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null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-12_H-768_A-12 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-2_H-128_A-2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-2_H-256_A-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-2_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-2_H-768_A-12 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-4_H-128_A-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-4_H-256_A-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-4_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-4_H-768_A-12 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-6_H-128_A-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-6_H-256_A-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-6_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-6_H-768_A-12 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-8_H-128_A-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-8_H-256_A-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-8_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-8_H-768_A-12 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers |
# BigBird base trivia-itc
This model is a fine-tune checkpoint of `bigbird-roberta-base`, fine-tuned on `trivia_qa` with `BigBirdForQuestionAnsweringHead` on its top.
Check out [this](https://colab.research.google.com/drive/1DVOm1VHjW0eKCayFq1N2GpY6GR9M4tJP?usp=sharing) to see how well `google/bigbird-base-trivia-it... | {"language": "en", "license": "apache-2.0", "datasets": ["trivia_qa"]} | google/bigbird-base-trivia-itc | null | [
"transformers",
"pytorch",
"jax",
"big_bird",
"question-answering",
"en",
"dataset:trivia_qa",
"arxiv:2007.14062",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.14062"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #big_bird #question-answering #en #dataset-trivia_qa #arxiv-2007.14062 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BigBird base trivia-itc
This model is a fine-tune checkpoint of 'bigbird-roberta-base', fine-tuned on 'trivia_qa' with 'BigBirdForQuestionAnsweringHead' on its top.
Check out this to see how well 'google/bigbird-base-trivia-itc' performs on question answering.
## How to use
Here is how to use this model to get t... | [
"# BigBird base trivia-itc\n\nThis model is a fine-tune checkpoint of 'bigbird-roberta-base', fine-tuned on 'trivia_qa' with 'BigBirdForQuestionAnsweringHead' on its top.\n\nCheck out this to see how well 'google/bigbird-base-trivia-itc' performs on question answering.",
"## How to use\n\nHere is how to use this ... | [
"TAGS\n#transformers #pytorch #jax #big_bird #question-answering #en #dataset-trivia_qa #arxiv-2007.14062 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BigBird base trivia-itc\n\nThis model is a fine-tune checkpoint of 'bigbird-roberta-base', fine-tuned on 'trivia_qa' with 'BigBirdForQue... |
summarization | transformers |
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["scientific_papers"], "model-index": [{"name": "google/bigbird-pegasus-large-arxiv", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "scientific_papers", "type": "scientific_papers", "config"... | google/bigbird-pegasus-large-arxiv | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"2007.14062"
] | [
"en"
] | TAGS
#transformers #pytorch #bigbird_pegasus #text2text-generation #summarization #en #dataset-scientific_papers #arxiv-2007.14062 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | [
"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. \n\nBigBir... | [
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"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based trans... |
summarization | transformers |
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["big_patent"]} | google/bigbird-pegasus-large-bigpatent | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.14062"
] | [
"en"
] | TAGS
#transformers #pytorch #bigbird_pegasus #text2text-generation #summarization #en #dataset-big_patent #arxiv-2007.14062 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | [
"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. \n\nBigBir... | [
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"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based transformer which extends... |
summarization | transformers |
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["scientific_papers"], "model-index": [{"name": "google/bigbird-pegasus-large-pubmed", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "scientific_papers", "type": "scientific_papers", "config... | google/bigbird-pegasus-large-pubmed | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"2007.14062"
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"en"
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|
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
BigBird was in... | [
"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. \n\nBigBir... | [
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"# BigBirdPegasus model (large)\n\nBigBird, is a sparse-attention based trans... |
null | transformers |
# BigBird base model
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
It is a pretrained model ... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "cc_news"]} | google/bigbird-roberta-base | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.14062"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #big_bird #pretraining #en #dataset-bookcorpus #dataset-wikipedia #dataset-cc_news #arxiv-2007.14062 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BigBird base model
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
It is a pretrained model ... | [
"# BigBird base model\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.\n\nIt is a pretraine... | [
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"# BigBird base model\n\nBigBird, is a sparse-attention based transformer which extends Transformer based mod... |
fill-mask | transformers |
# BigBird large model
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
It is a pretrained model... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "cc_news"]} | google/bigbird-roberta-large | null | [
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"dataset:wikipedia",
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"arxiv:2007.14062",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.14062"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #big_bird #fill-mask #en #dataset-bookcorpus #dataset-wikipedia #dataset-cc_news #arxiv-2007.14062 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BigBird large model
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
It is a pretrained model... | [
"# BigBird large model\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.\n\nIt is a pretrain... | [
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"# BigBird large model\n\nBigBird, is a sparse-attention based transformer which extends Transformer... |
text2text-generation | transformers |
# ByT5 - Base
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-base).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4mult... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/byt5-base | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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... | TAGS
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# ByT5 - Base
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT5... | [
"# ByT5 - Base\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream tas... | [
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text2text-generation | transformers |
# ByT5 - large
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-large).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4mu... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/byt5-large | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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... | TAGS
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# ByT5 - large
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT... | [
"# ByT5 - large\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream ta... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #multilingual #af #am #ar #az #be #bg #bn #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #ht #hu #hy #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml #... |
text2text-generation | transformers |
# ByT5 - Small
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4mu... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/byt5-small | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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... | TAGS
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# ByT5 - Small
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT... | [
"# ByT5 - Small\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream ta... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #multilingual #af #am #ar #az #be #bg #bn #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #ht #hu #hy #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml #... |
text2text-generation | transformers |
# ByT5 - xl
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-xl).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilin... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/byt5-xl | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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... | TAGS
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# ByT5 - xl
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT5 w... | [
"# ByT5 - xl\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.... | [
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text2text-generation | transformers |
# ByT5 - xxl
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-xxl).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multil... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/byt5-xxl | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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# ByT5 - xxl
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT5 ... | [
"# ByT5 - xxl\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task... | [
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feature-extraction | transformers |
# CANINE-c (CANINE pre-trained with autoregressive character loss)
Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) and ... | {"language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "he", "hi", "hu", "is", "io", "id", "ga", "it", "ja", "jv", "kn", "kk"... | google/canine-c | null | [
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... | TAGS
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# CANINE-c (CANINE pre-trained with autoregressive character loss)
Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation and first released in this repository.
... | [
"# CANINE-c (CANINE pre-trained with autoregressive character loss) \n\nPretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation and first released in this reposit... | [
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feature-extraction | transformers |
# CANINE-s (CANINE pre-trained with subword loss)
Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) and first released in... | {"language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "he", "hi", "hu", "is", "io", "id", "ga", "it", "ja", "jv", "kn", "kk"... | google/canine-s | null | [
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... | TAGS
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# CANINE-s (CANINE pre-trained with subword loss)
Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation and first released in this repository.
What's special a... | [
"# CANINE-s (CANINE pre-trained with subword loss) \n\nPretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation and first released in this repository. \n\nWhat's s... | [
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null | transformers |
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" in... | {"language": "en", "license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/electra-base-discriminator | null | [
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"1406.2661"
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] | TAGS
#transformers #pytorch #tf #jax #rust #electra #pretraining #en #arxiv-1406.2661 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input ... | [
"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish \"real\" input tokens vs \"fak... | [
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"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learn... |
fill-mask | transformers |
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" in... | {"language": "en", "license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/electra-base-generator | null | [
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|
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input ... | [
"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish \"real\" input tokens vs \"fak... | [
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"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language ... |
null | transformers |
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" in... | {"language": "en", "license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/electra-large-discriminator | null | [
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#transformers #pytorch #tf #jax #electra #pretraining #en #arxiv-1406.2661 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input ... | [
"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish \"real\" input tokens vs \"fak... | [
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"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. I... |
fill-mask | transformers |
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" in... | {"language": "en", "license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/electra-large-generator | null | [
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|
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input ... | [
"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish \"real\" input tokens vs \"fak... | [
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"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation le... |
null | transformers |
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" in... | {"language": "en", "license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/electra-small-discriminator | null | [
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|
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input ... | [
"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish \"real\" input tokens vs \"fak... | [
"TAGS\n#transformers #pytorch #tf #jax #electra #pretraining #en #arxiv-1406.2661 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. It can be us... |
fill-mask | transformers |
**WARNING**: This is the official generator checkpoint as in the [ELECTRA original codebase](https://github.com/google-research/electra). However, this model is not scaled properly for pre-training with [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator). The paper recommend... | {"language": "en", "license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/electra-small-generator | null | [
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"en"
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|
WARNING: This is the official generator checkpoint as in the ELECTRA original codebase. However, this model is not scaled properly for pre-training with google/electra-small-discriminator. The paper recommends a hyperparameter multiplier of 1/4 between the discriminator and generator for this given model to avoid trai... | [
"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish \"real\" input tokens vs \"fak... | [
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"## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators\n\nELECTRA is a new method for self-supervised language repres... |
null | transformers |
# FNet base model
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/google-research/tree/master/f_net)... | {"language": "en", "license": "apache-2.0", "tags": ["fnet"], "datasets": ["c4"]} | google/fnet-base | null | [
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"en"
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#transformers #pytorch #rust #fnet #pretraining #en #dataset-c4 #arxiv-2105.03824 #license-apache-2.0 #endpoints_compatible #region-us
| FNet base model
===============
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in this paper and first released in this repository.
This model is cased: it makes a difference between english and English. The model achieves 0.... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are\nthen of the form:\n\n\nWith probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in\nthe other cases, it's another... | [
"TAGS\n#transformers #pytorch #rust #fnet #pretraining #en #dataset-c4 #arxiv-2105.03824 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are\nthen of the form:\n\n\... |
null | transformers |
# FNet large model
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/google-research/tree/master/f_net... | {"language": "en", "license": "apache-2.0", "tags": ["fnet"], "datasets": ["c4"]} | google/fnet-large | null | [
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"2105.03824"
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"en"
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| FNet large model
================
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in this paper and first released in this repository.
This model is cased: it makes a difference between english and English. The model achieves ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nNote: The mask filling pipeline doesn't work exactly as the original model performs masking after converting to tokens. In masking pipeline an additional space is added after the [MASK].\n\n\nHere is how to use t... | [
"TAGS\n#transformers #pytorch #fnet #pretraining #en #dataset-c4 #arxiv-2105.03824 #license-apache-2.0 #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\nNote: The mask filling pipeline doesn't work exactly as the original ... |
null | transformers |
## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This checkpoint is the original MobileBert Optimized Uncased English:
[un... | {"language": "en", "license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/mobilebert-uncased | null | [
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"en"
] | TAGS
#transformers #pytorch #tf #rust #mobilebert #pretraining #en #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This checkpoint is the original MobileBert Optimized Uncased English:
unc... | [
"## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices\n\nMobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance\nbetween self-attentions and feed-forward networks.\n\nThis checkpoint is the original MobileBert Optimized Uncased Engl... | [
"TAGS\n#transformers #pytorch #tf #rust #mobilebert #pretraining #en #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices\n\nMobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a careful... |
text2text-generation | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebua... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/mt5-base | null | [
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... | TAGS
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Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galicia... | [
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based d... | [
"TAGS\n#transformers #pytorch #tf #jax #mt5 #text2text-generation #multilingual #af #am #ar #az #be #bg #bn #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #ht #hu #hy #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml ... |
text2text-generation | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebua... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/mt5-large | null | [
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... | TAGS
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Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galicia... | [
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based d... | [
"TAGS\n#transformers #pytorch #tf #jax #mt5 #text2text-generation #multilingual #af #am #ar #az #be #bg #bn #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #ht #hu #hy #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml ... |
text2text-generation | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebua... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/mt5-small | null | [
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... | TAGS
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Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galicia... | [
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based d... | [
"TAGS\n#transformers #pytorch #tf #jax #onnx #mt5 #text2text-generation #multilingual #af #am #ar #az #be #bg #bn #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #ht #hu #hy #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #m... |
text2text-generation | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebua... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/mt5-xl | null | [
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... | TAGS
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Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galicia... | [
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based d... | [
"TAGS\n#transformers #pytorch #tf #jax #mt5 #text2text-generation #multilingual #af #am #ar #az #be #bg #bn #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #ht #hu #hy #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml ... |
text2text-generation | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebua... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku"... | google/mt5-xxl | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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... | TAGS
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Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galicia... | [
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based d... | [
"TAGS\n#transformers #pytorch #tf #mt5 #text2text-generation #multilingual #af #am #ar #az #be #bg #bn #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #ht #hu #hy #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml #mn #... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 0k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different ... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_0k"]} | google/multiberts-seed_0-step_0k | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
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] | [
"en"
] | TAGS
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|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 0k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in th... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 0k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variat... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_0k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 0k\n\nMultiBERTs is a collection of checkpoints and... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1000k"]} | google/multiberts-seed_0-step_1000k | null | [
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"2106.16163",
"1908.08962"
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] | TAGS
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|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1000k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1000k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1000k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_100k"]} | google/multiberts-seed_0-step_100k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_100k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 100k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 100k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1100k"]} | google/multiberts-seed_0-step_1100k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1100k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1100k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1100k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1200k"]} | google/multiberts-seed_0-step_1200k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1200k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1200k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1200k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 120k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_120k"]} | google/multiberts-seed_0-step_120k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_120k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_120k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 120k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 120k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_120k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 120k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1300k"]} | google/multiberts-seed_0-step_1300k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1300k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1300k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1300k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1400k"]} | google/multiberts-seed_0-step_1400k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1400k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1400k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1400k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 140k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_140k"]} | google/multiberts-seed_0-step_140k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_140k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_140k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 140k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 140k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_140k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 140k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1500k"]} | google/multiberts-seed_0-step_1500k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1500k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1500k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1500k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1600k"]} | google/multiberts-seed_0-step_1600k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1600k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1600k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1600k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 160k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_160k"]} | google/multiberts-seed_0-step_160k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_160k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_160k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 160k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 160k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_160k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 160k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1700k"]} | google/multiberts-seed_0-step_1700k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1700k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1700k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1700k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1800k"]} | google/multiberts-seed_0-step_1800k | null | [
"transformers",
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"en",
"arxiv:2106.16163",
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"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1800k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1800k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 180k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_180k"]} | google/multiberts-seed_0-step_180k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
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"multiberts-seed_0-step_180k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_180k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 180k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 180k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_180k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 180k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_1900k"]} | google/multiberts-seed_0-step_1900k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_1900k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1900k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_1900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1900k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_2000k"]} | google/multiberts-seed_0-step_2000k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_2000k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_2000k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 2000k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_2000k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 2000k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_200k"]} | google/multiberts-seed_0-step_200k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_200k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 200k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 200k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 20k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_20k"]} | google/multiberts-seed_0-step_20k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_20k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_20k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 20k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 20k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_20k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 20k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_300k"]} | google/multiberts-seed_0-step_300k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_300k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 300k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 300k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_400k"]} | google/multiberts-seed_0-step_400k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_400k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 400k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 400k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 40k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_40k"]} | google/multiberts-seed_0-step_40k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_40k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_40k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 40k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 40k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_40k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 40k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_500k"]} | google/multiberts-seed_0-step_500k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_500k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 500k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 500k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_600k"]} | google/multiberts-seed_0-step_600k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_600k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 600k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 600k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 60k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_60k"]} | google/multiberts-seed_0-step_60k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_60k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_60k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 60k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 60k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_60k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 60k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_700k"]} | google/multiberts-seed_0-step_700k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_700k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 700k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 700k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_800k"]} | google/multiberts-seed_0-step_800k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_800k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 800k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 800k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 80k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_80k"]} | google/multiberts-seed_0-step_80k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_80k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_80k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 80k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 80k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_80k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 80k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0", "multiberts-seed_0-step_900k"]} | google/multiberts-seed_0-step_900k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"multiberts-seed_0-step_900k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 900k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #multiberts-seed_0-step_900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 900k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs - Seed 0
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variati... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_0"]} | google/multiberts-seed_0 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_0",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs - Seed 0
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in the initial weights and order of
tra... | [
"# MultiBERTs - Seed 0\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variations in the initial weights and or... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_0 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs - Seed 0\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. W... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 0k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different ... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_0k"]} | google/multiberts-seed_1-step_0k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_0k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_0k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 0k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in th... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 0k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variat... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_0k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 0k\n\nMultiBERTs is a collection of checkpoints and... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1000k"]} | google/multiberts-seed_1-step_1000k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1000k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1000k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1000k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1000k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1000k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_100k"]} | google/multiberts-seed_1-step_100k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_100k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 100k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 100k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1100k"]} | google/multiberts-seed_1-step_1100k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1100k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1100k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1100k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1100k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1200k"]} | google/multiberts-seed_1-step_1200k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1200k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1200k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1200k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 120k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_120k"]} | google/multiberts-seed_1-step_120k | null | [
"transformers",
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"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_120k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 120k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 120k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_120k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 120k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1300k"]} | google/multiberts-seed_1-step_1300k | null | [
"transformers",
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"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1300k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1300k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1400k"]} | google/multiberts-seed_1-step_1400k | null | [
"transformers",
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"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1400k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1400k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 140k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_140k"]} | google/multiberts-seed_1-step_140k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_140k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_140k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 140k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 140k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_140k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 140k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1500k"]} | google/multiberts-seed_1-step_1500k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1500k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1500k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1500k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1600k"]} | google/multiberts-seed_1-step_1600k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1600k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1600k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1600k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 160k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_160k"]} | google/multiberts-seed_1-step_160k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_160k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_160k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 160k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 160k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_160k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 160k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1700k"]} | google/multiberts-seed_1-step_1700k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1700k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1700k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1700k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1800k"]} | google/multiberts-seed_1-step_1800k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1800k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1800k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1800k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 180k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_180k"]} | google/multiberts-seed_1-step_180k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_180k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_180k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 180k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 180k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_180k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 180k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_1900k"]} | google/multiberts-seed_1-step_1900k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_1900k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1900k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_1900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1900k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_2000k"]} | google/multiberts-seed_1-step_2000k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_2000k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_2000k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 2000k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes var... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_2000k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 2000k\n\nMultiBERTs is a collection of checkpoin... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_200k"]} | google/multiberts-seed_1-step_200k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
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"multiberts-seed_1-step_200k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 200k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_200k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 200k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 20k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_20k"]} | google/multiberts-seed_1-step_20k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_20k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_20k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 20k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 20k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_20k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 20k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_300k"]} | google/multiberts-seed_1-step_300k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_300k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 300k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_300k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 300k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_400k"]} | google/multiberts-seed_1-step_400k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_400k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 400k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_400k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 400k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 40k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_40k"]} | google/multiberts-seed_1-step_40k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_40k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_40k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 40k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 40k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_40k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 40k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_500k"]} | google/multiberts-seed_1-step_500k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_500k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 500k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 500k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_600k"]} | google/multiberts-seed_1-step_600k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_600k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 600k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 600k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 60k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_60k"]} | google/multiberts-seed_1-step_60k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_60k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_60k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 60k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 60k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_60k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 60k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_1", "multiberts-seed_1-step_700k"]} | google/multiberts-seed_1-step_700k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_1",
"multiberts-seed_1-step_700k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 700k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_1 #multiberts-seed_1-step_700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 700k\n\nMultiBERTs is a collection of checkpoints... |
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