pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 | tokens_length listlengths 1 723 | input_texts listlengths 1 1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | transformers | # MultiBERTs Seed 4 Checkpoint 160k (uncased)
Seed 4 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-160k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 160k (uncased)
Seed 4 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 160k (uncased)\nSeed 4 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 160k (uncased)\nSeed 4 intermediate checkpoint 160k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 160k (uncased)\nSeed 4 intermediate checkpoint 160k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 1700k (uncased)
Seed 4 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-1700k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 1700k (uncased)
Seed 4 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 4 Checkpoint 1700k (uncased)\nSeed 4 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 1700k (uncased)\nSeed 4 intermediate checkpoint 1700k MultiBERTs (pretraine... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 1700k (uncased)\nSeed 4 intermediate checkpoint 1700k MultiBERTs (pretrained BERT... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 1800k (uncased)
Seed 4 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-1800k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 1800k (uncased)
Seed 4 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 4 Checkpoint 1800k (uncased)\nSeed 4 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 1800k (uncased)\nSeed 4 intermediate checkpoint 1800k MultiBERTs (pretraine... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 1800k (uncased)\nSeed 4 intermediate checkpoint 1800k MultiBERTs (pretrained BERT... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 180k (uncased)
Seed 4 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-180k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 180k (uncased)
Seed 4 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 180k (uncased)\nSeed 4 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 180k (uncased)\nSeed 4 intermediate checkpoint 180k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 180k (uncased)\nSeed 4 intermediate checkpoint 180k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 1900k (uncased)
Seed 4 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-1900k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 1900k (uncased)
Seed 4 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 4 Checkpoint 1900k (uncased)\nSeed 4 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 1900k (uncased)\nSeed 4 intermediate checkpoint 1900k MultiBERTs (pretraine... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 1900k (uncased)\nSeed 4 intermediate checkpoint 1900k MultiBERTs (pretrained BERT... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 2000k (uncased)
Seed 4 intermediate checkpoint 2000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-2000k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 2000k (uncased)
Seed 4 intermediate checkpoint 2000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 4 Checkpoint 2000k (uncased)\nSeed 4 intermediate checkpoint 2000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 2000k (uncased)\nSeed 4 intermediate checkpoint 2000k MultiBERTs (pretraine... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 2000k (uncased)\nSeed 4 intermediate checkpoint 2000k MultiBERTs (pretrained BERT... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 200k (uncased)
Seed 4 intermediate checkpoint 200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-200k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 200k (uncased)
Seed 4 intermediate checkpoint 200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 200k (uncased)\nSeed 4 intermediate checkpoint 200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 200k (uncased)\nSeed 4 intermediate checkpoint 200k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 200k (uncased)\nSeed 4 intermediate checkpoint 200k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 20k (uncased)
Seed 4 intermediate checkpoint 20k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-20k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 20k (uncased)
Seed 4 intermediate checkpoint 20k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
"# MultiBERTs Seed 4 Checkpoint 20k (uncased)\nSeed 4 intermediate checkpoint 20k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpoin... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 20k (uncased)\nSeed 4 intermediate checkpoint 20k MultiBERTs (pretrained BE... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 20k (uncased)\nSeed 4 intermediate checkpoint 20k MultiBERTs (pretrained BERT) mo... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 300k (uncased)
Seed 4 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-300k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 300k (uncased)
Seed 4 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 300k (uncased)\nSeed 4 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 300k (uncased)\nSeed 4 intermediate checkpoint 300k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 300k (uncased)\nSeed 4 intermediate checkpoint 300k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 400k (uncased)
Seed 4 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-400k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 400k (uncased)
Seed 4 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 400k (uncased)\nSeed 4 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 400k (uncased)\nSeed 4 intermediate checkpoint 400k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 400k (uncased)\nSeed 4 intermediate checkpoint 400k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 40k (uncased)
Seed 4 intermediate checkpoint 40k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-40k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 40k (uncased)
Seed 4 intermediate checkpoint 40k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
"# MultiBERTs Seed 4 Checkpoint 40k (uncased)\nSeed 4 intermediate checkpoint 40k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpoin... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 40k (uncased)\nSeed 4 intermediate checkpoint 40k MultiBERTs (pretrained BE... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 40k (uncased)\nSeed 4 intermediate checkpoint 40k MultiBERTs (pretrained BERT) mo... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 500k (uncased)
Seed 4 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-500k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 500k (uncased)
Seed 4 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 500k (uncased)\nSeed 4 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 500k (uncased)\nSeed 4 intermediate checkpoint 500k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 500k (uncased)\nSeed 4 intermediate checkpoint 500k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 600k (uncased)
Seed 4 intermediate checkpoint 600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-600k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 600k (uncased)
Seed 4 intermediate checkpoint 600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 600k (uncased)\nSeed 4 intermediate checkpoint 600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 600k (uncased)\nSeed 4 intermediate checkpoint 600k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 600k (uncased)\nSeed 4 intermediate checkpoint 600k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 60k (uncased)
Seed 4 intermediate checkpoint 60k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-60k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 60k (uncased)
Seed 4 intermediate checkpoint 60k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
"# MultiBERTs Seed 4 Checkpoint 60k (uncased)\nSeed 4 intermediate checkpoint 60k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpoin... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 60k (uncased)\nSeed 4 intermediate checkpoint 60k MultiBERTs (pretrained BE... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 60k (uncased)\nSeed 4 intermediate checkpoint 60k MultiBERTs (pretrained BERT) mo... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 700k (uncased)
Seed 4 intermediate checkpoint 700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-700k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 700k (uncased)
Seed 4 intermediate checkpoint 700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 700k (uncased)\nSeed 4 intermediate checkpoint 700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 700k (uncased)\nSeed 4 intermediate checkpoint 700k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 700k (uncased)\nSeed 4 intermediate checkpoint 700k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 800k (uncased)
Seed 4 intermediate checkpoint 800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-800k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 800k (uncased)
Seed 4 intermediate checkpoint 800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 800k (uncased)\nSeed 4 intermediate checkpoint 800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 800k (uncased)\nSeed 4 intermediate checkpoint 800k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 800k (uncased)\nSeed 4 intermediate checkpoint 800k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 80k (uncased)
Seed 4 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-80k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 80k (uncased)
Seed 4 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
"# MultiBERTs Seed 4 Checkpoint 80k (uncased)\nSeed 4 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpoin... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 80k (uncased)\nSeed 4 intermediate checkpoint 80k MultiBERTs (pretrained BE... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 80k (uncased)\nSeed 4 intermediate checkpoint 80k MultiBERTs (pretrained BERT) mo... |
null | transformers | # MultiBERTs Seed 4 Checkpoint 900k (uncased)
Seed 4 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4-900k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 4 Checkpoint 900k (uncased)
Seed 4 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 4 Checkpoint 900k (uncased)\nSeed 4 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 4 Checkpoint 900k (uncased)\nSeed 4 intermediate checkpoint 900k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-4 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 4 Checkpoint 900k (uncased)\nSeed 4 intermediate checkpoint 900k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-4 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer:... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeli... | [
63,
98,
307,
110,
27,
80,
42,
4,
208,
115,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (ML... |
null | transformers | # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-5 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer:... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeli... | [
63,
98,
307,
110,
27,
80,
42,
4,
208,
115,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (ML... |
null | transformers | # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-6 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer:... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeli... | [
63,
98,
307,
110,
27,
80,
42,
4,
208,
115,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (ML... |
null | transformers | # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-7 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer:... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeli... | [
63,
98,
307,
110,
27,
80,
42,
4,
208,
115,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (ML... |
null | transformers | # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-8 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer:... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeli... | [
63,
98,
307,
110,
27,
80,
42,
4,
208,
115,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (ML... |
null | transformers | # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-9 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer:... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeli... | [
63,
98,
307,
110,
27,
80,
42,
4,
208,
115,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 0 (uncased)\n\nSeed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (ML... |
fill-mask | transformers |
# UmBERTo Commoncrawl Cased
[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at [github.com/huggingface/transformers](https://huggingface.co/Musixmatch/umb... | {"language": "it"} | Musixmatch/umberto-commoncrawl-cased-v1 | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"it",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #camembert #fill-mask #it #autotrain_compatible #endpoints_compatible #has_space #region-us
| UmBERTo Commoncrawl Cased
=========================
UmBERTo is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at URL
Marco Lodola, Monument to Umberto Eco, Alessandria 2019
Dataset
-------
... | [
"#### Named Entity Recognition (NER)",
"#### Part of Speech (POS)\n\n\n\nUsage\n-----",
"##### Load UmBERTo with AutoModel, Autotokenizer:",
"##### Predict masked token:\n\n\nAll of the original datasets are publicly available or were released with the owners' grant. The datasets are all released under a CC0 ... | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #it #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Named Entity Recognition (NER)",
"#### Part of Speech (POS)\n\n\n\nUsage\n-----",
"##### Load UmBERTo with AutoModel, Autotokenizer:",
"##### Predict masked token:\n\n\nAll of... | [
36,
11,
17,
18,
221
] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #it #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### Named Entity Recognition (NER)#### Part of Speech (POS)\n\n\n\nUsage\n-----##### Load UmBERTo with AutoModel, Autotokenizer:##### Predict masked token:\n\n\nAll of the original datasets a... |
fill-mask | transformers |
# UmBERTo Wikipedia Uncased
[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at [github.com/huggingface/transformers](https://huggingface.co/Musixmatch/umb... | {"language": "it"} | Musixmatch/umberto-wikipedia-uncased-v1 | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"it",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #camembert #fill-mask #it #autotrain_compatible #endpoints_compatible #region-us
| UmBERTo Wikipedia Uncased
=========================
UmBERTo is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at URL
Marco Lodola, Monument to Umberto Eco, Alessandria 2019
Dataset
-------
... | [
"#### Named Entity Recognition (NER)",
"#### Part of Speech (POS)\n\n\n\nUsage\n-----",
"##### Load UmBERTo Wikipedia Uncased with AutoModel, Autotokenizer:",
"##### Predict masked token:\n\n\nAll of the original datasets are publicly available or were released with the owners' grant. The datasets are all rel... | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #it #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Named Entity Recognition (NER)",
"#### Part of Speech (POS)\n\n\n\nUsage\n-----",
"##### Load UmBERTo Wikipedia Uncased with AutoModel, Autotokenizer:",
"##### Predict masked token:\n\n\... | [
32,
11,
17,
21,
221
] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #it #autotrain_compatible #endpoints_compatible #region-us \n#### Named Entity Recognition (NER)#### Part of Speech (POS)\n\n\n\nUsage\n-----##### Load UmBERTo Wikipedia Uncased with AutoModel, Autotokenizer:##### Predict masked token:\n\n\nAll of the original dat... |
null | null | Source language: Finnish
Target language: Swedish
Training dataset: https://opus.nlpl.eu/
Framework toolkit: Fairseq
Model architecture: transformer_vaswani_wmt_en_de_big
https://github.com/MusserO/BERT-fused_fi-sv | {} | MusserO/transformer-opus-fi-sv | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Source language: Finnish
Target language: Swedish
Training dataset: URL
Framework toolkit: Fairseq
Model architecture: transformer_vaswani_wmt_en_de_big
URL | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | ## BERT model van het project Explainable AI | {"license": "eupl-1.1"} | Mustang/BERT_responsible_AI | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:eupl-1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-eupl-1.1 #autotrain_compatible #endpoints_compatible #region-us
| ## BERT model van het project Explainable AI | [
"## BERT model van het project Explainable AI"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-eupl-1.1 #autotrain_compatible #endpoints_compatible #region-us \n",
"## BERT model van het project Explainable AI"
] | [
37,
10
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-eupl-1.1 #autotrain_compatible #endpoints_compatible #region-us \n## BERT model van het project Explainable AI"
] |
fill-mask | transformers | # Ara-dialect-BERT
We used a pretrained model to further train it on [HARD-Arabic-Dataset](https://github.com/elnagara/HARD-Arabic-Dataset), the weights were initialized using [CAMeL-Lab](https://huggingface.co/CAMeL-Lab/bert-base-camelbert-msa-eighth) "bert-base-camelbert-msa-eighth" model
### Usage
The model weight... | {"language": "ar", "datasets": ["HARD-Arabic-Dataset"]} | MutazYoune/Ara_DialectBERT | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:HARD-Arabic-Dataset",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #ar #dataset-HARD-Arabic-Dataset #autotrain_compatible #endpoints_compatible #region-us
| # Ara-dialect-BERT
We used a pretrained model to further train it on HARD-Arabic-Dataset, the weights were initialized using CAMeL-Lab "bert-base-camelbert-msa-eighth" model
### Usage
The model weights can be loaded using 'transformers' library by HuggingFace.
Example using 'pipeline':
'''python
{'sequence': 'الفند... | [
"# Ara-dialect-BERT\nWe used a pretrained model to further train it on HARD-Arabic-Dataset, the weights were initialized using CAMeL-Lab \"bert-base-camelbert-msa-eighth\" model",
"### Usage\nThe model weights can be loaded using 'transformers' library by HuggingFace.\n\nExample using 'pipeline':\n\n'''python\n{'... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ar #dataset-HARD-Arabic-Dataset #autotrain_compatible #endpoints_compatible #region-us \n",
"# Ara-dialect-BERT\nWe used a pretrained model to further train it on HARD-Arabic-Dataset, the weights were initialized using CAMeL-Lab \"bert-base-camelbert-msa-eighth... | [
42,
48,
381
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ar #dataset-HARD-Arabic-Dataset #autotrain_compatible #endpoints_compatible #region-us \n# Ara-dialect-BERT\nWe used a pretrained model to further train it on HARD-Arabic-Dataset, the weights were initialized using CAMeL-Lab \"bert-base-camelbert-msa-eighth\" mod... |
text-generation | transformers |
# Modeus DialoGPT Model | {"tags": ["conversational"]} | Mythiie/DialoGPT-small-Modeus | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Modeus DialoGPT Model | [
"# Modeus DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Modeus DialoGPT Model"
] | [
39,
7
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Modeus DialoGPT Model"
] |
text-generation | null |
# My Awesome Model | {"tags": ["conversational"]} | N8Daawg/chat_bot | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] | [
8,
4
] | [
"TAGS\n#conversational #region-us \n# My Awesome Model"
] |
text-generation | null |
# Francesco's Machine Learning Discord BOT
| {"tags": ["conversational"]} | NASABOI/MachineLearningAI | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Francesco's Machine Learning Discord BOT
| [
"# Francesco's Machine Learning Discord BOT"
] | [
"TAGS\n#conversational #region-us \n",
"# Francesco's Machine Learning Discord BOT"
] | [
8,
9
] | [
"TAGS\n#conversational #region-us \n# Francesco's Machine Learning Discord BOT"
] |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "license": "mit", "tags": ["deberta-v3", "deberta-v2`", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/1epochv3 | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several ... | [
77,
34,
205
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE b... |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "license": "mit", "tags": ["deberta-v3", "deberta-v2`", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/2epochv3mlni | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several ... | [
77,
34,
205
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE b... |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "license": "mit", "tags": ["deberta-v3", "deberta-v2`", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/3epoch-3large | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 a... | [
81,
34,
205
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and sev... |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/ZSD-microsoft-v2xxlmnli | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v1",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 ... | [
77,
35,
176
] | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and se... |
zero-shot-classification | transformers | I tried to train v3 xl to mnli using my own training code and got this result. | {"language": "en", "license": "mit", "tags": ["deberta-v3", "deberta-v2`", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/deberta-v2-xlarge-mnli | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"zero-shot-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| I tried to train v3 xl to mnli using my own training code and got this result. | [] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
67
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "tags": ["deberta-v3", "deberta-mnli", "deberta", "deberta-v2"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/debertav3-mnli-snli-anli | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v3",
"deberta-mnli",
"deberta",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-mnli #deberta #zero-shot-classification #en #arxiv-2006.03654 #autotrain_compatible #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-mnli #deberta #zero-shot-classification #en #arxiv-2006.03654 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and s... | [
77,
34,
205
] | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-mnli #deberta #zero-shot-classification #en #arxiv-2006.03654 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m2m100_418M-fr
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the ... | {"license": "mit", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "base_model": "facebook/m2m100_418M", "model-index": [{"name": "m2m100_418M-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "... | NDugar/m2m100_418M-fr | null | [
"transformers",
"pytorch",
"safetensors",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:facebook/m2m100_418M",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-facebook/m2m100_418M #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| m2m100\_418M-fr
===============
This model is a fine-tuned version of facebook/m2m100\_418M on the kde4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7021
* Bleu: 51.1340
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #safetensors #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-facebook/m2m100_418M #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... | [
74,
112,
5,
46
] | [
"TAGS\n#transformers #pytorch #safetensors #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #base_model-facebook/m2m100_418M #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used d... |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/v2xl-again-mnli | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"deberta-v1",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchma... | [
69,
35,
176
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tas... |
zero-shot-classification | transformers |
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4103
- Accuracy: 0.9175
### Training hyperparameters
The following hyperparameters were used during trainin... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification", "base_model": "microsoft/deberta-v3-large"} | NDugar/v3-Large-mnli | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v1",
"deberta-mnli",
"zero-shot-classification",
"en",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| This model is a fine-tuned version of microsoft/deberta-v3-large on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4103
* Accuracy: 0.9175
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 6e-06
* train\_batch\_size... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparam... | [
82,
119,
5,
46
] | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters ... |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "license": "mit", "tags": ["deberta-v3", "deberta-v2`", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/v3large-1epoch | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0... | [
81,
34,
205
] | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and s... |
zero-shot-classification | transformers | ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repositor... | {"language": "en", "license": "mit", "tags": ["deberta-v3", "deberta-v2`", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | NDugar/v3large-2epoch | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check th... | [
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI.... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0... | [
81,
34,
205
] | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v3 #deberta-v2` #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and s... |
fill-mask | transformers | # MS-BERT
## Introduction
This repository provides codes and models of MS-BERT.
MS-BERT was pre-trained on notes from neurological examination for Multiple Sclerosis (MS) patients at St. Michael's Hospital in Toronto, Canada.
## Data
The dataset contained approximately 75,000 clinical notes, for about 5000 patients... | {} | NLP4H/ms_bert | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MS-BERT
## Introduction
This repository provides codes and models of MS-BERT.
MS-BERT was pre-trained on notes from neurological examination for Multiple Sclerosis (MS) patients at St. Michael's Hospital in Toronto, Canada.
## Data
The dataset contained approximately 75,000 clinical notes, for about 5000 patients... | [
"# MS-BERT",
"## Introduction\n\nThis repository provides codes and models of MS-BERT.\nMS-BERT was pre-trained on notes from neurological examination for Multiple Sclerosis (MS) patients at St. Michael's Hospital in Toronto, Canada.",
"## Data\n\nThe dataset contained approximately 75,000 clinical notes, for a... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# MS-BERT",
"## Introduction\n\nThis repository provides codes and models of MS-BERT.\nMS-BERT was pre-trained on notes from neurological examination for Multiple Sclerosis (MS) patients... | [
34,
4,
47,
145,
220,
64,
81,
184
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# MS-BERT## Introduction\n\nThis repository provides codes and models of MS-BERT.\nMS-BERT was pre-trained on notes from neurological examination for Multiple Sclerosis (MS) patients at St. Mich... |
fill-mask | transformers | This is SinBERT-large model. SinBERT models are pretrained on a large Sinhala monolingual corpus (sin-cc-15M) using RoBERTa. If you use this model, please cite *BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, LREC 2022* | {"language": ["si"], "license": ["mit"]} | NLPC-UOM/SinBERT-large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"si",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"si"
] | TAGS
#transformers #pytorch #roberta #fill-mask #si #license-mit #autotrain_compatible #endpoints_compatible #region-us
| This is SinBERT-large model. SinBERT models are pretrained on a large Sinhala monolingual corpus (sin-cc-15M) using RoBERTa. If you use this model, please cite *BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, LREC 2022* | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #si #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
34
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #si #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This is SinBERT-small model. SinBERT models are pretrained on a large Sinhala monolingual corpus (sin-cc-15M) using RoBERTa. If you use this model, please cite *BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, LREC 2022*
| {"language": ["si"], "license": "mit"} | NLPC-UOM/SinBERT-small | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"si",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"si"
] | TAGS
#transformers #pytorch #roberta #fill-mask #si #license-mit #autotrain_compatible #endpoints_compatible #region-us
| This is SinBERT-small model. SinBERT models are pretrained on a large Sinhala monolingual corpus (sin-cc-15M) using RoBERTa. If you use this model, please cite *BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, LREC 2022*
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #si #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
34
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #si #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-Japanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice), [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut), [TEDxJP](https://github.com/labor... | {"language": "ja", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "Wav2Vec2 Japanese by NTQAI", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice j... | NTQAI/wav2vec2-large-japanese | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"ja",
"dataset:common_voice",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #ja #dataset-common_voice #model-index #endpoints_compatible #region-us
| Wav2Vec2-Large-Japanese
=======================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice, JSUT, TEDxJP and some other data. This model is a model trained on public data. If you want to use trained model with more 600 hours of data and higher accuracy please contact nha282@URL
Wh... | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #ja #dataset-common_voice #model-index #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #ja #dataset-common_voice #model-index #endpoints_compatible #region-us \n"
] |
text-classification | transformers | ## How to use
```python
from simpletransformers.classification import ClassificationModel, ClassificationArgs
name_file = ['bash', 'c', 'c#', 'c++','css', 'haskell', 'java', 'javascript', 'lua', 'objective-c', 'perl', 'php', 'python','r','ruby', 'scala', 'sql', 'swift', 'vb.net']
deep_scc_model_args = Classification... | {} | NTUYG/DeepSCC-RoBERTa | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## How to use
| [
"## How to use"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## How to use"
] | [
34,
5
] | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n## How to use"
] |
text2text-generation | transformers | ## How to use
```python
import logging
from simpletransformers.seq2seq import Seq2SeqModel, Seq2SeqArgs
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
model_args = Seq2SeqArgs()
# 加载本地训练好的模型
model = Seq2SeqModel(
enco... | {} | NTUYG/SOTitle-java-BART | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| ## How to use
| [
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to use"
] | [
30,
5
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n## How to use"
] |
text-classification | transformers |
# Hungarian Sentence-level Sentiment Analysis with Finetuned huBERT Model
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: huBERT
- Finetuned on Hungarian Twitter Sentiment (H... | {"language": ["hu"], "license": "apache-2.0", "tags": ["text-classification"], "metrics": ["accuracy"], "widget": [{"text": "J\u00f3 reggelt! majd k\u00fcld\u00f6m az \u00e9lm\u00e9nyhoz\u00f3kat :)."}]} | NYTK/sentiment-hts2-hubert-hungarian | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #bert #text-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Hungarian Sentence-level Sentiment Analysis with Finetuned huBERT Model
=======================================================================
For further models, scripts and details, see our repository or our demo site.
* Pretrained model used: huBERT
* Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
* Labe... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
38
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: XLM-RoBERTa base
- Finetuned on Hungarian Twitter Sentime... | {"language": ["hu"], "license": "mit", "tags": ["text-classification"], "metrics": ["accuracy"], "widget": [{"text": "J\u00f3 reggelt! majd k\u00fcld\u00f6m az \u00e9lm\u00e9nyhoz\u00f3kat :)."}]} | NYTK/sentiment-hts2-xlm-roberta-hungarian | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"hu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #roberta #text-classification #hu #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa
==================================================================
For further models, scripts and details, see our repository or our demo site.
* Pretrained model used: XLM-RoBERTa base
* Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
* Labe... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #hu #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
34
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #hu #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Hungarian Sentence-level Sentiment Analysis with Finetuned huBERT Model
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: huBERT
- Finetuned on Hungarian Twitter Sentiment (H... | {"language": ["hu"], "license": "apache-2.0", "tags": ["text-classification"], "metrics": ["accuracy"], "widget": [{"text": "J\u00f3 reggelt! majd k\u00fcld\u00f6m az \u00e9lm\u00e9nyhoz\u00f3kat :)."}]} | NYTK/sentiment-hts5-hubert-hungarian | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #bert #text-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Hungarian Sentence-level Sentiment Analysis with Finetuned huBERT Model
=======================================================================
For further models, scripts and details, see our repository or our demo site.
* Pretrained model used: huBERT
* Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
* Labe... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
38
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: XLM-RoBERTa base
- Finetuned on Hungarian Twitter Sentime... | {"language": ["hu"], "license": "mit", "tags": ["text-classification"], "metrics": ["accuracy"], "widget": [{"text": "J\u00f3 reggelt! majd k\u00fcld\u00f6m az \u00e9lm\u00e9nyhoz\u00f3kat :)."}]} | NYTK/sentiment-hts5-xlm-roberta-hungarian | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"hu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #hu #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa
==================================================================
For further models, scripts and details, see our repository or our demo site.
* Pretrained model used: XLM-RoBERTa base
* Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
* Labe... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #hu #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
37
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #hu #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
# Hungarian Abstractive Summarization BART model
For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- BART base model (see Results Table - bold):
- Pretrained on Webcorpus 2.0
- Finetuned HI corpus (hvg.hu +... | {"language": ["hu"], "license": "apache-2.0", "tags": ["summarization"], "metrics": ["rouge"], "widget": [{"text": "A Tisza-parti v\u00e1ros \u00e1llatkertj\u00e9ben r\u00e9g\u00f3ta tartanak szurik\u00e1t\u00e1kat ( Suricata suricatta ) , de tavaly tavaszig nem siker\u00fclt szapor\u00edtani \u0151ket , annak ellen\u0... | NYTK/summarization-hi-bart-base-1024-hungarian | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Hungarian Abstractive Summarization BART model
==============================================
For further models, scripts and details, see our repository or our demo site.
* BART base model (see Results Table - bold):
+ Pretrained on Webcorpus 2.0
+ Finetuned HI corpus (URL + URL)
- Segments: 559.162
Limitati... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
# Hungarian Abstractive Summarization BART model
For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- BART base model (see Results Table - bold):
- Pretrained on Webcorpus 2.0
- Finetuned HI corpus (hvg.hu +... | {"language": ["hu"], "license": "apache-2.0", "tags": ["summarization"], "metrics": ["rouge"], "widget": [{"text": "A Tisza-parti v\u00e1ros \u00e1llatkertj\u00e9ben r\u00e9g\u00f3ta tartanak szurik\u00e1t\u00e1kat ( Suricata suricatta ) , de tavaly tavaszig nem siker\u00fclt szapor\u00edtani \u0151ket , annak ellen\u0... | NYTK/summarization-hi-bart-hungarian | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Hungarian Abstractive Summarization BART model
==============================================
For further models, scripts and details, see our repository or our demo site.
* BART base model (see Results Table - bold):
+ Pretrained on Webcorpus 2.0
+ Finetuned HI corpus (URL + URL)
- Segments: 559.162
Limitati... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
# Hungarian Abstractive Summarization BART model
For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- BART base model (see Results Table - bold):
- Pretrained on Webcorpus 2.0
- Finetuned NOL corpus (nol.hu)... | {"language": ["hu"], "license": "apache-2.0", "tags": ["summarization"], "metrics": ["rouge"], "widget": [{"text": "A Tisza-parti v\u00e1ros \u00e1llatkertj\u00e9ben r\u00e9g\u00f3ta tartanak szurik\u00e1t\u00e1kat ( Suricata suricatta ) , de tavaly tavaszig nem siker\u00fclt szapor\u00edtani \u0151ket , annak ellen\u0... | NYTK/summarization-nol-bart-hungarian | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Hungarian Abstractive Summarization BART model
==============================================
For further models, scripts and details, see our repository or our demo site.
* BART base model (see Results Table - bold):
+ Pretrained on Webcorpus 2.0
+ Finetuned NOL corpus (URL)
- Segments: 397,343
Limitations
-... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Hungarian GPT-2 news generator
For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained on Hungarian Wikipedia
- Finetuned on hin corpus (hvg.hu, index.hu, nol.hu)
## Results
| Model | Perplexity |
|... | {"language": ["hu"], "license": "mit", "tags": ["text-generation"], "widget": [{"text": "Szeptember v\u00e9g\u00e9n z\u00e1rul a balatoni szezon"}]} | NYTK/text-generation-news-gpt2-small-hungarian | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"hu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #hu #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hungarian GPT-2 news generator
==============================
For further models, scripts and details, see our repository or our demo site.
* Pretrained on Hungarian Wikipedia
* Finetuned on hin corpus (URL, URL, URL)
Results
-------
If you use this model, please cite the following paper:
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #hu #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
42
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #hu #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Hungarian GPT-2 poem generator in Petőfi style
For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained on Hungarian Wikipedia
- Finetuned on Petőfi Sándor összes költeményei
## Results
| Model | Per... | {"language": ["hu"], "license": "mit", "tags": ["text-generation"], "widget": [{"text": "Szegeden, janu\u00e1r v\u00e9g\u00e9n,"}]} | NYTK/text-generation-poem-petofi-gpt2-small-hungarian | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"hu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #hu #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Hungarian GPT-2 poem generator in Petőfi style
==============================================
For further models, scripts and details, see our repository or our demo site.
* Pretrained on Hungarian Wikipedia
* Finetuned on Petőfi Sándor összes költeményei
Results
-------
If you use this model, please cite the ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #hu #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
42
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #hu #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers |
# BART Translation model
For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Source language: English
- Target language: Hungarian
- BART base model:
- Pretrained on English WikiText-103 and Hungarian ... | {"language": ["en", "hu"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["sacrebleu", "chrf"], "widget": [{"text": "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter.", "example_title": "Translation: English-Hungarian"}]} | NYTK/translation-bart-128-en-hu | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"translation",
"en",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en",
"hu"
] | TAGS
#transformers #pytorch #bart #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BART Translation model
======================
For further models, scripts and details, see our repository or our demo site.
* Source language: English
* Target language: Hungarian
* BART base model:
+ Pretrained on English WikiText-103 and Hungarian Wikipedia
+ Finetuned on subcorpora from OPUS
- Segments: 56... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
# BART Translation model
For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Source language: English
- Target language: Hungarian
- Pretrained on English WikiText-103 and Hungarian Wikipedia
- Finetuned... | {"language": ["en", "hu"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["sacrebleu", "chrf"], "widget": [{"text": "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter.", "example_title": "Translation: English-Hungarian"}]} | NYTK/translation-bart-en-hu | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"translation",
"en",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en",
"hu"
] | TAGS
#transformers #pytorch #bart #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BART Translation model
======================
For further models, scripts and details, see our repository or our demo site.
* Source language: English
* Target language: Hungarian
* Pretrained on English WikiText-103 and Hungarian Wikipedia
* Finetuned on subcorpora from OPUS
+ Segments: 56.837.602
Limitations... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
# BART Translation model
For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Source language: Hungarian
- Target language: English
- Pretrained on English WikiText-103 and Hungarian Wikipedia
- Finetuned... | {"language": ["hu", "en"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["sacrebleu", "chrf"], "widget": [{"text": "Szeretn\u00e9m megragadni az alkalmat uram, hogy az enged\u00e9ly\u00e9t k\u00e9rjem, hogy tal\u00e1lkozhassak a l\u00e1ny\u00e1val."}]} | NYTK/translation-bart-hu-en | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"translation",
"hu",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu",
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #translation #hu #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BART Translation model
======================
For further models, scripts and details, see our repository or our demo site.
* Source language: Hungarian
* Target language: English
* Pretrained on English WikiText-103 and Hungarian Wikipedia
* Finetuned on subcorpora from OPUS
+ Segments: 56.837.602
Limitations... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #translation #hu #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #translation #hu #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
# Marian Translation model
For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp). There is a description of the REST API of our service.
This model has been traind with a [MarianNMT](https://github.com/marian-... | {"language": ["en", "hu"], "license": "gpl-3.0", "tags": ["translation"], "metrics": ["sacrebleu", "chrf"], "widget": [{"text": "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter.", "example_title": "Translation: English-Hungarian"}]} | NYTK/translation-marianmt-en-hu | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"hu",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en",
"hu"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #hu #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Marian Translation model
========================
For further models, scripts and details, see our repository or our demo site. There is a description of the REST API of our service.
This model has been traind with a MarianNMT v1.10.23; commit: 42f0b8b7 transformer-big typed environment.
This repository contains ou... | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #hu #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
45
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #hu #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
# mT5 Translation model
For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Source language: English
- Target language: Hungarian
- Pretrained model used: mT5-small
- Finetuned on subcorpora from OPUS
-... | {"language": ["en", "hu"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["sacrebleu", "chrf"], "widget": [{"text": "translate English to Hungarian: This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter."}]} | NYTK/translation-mt5-small-128-en-hu | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"translation",
"en",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en",
"hu"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5 Translation model
=====================
For further models, scripts and details, see our repository or our demo site.
* Source language: English
* Target language: Hungarian
* Pretrained model used: mT5-small
* Finetuned on subcorpora from OPUS
+ Segments: 56.837.602
* prefix: "translate English to Hungarian... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #en #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | nabarun/DialoGPT-small-joshua | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] | [
39,
4
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-down-sampled-evaluating-student-writing
This model is a fine-tuned version of [distilbert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-down-sampled-evaluating-student-writing", "results": []}]} | NahedAbdelgaber/distilbert-base-uncased-finetuned-down-sampled-evaluating-student-writing | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-down-sampled-evaluating-student-writing
=========================================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3408
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... | [
47,
103,
5,
40
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-evaluating-student-writing
This model is a fine-tuned version of [distilbert-base-uncased](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-evaluating-student-writing", "results": []}]} | NahedAbdelgaber/distilbert-base-uncased-finetuned-evaluating-student-writing | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-evaluating-student-writing
============================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9917
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... | [
47,
114,
5,
40
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# evaluating-student-writing-distibert-ner-with-metric
This model is a fine-tuned version of [NahedAbdelgaber/evaluating-student-w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "evaluating-student-writing-distibert-ner-with-metric", "results": []}]} | NahedAbdelgaber/evaluating-student-writing-distibert-ner-with-metric | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| evaluating-student-writing-distibert-ner-with-metric
====================================================
This model is a fine-tuned version of NahedAbdelgaber/evaluating-student-writing-distibert-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7535
* Precision: 0.0614
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... | [
47,
101,
5,
40
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# evaluating-student-writing-distibert-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "evaluating-student-writing-distibert-ner", "results": []}]} | NahedAbdelgaber/evaluating-student-writing-distibert-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| evaluating-student-writing-distibert-ner
========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7688
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... | [
47,
101,
5,
40
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
feature-extraction | transformers | Test
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("NakHyun/electra_kr_v1")
model = AutoModel.from_pretrained("NakHyun/electra_kr_v1")
| {} | NakHyun/electra_kr_v1 | null | [
"transformers",
"pytorch",
"electra",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #feature-extraction #endpoints_compatible #region-us
| Test
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("NakHyun/electra_kr_v1")
model = AutoModel.from_pretrained("NakHyun/electra_kr_v1")
| [] | [
"TAGS\n#transformers #pytorch #electra #feature-extraction #endpoints_compatible #region-us \n"
] | [
24
] | [
"TAGS\n#transformers #pytorch #electra #feature-extraction #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | NaliniK/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8239
* Matthews Correlation: 0.5495
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... | [
56,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
null | null | # Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple,... | {"title": "Pdf Table Extractor To CSV", "emoji": ";)", "colorFrom": "yellow", "colorTo": "green", "sdk": "streamlit", "app_file": "App_For_PDF_To_Dataframe.py", "pinned": false} | Nalla/PDF_To_CSV | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| # Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple,... | [
"# Configuration\n'title': _string_\nDisplay title for the Space\n'emoji': _string_\nSpace emoji (emoji-only character allowed)\n'colorFrom': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n'colorTo': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, in... | [
"TAGS\n#region-us \n",
"# Configuration\n'title': _string_\nDisplay title for the Space\n'emoji': _string_\nSpace emoji (emoji-only character allowed)\n'colorFrom': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n'colorTo': _string_\nColor for Thumbnail gradient (red... | [
5,
220
] | [
"TAGS\n#region-us \n# Configuration\n'title': _string_\nDisplay title for the Space\n'emoji': _string_\nSpace emoji (emoji-only character allowed)\n'colorFrom': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n'colorTo': _string_\nColor for Thumbnail gradient (red, yell... |
text-generation | transformers | # Aqua from Konosuba DialoGPT Model | {"tags": ["conversational"]} | NamPE/DialoGPT-medium-Aqua-konosuba | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Aqua from Konosuba DialoGPT Model | [
"# Aqua from Konosuba DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Aqua from Konosuba DialoGPT Model"
] | [
39,
10
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Aqua from Konosuba DialoGPT Model"
] |
text-generation | transformers | # Takanashi Rikka DialoGPT Model | {"tags": ["conversational"]} | NamPE/DialoGPT-medium-Takanashi-Rikka | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Takanashi Rikka DialoGPT Model | [
"# Takanashi Rikka DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Takanashi Rikka DialoGPT Model"
] | [
43,
10
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Takanashi Rikka DialoGPT Model"
] |
text-generation | transformers |
# Satou Hina DialoGPT Model | {"tags": ["conversational"]} | NamPE/DialoGPT-small-satouhina | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Satou Hina DialoGPT Model | [
"# Satou Hina DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Satou Hina DialoGPT Model"
] | [
39,
9
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Satou Hina DialoGPT Model"
] |
text-generation | transformers | # Bapibot | {"tags": ["conversational"]} | NanniKirby/DialoGPT-medium-bapi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Bapibot | [
"# Bapibot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bapibot"
] | [
39,
4
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Bapibot"
] |
text-generation | transformers |
# Bapibot | {"tags": ["conversational"]} | NanniKirby/bapismall | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Bapibot | [
"# Bapibot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bapibot"
] | [
39,
4
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Bapibot"
] |
summarization | transformers |
# Spanish RoBERTa2RoBERTa (roberta-base-bne) fine-tuned on MLSUM ES for summarization
## Model
[BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) (RoBERTa Checkpoint)
## Dataset
**MLSUM** is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it conta... | {"language": "es", "tags": ["summarization", "news"], "datasets": ["mlsum"], "widget": [{"text": "Al filo de las 22.00 horas del jueves, la Asamblea de Madrid vive un momento sorprendente: Vox decide no apoyar una propuesta del PP en favor del blindaje fiscal de la Comunidad. Se ha roto la unidad de los tres partidos d... | Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"news",
"es",
"dataset:mlsum",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #news #es #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us
| Spanish RoBERTa2RoBERTa (roberta-base-bne) fine-tuned on MLSUM ES for summarization
===================================================================================
Model
-----
BSC-TeMU/roberta-base-bne (RoBERTa Checkpoint)
Dataset
-------
MLSUM is the first large-scale MultiLingual SUMmarization dataset. Ob... | [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #news #es #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
53
] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #news #es #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# ByT5-base fine-tuned for Question Answering (on Tweets)
[ByT5](https://huggingface.co/google/byt5-base) base fine-tuned on [TweetQA](https://huggingface.co/datasets/tweet_qa) dataset for **Question Answering** downstream task.
# Details of ByT5 - Base 🧠
ByT5 is a tokenizer-free version of [Google's T5](https://a... | {"language": "en", "tags": ["qa", "Question Answering"], "datasets": ["tweet_qa"], "widget": [{"text": "question: how far away was the putt context: GET THE CIGAR READY! Miguel aces the 15th from 174 yards, and celebrates as only he knows how! The European Tour (@EuropeanTour) January, 15 2015"}]} | Narrativa/byt5-base-finetuned-tweet-qa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"qa",
"Question Answering",
"en",
"dataset:tweet_qa",
"arxiv:1907.06292",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1907.06292",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #qa #Question Answering #en #dataset-tweet_qa #arxiv-1907.06292 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5-base fine-tuned for Question Answering (on Tweets)
ByT5 base fine-tuned on TweetQA dataset for Question Answering downstream task.
# Details of ByT5 - Base
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on mC4 excluding any supervised... | [
"# ByT5-base fine-tuned for Question Answering (on Tweets)\nByT5 base fine-tuned on TweetQA dataset for Question Answering downstream task.",
"# Details of ByT5 - Base \n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\nByT5 was only pre-trained on mC4 excluding any... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #qa #Question Answering #en #dataset-tweet_qa #arxiv-1907.06292 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5-base fine-tuned for Question Answering (on Tweets)\nByT5 base fine-tuned on Tweet... | [
75,
38,
185,
261,
49
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #qa #Question Answering #en #dataset-tweet_qa #arxiv-1907.06292 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ByT5-base fine-tuned for Question Answering (on Tweets)\nByT5 base fine-tuned on TweetQA dat... |
text2text-generation | transformers |
# ByT5-base fine-tuned for Hate Speech Detection (on Tweets)
[ByT5](https://huggingface.co/google/byt5-base) base fine-tuned on [tweets hate speech detection](https://huggingface.co/datasets/tweets_hate_speech_detection) dataset for **Sequence Classification** downstream task.
# Details of ByT5 - Base 🧠
ByT5 is a ... | {"language": "en", "tags": ["hate", "speech"], "datasets": ["tweets_hate_speech_detection"], "widget": [{"text": "@user black lives really matter?"}]} | Narrativa/byt5-base-tweet-hate-detection | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"hate",
"speech",
"en",
"dataset:tweets_hate_speech_detection",
"arxiv:1907.06292",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1907.06292",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #hate #speech #en #dataset-tweets_hate_speech_detection #arxiv-1907.06292 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5-base fine-tuned for Hate Speech Detection (on Tweets)
ByT5 base fine-tuned on tweets hate speech detection dataset for Sequence Classification downstream task.
# Details of ByT5 - Base
ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.
ByT5 was only pre-trained on... | [
"# ByT5-base fine-tuned for Hate Speech Detection (on Tweets)\nByT5 base fine-tuned on tweets hate speech detection dataset for Sequence Classification downstream task.",
"# Details of ByT5 - Base \n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\nByT5 was only pre... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #hate #speech #en #dataset-tweets_hate_speech_detection #arxiv-1907.06292 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5-base fine-tuned for Hate Speech Detection (on Tweets)\nByT5 base f... | [
78,
41,
186,
217,
38,
49
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #hate #speech #en #dataset-tweets_hate_speech_detection #arxiv-1907.06292 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ByT5-base fine-tuned for Hate Speech Detection (on Tweets)\nByT5 base fine-tu... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilRoberta-stereotype
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "stereotype", "gender", "gender_bias"], "metrics": ["accuracy"], "widget": [{"text": "Cauterize is not just for fans of the guitarist or his other projects, but those that love music that is both aggressive and infectious and gave the album 4 out of 5 stars .... | Narrativa/distilroberta-finetuned-stereotype-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"stereotype",
"gender",
"gender_bias",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #stereotype #gender #gender_bias #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilRoberta-stereotype
========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0651
* Accuracy: 0.9892
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #stereotype #gender #gender_bias #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:... | [
58,
101,
5,
88
] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #stereotype #gender #gender_bias #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
text2text-generation | transformers |
# mT5-base fine-tuned on TyDiQA for multilingual Question Generation 🗺📖❓
[Google's mT5-base](https://huggingface.co/google/mt5-base) fine-tuned on [TyDi QA](https://huggingface.co/nlp/viewer/?dataset=tydiqa&config=secondary_task) (secondary task) for **multingual Question Generation** downstream task (by answer prep... | {"language": "multilingual", "datasets": ["tydiqa"], "widget": [{"text": "answer: monitoring and managing PR strategy including relations with the media and journalists context: Sof\u00eda has a degree in Communications and public relations agency experience where she was in charge of monitoring and managing PR strateg... | Narrativa/mT5-base-finetuned-tydiQA-question-generation | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"multilingual",
"dataset:tydiqa",
"arxiv:2010.11934",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2010.11934"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5-base fine-tuned on TyDiQA for multilingual Question Generation
==================================================================
Google's mT5-base fine-tuned on TyDi QA (secondary task) for multingual Question Generation downstream task (by answer prepending).
Details of mT5
--------------
Google's mT5
mT5... | [
"### WIP\n\n\nModel in Action\n---------------",
"### WIP\n\n\nCreated by: Narrativa\n\n\nAbout Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### WIP\n\n\nModel in Action\n---------------",
"### WIP\n\n\nCreated by: Narrativa\n\n\nAbout Narrativa: Natural Langu... | [
57,
23,
49
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### WIP\n\n\nModel in Action\n---------------### WIP\n\n\nCreated by: Narrativa\n\n\nAbout Narrativa: Natural Language Generati... |
text2text-generation | transformers |
# mT5-base fine-tuned on TyDiQA for multilingual QA 🗺📖❓
[Google's mT5-base](https://huggingface.co/google/mt5-base) fine-tuned on [TyDi QA](https://huggingface.co/nlp/viewer/?dataset=tydiqa&config=secondary_task) (secondary task) for **multingual Q&A** downstream task.
## Details of mT5
[Google's mT5](https://gith... | {"language": "multilingual", "datasets": ["tydiqa"], "widget": [{"text": "question: what does she do? context: Sof\u00eda has a degree in Communications and public relations agency experience where she was in charge of monitoring and managing PR strategy including relations with the media and journalists."}]} | Narrativa/mT5-base-finetuned-tydiQA-xqa | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"multilingual",
"dataset:tydiqa",
"arxiv:2010.11934",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2010.11934"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5-base fine-tuned on TyDiQA for multilingual QA
=================================================
Google's mT5-base fine-tuned on TyDi QA (secondary task) for multingual Q&A downstream task.
Details of mT5
--------------
Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Al... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
60
] | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #multilingual #dataset-tydiqa #arxiv-2010.11934 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers |
# mBART-large-50 fine-tuned onpus100 and opusbook for English to Portuguese translation.
[mBART-50](https://huggingface.co/facebook/mbart-large-50/) large fine-tuned on [opus100](https://huggingface.co/datasets/viewer/?dataset=opus100) dataset for **NMT** downstream task.
# Details of mBART-50 🧠
mBART-50 is a mult... | {"language": ["en", "pt"], "tags": ["translation"], "datasets": ["opus100", "opusbook"], "metrics": ["bleu"]} | Narrativa/mbart-large-50-finetuned-opus-en-pt-translation | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"en",
"pt",
"dataset:opus100",
"dataset:opusbook",
"arxiv:2008.00401",
"arxiv:2004.11867",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2008.00401",
"2004.11867"
] | [
"en",
"pt"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #translation #en #pt #dataset-opus100 #dataset-opusbook #arxiv-2008.00401 #arxiv-2004.11867 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mBART-large-50 fine-tuned onpus100 and opusbook for English to Portuguese translation.
mBART-50 large fine-tuned on opus100 dataset for NMT downstream task.
# Details of mBART-50
mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was int... | [
"# mBART-large-50 fine-tuned onpus100 and opusbook for English to Portuguese translation.\nmBART-50 large fine-tuned on opus100 dataset for NMT downstream task.",
"# Details of mBART-50 \n\nmBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the \"Multilingual Denoising Pretraining\" objective... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #en #pt #dataset-opus100 #dataset-opusbook #arxiv-2008.00401 #arxiv-2004.11867 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mBART-large-50 fine-tuned onpus100 and opusbook for English to Portuguese translation.\nm... | [
73,
41,
334,
31,
54,
45,
5,
38,
125,
17,
49
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #en #pt #dataset-opus100 #dataset-opusbook #arxiv-2008.00401 #arxiv-2004.11867 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# mBART-large-50 fine-tuned onpus100 and opusbook for English to Portuguese translation.\nmBART-5... |
translation | transformers |
# mBART-large-50 fine-tuned onpus100 and opusbook for Portuguese to English translation.
[mBART-50](https://huggingface.co/facebook/mbart-large-50/) large fine-tuned on [opus100](https://huggingface.co/datasets/viewer/?dataset=opus100) dataset for **NMT** downstream task.
# Details of mBART-50 🧠
mBART-50 is a mult... | {"language": ["pt", "en"], "tags": ["translation"], "datasets": ["opus100", "opusbook"], "metrics": ["bleu"]} | Narrativa/mbart-large-50-finetuned-opus-pt-en-translation | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"pt",
"en",
"dataset:opus100",
"dataset:opusbook",
"arxiv:2008.00401",
"arxiv:2004.11867",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2008.00401",
"2004.11867"
] | [
"pt",
"en"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #translation #pt #en #dataset-opus100 #dataset-opusbook #arxiv-2008.00401 #arxiv-2004.11867 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mBART-large-50 fine-tuned onpus100 and opusbook for Portuguese to English translation.
mBART-50 large fine-tuned on opus100 dataset for NMT downstream task.
# Details of mBART-50
mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was int... | [
"# mBART-large-50 fine-tuned onpus100 and opusbook for Portuguese to English translation.\nmBART-50 large fine-tuned on opus100 dataset for NMT downstream task.",
"# Details of mBART-50 \n\nmBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the \"Multilingual Denoising Pretraining\" objective... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #pt #en #dataset-opus100 #dataset-opusbook #arxiv-2008.00401 #arxiv-2004.11867 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mBART-large-50 fine-tuned onpus100 and opusbook for Portuguese to English translation.\nm... | [
73,
41,
334,
31,
54,
45,
5,
38,
125,
17,
49
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #pt #en #dataset-opus100 #dataset-opusbook #arxiv-2008.00401 #arxiv-2004.11867 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# mBART-large-50 fine-tuned onpus100 and opusbook for Portuguese to English translation.\nmBART-5... |
text-generation | transformers | # Spanish GPT-2 trained on [Spanish RAP Lyrics](https://www.kaggle.com/smunoz3801/9325-letras-de-rap-en-espaol)
Created by: [Narrativa](https://www.narrativa.com/)
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #... | {"language": "es", "license": "mit", "tags": ["GPT-2", "Rap", "Lyrics", "Songs"], "datasets": ["large_spanish_corpus"], "widget": [{"text": "D\u00e9jame contarte lo importante que es buscarte un plan\nNo para golpearles o ganarles, sino para darles paz\n"}]} | Narrativa/spanish-gpt2-finetuned-rap-lyrics | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"GPT-2",
"Rap",
"Lyrics",
"Songs",
"es",
"dataset:large_spanish_corpus",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #GPT-2 #Rap #Lyrics #Songs #es #dataset-large_spanish_corpus #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Spanish GPT-2 trained on Spanish RAP Lyrics
Created by: Narrativa
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI | [
"# Spanish GPT-2 trained on Spanish RAP Lyrics\n\n\nCreated by: Narrativa\n\nAbout Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #GPT-2 #Rap #Lyrics #Songs #es #dataset-large_spanish_corpus #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Spanish GPT-2 trained on Spanish RAP Lyrics\n\n\nCreated by: Narrativa\n\nAbout Narrativa: Natura... | [
62,
55
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #GPT-2 #Rap #Lyrics #Songs #es #dataset-large_spanish_corpus #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Spanish GPT-2 trained on Spanish RAP Lyrics\n\n\nCreated by: Narrativa\n\nAbout Narrativa: Natural Lang... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DeBERTa v3 small fine-tuned on hate_speech18 dataset for Hate Speech Detection
This model is a fine-tuned version of [microsoft/... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["hate_speech18"], "metrics": ["accuracy"], "widget": [{"text": "ok, so do we need to kill them too or are the slavs okay ? for some reason whenever i hear the word slav , the word slobber comes to mind and i picture a slobbering half breed creature lik... | Narrativaai/deberta-v3-small-finetuned-hate_speech18 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:hate_speech18",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #dataset-hate_speech18 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa v3 small fine-tuned on hate\_speech18 dataset for Hate Speech Detection
===============================================================================
This model is a fine-tuned version of microsoft/deberta-v3-small on the hate\_speech18 dataset.
It achieves the following results on the evaluation set:
* L... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #dataset-hate_speech18 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... | [
58,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #dataset-hate_speech18 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# RoBERTa-large-fake-news-detection-spanish
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggin... | {"language": "es", "tags": ["generated_from_trainer", "fake", "news", "competition"], "datasets": ["fakedes"], "metrics": ["f1", "accuracy"], "widget": [{"text": "La palabra \"haiga\", aceptada por la RAE [SEP] La palabra \"haiga\", aceptada por la RAE La Real Academia de la Lengua (RAE), ha aceptado el uso de \"HAIGA\... | Narrativaai/fake-news-detection-spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"fake",
"news",
"competition",
"es",
"dataset:fakedes",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #fake #news #competition #es #dataset-fakedes #autotrain_compatible #endpoints_compatible #has_space #region-us
| RoBERTa-large-fake-news-detection-spanish
=========================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-large-bne on an Spanish Fake News Dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7474
* F1: 0.7717
* Accuracy: 0.7797
>
> So, based on th... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #fake #news #competition #es #dataset-fakedes #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* l... | [
55,
101,
5,
12,
88
] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #fake #news #competition #es #dataset-fakedes #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-mlm
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-case... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "test-mlm", "results": []}]} | Narshion/bert-base-multilingual-cased-mwach | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
|
# test-mlm
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More ... | [
"# test-mlm\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6481",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"# test-mlm\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6481",
"## Model description\n\nMore information needed",
"## Intend... | [
17,
49,
7,
9,
9,
4,
95,
5,
44
] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n# test-mlm\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6481## Model description\n\nMore information needed## Intended uses & limitati... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-urgency
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co... | {"tags": ["generated_from_trainer"], "datasets": []} | Narshion/bert-base-multilingual-cased-urgency | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-multilingual-cased-urgency
====================================
This model is a fine-tuned version of bert-base-multilingual-cased on the mWACH NEO dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2797
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... | [
37,
103,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n... |
zero-shot-classification | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official reposi... | {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | Narsil/deberta-large-mnli-zero-cls | null | [
"transformers",
"pytorch",
"deberta",
"text-classification",
"deberta-v1",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check ... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI... | [
"TAGS\n#transformers #pytorch #deberta #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE... | [
70,
35,
176
] | [
"TAGS\n#transformers #pytorch #deberta #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE bench... |
text-generation | transformers |
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_... | {"language": "en", "license": "mit", "tags": ["exbert"], "pipeline_tag": "text-generation"} | Narsil/gpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #tflite #rust #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#transformers #pytorch #tf #jax #tflite #rust #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation re... | [
60,
64,
390,
118,
36
] | [
"TAGS\n#transformers #pytorch #tf #jax #tflite #rust #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies o... |
image-segmentation | generic | ## Keras semantic segmentation models on the 🤗Hub! 🐶 🐕 🐩
Image classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and revea... | {"license": "apache-2.0", "library_name": "generic", "tags": ["image-segmentation", "generic"], "pipeline_tag": "image-segmentation", "dataset": ["oxfort-iit pets"]} | Narsil/pet-segmentation | null | [
"generic",
"tf",
"image-segmentation",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#generic #tf #image-segmentation #license-apache-2.0 #has_space #region-us
| ## Keras semantic segmentation models on the Hub!
Image classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal their ... | [
"## Keras semantic segmentation models on the Hub! \n\nImage classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal... | [
"TAGS\n#generic #tf #image-segmentation #license-apache-2.0 #has_space #region-us \n",
"## Keras semantic segmentation models on the Hub! \n\nImage classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to ... | [
27,
277
] | [
"TAGS\n#generic #tf #image-segmentation #license-apache-2.0 #has_space #region-us \n## Keras semantic segmentation models on the Hub! \n\nImage classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know a... |
token-classification | transformers | Small change. again. again ? again.
| {} | Narsil/small | null | [
"transformers",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Small change. again. again ? again.
| [] | [
"TAGS\n#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
26
] | [
"TAGS\n#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | Small change. again. again ? again.
| {} | Narsil/small2 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Small change. again. again ? again.
| [] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
31
] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | ```python
import tempfile
from tokenizers import Tokenizer, models, processors
from transformers.tokenization_utils_fast import PreTrainedTo... | {} | Narsil/small_conversational_test | null | [
"transformers",
"albert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #albert #endpoints_compatible #region-us
|
Small change.
| [] | [
"TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] | [
14
] | [
"TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] |
null | transformers | ```python
import tempfile
from tokenizers import Tokenizer, models
from transformers import PreTrainedTokenizerFast
model_max_length = 4
vocab = [(chr(i), i) for i in range(256)]
tokenizer = Tokenizer(models.Unigram(vocab))
with tempfile.NamedTemporaryFile() as f:
tokenizer.save(f.name)
real_tokenizer = PreTr... | {} | Narsil/small_summarization_test | null | [
"transformers",
"albert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #albert #endpoints_compatible #region-us
|
config uses Albert which works with a minimal 'URL' | [] | [
"TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] | [
14
] | [
"TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] |
null | null | Говорили: "Погоди", уходил с дождём
Эта ночь нужна, переваривал сон
Вы порвали паруса, ожидая восторг
Это мой Тачтаун, это мой Гонконг
Надо созерцать, и не более того
Либо до конца переполох
Хитроматы пустот, наливай по сто
Забывай мой голос и меня самого
Забывай мой рай, я пропитый бадман
Добровольно приговаривал, а в... | {} | Nasvai1702/Night | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Говорили: "Погоди", уходил с дождём
Эта ночь нужна, переваривал сон
Вы порвали паруса, ожидая восторг
Это мой Тачтаун, это мой Гонконг
Надо созерцать, и не более того
Либо до конца переполох
Хитроматы пустот, наливай по сто
Забывай мой голос и меня самого
Забывай мой рай, я пропитый бадман
Добровольно приговаривал, а в... | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | Test for use in Google Colab :'( | {} | NathanZhu/GabHateCorpusTrained | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Test for use in Google Colab :'( | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
30
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Naturealbe/DialoGPT-small-harrypotter-2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] | [
39,
7
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Naturealbe/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] | [
39,
7
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.