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text2text-generation | transformers |
# T5-Efficient-TINY-NL32 (Deep-Narrow version)
T5-Efficient-TINY-NL32 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint ... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-tiny-nl32 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-TINY-NL32 (Deep-Narrow version)
============================================
T5-Efficient-TINY-NL32 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tunin... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-TINY-NL6 (Deep-Narrow version)
T5-Efficient-TINY-NL6 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint an... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-tiny-nl6 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-TINY-NL6 (Deep-Narrow version)
===========================================
T5-Efficient-TINY-NL6 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning T... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-TINY-NL8 (Deep-Narrow version)
T5-Efficient-TINY-NL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint an... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-tiny-nl8 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-TINY-NL8 (Deep-Narrow version)
===========================================
T5-Efficient-TINY-NL8 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning T... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-TINY (Deep-Narrow version)
T5-Efficient-TINY is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was re... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-tiny | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-TINY (Deep-Narrow version)
=======================================
T5-Efficient-TINY is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers
... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL-NL12 (Deep-Narrow version)
T5-Efficient-XL-NL12 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and ... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl-nl12 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL-NL12 (Deep-Narrow version)
==========================================
T5-Efficient-XL-NL12 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Tran... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL-NL16 (Deep-Narrow version)
T5-Efficient-XL-NL16 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and ... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl-nl16 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL-NL16 (Deep-Narrow version)
==========================================
T5-Efficient-XL-NL16 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Tran... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL-NL2 (Deep-Narrow version)
T5-Efficient-XL-NL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and wa... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl-nl2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL-NL2 (Deep-Narrow version)
=========================================
T5-Efficient-XL-NL2 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transfo... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL-NL28 (Deep-Narrow version)
T5-Efficient-XL-NL28 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and ... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl-nl28 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL-NL28 (Deep-Narrow version)
==========================================
T5-Efficient-XL-NL28 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Tran... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL-NL4 (Deep-Narrow version)
T5-Efficient-XL-NL4 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and wa... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl-nl4 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL-NL4 (Deep-Narrow version)
=========================================
T5-Efficient-XL-NL4 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transfo... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL-NL6 (Deep-Narrow version)
T5-Efficient-XL-NL6 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and wa... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl-nl6 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL-NL6 (Deep-Narrow version)
=========================================
T5-Efficient-XL-NL6 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transfo... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL-NL8 (Deep-Narrow version)
T5-Efficient-XL-NL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and wa... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl-nl8 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL-NL8 (Deep-Narrow version)
=========================================
T5-Efficient-XL-NL8 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transfo... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XL (Deep-Narrow version)
T5-Efficient-XL is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was releas... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xl | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XL (Deep-Narrow version)
=====================================
T5-Efficient-XL is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers
by *Yi... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XXL-NL4 (Deep-Narrow version)
T5-Efficient-XXL-NL4 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and ... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xxl-nl4 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XXL-NL4 (Deep-Narrow version)
==========================================
T5-Efficient-XXL-NL4 is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Tran... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-XXL (Deep-Narrow version)
T5-Efficient-XXL is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was rele... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-xxl | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10686"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| T5-Efficient-XXL (Deep-Narrow version)
======================================
T5-Efficient-XXL is a variation of Google's original T5 following the T5 model architecture.
It is a *pretrained-only* checkpoint and was released with the
paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers
by ... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the foll... | {"language": "en", "license": "apache-2.0", "tags": ["t5-lm-adapt"], "datasets": ["c4"]} | google/t5-large-lm-adapt | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"t5-lm-adapt",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
T5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:
- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should b... | [
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:\n\n- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuni... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes th... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "natural_questions"], "pipeline_tag": "text2text-generation"} | google/t5-large-ssm-nq | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-infe... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ).
Note: The model was fine-tuned on 100% of the train split... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "natural_questions"]} | google/t5-large-ssm-nqo | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-infe... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ).
Note: The model was fine-tuned on 90% of the train splits... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4) and subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.0890... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia"]} | google/t5-large-ssm | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4 and subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia.
Note: This model should be fine-tuned on a question answering downstream task before it is useable for cl... | [
"## Abstract\n\nIt has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without acce... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Abstract\n\nIt has recently been observed that neural la... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the foll... | {"language": "en", "license": "apache-2.0", "tags": ["t5-lm-adapt"], "datasets": ["c4"]} | google/t5-small-lm-adapt | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"t5-lm-adapt",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
T5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:
- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should b... | [
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:\n\n- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuni... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes th... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "natural_questions"], "pipeline_tag": "text2text-generation"} | google/t5-small-ssm-nq | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-infe... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ).
Note: The model was fine-tuned on 100% of the train split... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4) and subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.0890... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia"]} | google/t5-small-ssm | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4 and subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia.
Note: This model should be fine-tuned on a question answering downstream task before it is useable for cl... | [
"## Abstract\n\nIt has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without acce... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Abstract\n\nIt has recently been observed that neural la... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- ... | {"language": "en", "license": "apache-2.0", "datasets": ["c4"]} | google/t5-v1_1-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1
## Version 1.1
T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-tr... | [
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.\n\n- Pre-trained on C4 only... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared ... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- ... | {"language": "en", "license": "apache-2.0", "datasets": ["c4"]} | google/t5-v1_1-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1
## Version 1.1
T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-tr... | [
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.\n\n- Pre-trained on C4 only... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared ... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- ... | {"language": "en", "license": "apache-2.0", "datasets": ["c4"]} | google/t5-v1_1-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1
## Version 1.1
T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-tr... | [
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.\n\n- Pre-trained on C4 only... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared ... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- ... | {"language": "en", "license": "apache-2.0", "datasets": ["c4"]} | google/t5-v1_1-xl | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1
## Version 1.1
T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-tr... | [
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.\n\n- Pre-trained on C4 only... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared to th... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- ... | {"language": "en", "license": "apache-2.0", "datasets": ["c4"]} | google/t5-v1_1-xxl | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1
## Version 1.1
T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.
- Pre-tr... | [
"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning.\n\n- Pre-trained on C4 only... | [
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"## Version 1.1\n\nT5 Version 1.1 includes the following improvements compared to th... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the foll... | {"language": "en", "license": "apache-2.0", "tags": ["t5-lm-adapt"], "datasets": ["c4"]} | google/t5-xl-lm-adapt | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"t5-lm-adapt",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
T5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:
- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should b... | [
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:\n\n- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuni... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes th... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "natural_questions"], "pipeline_tag": "text2text-generation"} | google/t5-xl-ssm-nq | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ).
Note: The model was fine-tuned on 100% of the train split... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the foll... | {"language": "en", "license": "apache-2.0", "tags": ["t5-lm-adapt"], "datasets": ["c4"]} | google/t5-xxl-lm-adapt | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"t5-lm-adapt",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.05202",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
T5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:
- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.
- Dropout was turned off in pre-training (quality win). Dropout should b... | [
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes the following improvements compared to the original T5 model:\n\n- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here.\n\n- Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuni... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #t5-lm-adapt #en #dataset-c4 #arxiv-2002.05202 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Version 1.1 - LM-Adapted\n\nT5 Version 1.1 - LM Adapted includes th... |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "natural_questions"], "pipeline_tag": "text2text-generation"} | google/t5-xxl-ssm-nq | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ).
Note: The model was fine-tuned on 100% of the train split... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "natural_questions"]} | google/t5-xxl-ssm-nqo | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Natural Questions (NQ).
Note: The model was fine-tuned on 90% of the train splits... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-natural_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "trivia_qa"]} | google/t5-xxl-ssm-tqa | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:trivia_qa",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-trivia_qa #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Trivia QA (TQA).
Note: The model was fine-tuned on 100% of the train splits of Tr... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-trivia_qa #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "trivia_qa"]} | google/t5-xxl-ssm-tqao | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:trivia_qa",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-trivia_qa #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Trivia QA (TQA).
Note: The model was fine-tuned on 90% of the train splits of Tri... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-trivia_qa #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "web_questions"]} | google/t5-xxl-ssm-wq | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:web_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"re... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Web Questions (WQ).
Note: The model was fine-tuned on 100% of the train splits of... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "web_questions"]} | google/t5-xxl-ssm-wqo | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:web_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"re... | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Web Questions (WQ).
Note: The model was fine-tuned on 90% of the train splits of ... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4) and subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.0890... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia"]} | google/t5-xxl-ssm | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Google's T5 for Closed Book Question Answering.
The model was pre-trained using T5's denoising objective on C4 and subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia.
Note: This model should be fine-tuned on a question answering downstream task before it is useable for cl... | [
"## Abstract\n\nIt has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without acce... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Abstract\n\nIt has recently been observed that neural languag... |
table-question-answering | transformers |
# TAPAS base model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and a... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "table-question-answering"], "datasets": ["msr_sqa"]} | google/tapas-base-finetuned-sqa | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| TAPAS base model fine-tuned on Sequential Question Answering (SQA)
==================================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_sqa\_inter\_masklm\_base\_reset' checkpoint of the original Github repository.
This model wa... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:",
"### Fine-tuning\n\n\nThe model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.\nIn thi... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The input... |
text-classification | transformers |
# TAPAS base model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model w... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "sequence-classification"], "datasets": ["tab_fact"]} | google/tapas-base-finetuned-tabfact | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"text-classification",
"sequence-classification",
"en",
"dataset:tab_fact",
"arxiv:2010.00571",
"arxiv:2004.02349",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.00571",
"2004.02349"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TAPAS base model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_base_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step... | [
"# TAPAS base model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_base_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an addition... | [
"TAGS\n#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TAPAS base model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 ve... |
table-question-answering | transformers |
# TAPAS base model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM ... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["wikisql"]} | google/tapas-base-finetuned-wikisql-supervised | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1709.00103",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1709.00103"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# TAPAS base model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_base_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors cal... | [
"# TAPAS base model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_base_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the auth... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# TAPAS base model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which c... |
table-question-answering | transformers |
# TAPAS base model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["wikitablequestions"]} | google/tapas-base-finetuned-wtq | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1508.00305"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| TAPAS base model fine-tuned on WikiTable Questions (WTQ)
========================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_wtq\_wikisql\_sqa\_inter\_masklm\_base\_reset' checkpoint of the original Github repository.
This model was pre-... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nThe authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts.",
"### Fine-tuning\n\n\nThe model was fine-tu... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabul... |
fill-mask | transformers | This model corresponds to **tapas_masklm_base_reset** of the [original repository](https://github.com/google-research/tapas).
Here's how you can use it:
```python
from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch
tokenizer = TapasTokenizer.from_pretrained("google/tapas-base-m... | {} | google/tapas-base-masklm | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"tapas",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model corresponds to tapas_masklm_base_reset of the original repository.
Here's how you can use it:
| [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# TAPAS base model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which th... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "TapasModel"]} | google/tapas-base | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"feature-extraction",
"TapasModel",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# TAPAS base model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_base_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It... | [
"# TAPAS base model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_base_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the authors call intermediate pre-train... | [
"TAGS\n#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# TAPAS base model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to t... |
table-question-answering | transformers |
# TAPAS large model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["msr_sqa"]} | google/tapas-large-finetuned-sqa | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| TAPAS large model fine-tuned on Sequential Question Answering (SQA)
===================================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_sqa\_inter\_masklm\_large\_reset' checkpoint of the original Github repository.
This model... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:",
"### Fine-tuning\n\n\nThe model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.\nIn thi... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The input... |
text-classification | transformers |
# TAPAS large model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "sequence-classification"], "datasets": ["tab_fact"]} | google/tapas-large-finetuned-tabfact | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"text-classification",
"sequence-classification",
"en",
"dataset:tab_fact",
"arxiv:2010.00571",
"arxiv:2004.02349",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.00571",
"2004.02349"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TAPAS large model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_large_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional st... | [
"# TAPAS large model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_large_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additi... | [
"TAGS\n#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TAPAS large model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 v... |
table-question-answering | transformers |
# TAPAS large model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on ML... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["wikisql"]} | google/tapas-large-finetuned-wikisql-supervised | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1709.00103",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1709.00103"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# TAPAS large model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_large_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors c... | [
"# TAPAS large model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_large_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the au... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# TAPAS large model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which ... |
table-question-answering | transformers |
# TAPAS large model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM a... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "table-question-answering"], "datasets": ["wikitablequestions"]} | google/tapas-large-finetuned-wtq | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"tapas",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1508.00305"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| TAPAS large model fine-tuned on WikiTable Questions (WTQ)
=========================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_wtq\_wikisql\_sqa\_inter\_masklm\_large\_reset' checkpoint of the original Github repository.
This model was p... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nThe authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts.",
"### Fine-tuning\n\n\nThe model was fine-tu... | [
"TAGS\n#transformers #pytorch #tf #safetensors #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece ... |
fill-mask | transformers | This model corresponds to **tapas_masklm_large_reset** of the [original repository](https://github.com/google-research/tapas).
Here's how you can use it:
```python
from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch
tokenizer = TapasTokenizer.from_pretrained("google/tapas-large... | {} | google/tapas-large-masklm | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model corresponds to tapas_masklm_large_reset of the original repository.
Here's how you can use it:
| [] | [
"TAGS\n#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# TAPAS large model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which ... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "TapasModel"]} | google/tapas-large | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"feature-extraction",
"TapasModel",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS large model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_large_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. ... | [
"# TAPAS large model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_large_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the authors call intermediate pre-tra... | [
"TAGS\n#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TAPAS large model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_... |
table-question-answering | transformers |
# TAPAS medium model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM a... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["msr_sqa"]} | google/tapas-medium-finetuned-sqa | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
| TAPAS medium model fine-tuned on Sequential Question Answering (SQA)
====================================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_sqa\_inter\_masklm\_medium\_reset' checkpoint of the original Github repository.
This mo... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:",
"### Fine-tuning\n\n\nThe model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.\nIn thi... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the mo... |
text-classification | transformers |
# TAPAS medium model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This mod... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "sequence-classification"], "datasets": ["tab_fact"]} | google/tapas-medium-finetuned-tabfact | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"text-classification",
"sequence-classification",
"en",
"dataset:tab_fact",
"arxiv:2010.00571",
"arxiv:2004.02349",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.00571",
"2004.02349"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TAPAS medium model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_medium_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional ... | [
"# TAPAS medium model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_medium_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an addi... | [
"TAGS\n#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TAPAS medium model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 ... |
table-question-answering | transformers |
# TAPAS medium model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on ... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["wikisql"]} | google/tapas-medium-finetuned-wikisql-supervised | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1709.00103",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1709.00103"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# TAPAS medium model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_medium_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors... | [
"# TAPAS medium model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_medium_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the ... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# TAPAS medium model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which... |
table-question-answering | transformers |
# TAPAS medium model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "table-question-answering"], "datasets": ["wikitablequestions"]} | google/tapas-medium-finetuned-wtq | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1508.00305"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #region-us
| TAPAS medium model fine-tuned on WikiTable Questions (WTQ)
==========================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_wtq\_wikisql\_sqa\_inter\_masklm\_medium\_reset' checkpoint of the original Github repository.
This model wa... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nThe authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts.",
"### Fine-tuning\n\n\nThe model was fine-tu... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of... |
fill-mask | transformers | This model corresponds to **tapas_masklm_medium_reset** of the [original repository](https://github.com/google-research/tapas).
Here's how you can use it:
```python
from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch
tokenizer = TapasTokenizer.from_pretrained("google/tapas-medi... | {} | google/tapas-medium-masklm | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model corresponds to tapas_masklm_medium_reset of the original repository.
Here's how you can use it:
| [] | [
"TAGS\n#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# TAPAS medium model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step whic... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "TapasModel"]} | google/tapas-medium | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"feature-extraction",
"TapasModel",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS medium model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_medium_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training... | [
"# TAPAS medium model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_medium_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the authors call intermediate pre-t... | [
"TAGS\n#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TAPAS medium model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas... |
table-question-answering | transformers |
# TAPAS mini model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and a... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["msr_sqa"]} | google/tapas-mini-finetuned-sqa | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
| TAPAS mini model fine-tuned on Sequential Question Answering (SQA)
==================================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_sqa\_inter\_masklm\_mini\_reset' checkpoint of the original Github repository.
This model wa... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:",
"### Fine-tuning\n\n\nThe model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.\nIn thi... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the mo... |
text-classification | transformers |
# TAPAS mini model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model w... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "sequence-classification"], "datasets": ["tab_fact"]} | google/tapas-mini-finetuned-tabfact | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"text-classification",
"sequence-classification",
"en",
"dataset:tab_fact",
"arxiv:2010.00571",
"arxiv:2004.02349",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.00571",
"2004.02349"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TAPAS mini model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_mini_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step... | [
"# TAPAS mini model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_mini_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an addition... | [
"TAGS\n#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TAPAS mini model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 ve... |
table-question-answering | transformers |
# TAPAS mini model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "table-question-answering"], "datasets": ["wikitablequestions"]} | google/tapas-mini-finetuned-wtq | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1508.00305"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| TAPAS mini model fine-tuned on WikiTable Questions (WTQ)
========================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_wtq\_wikisql\_sqa\_inter\_masklm\_mini\_reset' checkpoint of the original Github repository.
This model was pre-... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nThe authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts.",
"### Fine-tuning\n\n\nThe model was fine-tu... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabul... |
fill-mask | transformers | This model corresponds to **tapas_masklm_mini_reset** of the [original repository](https://github.com/google-research/tapas).
Here's how you can use it:
```python
from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch
tokenizer = TapasTokenizer.from_pretrained("google/tapas-mini-m... | {} | google/tapas-mini-masklm | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model corresponds to tapas_masklm_mini_reset of the original repository.
Here's how you can use it:
| [] | [
"TAGS\n#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# TAPAS mini model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which th... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "TapasModel"]} | google/tapas-mini | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"feature-extraction",
"TapasModel",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS mini model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_mini_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It... | [
"# TAPAS mini model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_mini_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the authors call intermediate pre-train... | [
"TAGS\n#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TAPAS mini model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_i... |
table-question-answering | transformers |
# TAPAS small model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["msr_sqa"]} | google/tapas-small-finetuned-sqa | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
| TAPAS small model fine-tuned on Sequential Question Answering (SQA)
===================================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_sqa\_inter\_masklm\_small\_reset' checkpoint of the original Github repository.
This model... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:",
"### Fine-tuning\n\n\nThe model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.\nIn thi... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the mo... |
text-classification | transformers |
# TAPAS small model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "sequence-classification"], "datasets": ["tab_fact"]} | google/tapas-small-finetuned-tabfact | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"text-classification",
"sequence-classification",
"en",
"dataset:tab_fact",
"arxiv:2010.00571",
"arxiv:2004.02349",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.00571",
"2004.02349"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TAPAS small model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_small_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional st... | [
"# TAPAS small model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_small_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additi... | [
"TAGS\n#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TAPAS small model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 v... |
table-question-answering | transformers |
# TAPAS small model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on ML... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["wikisql"]} | google/tapas-small-finetuned-wikisql-supervised | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1709.00103",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1709.00103"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS small model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_small_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors c... | [
"# TAPAS small model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which can be used. The default version corresponds to the 'tapas_wikisql_sqa_inter_masklm_small_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the au... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikisql #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1709.00103 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TAPAS small model fine-tuned on WikiSQL (in a supervised fashion)\n\nhis model has 2 versions which can be used... |
table-question-answering | transformers |
# TAPAS small model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM a... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "table-question-answering"], "datasets": ["wikitablequestions"]} | google/tapas-small-finetuned-wtq | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1508.00305"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| TAPAS small model fine-tuned on WikiTable Questions (WTQ)
=========================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_wtq\_wikisql\_sqa\_inter\_masklm\_small\_reset' checkpoint of the original Github repository.
This model was p... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nThe authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts.",
"### Fine-tuning\n\n\nThe model was fine-tu... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wikitablequestions #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabul... |
fill-mask | transformers | This model corresponds to **tapas_masklm_small_reset** of the [original repository](https://github.com/google-research/tapas).
Here's how you can use it:
```python
from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch
tokenizer = TapasTokenizer.from_pretrained("google/tapas-small... | {} | google/tapas-small-masklm | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model corresponds to tapas_masklm_small_reset of the original repository.
Here's how you can use it:
| [] | [
"TAGS\n#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# TAPAS small model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which ... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "TapasModel"]} | google/tapas-small | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"feature-extraction",
"TapasModel",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS small model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_small_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. ... | [
"# TAPAS small model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_small_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the authors call intermediate pre-tra... | [
"TAGS\n#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TAPAS small model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_... |
table-question-answering | transformers |
# TAPAS tiny model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_tiny_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and a... | {"language": "en", "license": "apache-2.0", "tags": ["tapas"], "datasets": ["msr_sqa"]} | google/tapas-tiny-finetuned-sqa | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
| TAPAS tiny model fine-tuned on Sequential Question Answering (SQA)
==================================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_sqa\_inter\_masklm\_tiny\_reset' checkpoint of the original Github repository.
This model wa... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:",
"### Fine-tuning\n\n\nThe model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.\nIn thi... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-msr_sqa #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the mo... |
text-classification | transformers |
# TAPAS tiny model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_tiny_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model w... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "sequence-classification"], "datasets": ["tab_fact"]} | google/tapas-tiny-finetuned-tabfact | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"text-classification",
"sequence-classification",
"en",
"dataset:tab_fact",
"arxiv:2010.00571",
"arxiv:2004.02349",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.00571",
"2004.02349"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TAPAS tiny model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_tiny_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step... | [
"# TAPAS tiny model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_tabfact_inter_masklm_tiny_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an addition... | [
"TAGS\n#transformers #pytorch #tf #tapas #text-classification #sequence-classification #en #dataset-tab_fact #arxiv-2010.00571 #arxiv-2004.02349 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TAPAS tiny model fine-tuned on Tabular Fact Checking (TabFact) \n\nThis model has 2 ve... |
table-question-answering | transformers |
# TAPAS tiny model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_tiny_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "table-question-answering"], "datasets": ["wtq"]} | google/tapas-tiny-finetuned-wtq | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wtq",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571",
"1508.00305"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wtq #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| TAPAS tiny model fine-tuned on WikiTable Questions (WTQ)
========================================================
This model has 2 versions which can be used. The default version corresponds to the 'tapas\_wtq\_wikisql\_sqa\_inter\_masklm\_tiny\_reset' checkpoint of the original Github repository.
This model was pre-... | [
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nThe authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts.",
"### Fine-tuning\n\n\nThe model was fine-tu... | [
"TAGS\n#transformers #pytorch #tf #tapas #table-question-answering #en #dataset-wtq #arxiv-2004.02349 #arxiv-2010.00571 #arxiv-1508.00305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,... |
fill-mask | transformers | This model corresponds to **tapas_masklm_tiny_reset** of the [original repository](https://github.com/google-research/tapas).
Here's how you can use it:
```python
from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch
tokenizer = TapasTokenizer.from_pretrained("google/tapas-tiny-m... | {} | google/tapas-tiny-masklm | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model corresponds to tapas_masklm_tiny_reset of the original repository.
Here's how you can use it:
| [] | [
"TAGS\n#transformers #pytorch #tf #tapas #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# TAPAS tiny model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_tiny_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which th... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "TapasModel"]} | google/tapas-tiny | null | [
"transformers",
"pytorch",
"tf",
"tapas",
"feature-extraction",
"TapasModel",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS tiny model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_tiny_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It... | [
"# TAPAS tiny model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_tiny_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the authors call intermediate pre-train... | [
"TAGS\n#transformers #pytorch #tf #tapas #feature-extraction #TapasModel #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TAPAS tiny model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_i... |
feature-extraction | transformers |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false} | google/vit-base-patch16-224-in21k | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repo... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in thi... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, ... |
image-classification | transformers |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transfo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k", "imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapo... | google/vit-base-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transfor... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Tr... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Trans... |
image-classification | transformers |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transfo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet", "imagenet-21k"]} | google/vit-base-patch16-384 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transfor... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Tr... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transfor... |
feature-extraction | transformers |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false} | google/vit-base-patch32-224-in21k | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repo... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in thi... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 clas... |
image-classification | transformers |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transfo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k", "imagenet-21k"]} | google/vit-base-patch32-384 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transfor... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Tr... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model... |
feature-extraction | transformers |
# Vision Transformer (huge-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false} | google/vit-huge-patch14-224-in21k | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us
|
# Vision Transformer (huge-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repo... | [
"# Vision Transformer (huge-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in thi... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us \n",
"# Vision Transformer (huge-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 clas... |
feature-extraction | transformers |
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy e... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false} | google/vit-large-patch16-224-in21k | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this rep... | [
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in th... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images,... |
image-classification | transformers |
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transf... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet-1k", "imagenet-21k"]} | google/vit-large-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
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"vit",
"image-classification",
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"dataset:imagenet-1k",
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"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transfo... | [
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: T... | [
"TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT)... |
image-classification | transformers |
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transf... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | google/vit-large-patch16-384 | null | [
"transformers",
"pytorch",
"tf",
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"vit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transfo... | [
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: T... | [
"TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-tra... |
feature-extraction | transformers |
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy e... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false} | google/vit-large-patch32-224-in21k | null | [
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this rep... | [
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in th... | [
"TAGS\n#transformers #pytorch #tf #jax #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 class... |
image-classification | transformers |
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transf... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]} | google/vit-large-patch32-384 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transfo... | [
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: T... | [
"TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) mo... |
text-classification | transformers | # Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).
## Data
The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/nikhileswarkomati/suicide-watch) obtained from Kaggle. The dataset was scraped from Reddit and consis... | {} | gooohjy/suicidal-electra | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).
## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes -... | [
"# Suicidal-ELECTRA\nThis text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).",
"## Data\nThe model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between ... | [
"TAGS\n#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Suicidal-ELECTRA\nThis text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).",
"## Data\nThe model was trained on the Suicide and Depression D... |
null | null | https://www.geogebra.org/m/awcxgj4g
https://www.geogebra.org/m/tx9tme6s
https://www.geogebra.org/m/yx5yyjmx | {} | gorave/gorave | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Turkish GPT2 Model Finetuned
# Türkçe GPT2 Modeli
## Model description
This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020
Live demo based on this work at : https://www.metayazar.com/
Fine tuned writer on this model: https://huggingface... | {"language": ["tr"], "license": "apache-2.0", "tags": ["gpt2", "turkish"], "datasets": ["wikipedia-turkish"], "metrics": ["perplexity", "accuracy"], "widget": [{"text": "Bu yaz\u0131y\u0131 bir bilgisayar yazd\u0131. Yazarken", "context": ""}, {"text": "\u0130nternete kolay eri\u015fim sayesinde d\u00fcnya daha da k\u0... | gorkemgoknar/gpt2-small-turkish | null | [
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"jax",
"gpt2",
"text-generation",
"turkish",
"tr",
"dataset:wikipedia-turkish",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #turkish #tr #dataset-wikipedia-turkish #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Turkish GPT2 Model Finetuned
============================
Türkçe GPT2 Modeli
==================
Model description
-----------------
This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020
Live demo based on this work at : URL
Fine tuned w... | [
"#### How to use",
"#### Install",
"#### Generate 1 word",
"#### Generate Full Sequence",
"#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral.\n\n\nTraining data\n-----... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #turkish #tr #dataset-wikipedia-turkish #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"#### How to use",
"#### Install",
"#### Generate 1 word",
"#### Generate Full Sequence",
"#### Limit... |
text-generation | transformers |
# Turkish AI Writer based on GPT2-Small
# Türkçe Yapay Zeka Yazarı
## Model description
This model is enhanced version of gpt2-small-turkish finetuned version. In addition to 28-10-2020 Wikipedia Turkish article dump this model is trained with more than 400 classic novels and plays in Turkish (Including Dostoyevski,... | {"language": ["tr"], "license": "apache-2.0", "tags": ["gpt2", "turkish", "aiwriter", "finetuned"], "datasets": ["wikipedia-turkish", "custom-book-corpus"], "metrics": ["perplexity", "accuracy"], "widget": [{"text": "Bir zaman topu olan ama k\u00f6pe\u011fi olmayan bir \u00e7ocuk vard\u0131. Parkta", "context": ""}, {"... | gorkemgoknar/gpt2-turkish-writer | null | [
"transformers",
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"jax",
"gpt2",
"text-generation",
"turkish",
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"dataset:wikipedia-turkish",
"dataset:custom-book-corpus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us... | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #turkish #aiwriter #finetuned #tr #dataset-wikipedia-turkish #dataset-custom-book-corpus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Turkish AI Writer based on GPT2-Small
=====================================
Türkçe Yapay Zeka Yazarı
========================
Model description
-----------------
This model is enhanced version of gpt2-small-turkish finetuned version. In addition to 28-10-2020 Wikipedia Turkish article dump this model is trained w... | [
"#### How to use",
"#### Install",
"#### Generate 1 word",
"#### Generate Full Sequence",
"#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much p... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #turkish #aiwriter #finetuned #tr #dataset-wikipedia-turkish #dataset-custom-book-corpus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### How to use",
"#### Install",
"#### Gene... |
text-generation | transformers | # GPT2 Persona Chatbot based on Movie Characters
Model used for https://www.metayazar.com/chatbot
GPT2 Small Trained on movie scripts (especially Sci-fi)
Usual HF api will not work see HF Spaces for demo usage https://huggingface.co/spaces/gorkemgoknar/moviechatbot
This work is based on Persona Chatbot originally ... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["gpt2", "conversational"], "widget": [{"text": "Hello there", "context": "Gandalf"}]} | gorkemgoknar/gpt2chatbotenglish | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # GPT2 Persona Chatbot based on Movie Characters
Model used for URL
GPT2 Small Trained on movie scripts (especially Sci-fi)
Usual HF api will not work see HF Spaces for demo usage URL
This work is based on Persona Chatbot originally done by Hugging Face team (URL
For cleaning movie scripts I also provide cleaner ... | [
"# GPT2 Persona Chatbot based on Movie Characters\nModel used for URL\n\nGPT2 Small Trained on movie scripts (especially Sci-fi) \n\nUsual HF api will not work see HF Spaces for demo usage URL\n\n\nThis work is based on Persona Chatbot originally done by Hugging Face team (URL\n\nFor cleaning movie scripts I also p... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2 Persona Chatbot based on Movie Characters\nModel used for URL\n\nGPT2 Small Trained on movie scripts (especially Sci... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Turkish
Note: This model is trained with 5 Turkish movies additional to common voice dataset.
Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good.
Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogu... | {"language": ["tr"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "movies"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large Turkish with extended dataset by Gorkem Goknar", "results": [{"task": {"type": "au... | gorkemgoknar/wav2vec2-large-xlsr-53-turkish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"dataset:movies",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #dataset-movies #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Turkish
Note: This model is trained with 5 Turkish movies additional to common voice dataset.
Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good.
Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogu... | [
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nNote: This model is trained with 5 Turkish movies additional to common voice dataset.\nAlthough WER is high (50%) per common voice test dataset, performance from \"other sources \" seems pretty good.\n\nDisclaimer: Please use another wav2vec2-tr model in hub for \"clean environ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #dataset-movies #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nNote: This model is trained with 5 Turkish movies a... |
null | null | test | {} | gottaegbert/nolibox | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| test | [] | [
"TAGS\n#region-us \n"
] |
summarization | transformers |
# Introduction
This model checkpoint is obtained by first fine-tuning the sshleifer/distilbart-cnn-6-6 summarization checkpoint on the SQuAD dataset. After this, the 6-6 fine-tuned model is distilled down to a 3-3 model which gives us the final checkpoint. [GitHub Link for training scripts.](https://github.com/darth-... | {"language": "en", "license": "apache-2.0", "tags": ["question-generation", "summarization"], "datasets": ["squad"]} | gpssohi/distilbart-qgen-3-3 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question-generation",
"summarization",
"en",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question-generation #summarization #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Introduction
============
This model checkpoint is obtained by first fine-tuning the sshleifer/distilbart-cnn-6-6 summarization checkpoint on the SQuAD dataset. After this, the 6-6 fine-tuned model is distilled down to a 3-3 model which gives us the final checkpoint. GitHub Link for training scripts.
Usage
=====
... | [
"### Preprocessing\n\n\nThe first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.",
"### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number o... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question-generation #summarization #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe first step is to remove questions which don't have answers. After that, we split the train se... |
summarization | transformers |
# Introduction
This model checkpoint is obtained by fine-tuning the `sshleifer/distilbart-cnn-6-6` summarization checkpoint on the SQuAD dataset. [GitHub Link for training scripts.](https://github.com/darth-c0d3r/bart-question-generation)
# Usage
The input format is as follows: `[answer] <s> [passage]`. The model w... | {"language": "en", "license": "apache-2.0", "tags": ["summarization", "question-generation"], "datasets": ["squad"]} | gpssohi/distilbart-qgen-6-6 | null | [
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"safetensors",
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"summarization",
"question-generation",
"en",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #summarization #question-generation #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Introduction
============
This model checkpoint is obtained by fine-tuning the 'sshleifer/distilbart-cnn-6-6' summarization checkpoint on the SQuAD dataset. GitHub Link for training scripts.
Usage
=====
The input format is as follows: '[answer] ~~[passage]'. The model will predict the question that corresponds t... | [
"### Preprocessing\n\n\nThe first step is to remove questions that don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.",
"### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number of... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #summarization #question-generation #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe first step is to remove questions that don't have answers. After that, we split ... |
text-generation | transformers |
#waifu bot | {"tags": ["conversational"]} | grayson124/chatbotwaifu | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#waifu bot | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
audio-to-audio | asteroid |
## Asteroid model `groadabike/ConvTasNet_DAMP-VSEP_enhboth`
Imported from [Zenodo](https://zenodo.org/record/3994193)
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
```yaml
data:
channels: 1
n_sr... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["DAMP-VSEP"]} | groadabike/ConvTasNet_DAMP-VSEP_enhboth | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:DAMP-VSEP",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-DAMP-VSEP #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'groadabike/ConvTasNet_DAMP-VSEP_enhboth'
Imported from Zenodo
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
### Results:
### License notice:
This work "ConvTasNet_DAMP-VSEP_enhbot... | [
"## Asteroid model 'groadabike/ConvTasNet_DAMP-VSEP_enhboth'\nImported from Zenodo",
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvT... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-DAMP-VSEP #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'groadabike/ConvTasNet_DAMP-VSEP_enhboth'\nImported from Zenodo",
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on ... |
audio-to-audio | asteroid |
## Description:
This model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.
It was trained on the `singing/accompaniment` task of the `DAMP-VSEP` dataset.
## Training config:
```yaml
data:
channels: 1
emb_model: 'no'
metadata_path: metadata
mixture: remix
root_path: /fastdata/acp13gr/DAMP... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["DAMP-VSEP", "Singing/Accompaniment Separation"]} | groadabike/ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #license-cc-by-sa-4.0 #region-us
|
## Description:
This model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.
It was trained on the 'singing/accompaniment' task of the 'DAMP-VSEP' dataset.
## Training config:
## Results:
## License notice:
This is important, please fill it, if you need help, you can ask on Asteroid's slack.... | [
"## Description:\nThis model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.\nIt was trained on the 'singing/accompaniment' task of the 'DAMP-VSEP' dataset.",
"## Training config:",
"## Results:",
"## License notice:\n\n This is important, please fill it, if you need help, you can ask on Ast... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #license-cc-by-sa-4.0 #region-us \n",
"## Description:\nThis model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.\nIt was trained on the 'singing/accompaniment' task of the 'DAMP-VSEP' dataset.",
"## Training config:",
"## Results... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-escape
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-escape", "results": []}]} | groar/distilgpt2-finetuned-escape | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-finetuned-escape
This model is a fine-tuned version of distilgpt2 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# distilgpt2-finetuned-escape\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-finetuned-escape\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | groar/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6895
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-1.3B-finetuned-escape
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape", "results": []}]} | groar/gpt-neo-1.3B-finetuned-escape | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# gpt-neo-1.3B-finetuned-escape\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model descript... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-1.3B-finetuned-escape2
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape2", "results": []}]} | groar/gpt-neo-1.3B-finetuned-escape2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape2
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# gpt-neo-1.3B-finetuned-escape2\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape2\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model descrip... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-1.3B-finetuned-escape3
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape3", "results": []}]} | groar/gpt-neo-1.3B-finetuned-escape3 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape3
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# gpt-neo-1.3B-finetuned-escape3\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape3\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model descrip... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-1.3B-finetuned-escape5
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape5", "results": []}]} | groar/gpt-neo-1.3B-finetuned-escape5 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape5
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# gpt-neo-1.3B-finetuned-escape5\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape5\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model descrip... |
text-generation | transformers |
#Rick DialoGPT Model | {"tags": ["conversational"]} | grounddominator/DialoGPT-lar-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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