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null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 40k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_40k"]} | google/multiberts-seed_4-step_40k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_40k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_40k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 40k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 40k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_40k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 40k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_500k"]} | google/multiberts-seed_4-step_500k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_500k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 500k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_500k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 500k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_600k"]} | google/multiberts-seed_4-step_600k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_600k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 600k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_600k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 600k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 60k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_60k"]} | google/multiberts-seed_4-step_60k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_60k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_60k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 60k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 60k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_60k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 60k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_700k"]} | google/multiberts-seed_4-step_700k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_700k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 700k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_700k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 700k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_800k"]} | google/multiberts-seed_4-step_800k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_800k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 800k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_800k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 800k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 80k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_80k"]} | google/multiberts-seed_4-step_80k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_80k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_80k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 80k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in t... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 80k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes varia... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_80k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 80k\n\nMultiBERTs is a collection of checkpoints a... |
null | transformers |
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differen... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4", "multiberts-seed_4-step_900k"]} | google/multiberts-seed_4-step_900k | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"multiberts-seed_4-step_900k",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in ... | [
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 900k\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes vari... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #multiberts-seed_4-step_900k #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 900k\n\nMultiBERTs is a collection of checkpoints... |
null | transformers |
# MultiBERTs - Seed 4
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variati... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_4"]} | google/multiberts-seed_4 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_4",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs - Seed 4
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in the initial weights and order of
tra... | [
"# MultiBERTs - Seed 4\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variations in the initial weights and or... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_4 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs - Seed 4\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. W... |
null | transformers |
# MultiBERTs - Seed 5
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variati... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_5"]} | google/multiberts-seed_5 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_5",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_5 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs - Seed 5
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in the initial weights and order of
tra... | [
"# MultiBERTs - Seed 5\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variations in the initial weights and or... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_5 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs - Seed 5\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. W... |
null | transformers |
# MultiBERTs - Seed 6
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variati... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_6"]} | google/multiberts-seed_6 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_6",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_6 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
|
# MultiBERTs - Seed 6
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in the initial weights and order of
tra... | [
"# MultiBERTs - Seed 6\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variations in the initial weights and or... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_6 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs - Seed 6\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. W... |
null | transformers |
# MultiBERTs - Seed 7
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variati... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_7"]} | google/multiberts-seed_7 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"multiberts",
"multiberts-seed_7",
"en",
"arxiv:2106.16163",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
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"en"
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|
# MultiBERTs - Seed 7
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in the initial weights and order of
tra... | [
"# MultiBERTs - Seed 7\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variations in the initial weights and or... | [
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"# MultiBERTs - Seed 7\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. W... |
null | transformers |
# MultiBERTs - Seed 8
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variati... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_8"]} | google/multiberts-seed_8 | null | [
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"region:us"
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|
# MultiBERTs - Seed 8
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in the initial weights and order of
tra... | [
"# MultiBERTs - Seed 8\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variations in the initial weights and or... | [
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"# MultiBERTs - Seed 8\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. W... |
null | transformers |
# MultiBERTs - Seed 9
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variati... | {"language": "en", "license": "apache-2.0", "tags": ["multiberts", "multiberts-seed_9"]} | google/multiberts-seed_9 | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.16163",
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] | [
"en"
] | TAGS
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|
# MultiBERTs - Seed 9
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
the original BERT model but
with different random seeds, which causes variations in the initial weights and order of
tra... | [
"# MultiBERTs - Seed 9\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. We provide 25 BERT-base models trained with\nsimilar hyper-parameters as\nthe original BERT model but\nwith different random seeds, which causes variations in the initial weights and or... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #multiberts #multiberts-seed_9 #en #arxiv-2106.16163 #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs - Seed 9\n\nMultiBERTs is a collection of checkpoints and a statistical library to support\nrobust research on BERT. W... |
fill-mask | transformers | MuRIL: Multilingual Representations for Indian Languages
===
MuRIL is a BERT model pre-trained on 17 Indian languages and their transliterated counterparts. We have released the pre-trained model (with the MLM layer intact, enabling masked word predictions) in this repository. We have also released the encoder on [TFHu... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/muril-base-cased | null | [
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] | [] | TAGS
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| MuRIL: Multilingual Representations for Indian Languages
========================================================
MuRIL is a BERT model pre-trained on 17 Indian languages and their transliterated counterparts. We have released the pre-trained model (with the MLM layer intact, enabling masked word predictions) in this... | [
"### Trainable parameters\n\n\nAll parameters in the module are trainable, and fine-tuning all parameters is\nthe recommended practice.\n\n\nUses & Limitations\n------------------\n\n\nThis model is intended to be used for a variety of downstream NLP tasks for\nIndian languages. This model is trained on translitera... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #arxiv-2103.10730 #arxiv-1810.04805 #arxiv-1911.02116 #arxiv-2003.11080 #arxiv-2009.05166 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Trainable parameters\n\n\nAll parameters in the module are trainable, an... |
feature-extraction | transformers | # MuRIL Large
Multilingual Representations for Indian Languages : A BERT Large (24L) model pre-trained on 17 Indian languages, and their transliterated counterparts.
## Overview
This model uses a BERT large architecture [1] pretrained from scratch using the
Wikipedia [2], Common Crawl [3], PMINDIA [4] and Dakshina [5... | {} | google/muril-large-cased | null | [
"transformers",
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"feature-extraction",
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"arxiv:2003.11080",
"arxiv:2009.05166",
"arxiv:2103.10730",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805",
"1911.02116",
"2003.11080",
"2009.05166",
"2103.10730"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-1810.04805 #arxiv-1911.02116 #arxiv-2003.11080 #arxiv-2009.05166 #arxiv-2103.10730 #endpoints_compatible #region-us
| MuRIL Large
===========
Multilingual Representations for Indian Languages : A BERT Large (24L) model pre-trained on 17 Indian languages, and their transliterated counterparts.
Overview
--------
This model uses a BERT large architecture [1] pretrained from scratch using the
Wikipedia [2], Common Crawl [3], PMINDIA... | [
"### Trainable parameters\n\n\nAll parameters in the module are trainable, and fine-tuning all parameters is\nthe recommended practice.\n\n\nUses & Limitations\n------------------\n\n\nThis model is intended to be used for a variety of downstream NLP tasks for\nIndian languages. This model is trained on translitera... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-1810.04805 #arxiv-1911.02116 #arxiv-2003.11080 #arxiv-2009.05166 #arxiv-2103.10730 #endpoints_compatible #region-us \n",
"### Trainable parameters\n\n\nAll parameters in the module are trainable, and fine-tuning all parameters is\nthe recommended prac... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-aeslc | null | [
"transformers",
"pytorch",
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"text2text-generation",
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"en",
"arxiv:1912.08777",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on D... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-arxiv | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
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"en",
"arxiv:1912.08777",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on D... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-billsum | null | [
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"en",
"arxiv:1912.08777",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on D... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-cnn_dailymail | null | [
"transformers",
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"en",
"arxiv:1912.08777",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #rust #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #rust #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Li... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"], "datasets": ["gigaword"]} | google/pegasus-gigaword | null | [
"transformers",
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"arxiv:1912.08777",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #dataset-gigaword #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #dataset-gigaword #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-large | null | [
"transformers",
"pytorch",
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"summarization",
"en",
"arxiv:1912.08777",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #tf #jax #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J.... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-multi_news | null | [
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"arxiv:1912.08777",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on D... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-newsroom | null | [
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"1912.08777"
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"en"
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#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
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summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-pubmed | null | [
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"1912.08777"
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"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
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summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-reddit_tifu | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
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summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | google/pegasus-wikihow | null | [
"transformers",
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"text2text-generation",
"summarization",
"en",
"arxiv:1912.08777",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on D... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"], "model-index": [{"name": "google/pegasus-xsum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "train"}, "metrics": [{"type": "rouge", "value": 21.8096, "name": "ROUGE-1",... | google/pegasus-xsum | null | [
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"jax",
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"summarization",
"en",
"arxiv:1912.08777",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #tf #jax #pegasus #text2text-generation #summarization #en #arxiv-1912.08777 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh... |
null | transformers |
# realm-cc-news-pretrained-embedder
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/lan... | {"language": "en", "license": "apache-2.0"} | google/realm-cc-news-pretrained-embedder | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us
|
# realm-cc-news-pretrained-embedder
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-cc-news-pretrained-embedder",
"## Model description\n\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.\n\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
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"# realm-cc-news-pretrained-embedder",
"## Model description\n\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google ... |
null | transformers |
# realm-cc-news-pretrained-encoder
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/lang... | {"language": "en", "license": "apache-2.0"} | google/realm-cc-news-pretrained-encoder | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us
|
# realm-cc-news-pretrained-encoder
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-cc-news-pretrained-encoder",
"## Model description\n\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.\n\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
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"# realm-cc-news-pretrained-encoder",
"## Model description\n\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google L... |
null | transformers |
# realm-cc-news-pretrained-openqa
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-researc... | {"language": "en", "license": "apache-2.0"} | google/realm-cc-news-pretrained-openqa | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us
|
# realm-cc-news-pretrained-openqa
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-cc-news-pretrained-openqa",
"## Model description\r\n\r\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# realm-cc-news-pretrained-openqa",
"## Model description\r\n\r\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Googl... |
null | transformers |
# realm-cc-news-pretrained-scorer
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/langu... | {"language": "en", "license": "apache-2.0"} | google/realm-cc-news-pretrained-scorer | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us
|
# realm-cc-news-pretrained-scorer
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-cc-news-pretrained-scorer",
"## Model description\n\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.\n\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# realm-cc-news-pretrained-scorer",
"## Model description\n\nThe REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google La... |
null | transformers |
# realm-orqa-nq-openqa
## Model description
The REALM checkpoint finetuned with Natural Questions(NQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/language/tree/master/language/realm).
... | {"language": "en", "license": "apache-2.0"} | google/realm-orqa-nq-openqa | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# realm-orqa-nq-openqa
## Model description
The REALM checkpoint finetuned with Natural Questions(NQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-orqa-nq-openqa",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with Natural Questions(NQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# realm-orqa-nq-openqa",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with Natural Questions(NQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe origina... |
null | transformers |
# realm-orqa-nq-reader
## Model description
The REALM checkpoint finetuned with Natural Question(NQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/language/tree/master/language/realm).
... | {"language": "en", "license": "apache-2.0"} | google/realm-orqa-nq-reader | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us
|
# realm-orqa-nq-reader
## Model description
The REALM checkpoint finetuned with Natural Question(NQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-orqa-nq-reader",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with Natural Question(NQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# realm-orqa-nq-reader",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with Natural Question(NQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, cod... |
null | transformers |
# realm-orqa-wq-openqa
## Model description
The REALM checkpoint finetuned with Web Questions(WQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/language/tree/master/language/realm).
##... | {"language": "en", "license": "apache-2.0"} | google/realm-orqa-wq-openqa | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us
|
# realm-orqa-wq-openqa
## Model description
The REALM checkpoint finetuned with Web Questions(WQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-orqa-wq-openqa",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with Web Questions(WQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
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"# realm-orqa-wq-openqa",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with Web Questions(WQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, code, ... |
null | transformers |
# realm-orqa-wq-reader
## Model description
The REALM checkpoint finetuned with WebQuestions(WQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/language/tree/master/language/realm).
## ... | {"language": "en", "license": "apache-2.0"} | google/realm-orqa-wq-reader | null | [
"transformers",
"pytorch",
"realm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us
|
# realm-orqa-wq-reader
## Model description
The REALM checkpoint finetuned with WebQuestions(WQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found here.
## Usage
| [
"# realm-orqa-wq-reader",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with WebQuestions(WQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, code, and checkpoints can be found here.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #realm #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"# realm-orqa-wq-reader",
"## Model description\r\n\r\nThe REALM checkpoint finetuned with WebQuestions(WQ) dataset, converted from the TF checkpoint provided by Google Language.\r\n\r\nThe original paper, code, a... |
text-generation | transformers | ## Reformer Model trained on "Crime and Punishment"
Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English.
Crime and Punishment training data was taken from `gs://trax-ml/reformer/crime-and-punishment-2554.txt` and contains
roughly 0.5M tokens.
The ReformerLM model was trai... | {} | google/reformer-crime-and-punishment | null | [
"transformers",
"pytorch",
"rust",
"reformer",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #rust #reformer #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Reformer Model trained on "Crime and Punishment"
Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English.
Crime and Punishment training data was taken from 'gs://trax-ml/reformer/URL' and contains
roughly 0.5M tokens.
The ReformerLM model was trained in flax using colab no... | [
"## Reformer Model trained on \"Crime and Punishment\" \n\nCrime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English. \n\nCrime and Punishment training data was taken from 'gs://trax-ml/reformer/URL' and contains \nroughly 0.5M tokens. \n\nThe ReformerLM model was trained in flax ... | [
"TAGS\n#transformers #pytorch #rust #reformer #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Reformer Model trained on \"Crime and Punishment\" \n\nCrime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English. \n\nCrime and Punishment tr... |
text-generation | transformers | ## Reformer Language model on character level and trained on enwik8.
*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in
the scope of the *Hutter prize*: https://en.wikipedia.org/wiki/Hutter_Prize.
`reformer-enwik8` was pretrained on the first 90M ... | {} | google/reformer-enwik8 | null | [
"transformers",
"pytorch",
"reformer",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #reformer #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Reformer Language model on character level and trained on enwik8.
*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in
the scope of the *Hutter prize*: URL
'reformer-enwik8' was pretrained on the first 90M chars of *enwik8* whereas the text was c... | [
"## Reformer Language model on character level and trained on enwik8. \n\n*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in \nthe scope of the *Hutter prize*: URL\n\n'reformer-enwik8' was pretrained on the first 90M chars of *enwik8* whereas the ... | [
"TAGS\n#transformers #pytorch #reformer #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Reformer Language model on character level and trained on enwik8. \n\n*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e... |
null | transformers |
# RemBERT (for classification)
Pretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective. It was introduced in the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821). A direct export of the model checkpoint was first made available ... | {"language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "bs", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km"... | google/rembert | null | [
"transformers",
"pytorch",
"tf",
"rembert",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"bs",
"ca",
"ceb",
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"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha"... | null | 2022-03-02T23:29:05+00:00 | [
"2010.12821"
] | [
"multilingual",
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"am",
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"az",
"be",
"bg",
"bn",
"bs",
"ca",
"ceb",
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"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",
"hmn",
"hr",
"ht",
"hu",
... | TAGS
#transformers #pytorch #tf #rembert #multilingual #af #am #ar #az #be #bg #bn #bs #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #hr #ht #hu #hy #id #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml #mn #mr #ms #mt #... |
# RemBERT (for classification)
Pretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective. It was introduced in the paper Rethinking embedding coupling in pre-trained language models. A direct export of the model checkpoint was first made available in this repository. This version of ... | [
"# RemBERT (for classification) \n\nPretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective. It was introduced in the paper Rethinking embedding coupling in pre-trained language models. A direct export of the model checkpoint was first made available in this repository. This versi... | [
"TAGS\n#transformers #pytorch #tf #rembert #multilingual #af #am #ar #az #be #bg #bn #bs #ca #ceb #co #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fil #fr #fy #ga #gd #gl #gu #ha #haw #hi #hmn #hr #ht #hu #hy #id #ig #is #it #iw #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lb #lo #lt #lv #mg #mi #mk #ml #mn #mr #ms... |
summarization | transformers |
# Roberta2Roberta_L-24_bbc EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_bbc/1).
The model is an encoder-decoder model that was ... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["xsum"]} | google/roberta2roberta_L-24_bbc | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:xsum",
"arxiv:1907.12461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12461"
] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-xsum #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Roberta2Roberta_L-24_bbc EncoderDecoder model
The model was introduced in
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.
The model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder
and decoder and fine-tu... | [
"# Roberta2Roberta_L-24_bbc EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. \n\nThe model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder \nand decoder a... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-xsum #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta2Roberta_L-24_bbc EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shas... |
summarization | transformers |
# Roberta2Roberta_L-24_cnn_daily_mail EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_cnndm/1).
The model is an encoder-decoder mo... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"]} | google/roberta2roberta_L-24_cnn_daily_mail | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"arxiv:1907.12461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12461"
] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Roberta2Roberta_L-24_cnn_daily_mail EncoderDecoder model
The model was introduced in
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.
The model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder
and decoder ... | [
"# Roberta2Roberta_L-24_cnn_daily_mail EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. \n\nThe model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder \nan... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Roberta2Roberta_L-24_cnn_daily_mail EncoderDecoder model\n\nThe model was introduced in \nt... |
text2text-generation | transformers |
# Roberta2Roberta_L-24_discofuse EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_discofuse/1).
The model is an encoder-decoder mod... | {"language": "en", "license": "apache-2.0", "datasets": ["discofuse"]} | google/roberta2roberta_L-24_discofuse | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:discofuse",
"arxiv:1907.12461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12461"
] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #en #dataset-discofuse #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Roberta2Roberta_L-24_discofuse EncoderDecoder model
The model was introduced in
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.
The model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder
and decoder and f... | [
"# Roberta2Roberta_L-24_discofuse EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. \n\nThe model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder \nand dec... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #en #dataset-discofuse #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Roberta2Roberta_L-24_discofuse EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Roth... |
summarization | transformers |
# Roberta2Roberta_L-24_gigaword EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_gigaword/1).
The model is an encoder-decoder model... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["gigaword"]} | google/roberta2roberta_L-24_gigaword | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:gigaword",
"arxiv:1907.12461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12461"
] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-gigaword #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Roberta2Roberta_L-24_gigaword EncoderDecoder model
The model was introduced in
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.
The model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder
and decoder and fi... | [
"# Roberta2Roberta_L-24_gigaword EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. \n\nThe model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder \nand deco... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-gigaword #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta2Roberta_L-24_gigaword EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Ro... |
text2text-generation | transformers |
# Roberta2Roberta_L-24_wikisplit EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_cnndm/1).
The model is an encoder-decoder model t... | {"language": "en", "license": "apache-2.0"} | google/roberta2roberta_L-24_wikisplit | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"arxiv:1907.12461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12461"
] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #en #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Roberta2Roberta_L-24_wikisplit EncoderDecoder model
The model was introduced in
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.
The model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder
and decoder and f... | [
"# Roberta2Roberta_L-24_wikisplit EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. \n\nThe model is an encoder-decoder model that was initialized on the 'roberta-large' checkpoints for both the encoder \nand dec... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #en #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Roberta2Roberta_L-24_wikisplit EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, ... |
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/t5-11b-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/t5-11b-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/t5-11b-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/t5-11b-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/t5-11b-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"
] |
null | null |
[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/t5-11b-ssm-wqo | null | [
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:web_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.08909",
"1910.10683"
] | [
"en"
] | TAGS
#en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #has_space #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#en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #has_space #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/t5-11b-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... |
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/t5-3b-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/t5-3b-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) and subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.0890... | {"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia"]} | google/t5-3b-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... |
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-base-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 |
# T5-Efficient-BASE-DL2 (Deep-Narrow version)
T5-Efficient-BASE-DL2 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-base-dl2 | 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-BASE-DL2 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-DL2 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-BASE-DL4 (Deep-Narrow version)
T5-Efficient-BASE-DL4 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-base-dl4 | 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-BASE-DL4 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-DL4 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-BASE-DL6 (Deep-Narrow version)
T5-Efficient-BASE-DL6 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-base-dl6 | 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-BASE-DL6 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-DL6 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-BASE-DL8 (Deep-Narrow version)
T5-Efficient-BASE-DL8 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-base-dl8 | 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-BASE-DL8 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-DL8 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-BASE-DM1000 (Deep-Narrow version)
T5-Efficient-BASE-DM1000 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* checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-dm1000 | 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-BASE-DM1000 (Deep-Narrow version)
==============================================
T5-Efficient-BASE-DM1000 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... | [] | [
"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-BASE-DM2000 (Deep-Narrow version)
T5-Efficient-BASE-DM2000 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* checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-dm2000 | 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-BASE-DM2000 (Deep-Narrow version)
==============================================
T5-Efficient-BASE-DM2000 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... | [] | [
"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-BASE-DM256 (Deep-Narrow version)
T5-Efficient-BASE-DM256 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* checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-dm256 | 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-BASE-DM256 (Deep-Narrow version)
=============================================
T5-Efficient-BASE-DM256 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-tu... | [] | [
"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-BASE-DM512 (Deep-Narrow version)
T5-Efficient-BASE-DM512 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* checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-dm512 | 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-BASE-DM512 (Deep-Narrow version)
=============================================
T5-Efficient-BASE-DM512 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-tu... | [] | [
"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-BASE-EL16 (Deep-Narrow version)
T5-Efficient-BASE-EL16 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-base-el16 | 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-BASE-EL16 (Deep-Narrow version)
============================================
T5-Efficient-BASE-EL16 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-BASE-EL2 (Deep-Narrow version)
T5-Efficient-BASE-EL2 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-base-el2 | 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-BASE-EL2 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-EL2 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-BASE-EL4 (Deep-Narrow version)
T5-Efficient-BASE-EL4 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-base-el4 | 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-BASE-EL4 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-EL4 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-BASE-EL6 (Deep-Narrow version)
T5-Efficient-BASE-EL6 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-base-el6 | 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-BASE-EL6 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-EL6 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-BASE-EL8 (Deep-Narrow version)
T5-Efficient-BASE-EL8 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-base-el8 | 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-BASE-EL8 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-EL8 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-BASE-FF1000 (Deep-Narrow version)
T5-Efficient-BASE-FF1000 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* checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-ff1000 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-FF1000 (Deep-Narrow version)
==============================================
T5-Efficient-BASE-FF1000 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... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-FF12000 (Deep-Narrow version)
T5-Efficient-BASE-FF12000 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* check... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-ff12000 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-FF12000 (Deep-Narrow version)
===============================================
T5-Efficient-BASE-FF12000 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 F... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-FF2000 (Deep-Narrow version)
T5-Efficient-BASE-FF2000 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* checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-ff2000 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-FF2000 (Deep-Narrow version)
==============================================
T5-Efficient-BASE-FF2000 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... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-FF6000 (Deep-Narrow version)
T5-Efficient-BASE-FF6000 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* checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-ff6000 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-FF6000 (Deep-Narrow version)
==============================================
T5-Efficient-BASE-FF6000 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... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-FF9000 (Deep-Narrow version)
T5-Efficient-BASE-FF9000 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* checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-ff9000 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-FF9000 (Deep-Narrow version)
==============================================
T5-Efficient-BASE-FF9000 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... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-KV128 (Deep-Narrow version)
T5-Efficient-BASE-KV128 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* checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-kv128 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-KV128 (Deep-Narrow version)
=============================================
T5-Efficient-BASE-KV128 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-tu... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-KV16 (Deep-Narrow version)
T5-Efficient-BASE-KV16 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-base-kv16 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-KV16 (Deep-Narrow version)
============================================
T5-Efficient-BASE-KV16 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-KV256 (Deep-Narrow version)
T5-Efficient-BASE-KV256 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* checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-kv256 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-KV256 (Deep-Narrow version)
=============================================
T5-Efficient-BASE-KV256 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-tu... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-KV32 (Deep-Narrow version)
T5-Efficient-BASE-KV32 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-base-kv32 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-KV32 (Deep-Narrow version)
============================================
T5-Efficient-BASE-KV32 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NH16 (Deep-Narrow version)
T5-Efficient-BASE-NH16 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-base-nh16 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NH16 (Deep-Narrow version)
============================================
T5-Efficient-BASE-NH16 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NH24 (Deep-Narrow version)
T5-Efficient-BASE-NH24 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-base-nh24 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NH24 (Deep-Narrow version)
============================================
T5-Efficient-BASE-NH24 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NH32 (Deep-Narrow version)
T5-Efficient-BASE-NH32 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-base-nh32 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NH32 (Deep-Narrow version)
============================================
T5-Efficient-BASE-NH32 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NH8 (Deep-Narrow version)
T5-Efficient-BASE-NH8 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-base-nh8 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NH8 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-NH8 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NL16 (Deep-Narrow version)
T5-Efficient-BASE-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 ... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-nl16 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NL16 (Deep-Narrow version)
============================================
T5-Efficient-BASE-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-tunin... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NL2 (Deep-Narrow version)
T5-Efficient-BASE-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 an... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-nl2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NL2 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-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 T... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NL24 (Deep-Narrow version)
T5-Efficient-BASE-NL24 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-base-nl24 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NL24 (Deep-Narrow version)
============================================
T5-Efficient-BASE-NL24 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NL32 (Deep-Narrow version)
T5-Efficient-BASE-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-base-nl32 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NL32 (Deep-Narrow version)
============================================
T5-Efficient-BASE-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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NL36 (Deep-Narrow version)
T5-Efficient-BASE-NL36 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-base-nl36 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NL36 (Deep-Narrow version)
============================================
T5-Efficient-BASE-NL36 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 #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NL4 (Deep-Narrow version)
T5-Efficient-BASE-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 an... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-base-nl4 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"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 #text-generation-inference #region-us
| T5-Efficient-BASE-NL4 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-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 T... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #deep-narrow #en #dataset-c4 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# T5-Efficient-BASE-NL40 (Deep-Narrow version)
T5-Efficient-BASE-NL40 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-base-nl40 | 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-BASE-NL40 (Deep-Narrow version)
============================================
T5-Efficient-BASE-NL40 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-BASE-NL48 (Deep-Narrow version)
T5-Efficient-BASE-NL48 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-base-nl48 | 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-BASE-NL48 (Deep-Narrow version)
============================================
T5-Efficient-BASE-NL48 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-BASE-NL8 (Deep-Narrow version)
T5-Efficient-BASE-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-base-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-BASE-NL8 (Deep-Narrow version)
===========================================
T5-Efficient-BASE-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-BASE (Deep-Narrow version)
T5-Efficient-BASE 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-base | 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-BASE (Deep-Narrow version)
=======================================
T5-Efficient-BASE 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-LARGE-DL12 (Deep-Narrow version)
T5-Efficient-LARGE-DL12 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* checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-large-dl12 | 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-LARGE-DL12 (Deep-Narrow version)
=============================================
T5-Efficient-LARGE-DL12 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-tu... | [] | [
"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-LARGE-DL16 (Deep-Narrow version)
T5-Efficient-LARGE-DL16 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* checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-large-dl16 | 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-LARGE-DL16 (Deep-Narrow version)
=============================================
T5-Efficient-LARGE-DL16 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-tu... | [] | [
"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-LARGE-DL2 (Deep-Narrow version)
T5-Efficient-LARGE-DL2 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-large-dl2 | 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-LARGE-DL2 (Deep-Narrow version)
============================================
T5-Efficient-LARGE-DL2 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-LARGE-DL32 (Deep-Narrow version)
T5-Efficient-LARGE-DL32 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* checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-large-dl32 | 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-LARGE-DL32 (Deep-Narrow version)
=============================================
T5-Efficient-LARGE-DL32 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-tu... | [] | [
"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-LARGE-DL4 (Deep-Narrow version)
T5-Efficient-LARGE-DL4 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-large-dl4 | 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-LARGE-DL4 (Deep-Narrow version)
============================================
T5-Efficient-LARGE-DL4 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-LARGE-DL6 (Deep-Narrow version)
T5-Efficient-LARGE-DL6 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-large-dl6 | 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-LARGE-DL6 (Deep-Narrow version)
============================================
T5-Efficient-LARGE-DL6 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-LARGE-DL8 (Deep-Narrow version)
T5-Efficient-LARGE-DL8 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-large-dl8 | 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-LARGE-DL8 (Deep-Narrow version)
============================================
T5-Efficient-LARGE-DL8 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-LARGE-DM128 (Deep-Narrow version)
T5-Efficient-LARGE-DM128 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* checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-large-dm128 | 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-LARGE-DM128 (Deep-Narrow version)
==============================================
T5-Efficient-LARGE-DM128 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... | [] | [
"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-LARGE-DM2000 (Deep-Narrow version)
T5-Efficient-LARGE-DM2000 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* check... | {"language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "datasets": ["c4"], "inference": false} | google/t5-efficient-large-dm2000 | 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-LARGE-DM2000 (Deep-Narrow version)
===============================================
T5-Efficient-LARGE-DM2000 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 F... | [] | [
"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"
] |
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