-webcrawl #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Uzbek news category classifier (based on UzBERT)
UzBERT fine-tuned to classify news articles into one of the following
categories:
- дунё
- жамият
- жиноят
- иқтисодиёт
- маданият
- реклама
- саломатлик
- сиёсат
- спорт
- фан ва техника
- шоу-бизнес
## How to use
## Fine-tuning data
Fine-tuned on ~60K news art... | [
"# Uzbek news category classifier (based on UzBERT)\n\nUzBERT fine-tuned to classify news articles into one of the following\ncategories:\n\n- дунё\n- жамият\n- жиноят\n- иқтисодиёт\n- маданият\n- реклама\n- саломатлик\n- сиёсат\n- спорт\n- фан ва техника\n- шоу-бизнес",
"## How to use",
"## Fine-tuning data\nF... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #uzbek #cyrillic #news category classifier #uz #dataset-webcrawl #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Uzbek news category classifier (based on UzBERT)\n\nUzBERT fine-tuned to classify news artic... |
fill-mask | transformers |
# UzBERT base model (uncased)
Pretrained model on Uzbek language (Cyrillic script) using a masked
language modeling and next sentence prediction objectives.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pip... | {"language": "uz", "license": "mit", "tags": ["uzbert", "uzbek", "bert", "cyrillic"], "datasets": ["webcrawl"]} | coppercitylabs/uzbert-base-uncased | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"uzbert",
"uzbek",
"cyrillic",
"uz",
"dataset:webcrawl",
"arxiv:2108.09814",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.09814"
] | [
"uz"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #uzbert #uzbek #cyrillic #uz #dataset-webcrawl #arxiv-2108.09814 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# UzBERT base model (uncased)
Pretrained model on Uzbek language (Cyrillic script) using a masked
language modeling and next sentence prediction objectives.
## How to use
You can use this model directly with a pipeline for masked language modeling:
## Training data
UzBERT model was pretrained on \~625K news art... | [
"# UzBERT base model (uncased)\n\nPretrained model on Uzbek language (Cyrillic script) using a masked\nlanguage modeling and next sentence prediction objectives.",
"## How to use\n\nYou can use this model directly with a pipeline for masked language modeling:",
"## Training data\n\nUzBERT model was pretrained o... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #uzbert #uzbek #cyrillic #uz #dataset-webcrawl #arxiv-2108.09814 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# UzBERT base model (uncased)\n\nPretrained model on Uzbek language (Cyrillic script) using a masked\nlanguage mode... |
text-generation | transformers |
# Rick Sanchez | {"tags": ["conversational"]} | cosmic/DialoGPT-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez | [
"# Rick Sanchez"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez"
] |
text-generation | transformers | # Harry Potter DialoGPT Model | {"tags": ["conversational"]} | cosmicray001/prod-harry | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | cosmicray001/small-harry | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text2text-generation | transformers |
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train mo... | {"language": "ko"} | cosmoquester/bart-ko-base | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us
| Pretrained BART in Korean
=========================
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by TPU Research Cloud program.
The script which is used to pre-train model is here... | [
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
text2text-generation | transformers |
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train mo... | {"language": "ko"} | cosmoquester/bart-ko-mini | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us
| Pretrained BART in Korean
=========================
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by TPU Research Cloud program.
The script which is used to pre-train model is here... | [
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
text2text-generation | transformers |
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train mo... | {"language": "ko"} | cosmoquester/bart-ko-small | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us
| Pretrained BART in Korean
=========================
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by TPU Research Cloud program.
The script which is used to pre-train model is here... | [
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-eo
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "eo", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Esperanto by Charles Pierse", "results": [{"task": {"type": "automatic-speech-recognition", "name": ... | cpierse/wav2vec2-large-xlsr-53-esperanto | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"eo",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-eo
Fine-tuned facebook/wav2vec2-large-xlsr-53 on esperanto using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# Wav2Vec2-Large-XLSR-53-eo \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on esperanto using the Common Voice dataset. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe m... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-eo \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on e... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Irish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Irish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mo... | {"language": "ga-IE", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "cpierse/wav2vec2-large-xlsr-53-irish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "S... | cpierse/wav2vec2-large-xlsr-53-irish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga-IE"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Irish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Irish using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalua... | [
"# Wav2Vec2-Large-XLSR-53-Irish \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Irish using the Common Voice dataset. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Irish \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Irish using the Common Voi... |
token-classification | transformers |
# Named Entity Recognition based on FERNET-CC_sk
This model is a fine-tuned version of [fav-kky/FERNET-CC_sk](https://huggingface.co/fav-kky/FERNET-CC_sk) on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1763
- Precision: 0.9360
- Recall: 0.9472
- F1: 0.9416
- Accuracy... | {"language": ["sk"], "license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "inference": false, "model-index": [{"name": "fernet-sk-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "data... | crabz/FERNET-CC_sk-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"sk",
"dataset:wikiann",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sk"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #sk #dataset-wikiann #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #region-us
| Named Entity Recognition based on FERNET-CC\_sk
===============================================
This model is a fine-tuned version of fav-kky/FERNET-CC\_sk on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1763
* Precision: 0.9360
* Recall: 0.9472
* F1: 0.9416
* Accur... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Tra... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #sk #dataset-wikiann #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_ba... |
token-classification | transformers |
# Named Entity Recognition based on bertoslav-limited
This model is a fine-tuned version of [crabz/bertoslav-limited](https://huggingface.co/crabz/bertoslav-limited) on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2119
- Precision: 0.8986
- Recall: 0.9174
- F1: 0.9079... | {"language": ["sk"], "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "inference": false, "model-index": [{"name": "bertoslav-limited-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikian... | crabz/bertoslav-limited-ner | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"sk",
"dataset:wikiann",
"model-index",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sk"
] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #sk #dataset-wikiann #model-index #autotrain_compatible #region-us
| Named Entity Recognition based on bertoslav-limited
===================================================
This model is a fine-tuned version of crabz/bertoslav-limited on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2119
* Precision: 0.8986
* Recall: 0.9174
* F1: 0.90... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Tra... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #sk #dataset-wikiann #model-index #autotrain_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* e... |
token-classification | transformers |
# Named Entity Recognition based on SlovakBERT
This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1600
- Precision: 0.9327
- Recall: 0.9470
- F1: 0.9398
- Accuracy: 0.... | {"language": ["sk"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "inference": false, "widget": [{"text": "Zuzana \u010caputov\u00e1 sa narodila 21. j\u00fana 1973 v Bratislave.", "example_title": "Named Entity Recognition"}], "base... | crabz/slovakbert-ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"sk",
"dataset:wikiann",
"base_model:gerulata/slovakbert",
"license:mit",
"model-index",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sk"
] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #sk #dataset-wikiann #base_model-gerulata/slovakbert #license-mit #model-index #autotrain_compatible #has_space #region-us
| Named Entity Recognition based on SlovakBERT
============================================
This model is a fine-tuned version of gerulata/slovakbert on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1600
* Precision: 0.9327
* Recall: 0.9470
* F1: 0.9398
* Accuracy: 0.9... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\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: 15.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #sk #dataset-wikiann #base_model-gerulata/slovakbert #license-mit #model-index #autotrain_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Frisian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Frisian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "fy-NL", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Frisian XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech ... | crang/wav2vec2-large-xlsr-53-frisian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fy-NL"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Frisian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Frisian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# Wav2Vec2-Large-XLSR-53-Frisian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Frisian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Frisian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Frisian using the Common ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tatar using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | {"language": "tt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Tatar XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recog... | crang/wav2vec2-large-xlsr-53-tatar | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tt"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated... | [
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common ... |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: locat... | {"language": ["multilingual", "bg", "mk"], "license": "mit", "tags": ["labse", "ner"]} | creat89/NER_FEDA_Bg | null | [
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"bg",
"mk",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"bg",
"mk"
] | TAGS
#transformers #pytorch #bert #labse #ner #multilingual #bg #mk #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: locat... | [] | [
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #bg #mk #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
4. Turku (DATE, EVT, LOC, ORG... | {"language": ["multilingual", "cs"], "license": "mit", "tags": ["labse", "ner"]} | creat89/NER_FEDA_Cs | null | [
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"cs",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"cs"
] | TAGS
#transformers #pytorch #bert #labse #ner #multilingual #cs #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
4. Turku (DATE, EVT, LOC, ORG... | [] | [
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER,... | {"language": ["multilingual", "ru", "bg", "mk", "uk", "fi"], "license": "mit", "tags": ["labse", "ner"]} | creat89/NER_FEDA_Cyrillic1 | null | [
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi"
] | TAGS
#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER,... | [] | [
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER,... | {"language": ["multilingual", "ru", "bg", "mk", "uk", "fi"], "license": "mit", "tags": ["labse", "ner"]} | creat89/NER_FEDA_Cyrillic2 | null | [
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi"
] | TAGS
#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER,... | [] | [
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5.... | {"language": ["multilingual", "cs", "pl", "sl", "fi"], "license": "mit", "tags": ["labse", "ner"]} | creat89/NER_FEDA_Latin1 | null | [
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"cs",
"pl",
"sl",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"cs",
"pl",
"sl",
"fi"
] | TAGS
#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5.... | [] | [
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5.... | {"language": ["multilingual", "cs", "pl", "sl", "fi"], "license": "mit", "tags": ["labse", "ner"]} | creat89/NER_FEDA_Latin2 | null | [
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"cs",
"pl",
"sl",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"cs",
"pl",
"sl",
"fi"
] | TAGS
#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5.... | [] | [
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a Polish NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on Polish BERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. NKJP (DATE, GEOPOLIT, LOC, ORG, PE... | {"language": ["pl"], "license": "mit", "tags": ["polish_bert", "ner"]} | creat89/NER_FEDA_Pl | null | [
"transformers",
"pytorch",
"bert",
"polish_bert",
"ner",
"pl",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #bert #polish_bert #ner #pl #license-mit #endpoints_compatible #region-us
|
This is a Polish NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on Polish BERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. NKJP (DATE, GEOPOLIT, LOC, ORG, PE... | [] | [
"TAGS\n#transformers #pytorch #bert #polish_bert #ner #pl #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a Russian NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on RuBERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
PER... | {"language": ["ru"], "license": "mit", "tags": ["rubert", "ner"]} | creat89/NER_FEDA_Ru | null | [
"transformers",
"pytorch",
"bert",
"rubert",
"ner",
"ru",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #bert #rubert #ner #ru #license-mit #endpoints_compatible #region-us
|
This is a Russian NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on RuBERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
PER... | [] | [
"TAGS\n#transformers #pytorch #bert #rubert #ner #ru #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on CroSloEngual (https://huggingface.co/EMBEDDIA/crosloengual-bert) and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SSJ500k (... | {"language": ["hr", "sl", "en", "multilingual"], "license": "mit", "tags": ["CroSloEngual", "ner"]} | creat89/NER_FEDA_Sl | null | [
"transformers",
"pytorch",
"bert",
"CroSloEngual",
"ner",
"hr",
"sl",
"en",
"multilingual",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr",
"sl",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on CroSloEngual (URL and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SSJ500k (LOC, MISC, ORG, PER)
PER: person, LOC: locatio... | [] | [
"TAGS\n#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. NER-UK (LOC, MISC, ORG, PER)
4. Turku (DATE, EVT, LOC, ORG, PER, PRO... | {"language": ["multilingual", "uk"], "license": "mit", "tags": ["labse", "ner"]} | creat89/NER_FEDA_Uk | null | [
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"uk",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"uk"
] | TAGS
#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. NER-UK (LOC, MISC, ORG, PER)
4. Turku (DATE, EVT, LOC, ORG, PER, PRO... | [] | [
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# MyModel
## Model description
This is the `BART-TL-all` model from the paper [BART-TL: Weakly-Supervised Topic Label Generation](https://www.aclweb.org/anthology/2021.eacl-main.121.pdf). We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in prev... | {"language": ["en"], "license": "apache-2.0", "tags": ["topic labeling"], "metrics": ["ndcg"], "<!-- thumbnail": "https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png -->"} | cristian-popa/bart-tl-all | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"topic labeling",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MyModel
=======
Model description
-----------------
This is the 'BART-TL-all' model from the paper BART-TL: Weakly-Supervised Topic Label Generation. We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in previous State of the Art works.
For mo... | [
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.",
"#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelate... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for ... |
text2text-generation | transformers |
# MyModel
## Model description
This is the `BART-TL-ng` model from the paper [BART-TL: Weakly-Supervised Topic Label Generation](https://www.aclweb.org/anthology/2021.eacl-main.121.pdf). We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in previ... | {"language": ["en"], "license": "apache-2.0", "tags": ["topic labeling"], "metrics": ["ndcg"], "<!-- thumbnail": "https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png -->"} | cristian-popa/bart-tl-ng | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"topic labeling",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MyModel
=======
Model description
-----------------
This is the 'BART-TL-ng' model from the paper BART-TL: Weakly-Supervised Topic Label Generation. We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in previous State of the Art works.
For mor... | [
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.",
"#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelate... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for ... |
translation | null |
### Preprocessing
1. Normalisation and tokenisation with moses scripts
2. truecased with model docgWP.tcmodel.[LAN] and moses scripts
3. bped with model model.caesen40k.bpe and subword-nmt
- Note: no prepended tag for multilinguality
### Training Data
1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, Global... | {"language": ["ca", "es", "en"], "tags": ["translation"]} | cristinae/marian_caes2en | null | [
"translation",
"ca",
"es",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ca",
"es",
"en"
] | TAGS
#translation #ca #es #en #region-us
|
### Preprocessing
1. Normalisation and tokenisation with moses scripts
2. truecased with model docgWP.tcmodel.[LAN] and moses scripts
3. bped with model URL and subword-nmt
- Note: no prepended tag for multilinguality
### Training Data
1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoices
* Bilingu... | [
"### Preprocessing\n1. Normalisation and tokenisation with moses scripts\n2. truecased with model docgWP.tcmodel.[LAN] and moses scripts\n3. bped with model URL and subword-nmt\n- Note: no prepended tag for multilinguality",
"### Training Data\n1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoi... | [
"TAGS\n#translation #ca #es #en #region-us \n",
"### Preprocessing\n1. Normalisation and tokenisation with moses scripts\n2. truecased with model docgWP.tcmodel.[LAN] and moses scripts\n3. bped with model URL and subword-nmt\n- Note: no prepended tag for multilinguality",
"### Training Data\n1. Bilingual es-ca:... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec-timit
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-timit", "results": []}]} | cristinakuo/wav2vec-timit | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec-timit
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
Th... | [
"# wav2vec-timit\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"##... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec-timit\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"##... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-latino40
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-latino40", "results": []}]} | cristinakuo/wav2vec2-latino40 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-latino40
=================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8795
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_b... |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-MiniLM-L-12-v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-MiniLM-L-2-v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-MiniLM-L-4-v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-MiniLM-L-6-v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-TinyBERT-L-2-v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-TinyBERT-L-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-TinyBERT-L-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-TinyBERT-L-6 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | {"license": "apache-2.0"} | cross-encoder/ms-marco-electra-base | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Re... | [] | [
"TAGS\n#transformers #pytorch #electra #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS MARCO - EN-DE
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: See [SBERT.net Retrieve & R... | {"license": "apache-2.0"} | cross-encoder/msmarco-MiniLM-L12-en-de-v1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Cross-Encoder for MS MARCO - EN-DE
==================================
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: See URL Retrieve & Re-rank.
The training code i... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Cross-Encoder for MS MARCO - EN-DE
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: See [SBERT.net Retrieve & R... | {"license": "apache-2.0"} | cross-encoder/msmarco-MiniLM-L6-en-de-v1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Cross-Encoder for MS MARCO - EN-DE
==================================
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: See URL Retrieve & Re-rank.
The training code i... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNL... | {"language": "en", "license": "apache-2.0", "tags": ["MiniLMv2"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-MiniLM2-L6-H768 | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #MiniLMv2 #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Pe... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutr... | [
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"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cr... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNL... | {"language": "en", "license": "apache-2.0", "tags": ["deberta-base-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-deberta-base | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta #text-classification #deberta-base-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Pe... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutr... | [
"TAGS\n#transformers #pytorch #deberta #text-classification #deberta-base-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using Sen... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)
## Tr... | {"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-deberta-v3-base | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-base",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the l... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores correspondin... | [
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"# Cross-Encoder for Natural Language Inference\nThis model was trained using Sen... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)
## ... | {"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-large"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-deberta-v3-large | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-large",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the ... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores correspondi... | [
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"# Cross-Encoder for Natural Language Inference\nThis model was trained using Se... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small)
## ... | {"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-small"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-deberta-v3-small | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-small",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-small #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the ... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores correspondi... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-small #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was train... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall)
#... | {"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-xsmall"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-deberta-v3-xsmall | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-xsmall",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-xsmall #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores correspond... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-xsmall #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trai... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNL... | {"language": "en", "license": "apache-2.0", "tags": ["distilroberta-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-distilroberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"distilroberta-base",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #distilroberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Pe... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutr... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #distilroberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained usi... |
zero-shot-classification | transformers |
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNL... | {"language": "en", "license": "apache-2.0", "tags": ["roberta-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | cross-encoder/nli-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"roberta-base",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #roberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Pe... | [
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutr... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #roberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using Sen... |
text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? Th... | {"license": "apache-2.0"} | cross-encoder/qnli-distilroberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"arxiv:1804.07461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.07461"
] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into ... | [
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD da... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Tra... |
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