pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags sequencelengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv sequencelengths 0 201 | languages sequencelengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists sequencelengths 0 722 | processed_texts sequencelengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fill-mask | transformers |
# ALBERT Base v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-base-v1 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Base v1
==============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\... |
fill-mask | transformers |
# ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-base-v2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #safetensors #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Base v2
==============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #safetensors #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modelin... |
fill-mask | transformers |
# ALBERT Large v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-large-v1 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Large v1
===============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to u... |
fill-mask | transformers |
# ALBERT Large v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-large-v2 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT Large v2
===============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHer... |
fill-mask | transformers |
# ALBERT XLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xlarge-v1 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT XLarge v1
================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The tea... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHer... |
fill-mask | transformers |
# ALBERT XLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xlarge-v2 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT XLarge v2
================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The tea... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to u... |
fill-mask | transformers |
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xxlarge-v1 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ALBERT XXLarge v1
=================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to u... |
fill-mask | transformers |
# ALBERT XXLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | albert/albert-xxlarge-v2 | null | [
"transformers",
"pytorch",
"tf",
"rust",
"safetensors",
"albert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #rust #safetensors #albert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ALBERT XXLarge v2
=================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #rust #safetensors #albert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked la... |
fill-mask | transformers |
# BERT base model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference bet... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | google-bert/bert-base-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERT base model (cased)
=======================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between
english and English.
Disclaimer: The team releasin... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked langu... |
fill-mask | transformers |
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
### Model Descri... | {"language": "zh"} | google-bert/bert-base-chinese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1810.04805"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #zh #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Bert-base-chinese
## Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- How to Get Started With the Model
## Model Details
### Model Description
This model has been pre-trained for Chinese, training and random input masking has been applied independently to word p... | [
"# Bert-base-chinese",
"## Table of Contents\n- Model Details\n- Uses\n- Risks, Limitations and Biases\n- Training\n- Evaluation\n- How to Get Started With the Model",
"## Model Details",
"### Model Description\n\nThis model has been pre-trained for Chinese, training and random input masking has been applied ... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #zh #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Bert-base-chinese",
"## Table of Contents\n- Model Details\n- Uses\n- Risks, Limitations and Biases\n- Training\n- Evaluation\n- How to Get Started... |
fill-mask | transformers |
<a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT
## Overview
**Language model:** bert-base-cased
**L... | {"language": "de", "license": "mit", "tags": ["exbert"], "thumbnail": "https://static.tildacdn.com/tild6438-3730-4164-b266-613634323466/german_bert.png"} | google-bert/bert-base-german-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"bert",
"fill-mask",
"exbert",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #onnx #safetensors #bert #fill-mask #exbert #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
<a href="URL
<img width="300px" src="URL
</a>
# German BERT
!bert_image
## Overview
Language model: bert-base-cased
Language: German
Training data: Wiki, OpenLegalData, News (~ 12GB)
Eval data: Conll03 (NER), GermEval14 (NER), GermEval18 (Classification), GNAD (Classification)
Infrastructure: 1x TPU v2
Pu... | [
"# German BERT\n!bert_image",
"## Overview\nLanguage model: bert-base-cased \nLanguage: German \nTraining data: Wiki, OpenLegalData, News (~ 12GB) \nEval data: Conll03 (NER), GermEval14 (NER), GermEval18 (Classification), GNAD (Classification) \nInfrastructure: 1x TPU v2 \nPublished: Jun 14th, 2019\n\nUpdat... | [
"TAGS\n#transformers #pytorch #tf #jax #onnx #safetensors #bert #fill-mask #exbert #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# German BERT\n!bert_image",
"## Overview\nLanguage model: bert-base-cased \nLanguage: German \nTraining data: Wiki, OpenLegalData, News (~ 12GB) \... |
fill-mask | transformers |
This model is the same as [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-cased) for details on the model. | {"language": "de", "license": "mit"} | google-bert/bert-base-german-dbmdz-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
This model is the same as dbmdz/bert-base-german-cased. See the dbmdz/bert-base-german-cased model card for details on the model. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.
| {"language": "de", "license": "mit"} | google-bert/bert-base-german-dbmdz-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
This model is the same as dbmdz/bert-base-german-uncased. See the dbmdz/bert-base-german-cased model card for details on the model.
| [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 9