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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Indonesian-GSD
| Feature | Description |
| --- | --- |
| **Name** | `id_udv25_indonesiangsd_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimenta... | {"language": ["id"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/id_udv25_indonesiangsd_trf | null | [
"spacy",
"token-classification",
"id",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#spacy #token-classification #id #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Indonesian-GSD
### Label Scheme
View label scheme (1325 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1325 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #id #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1325 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Korean-GSD
| Feature | Description |
| --- | --- |
| **Name** | `ko_udv25_koreangsd_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | {"language": ["ko"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/ko_udv25_koreangsd_trf | null | [
"spacy",
"token-classification",
"ko",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Korean-GSD
### Label Scheme
View label scheme (2415 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (2415 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2415 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Korean-Kaist
| Feature | Description |
| --- | --- |
| **Name** | `ko_udv25_koreankaist_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | {"language": ["ko"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/ko_udv25_koreankaist_trf | null | [
"spacy",
"token-classification",
"ko",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Korean-Kaist
### Label Scheme
View label scheme (5329 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (5329 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ko #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (5329 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Lithuanian-ALKSNIS
| Feature | Description |
| --- | --- |
| **Name** | `lt_udv25_lithuanianalksnis_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exp... | {"language": ["lt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/lt_udv25_lithuanianalksnis_trf | null | [
"spacy",
"token-classification",
"lt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Lithuanian-ALKSNIS
### Label Scheme
View label scheme (3674 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (3674 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #lt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3674 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Latvian-LVTB
| Feature | Description |
| --- | --- |
| **Name** | `lv_udv25_latvianlvtb_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | {"language": ["lv"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/lv_udv25_latvianlvtb_trf | null | [
"spacy",
"token-classification",
"lv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lv"
] | TAGS
#spacy #token-classification #lv #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Latvian-LVTB
### Label Scheme
View label scheme (6012 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (6012 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #lv #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6012 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Norwegian-Bokmaal
| Feature | Description |
| --- | --- |
| **Name** | `nb_udv25_norwegianbokmaal_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exper... | {"language": ["nb"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/nb_udv25_norwegianbokmaal_trf | null | [
"spacy",
"token-classification",
"nb",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nb"
] | TAGS
#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Norwegian-Bokmaal
### Label Scheme
View label scheme (1240 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1240 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1240 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Norwegian-Nynorsk
| Feature | Description |
| --- | --- |
| **Name** | `nb_udv25_norwegiannynorsk_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exper... | {"language": ["nb"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/nb_udv25_norwegiannynorsk_trf | null | [
"spacy",
"token-classification",
"nb",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nb"
] | TAGS
#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Norwegian-Nynorsk
### Label Scheme
View label scheme (1400 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1400 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nb #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1400 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Dutch-Alpino
| Feature | Description |
| --- | --- |
| **Name** | `nl_udv25_dutchalpino_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | {"language": ["nl"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/nl_udv25_dutchalpino_trf | null | [
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Dutch-Alpino
### Label Scheme
View label scheme (1712 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1712 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1712 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Dutch-LassySmall
| Feature | Description |
| --- | --- |
| **Name** | `nl_udv25_dutchlassysmall_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experim... | {"language": ["nl"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/nl_udv25_dutchlassysmall_trf | null | [
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Dutch-LassySmall
### Label Scheme
View label scheme (1070 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1070 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nl #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1070 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Polish-LFG
| Feature | Description |
| --- | --- |
| **Name** | `pl_udv25_polishlfg_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | {"language": ["pl"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | explosion/pl_udv25_polishlfg_trf | null | [
"spacy",
"token-classification",
"pl",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Polish-LFG
### Label Scheme
View label scheme (4947 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4947 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4947 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Portuguese-Bosque
| Feature | Description |
| --- | --- |
| **Name** | `pt_udv25_portuguesebosque_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exper... | {"language": ["pt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/pt_udv25_portuguesebosque_trf | null | [
"spacy",
"token-classification",
"pt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Portuguese-Bosque
### Label Scheme
View label scheme (2079 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (2079 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2079 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Romanian-Nonstandard
| Feature | Description |
| --- | --- |
| **Name** | `ro_udv25_romaniannonstandard_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, ... | {"language": ["ro"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/ro_udv25_romaniannonstandard_trf | null | [
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Romanian-Nonstandard
### Label Scheme
View label scheme (7445 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (7445 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (7445 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Romanian-RRT
| Feature | Description |
| --- | --- |
| **Name** | `ro_udv25_romanianrrt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | {"language": ["ro"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/ro_udv25_romanianrrt_trf | null | [
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Romanian-RRT
### Label Scheme
View label scheme (3096 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (3096 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3096 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Russian-GSD
| Feature | Description |
| --- | --- |
| **Name** | `ru_udv25_russiangsd_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit... | {"language": ["ru"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/ru_udv25_russiangsd_trf | null | [
"spacy",
"token-classification",
"ru",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#spacy #token-classification #ru #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Russian-GSD
### Label Scheme
View label scheme (3014 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (3014 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ru #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3014 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Slovak-SNK
| Feature | Description |
| --- | --- |
| **Name** | `sk_udv25_slovaksnk_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | {"language": ["sk"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/sk_udv25_slovaksnk_trf | null | [
"spacy",
"token-classification",
"sk",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sk"
] | TAGS
#spacy #token-classification #sk #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Slovak-SNK
### Label Scheme
View label scheme (4879 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4879 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #sk #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4879 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Serbian-SET
| Feature | Description |
| --- | --- |
| **Name** | `sr_udv25_serbianset_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit... | {"language": ["sr"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/sr_udv25_serbianset_trf | null | [
"spacy",
"token-classification",
"sr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sr"
] | TAGS
#spacy #token-classification #sr #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Serbian-SET
### Label Scheme
View label scheme (2603 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (2603 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #sr #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2603 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Swedish-Talbanken
| Feature | Description |
| --- | --- |
| **Name** | `sv_udv25_swedishtalbanken_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exper... | {"language": ["sv"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/sv_udv25_swedishtalbanken_trf | null | [
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#spacy #token-classification #sv #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Swedish-Talbanken
### Label Scheme
View label scheme (1206 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1206 labels for 6 components)",
"### Accuracy"
] | [
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"### Label Scheme\n\n\n\nView label scheme (1206 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Vietnamese-VTB
| Feature | Description |
| --- | --- |
| **Name** | `vi_udv25_vietnamesevtb_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimenta... | {"language": ["vi"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/vi_udv25_vietnamesevtb_trf | null | [
"spacy",
"token-classification",
"vi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#spacy #token-classification #vi #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Vietnamese-VTB
### Label Scheme
View label scheme (81 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (81 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #vi #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (81 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Old_French-SRCMF
| Feature | Description |
| --- | --- |
| **Name** | `xx_udv25_oldfrenchsrcmf_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experime... | {"language": ["multilingual"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/xx_udv25_oldfrenchsrcmf_trf | null | [
"spacy",
"token-classification",
"multilingual",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#spacy #token-classification #multilingual #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Old\_French-SRCMF
### Label Scheme
View label scheme (16214 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (16214 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #multilingual #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (16214 labels for 6 components)",
"### Accuracy"
] |
text-generation | transformers | #peppa pig chat bot | {"tags": ["conversational"]} | f00d4tehg0dz/Peppa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #peppa pig chat bot | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | #yoda chat bot | {"tags": ["conversational"]} | f00d4tehg0dz/Yoda | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #yoda chat bot | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers | # Italian Legal Named Entity Recognition (NER)
ELECTRA-based model trained to extract entities of interest from Italian civil judgments issued by the Corte Suprema di Cassazione.
## Dataset
The model has been fine-tuned on 9000 judgments from 2016 to 2021 (1500 per year), labeled with a combination of rule-based and m... | {"language": ["it"], "tags": ["legal"], "widget": [{"text": "la seguente SENTENZA sul ricorso 24817-2015 proposto da: ANDREA FORMISANO, elettivamente domiciliato in ROMA VIA S. TOMMASO D'AQUINO 7, presso lo studio dell'avvocato CARLO BORELLO, che lo rappresenta e difende giusta delega in calce; - ricorrente - contro SO... | fabiod20/italian-legal-ner | null | [
"transformers",
"pytorch",
"electra",
"token-classification",
"legal",
"it",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #electra #token-classification #legal #it #autotrain_compatible #endpoints_compatible #has_space #region-us
| Italian Legal Named Entity Recognition (NER)
============================================
ELECTRA-based model trained to extract entities of interest from Italian civil judgments issued by the Corte Suprema di Cassazione.
Dataset
-------
The model has been fine-tuned on 9000 judgments from 2016 to 2021 (1500 per ... | [] | [
"TAGS\n#transformers #pytorch #electra #token-classification #legal #it #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "opus-mt-en-de-finetuned-en-to-de-wd01-fp16false", "results": []}]} | fabiogr/opus-mt-en-de-finetuned-en-to-de-wd01-fp16false | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-en-de-finetuned-en-to-de-wd01-fp16false\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dat... |
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. -->
# bert-base-uncased-ag_news
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["ag_news"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-ag_news", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ag_news", "type": "ag_news", "args": "d... | fabriceyhc/bert-base-uncased-ag_news | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:ag_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-ag_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-ag\_news
==========================
This model is a fine-tuned version of bert-base-uncased on the ag\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3284
* Accuracy: 0.9375
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-ag_news #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\\_rate: ... |
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. -->
# bert-base-uncased-amazon_polarity
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["amazon_polarity"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-amazon_polarity", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_polarity", "type... | fabriceyhc/bert-base-uncased-amazon_polarity | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:amazon_polarity",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-amazon\_polarity
==================================
This model is a fine-tuned version of bert-base-uncased on the amazon\_polarity dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2945
* Accuracy: 0.9465
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\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 #bert #text-classification #generated_from_trainer #sibyl #dataset-amazon_polarity #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\... |
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. -->
# bert-base-uncased-dbpedia_14
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["dbpedia_14"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-dbpedia_14", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "dbpedia_14", "type": "dbpedia_14"... | fabriceyhc/bert-base-uncased-dbpedia_14 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:dbpedia_14",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-dbpedia_14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-dbpedia\_14
=============================
This model is a fine-tuned version of bert-base-uncased on the dbpedia\_14 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0547
* Accuracy: 0.9903
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-dbpedia_14 #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... |
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. -->
# bert-base-uncased-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"},... | fabriceyhc/bert-base-uncased-imdb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-uncased-imdb
======================
This model is a fine-tuned version of bert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4942
* Accuracy: 0.9126
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
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. -->
# bert-base-uncased-yahoo_answers_topics
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["yahoo_answers_topics"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-yahoo_answers_topics", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "yahoo_answers... | fabriceyhc/bert-base-uncased-yahoo_answers_topics | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:yahoo_answers_topics",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-yahoo\_answers\_topics
========================================
This model is a fine-tuned version of bert-base-uncased on the yahoo\_answers\_topics dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8092
* Accuracy: 0.7499
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #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* lear... |
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. -->
# bert-base-uncased-yelp_polarity
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["yelp_polarity"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-yelp_polarity", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "yelp_polarity", "type": "ye... | fabriceyhc/bert-base-uncased-yelp_polarity | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:yelp_polarity",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yelp_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-yelp\_polarity
================================
This model is a fine-tuned version of bert-base-uncased on the yelp\_polarity dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3222
* Accuracy: 0.9516
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\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 #bert #text-classification #generated_from_trainer #sibyl #dataset-yelp_polarity #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\\_... |
feature-extraction | transformers |
# BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://gi... | {"language": "en", "license": "apache-2.0"} | facebook/bart-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository.
Disclaimer: The team releasing BART did not w... | [
"# BART (base-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART d... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BART (base-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pr... |
summarization | transformers | # BART (large-sized model), fine-tuned on CNN Daily Mail
BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Co... | {"language": ["en"], "license": "mit", "datasets": ["cnn_dailymail"], "pipeline_tag": "summarization", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "model-index": [{"name": "facebook/bart-large-cnn", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cn... | facebook/bart-large-cnn | null | [
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"model-index",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #safetensors #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BART (large-sized model), fine-tuned on CNN Daily Mail
BART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in ... | [
"# BART (large-sized model), fine-tuned on CNN Daily Mail \n\nBART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first relea... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #safetensors #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BART (large-sized model), fine-tuned on CNN Daily Mail \n\nBART mode... |
zero-shot-classification | transformers |
# bart-large-mnli
This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset.
Additional information about this model:
- The [bart-large](https://huggingface.co/facebook/bart-large) model page
- [... | {"license": "mit", "datasets": ["multi_nli"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "pipeline_tag": "zero-shot-classification"} | facebook/bart-large-mnli | null | [
"transformers",
"pytorch",
"jax",
"rust",
"safetensors",
"bart",
"text-classification",
"zero-shot-classification",
"dataset:multi_nli",
"arxiv:1910.13461",
"arxiv:1909.00161",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461",
"1909.00161"
] | [] | TAGS
#transformers #pytorch #jax #rust #safetensors #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bart-large-mnli
This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.
Additional information about this model:
- The bart-large model page
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
- BART fairseq implemen... | [
"# bart-large-mnli\n\nThis is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.\n\nAdditional information about this model:\n- The bart-large model page\n- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension\n\n- BART fair... | [
"TAGS\n#transformers #pytorch #jax #rust #safetensors #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bart-large-mnli\n\nThis is the checkpoint for bart-large after be... |
summarization | transformers | ### Bart model finetuned on xsum
docs: https://huggingface.co/transformers/model_doc/bart.html
finetuning: examples/seq2seq/ (as of Aug 20, 2020)
Metrics: ROUGE > 22 on xsum.
variants: search for distilbart
paper: https://arxiv.org/abs/1910.13461 | {"language": ["en"], "license": "mit", "tags": ["summarization"], "model-index": [{"name": "facebook/bart-large-xsum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailymail", "config": "3.0.0", "split": "test"}, "metrics": [{"type": "rouge",... | facebook/bart-large-xsum | null | [
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"tf",
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"rust",
"bart",
"text2text-generation",
"summarization",
"en",
"arxiv:1910.13461",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #bart #text2text-generation #summarization #en #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Bart model finetuned on xsum
docs: URL
finetuning: examples/seq2seq/ (as of Aug 20, 2020)
Metrics: ROUGE > 22 on xsum.
variants: search for distilbart
paper: URL | [
"### Bart model finetuned on xsum\n\ndocs: URL\n\nfinetuning: examples/seq2seq/ (as of Aug 20, 2020)\n\nMetrics: ROUGE > 22 on xsum.\n\nvariants: search for distilbart\n\npaper: URL"
] | [
"TAGS\n#transformers #pytorch #tf #jax #rust #bart #text2text-generation #summarization #en #arxiv-1910.13461 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Bart model finetuned on xsum\n\ndocs: URL\n\nfinetuning: examples/seq2seq/ (as of Aug 20, 2020)\n\nMet... |
feature-extraction | transformers |
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://g... | {"language": "en", "license": "apache-2.0"} | facebook/bart-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository.
Disclaimer: The team releasing BART did not ... | [
"# BART (large-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART ... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BART (large-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-trai... |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural m... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | facebook/blenderbot-1B-distill | null | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
"TAGS\n#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatb... |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural m... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | facebook/blenderbot-3B | null | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
"TAGS\n#transformers #pytorch #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatb... |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot]( https://arxiv.org/abs/2004.13637)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural ... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | facebook/blenderbot-400M-distill | null | [
"transformers",
"pytorch",
"tf",
"jax",
"blenderbot",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:2004.13637",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13637"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-2004.13637 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
"TAGS\n#transformers #pytorch #tf #jax #blenderbot #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-2004.13637 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\n+ Paper: Recipes for building an open-dom... |
text-generation | transformers |
# 🚨🚨**IMPORTANT**🚨🚨
**This model is deprecated! Please use the identical model** **https://huggingface.co/facebook/blenderbot_small-90M instead**
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipe... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | facebook/blenderbot-90M | null | [
"transformers",
"pytorch",
"blenderbot-small",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #pytorch #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# IMPORTANT
This model is deprecated! Please use the identical model URL instead
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scalin... | [
"# IMPORTANT\n\nThis model is deprecated! Please use the identical model URL instead",
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work ha... | [
"TAGS\n#transformers #pytorch #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# IMPORTANT\n\nThis model is deprecated! Please use the identical mo... |
text-generation | transformers |
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural m... | {"language": ["en"], "license": "apache-2.0", "tags": ["convAI", "conversational", "facebook"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | facebook/blenderbot_small-90M | null | [
"transformers",
"pytorch",
"tf",
"jax",
"blenderbot-small",
"text2text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #blenderbot-small #text2text-generation #convAI #conversational #facebook #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
+ Paper: Recipes for building an open-domain chatbot
+ Original PARLAI Code
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are train... | [
"## Model description\n\n+ Paper: Recipes for building an open-domain chatbot\n+ Original PARLAI Code",
"### Abstract\n\n\nBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data t... | [
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"## Model description\n\n+ Paper: Recipes for building an op... |
feature-extraction | transformers | This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is avail... | {"tags": ["feature-extraction"], "pipeline_tag": "feature-extraction"} | facebook/contriever-msmarco | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2112.09118",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09118"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2112.09118 #endpoints_compatible #has_space #region-us
| This model is the finetuned version of the pre-trained contriever model available here URL following the approach described in Towards Unsupervised Dense Information Retrieval with Contrastive Learning. The associated GitHub repository is available here URL
## Usage (HuggingFace Transformers)
Using the model directly ... | [
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2112.09118 #endpoints_compatible #has_space #region-us \n",
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
null | transformers | This model has been trained without supervision following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Tr... | {} | facebook/contriever | null | [
"transformers",
"pytorch",
"bert",
"arxiv:2112.09118",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.09118"
] | [] | TAGS
#transformers #pytorch #bert #arxiv-2112.09118 #endpoints_compatible #has_space #region-us
| This model has been trained without supervision following the approach described in Towards Unsupervised Dense Information Retrieval with Contrastive Learning. The associated GitHub repository is available here URL
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers requi... | [
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] | [
"TAGS\n#transformers #pytorch #bert #arxiv-2112.09118 #endpoints_compatible #has_space #region-us \n",
"## Usage (HuggingFace Transformers)\nUsing the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding."
] |
image-classification | transformers |
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapo... | facebook/convnext-base-224-22k-1k | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model... | [
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for th... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine... |
image-classification | transformers |
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | facebook/convnext-base-224-22k | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writt... | [
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has be... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was intro... |
image-classification | transformers |
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing C... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/convnext-base-224 | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
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|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writte... | [
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has bee... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introdu... |
image-classification | transformers |
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapo... | facebook/convnext-base-384-22k-1k | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model... | [
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for th... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on Ima... |
image-classification | transformers |
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing C... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/convnext-base-384 | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (base-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writte... | [
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has bee... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (base-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the ... |
image-classification | transformers |
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt)... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | facebook/convnext-large-224-22k-1k | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this mode... | [
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for t... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resoluti... |
image-classification | transformers |
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | facebook/convnext-large-224-22k | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writ... | [
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has b... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was in... |
image-classification | transformers |
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/convnext-large-224 | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writt... | [
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has be... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the... |
image-classification | transformers |
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt)... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapo... | facebook/convnext-large-384-22k-1k | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this mode... | [
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for t... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on Im... |
image-classification | transformers |
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/convnext-large-384 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writt... | [
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has be... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 384x384. It was intr... |
image-classification | transformers |
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/convnext-small-224 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (large-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writt... | [
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has be... | [
"TAGS\n#transformers #pytorch #tf #safetensors #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (large-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was intr... |
image-classification | transformers |
# ConvNeXT (tiny-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing C... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | facebook/convnext-tiny-224 | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXT (tiny-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writte... | [
"# ConvNeXT (tiny-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has bee... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (tiny-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introdu... |
image-classification | transformers |
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapo... | facebook/convnext-xlarge-224-22k-1k | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been writ... | [
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has b... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. I... |
image-classification | transformers |
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasin... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exampl... | facebook/convnext-xlarge-224-22k | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been wri... | [
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has ... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in ... |
image-classification | transformers |
# ConvNeXT (xlarge-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapo... | facebook/convnext-xlarge-384-22k-1k | null | [
"transformers",
"pytorch",
"tf",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXT (xlarge-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Disclaimer: The team releasing ConvNeXT did not write a model card for this mod... | [
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing ConvNeXT did not write a model card for ... | [
"TAGS\n#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ConvNeXT (xlarge-sized model) \n\nConvNeXT model pre-trained on ImageNet-22k and fine... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Base-100h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your spee... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/data2vec-audio-base-100h | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Base-100h
Facebook's Data2Vec
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael ... | [
"# Data2Vec-Audio-Base-100h\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao... | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Base-100h\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 100 hours of Librispeech on... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Base-10m
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your spee... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/data2vec-audio-base-10m | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Base-10m
Facebook's Data2Vec
The base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael ... | [
"# Data2Vec-Audio-Base-10m\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao... | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Base-10m\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 10 minutes of Librispeech on... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Base-960h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your spee... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingf... | facebook/data2vec-audio-base-960h | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Data2Vec-Audio-Base-960h
========================
Facebook's Data2Vec
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun... | [] | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# Data2Vec-Audio-Base
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: T... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/data2vec-audio-base | null | [
"transformers",
"pytorch",
"data2vec-audio",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Data2Vec-Audio-Base
Facebook's Data2Vec
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokeniz... | [
"# Data2Vec-Audio-Base\n\nFacebook's Data2Vec\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition,... | [
"TAGS\n#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Data2Vec-Audio-Base\n\nFacebook's Data2Vec\n\nThe base model pretrained on 16kHz sampled speech audio. When using the mod... |
feature-extraction | transformers |
# Data2Vec-Text base model
Pretrained model on English language using the *data2vec* objective. It was introduced in
[this paper](https://arxiv.org/abs/2202.03555) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/main/examples/data2vec). This model is case-sensitive: it
makes a differen... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | facebook/data2vec-text-base | null | [
"transformers",
"pytorch",
"data2vec-text",
"feature-extraction",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2202.03555",
"arxiv:1806.02847",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03555",
"1806.02847"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-text #feature-extraction #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2202.03555 #arxiv-1806.02847 #license-mit #endpoints_compatible #has_space #region-us
|
# Data2Vec-Text base model
Pretrained model on English language using the *data2vec* objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing Data2Vec-Text did not write a model ca... | [
"# Data2Vec-Text base model\n\nPretrained model on English language using the *data2vec* objective. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it\nmakes a difference between english and English.\n\nDisclaimer: The team releasing Data2Vec-Text did not write... | [
"TAGS\n#transformers #pytorch #data2vec-text #feature-extraction #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2202.03555 #arxiv-1806.02847 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Data2Vec-Text base model\n\nPretrained model on English language using the *data2vec* objective. ... |
image-classification | transformers |
# Distilled Data-efficient Image Transformer (base-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation thr... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | facebook/deit-base-distilled-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Distilled Data-efficient Image Transformer (base-sized model)
=============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Tr... | [
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appr... | [
"TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, Dei... |
image-classification | transformers |
# Distilled Data-efficient Image Transformer (base-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained at resolution 224x224 and fine-tuned at resolution 384x384 on ImageNet-1k (1 million images, 1,000 classes). It was first introduced in the paper [Training data-efficient image transform... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | facebook/deit-base-distilled-patch16-384 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Distilled Data-efficient Image Transformer (base-sized model)
=============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained at resolution 224x224 and fine-tuned at resolution 384x384 on ImageNet-1k (1 million images, 1,000 classes). It was first intr... | [
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appr... | [
"TAGS\n#transformers #pytorch #tf #safetensors #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into D... |
image-classification | transformers |
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](http... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet-1k"]} | facebook/deit-base-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Data-efficient Image Transformer (base-sized model)
===================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image tr... | [
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor ... | [
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTMo... |
image-classification | transformers |
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 384x384. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](htt... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet-1k"]} | facebook/deit-base-patch16-384 | null | [
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Data-efficient Image Transformer (base-sized model)
===================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 384x384. It was first introduced in the paper Training data-efficient image tr... | [
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor ... | [
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTF... |
image-classification | transformers |
# Distilled Data-efficient Image Transformer (small-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation th... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | facebook/deit-small-distilled-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Distilled Data-efficient Image Transformer (small-sized model)
==============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper ... | [
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appr... | [
"TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageCl... |
image-classification | transformers |
# Data-efficient Image Transformer (small-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](ht... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet-1k"]} | facebook/deit-small-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Data-efficient Image Transformer (small-sized model)
====================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image ... | [
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor ... | [
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet-1k #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTF... |
image-classification | transformers |
# Distilled Data-efficient Image Transformer (tiny-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation th... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | facebook/deit-tiny-distilled-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
"deit",
"image-classification",
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"dataset:imagenet",
"arxiv:2012.12877",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Distilled Data-efficient Image Transformer (tiny-sized model)
=============================================================
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Tr... | [
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appr... | [
"TAGS\n#transformers #pytorch #tf #deit #image-classification #vision #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a distilled ViT model, you can plug it into DeiTModel, Dei... |
image-classification | transformers |
# Data-efficient Image Transformer (tiny-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](htt... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | facebook/deit-tiny-patch16-224 | null | [
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.12877",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #vit #image-classification #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Data-efficient Image Transformer (tiny-sized model)
===================================================
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image tr... | [
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor ... | [
"TAGS\n#transformers #pytorch #tf #vit #image-classification #dataset-imagenet #arxiv-2012.12877 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nSince this model is a more efficiently trained ViT model, you can plug it into ViTModel... |
object-detection | transformers |
# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion... | {"license": "apache-2.0", "tags": ["object-detection"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "example_title": ... | facebook/detr-resnet-101-dc5 | null | [
"transformers",
"pytorch",
"safetensors",
"detr",
"object-detection",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.12872"
] | [] | TAGS
#transformers #pytorch #safetensors #detr #object-detection #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this r... | [
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in ... | [
"TAGS\n#transformers #pytorch #safetensors #detr #object-detection #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end o... |
image-segmentation | transformers |
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released ... | {"license": "apache-2.0", "tags": ["image-segmentation", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg", "example_title": "Dog & Cat"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/construction-site.jpg", ... | facebook/detr-resnet-101-panoptic | null | [
"transformers",
"pytorch",
"safetensors",
"detr",
"image-segmentation",
"vision",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.12872"
] | [] | TAGS
#transformers #pytorch #safetensors #detr #image-segmentation #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Disclaimer: Th... | [
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDiscla... | [
"TAGS\n#transformers #pytorch #safetensors #detr #image-segmentation #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 20... |
object-detection | transformers |
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first r... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | facebook/detr-resnet-101 | null | [
"transformers",
"pytorch",
"safetensors",
"detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.12872"
] | [] | TAGS
#transformers #pytorch #safetensors #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-101 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Discla... | [
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n... | [
"TAGS\n#transformers #pytorch #safetensors #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-101 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017... |
image-segmentation | transformers |
# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. a... | {"license": "apache-2.0", "tags": ["image-segmentation"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg", "example_title": "Dog & Cat"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/construction-site.jpg", "example_t... | facebook/detr-resnet-50-dc5-panoptic | null | [
"transformers",
"pytorch",
"safetensors",
"detr",
"image-segmentation",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.12872"
] | [] | TAGS
#transformers #pytorch #safetensors #detr #image-segmentation #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository... | [
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repo... | [
"TAGS\n#transformers #pytorch #safetensors #detr #image-segmentation #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end ... |
object-detection | transformers |
# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion ... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | facebook/detr-resnet-50-dc5 | null | [
"transformers",
"pytorch",
"safetensors",
"detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.12872"
] | [] | TAGS
#transformers #pytorch #safetensors #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this re... | [
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in t... | [
"TAGS\n#transformers #pytorch #safetensors #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone (dilated C5 stage)\n\nDEtection TRansformer (DETR) model trained end-t... |
image-segmentation | transformers |
# DETR (End-to-End Object Detection) model with ResNet-50 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released i... | {"license": "apache-2.0", "tags": ["image-segmentation", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "example_title": "Football Match"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg"... | facebook/detr-resnet-50-panoptic | null | [
"transformers",
"pytorch",
"detr",
"image-segmentation",
"vision",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.12872"
] | [] | TAGS
#transformers #pytorch #detr #image-segmentation #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-50 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Disclaimer: The... | [
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\nDisclai... | [
"TAGS\n#transformers #pytorch #detr #image-segmentation #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (1... |
object-detection | transformers |
# DETR (End-to-End Object Detection) model with ResNet-50 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first re... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | facebook/detr-resnet-50 | null | [
"transformers",
"pytorch",
"detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2005.12872",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.12872"
] | [] | TAGS
#transformers #pytorch #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DETR (End-to-End Object Detection) model with ResNet-50 backbone
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Disclai... | [
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. \n\... | [
"TAGS\n#transformers #pytorch #detr #object-detection #vision #dataset-coco #arxiv-2005.12872 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DETR (End-to-End Object Detection) model with ResNet-50 backbone\n\nDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detect... |
feature-extraction | transformers |
# Vision Transformer (base-sized model, patch size 16) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jég... | {"license": "apache-2.0", "tags": ["dino", "vision"], "datasets": ["imagenet-1k"]} | facebook/dino-vitb16 | null | [
"transformers",
"pytorch",
"tf",
"vit",
"feature-extraction",
"dino",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.14294",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.14294"
] | [] | TAGS
#transformers #pytorch #tf #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model, patch size 16) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski,... | [
"# Vision Transformer (base-sized model, patch size 16) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojan... | [
"TAGS\n#transformers #pytorch #tf #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model, patch size 16) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO ... |
feature-extraction | transformers |
# Vision Transformer (base-sized model, patch size 8) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégo... | {"license": "apache-2.0", "tags": ["dino", "vision"], "datasets": ["imagenet-1k"]} | facebook/dino-vitb8 | null | [
"transformers",
"pytorch",
"vit",
"feature-extraction",
"dino",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.14294",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.14294"
] | [] | TAGS
#transformers #pytorch #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model, patch size 8) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, ... | [
"# Vision Transformer (base-sized model, patch size 8) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojano... | [
"TAGS\n#transformers #pytorch #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model, patch size 8) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO metho... |
feature-extraction | transformers |
# Vision Transformer (small-sized model, patch size 16) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jé... | {"license": "apache-2.0", "tags": ["dino", "vision"], "datasets": ["imagenet-1k"]} | facebook/dino-vits16 | null | [
"transformers",
"pytorch",
"vit",
"feature-extraction",
"dino",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.14294",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.14294"
] | [] | TAGS
#transformers #pytorch #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision Transformer (small-sized model, patch size 16) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski... | [
"# Vision Transformer (small-sized model, patch size 16) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Boja... | [
"TAGS\n#transformers #pytorch #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (small-sized model, patch size 16) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO met... |
feature-extraction | transformers |
# Vision Transformer (small-sized model, patch size 8) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jég... | {"license": "apache-2.0", "tags": ["dino", "vision"], "datasets": ["imagenet-1k"]} | facebook/dino-vits8 | null | [
"transformers",
"pytorch",
"vit",
"feature-extraction",
"dino",
"vision",
"dataset:imagenet-1k",
"arxiv:2104.14294",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.14294"
] | [] | TAGS
#transformers #pytorch #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision Transformer (small-sized model, patch size 8) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski,... | [
"# Vision Transformer (small-sized model, patch size 8) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojan... | [
"TAGS\n#transformers #pytorch #vit #feature-extraction #dino #vision #dataset-imagenet-1k #arxiv-2104.14294 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (small-sized model, patch size 8) trained using DINO \n\nVision Transformer (ViT) model trained using the DINO meth... |
null | transformers |
# `dpr-ctx_encoder-multiset-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environment... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["dpr"], "datasets": ["nq_open"], "inference": false} | facebook/dpr-ctx_encoder-multiset-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1702.08734",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #dpr #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us
| 'dpr-ctx\_encoder-multiset-base'
================================
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Mod... | [
"#### Direct Use\n\n\n'dpr-ctx\\_encoder-multiset-base', 'dpr-question\\_encoder-multiset-base', and 'dpr-reader-multiset-base' can be used for the task of open-domain question answering.",
"#### Misuse and Out-of-scope Use\n\n\nThe model should not be used to intentionally create hostile or alienating environmen... | [
"TAGS\n#transformers #pytorch #tf #dpr #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us \n",
"#### Direct Use\n\n\n'dpr-ctx\\_encoder-multiset-base', 'dpr-question\\_encoder-multiset-base', and 'dpr-reader-multiset-base' can be used for the tas... |
null | transformers |
# `dpr-ctx_encoder-single-nq-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmen... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["dpr"], "datasets": ["nq_open"], "inference": false} | facebook/dpr-ctx_encoder-single-nq-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1702.08734",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #dpr #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us
| 'dpr-ctx\_encoder-single-nq-base'
=================================
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
M... | [
"#### Direct Use\n\n\n'dpr-ctx\\_encoder-single-nq-base', 'dpr-question-encoder-single-nq-base', and 'dpr-reader-single-nq-base' can be used for the task of open-domain question answering.",
"#### Misuse and Out-of-scope Use\n\n\nThe model should not be used to intentionally create hostile or alienating environme... | [
"TAGS\n#transformers #pytorch #tf #dpr #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us \n",
"#### Direct Use\n\n\n'dpr-ctx\\_encoder-single-nq-base', 'dpr-question-encoder-single-nq-base', and 'dpr-reader-single-nq-base' can be used for the ta... |
feature-extraction | transformers |
# `dpr-question_encoder-multiset-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Enviro... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["dpr"], "datasets": ["nq_open", "trivia_qa", "web_questions", "trec"], "inference": false} | facebook/dpr-question_encoder-multiset-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"feature-extraction",
"en",
"dataset:nq_open",
"dataset:trivia_qa",
"dataset:web_questions",
"dataset:trec",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1702.08734",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #dpr #feature-extraction #en #dataset-nq_open #dataset-trivia_qa #dataset-web_questions #dataset-trec #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us
| 'dpr-question\_encoder-multiset-base'
=====================================
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Aut... | [
"#### Direct Use\n\n\n'dpr-question\\_encoder-multiset-base', 'dpr-ctx\\_encoder-multiset-base', and 'dpr-reader-multiset-base' can be used for the task of open-domain question answering.",
"#### Misuse and Out-of-scope Use\n\n\nThe model should not be used to intentionally create hostile or alienating environmen... | [
"TAGS\n#transformers #pytorch #tf #dpr #feature-extraction #en #dataset-nq_open #dataset-trivia_qa #dataset-web_questions #dataset-trec #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us \n",
"#### Direct Use\n\n\n'dpr-question\\_encoder-multiset-base', 'dpr-ctx\\_en... |
feature-extraction | transformers |
# `dpr-question_encoder-single-nq-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Envir... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["dpr"], "datasets": ["nq_open"], "inference": false} | facebook/dpr-question_encoder-single-nq-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"feature-extraction",
"en",
"dataset:nq_open",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1702.08734",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #dpr #feature-extraction #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us
| 'dpr-question\_encoder-single-nq-base'
======================================
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card A... | [
"#### Direct Use\n\n\n'dpr-question\\_encoder-single-nq-base', 'dpr-ctx\\_encoder-single-nq-base', and 'dpr-reader-single-nq-base' can be used for the task of open-domain question answering.",
"#### Misuse and Out-of-scope Use\n\n\nThe model should not be used to intentionally create hostile or alienating environ... | [
"TAGS\n#transformers #pytorch #tf #dpr #feature-extraction #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us \n",
"#### Direct Use\n\n\n'dpr-question\\_encoder-single-nq-base', 'dpr-ctx\\_encoder-single-nq-base', and 'dpr-reader-single-nq-base' ... |
null | transformers |
# `dpr-reader-multiset-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Im... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["dpr"], "datasets": ["nq_open", "trivia_qa", "web_questions", "trec"], "inference": false} | facebook/dpr-reader-multiset-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"dataset:trivia_qa",
"dataset:web_questions",
"dataset:trec",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1702.08734",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #dpr #en #dataset-nq_open #dataset-trivia_qa #dataset-web_questions #dataset-trec #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #region-us
| 'dpr-reader-multiset-base'
==========================
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-... | [
"#### Direct Use\n\n\n'dpr-reader-multiset-base', 'dpr-question\\_encoder-multiset-base', and 'dpr-ctx\\_encoder-multiset-base' can be used for the task of open-domain question answering.",
"#### Misuse and Out-of-scope Use\n\n\nThe model should not be used to intentionally create hostile or alienating environmen... | [
"TAGS\n#transformers #pytorch #tf #dpr #en #dataset-nq_open #dataset-trivia_qa #dataset-web_questions #dataset-trec #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #region-us \n",
"#### Direct Use\n\n\n'dpr-reader-multiset-base', 'dpr-question\\_encoder-multiset-base', and 'dpr-ctx\\_e... |
null | transformers |
`dpr-reader-single-nq-base`
# Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Impa... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["dpr"], "datasets": ["nq_open"], "inference": false} | facebook/dpr-reader-single-nq-base | null | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1702.08734",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #dpr #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us
| 'dpr-reader-single-nq-base'
Table of Contents
=================
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
Model Descr... | [
"#### Direct Use\n\n\n'dpr-reader-single-nq-base', 'dpr-ctx\\_encoder-single-nq-base', and 'dpr-question\\_encoder-single-nq-base' can be used for the task of open-domain question answering.",
"#### Misuse and Out-of-scope Use\n\n\nThe model should not be used to intentionally create hostile or alienating environ... | [
"TAGS\n#transformers #pytorch #tf #dpr #en #dataset-nq_open #arxiv-2004.04906 #arxiv-1702.08734 #arxiv-1910.09700 #license-cc-by-nc-4.0 #has_space #region-us \n",
"#### Direct Use\n\n\n'dpr-reader-single-nq-base', 'dpr-ctx\\_encoder-single-nq-base', and 'dpr-question\\_encoder-single-nq-base' can be used for the ... |
fill-mask | transformers | This repository has been deprecated and will be deleted shortly. All ESM models have been moved to their official names to match their naming at the original FAIR repo. You can now find the ESM-1b model at [facebook/esm1b_t33_650M_UR50S](https://huggingface.co/facebook/esm1b_t33_650M_UR50S). | {} | facebook/esm-1b | null | [
"transformers",
"pytorch",
"safetensors",
"esm",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #esm #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| This repository has been deprecated and will be deleted shortly. All ESM models have been moved to their official names to match their naming at the original FAIR repo. You can now find the ESM-1b model at facebook/esm1b_t33_650M_UR50S. | [] | [
"TAGS\n#transformers #pytorch #safetensors #esm #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-to-speech | fairseq | # fastspeech2-en-200_speaker-cv4
[FastSpeech 2](https://arxiv.org/abs/2006.04558) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- English
- 200 male/female voices (random speaker when using the widget)
-... | {"language": "en", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech", "multi-speaker"], "datasets": ["common_voice"], "task": "text-to-speech", "widget": [{"text": "Hello, this is a test run.", "example_title": "Hello, this is a test run."}]} | facebook/fastspeech2-en-200_speaker-cv4 | null | [
"fairseq",
"audio",
"text-to-speech",
"multi-speaker",
"en",
"dataset:common_voice",
"arxiv:2006.04558",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.04558",
"2109.06912"
] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #multi-speaker #en #dataset-common_voice #arxiv-2006.04558 #arxiv-2109.06912 #has_space #region-us
| # fastspeech2-en-200_speaker-cv4
FastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):
- English
- 200 male/female voices (random speaker when using the widget)
- Trained on Common Voice v4
## Usage
See also fairseq S^2 example.
| [
"# fastspeech2-en-200_speaker-cv4\n\nFastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):\n- English\n- 200 male/female voices (random speaker when using the widget)\n- Trained on Common Voice v4",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #multi-speaker #en #dataset-common_voice #arxiv-2006.04558 #arxiv-2109.06912 #has_space #region-us \n",
"# fastspeech2-en-200_speaker-cv4\n\nFastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):\n- English\n- 200 male/female voices (random speaker when using the w... |
text-to-speech | fairseq | # fastspeech2-en-ljspeech
[FastSpeech 2](https://arxiv.org/abs/2006.04558) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- English
- Single-speaker female voice
- Trained on [LJSpeech](https://keithito.c... | {"language": "en", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["ljspeech"], "task": "text-to-speech", "widget": [{"text": "Hello, this is a test run.", "example_title": "Hello, this is a test run."}]} | facebook/fastspeech2-en-ljspeech | null | [
"fairseq",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:2006.04558",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.04558",
"2109.06912"
] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #en #dataset-ljspeech #arxiv-2006.04558 #arxiv-2109.06912 #has_space #region-us
| # fastspeech2-en-ljspeech
FastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):
- English
- Single-speaker female voice
- Trained on LJSpeech
## Usage
See also fairseq S^2 example.
| [
"# fastspeech2-en-ljspeech\n\nFastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):\n- English\n- Single-speaker female voice\n- Trained on LJSpeech",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #en #dataset-ljspeech #arxiv-2006.04558 #arxiv-2109.06912 #has_space #region-us \n",
"# fastspeech2-en-ljspeech\n\nFastSpeech 2 text-to-speech model from fairseq S^2 (paper/code):\n- English\n- Single-speaker female voice\n- Trained on LJSpeech",
"## Usage\n\n\n\nSee also ... |
feature-extraction | transformers |
# Hubert-Base
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This mo... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/hubert-base-ls960 | null | [
"transformers",
"pytorch",
"tf",
"hubert",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2106.07447",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.07447"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #hubert #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2106.07447 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Hubert-Base
Facebook's Hubert
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should ... | [
"# Hubert-Base \n\nFacebook's Hubert\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokeniz... | [
"TAGS\n#transformers #pytorch #tf #hubert #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2106.07447 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Hubert-Base \n\nFacebook's Hubert\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure ... |
feature-extraction | transformers |
# Hubert-Large
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This ... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["libri-light"]} | facebook/hubert-large-ll60k | null | [
"transformers",
"pytorch",
"tf",
"hubert",
"feature-extraction",
"speech",
"en",
"dataset:libri-light",
"arxiv:2106.07447",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.07447"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #hubert #feature-extraction #speech #en #dataset-libri-light #arxiv-2106.07447 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Hubert-Large
Facebook's Hubert
The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer shoul... | [
"# Hubert-Large \n\nFacebook's Hubert\n\nThe large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a token... | [
"TAGS\n#transformers #pytorch #tf #hubert #feature-extraction #speech #en #dataset-libri-light #arxiv-2106.07447 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Hubert-Large \n\nFacebook's Hubert\n\nThe large model pretrained on 16kHz sampled speech audio. When using the model make sure th... |
automatic-speech-recognition | transformers |
# Hubert-Large-Finetuned
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sa... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["libri-light", "librispeech_asr"], "model-index": [{"name": "hubert-large-ls960-ft", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recogni... | facebook/hubert-large-ls960-ft | null | [
"transformers",
"pytorch",
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"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
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"arxiv:2106.07447",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.07447"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #hubert #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-libri-light #dataset-librispeech_asr #arxiv-2106.07447 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Hubert-Large-Finetuned
Facebook's Hubert
The large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
The model is a fine-tuned version of hubert-large-ll60k.
Paper
Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hun... | [
"# Hubert-Large-Finetuned\n\nFacebook's Hubert\n\nThe large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nThe model is a fine-tuned version of hubert-large-ll60k.\n\nPaper\n\nAuthors: Wei-Ning Hsu, Benjamin ... | [
"TAGS\n#transformers #pytorch #tf #hubert #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-libri-light #dataset-librispeech_asr #arxiv-2106.07447 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Hubert-Large-Finetuned\n\nFacebook's Hubert\n\nThe lar... |
feature-extraction | transformers |
# Hubert-Extra-Large
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The extra large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["libri-light"]} | facebook/hubert-xlarge-ll60k | null | [
"transformers",
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"hubert",
"feature-extraction",
"speech",
"en",
"dataset:libri-light",
"arxiv:2106.07447",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.07447"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #hubert #feature-extraction #speech #en #dataset-libri-light #arxiv-2106.07447 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Hubert-Extra-Large
Facebook's Hubert
The extra large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Cla... | [
"# Hubert-Extra-Large \n\nFacebook's Hubert\n\nThe extra large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, In... | [
"TAGS\n#transformers #pytorch #tf #hubert #feature-extraction #speech #en #dataset-libri-light #arxiv-2106.07447 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Hubert-Extra-Large \n\nFacebook's Hubert\n\nThe extra large model pretrained on 16kHz sampled speech audio. When using the model ... |
automatic-speech-recognition | transformers |
# Hubert-Extra-Large-Finetuned
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The extra large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech inpu... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["libri-light", "librispeech_asr"], "model-index": [{"name": "hubert-large-ls960-ft", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recogni... | facebook/hubert-xlarge-ls960-ft | null | [
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"safetensors",
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"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
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"dataset:librispeech_asr",
"arxiv:2106.07447",
"license:apache-2.0",
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"endpoints_compatible",
"has_space",
"... | null | 2022-03-02T23:29:05+00:00 | [
"2106.07447"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #hubert #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-libri-light #dataset-librispeech_asr #arxiv-2106.07447 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Hubert-Extra-Large-Finetuned
Facebook's Hubert
The extra large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
The model is a fine-tuned version of hubert-xlarge-ll60k.
Paper
Authors: Wei-Ning Hsu, Benjamin B... | [
"# Hubert-Extra-Large-Finetuned\n\nFacebook's Hubert\n\nThe extra large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nThe model is a fine-tuned version of hubert-xlarge-ll60k.\n\nPaper\n\nAuthors: Wei-Ning H... | [
"TAGS\n#transformers #pytorch #tf #safetensors #hubert #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-libri-light #dataset-librispeech_asr #arxiv-2106.07447 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Hubert-Extra-Large-Finetuned\n\nFacebook'... |
null | null |
# <p align="center"> IC-GAN: Instance-Conditioned GAN </p>
Official Pytorch code of [Instance-Conditioned GAN](https://arxiv.org/abs/2109.05070) by Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.
## Generate images with ... | {"license": "cc-by-nc-4.0", "tags": ["image-generation", "conditional-image-generation", "generative-model"], "library": "pytorch"} | facebook/ic_gan | null | [
"image-generation",
"conditional-image-generation",
"generative-model",
"arxiv:2109.05070",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05070"
] | [] | TAGS
#image-generation #conditional-image-generation #generative-model #arxiv-2109.05070 #license-cc-by-nc-4.0 #region-us
| IC-GAN: Instance-Conditioned GAN
=================================
Official Pytorch code of Instance-Conditioned GAN by Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.
!IC-GAN results
Generate images with IC-GAN in a Colab Notebook
-----------------------------------------... | [
"#### Other backbones\n\n\nTo be able to run IC-GAN with other backbones, we provide some orientative steps:\n\n* Place the new backbone code in a new folder under 'ic\\_gan' ('ic\\_gan/new\\_backbone').\n* Modify the relevant piece of code in the GAN architecture to allow instance features as conditionings (for bo... | [
"TAGS\n#image-generation #conditional-image-generation #generative-model #arxiv-2109.05070 #license-cc-by-nc-4.0 #region-us \n",
"#### Other backbones\n\n\nTo be able to run IC-GAN with other backbones, we provide some orientative steps:\n\n* Place the new backbone code in a new folder under 'ic\\_gan' ('ic\\_gan... |
text2text-generation | transformers |
# M2M100 1.2B
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
The model that can... | {"language": ["multilingual", "af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn",... | facebook/m2m100_1.2B | null | [
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... | TAGS
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# M2M100 1.2B
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this paper and first released in this repository.
The model that can directly translate between the 9,900 directions of 100 languages.
To translate into a target language,... | [
"# M2M100 1.2B\n\nM2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.\nIt was introduced in this paper and first released in this repository.\n\nThe model that can directly translate between the 9,900 directions of 100 languages.\nTo translate into a target... | [
"TAGS\n#transformers #pytorch #rust #m2m_100 #text2text-generation #multilingual #af #am #ar #ast #az #ba #be #bg #bn #br #bs #ca #ceb #cs #cy #da #de #el #en #es #et #fa #ff #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #ht #hu #hy #id #ig #ilo #is #it #ja #jv #ka #kk #km #kn #ko #lb #lg #ln #lo #lt #lv #mg #mk #ml ... |
text2text-generation | transformers |
# M2M100 418M
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
The model that can... | {"language": ["multilingual", "af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn",... | facebook/m2m100_418M | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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... | TAGS
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# M2M100 418M
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this paper and first released in this repository.
The model that can directly translate between the 9,900 directions of 100 languages.
To translate into a target language,... | [
"# M2M100 418M\n\nM2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.\nIt was introduced in this paper and first released in this repository.\n\nThe model that can directly translate between the 9,900 directions of 100 languages.\nTo translate into a target... | [
"TAGS\n#transformers #pytorch #rust #m2m_100 #text2text-generation #multilingual #af #am #ar #ast #az #ba #be #bg #bn #br #bs #ca #ceb #cs #cy #da #de #el #en #es #et #fa #ff #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #ht #hu #hy #id #ig #ilo #is #it #ja #jv #ka #kk #km #kn #ko #lb #lg #ln #lo #lt #lv #mg #mk #ml ... |
image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresear... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | facebook/maskformer-swin-base-ade | null | [
"transformers",
"pytorch",
"maskformer",
"vision",
"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.06278"
] | [] | TAGS
#transformers #pytorch #maskformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2107.06278 #license-other #endpoints_compatible #has_space #region-us
|
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model card... | [
"# MaskFormer\n\nMaskFormer model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a mo... | [
"TAGS\n#transformers #pytorch #maskformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2107.06278 #license-other #endpoints_compatible #has_space #region-us \n",
"# MaskFormer\n\nMaskFormer model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the p... |
image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (base-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["coco"], "widget": [{"src": "http://images.cocodataset.org/val2017/000000039769.jpg", "example_title": "Cats"}, {"src": "http://images.cocodataset.org/val2017/000000039770.jpg", "example_title": "Castle"}]} | facebook/maskformer-swin-base-coco | null | [
"transformers",
"pytorch",
"maskformer",
"vision",
"image-segmentation",
"dataset:coco",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.06278"
] | [] | TAGS
#transformers #pytorch #maskformer #vision #image-segmentation #dataset-coco #arxiv-2107.06278 #license-other #endpoints_compatible #has_space #region-us
|
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (base-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model card f... | [
"# MaskFormer\n\nMaskFormer model trained on COCO panoptic segmentation (base-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a mode... | [
"TAGS\n#transformers #pytorch #maskformer #vision #image-segmentation #dataset-coco #arxiv-2107.06278 #license-other #endpoints_compatible #has_space #region-us \n",
"# MaskFormer\n\nMaskFormer model trained on COCO panoptic segmentation (base-sized version, Swin backbone). It was introduced in the paper Per-Pixe... |
image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresea... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | facebook/maskformer-swin-large-ade | null | [
"transformers",
"pytorch",
"maskformer",
"vision",
"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.06278"
] | [] | TAGS
#transformers #pytorch #maskformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2107.06278 #license-other #endpoints_compatible #has_space #region-us
|
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model car... | [
"# MaskFormer\n\nMaskFormer model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a m... | [
"TAGS\n#transformers #pytorch #maskformer #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2107.06278 #license-other #endpoints_compatible #has_space #region-us \n",
"# MaskFormer\n\nMaskFormer model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the ... |
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