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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 18:33:19
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11.7k
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timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-12 18:33:14
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masakhane/afrimt5_lug_en_news
|
masakhane
| 2022-09-24T15:06:37Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:10:05Z |
---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/afribyt5_en_lug_news
|
masakhane
| 2022-09-24T15:06:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:11:07Z |
---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/byt5_lug_en_news
|
masakhane
| 2022-09-24T15:06:35Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:12:34Z |
---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/byt5_en_lug_news
|
masakhane
| 2022-09-24T15:06:34Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:12:52Z |
---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_news
|
masakhane
| 2022-09-24T15:06:31Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:19:41Z |
---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/mbart50_en_lug_news
|
masakhane
| 2022-09-24T15:06:31Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:16:44Z |
---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_rel_news_ft
|
masakhane
| 2022-09-24T15:06:28Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:21:04Z |
---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_rel_ft
|
masakhane
| 2022-09-24T15:06:26Z | 99 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:22:09Z |
---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_rel_news_ft
|
masakhane
| 2022-09-24T15:06:26Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:21:22Z |
---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_rel
|
masakhane
| 2022-09-24T15:06:25Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T11:24:31Z |
---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/afrimt5_en_pcm_news
|
masakhane
| 2022-09-24T15:06:23Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"en",
"pcm",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-09T13:04:05Z |
---
language:
- en
- pcm
license: afl-3.0
---
|
masakhane/afrimt5_pcm_en_news
|
masakhane
| 2022-09-24T15:06:22Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"pcm",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-09T13:04:23Z |
---
language:
- pcm
- en
license: afl-3.0
---
|
masakhane/byt5_pcm_en_news
|
masakhane
| 2022-09-24T15:06:21Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"pcm",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T06:42:00Z |
---
language:
- pcm
- en
license: afl-3.0
---
|
masakhane/mbart50_en_pcm_news
|
masakhane
| 2022-09-24T15:06:19Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"pcm",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T08:53:42Z |
---
language:
- en
- pcm
license: afl-3.0
---
|
masakhane/mbart50_pcm_en_news
|
masakhane
| 2022-09-24T15:06:18Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"pcm",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T08:53:57Z |
---
language:
- pcm
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_pcm_rel_ft
|
masakhane
| 2022-09-24T15:06:14Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"pcm",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T08:58:06Z |
---
language:
- en
- pcm
license: afl-3.0
---
|
masakhane/m2m100_418M_pcm_en_rel_news_ft
|
masakhane
| 2022-09-24T15:06:13Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"pcm",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T08:57:44Z |
---
language:
- pcm
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_pcm_rel
|
masakhane
| 2022-09-24T15:06:12Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"pcm",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T08:59:06Z |
---
language:
- en
- pcm
license: afl-3.0
---
|
masakhane/afrimt5_yor_en_news
|
masakhane
| 2022-09-24T15:06:11Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"yor",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:11:28Z |
---
language:
- yor
- en
license: afl-3.0
---
|
masakhane/afribyt5_yor_en_news
|
masakhane
| 2022-09-24T15:06:10Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"yor",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:13:17Z |
---
language:
- yor
- en
license: afl-3.0
---
|
masakhane/afribyt5_en_yor_news
|
masakhane
| 2022-09-24T15:06:09Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"yor",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:13:36Z |
---
language:
- en
- yor
license: afl-3.0
---
|
masakhane/mbart50_en_yor_news
|
masakhane
| 2022-09-24T15:06:07Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"yor",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:15:22Z |
---
language:
- en
- yor
license: afl-3.0
---
|
masakhane/m2m100_418M_en_yor_rel_news
|
masakhane
| 2022-09-24T15:06:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"yor",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:20:26Z |
---
language:
- en
- yor
license: afl-3.0
---
|
masakhane/m2m100_418M_yor_en_rel_news_ft
|
masakhane
| 2022-09-24T15:06:03Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"yor",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:21:05Z |
---
language:
- yor
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_yor_rel_news_ft
|
masakhane
| 2022-09-24T15:06:03Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"yor",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:20:48Z |
---
language:
- en
- yor
license: afl-3.0
---
|
masakhane/m2m100_418M_yor_en_rel_ft
|
masakhane
| 2022-09-24T15:06:02Z | 104 | 1 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"yor",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:21:44Z |
---
language:
- yor
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_yor_en_rel
|
masakhane
| 2022-09-24T15:06:02Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"yor",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T12:22:03Z |
---
language:
- yor
- en
license: afl-3.0
---
|
masakhane/afrimt5_swa_en_news
|
masakhane
| 2022-09-24T15:06:00Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"swa",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T09:01:34Z |
---
language:
- swa
- en
license: afl-3.0
---
|
masakhane/afrimbart_swa_en_news
|
masakhane
| 2022-09-24T15:06:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"swa",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:09:23Z |
---
language:
- swa
- en
license: afl-3.0
---
|
masakhane/afrimbart_en_swa_news
|
masakhane
| 2022-09-24T15:05:59Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"swa",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:09:43Z |
---
language:
- en
- swa
license: afl-3.0
---
|
masakhane/afribyt5_en_swa_news
|
masakhane
| 2022-09-24T15:05:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"swa",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:10:18Z |
---
language:
- en
- swa
license: afl-3.0
---
|
masakhane/afribyt5_swa_en_news
|
masakhane
| 2022-09-24T15:05:58Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"swa",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:10:03Z |
---
language:
- swa
- en
license: afl-3.0
---
|
masakhane/byt5_swa_en_news
|
masakhane
| 2022-09-24T15:05:58Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"swa",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:10:57Z |
---
language:
- swa
- en
license: afl-3.0
---
|
masakhane/byt5_en_swa_news
|
masakhane
| 2022-09-24T15:05:57Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"swa",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:10:38Z |
---
language:
- en
- swa
license: afl-3.0
---
|
masakhane/mt5_en_swa_news
|
masakhane
| 2022-09-24T15:05:57Z | 99 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"en",
"swa",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:11:39Z |
---
language:
- en
- swa
license: afl-3.0
---
|
masakhane/m2m100_418M_en_swa_rel_news
|
masakhane
| 2022-09-24T15:05:54Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"swa",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:13:30Z |
---
language:
- en
- swa
license: afl-3.0
---
|
masakhane/m2m100_418M_swa_en_news
|
masakhane
| 2022-09-24T15:05:53Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"swa",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:12:54Z |
---
language:
- swa
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_swa_rel_ft
|
masakhane
| 2022-09-24T15:05:52Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"swa",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:14:29Z |
---
language:
- en
- swa
license: afl-3.0
---
|
masakhane/m2m100_418M_swa_en_rel_news_ft
|
masakhane
| 2022-09-24T15:05:51Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"swa",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:14:10Z |
---
language:
- swa
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_swa_en_rel
|
masakhane
| 2022-09-24T15:05:50Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"swa",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:15:07Z |
---
language:
- swa
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_swa_rel
|
masakhane
| 2022-09-24T15:05:50Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"swa",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T10:15:23Z |
---
language:
- en
- swa
license: afl-3.0
---
|
masakhane/afrimt5_en_tsn_news
|
masakhane
| 2022-09-24T15:05:49Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"en",
"tsn",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T13:48:47Z |
---
language:
- en
- tsn
license: afl-3.0
---
|
masakhane/afrimbart_tsn_en_news
|
masakhane
| 2022-09-24T15:05:48Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"tsn",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T13:49:34Z |
---
language:
- tsn
- en
license: afl-3.0
---
|
masakhane/afribyt5_tsn_en_news
|
masakhane
| 2022-09-24T15:05:47Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"tsn",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T13:52:20Z |
---
language:
- tsn
- en
license: afl-3.0
---
|
masakhane/byt5_tsn_en_news
|
masakhane
| 2022-09-24T15:05:47Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"tsn",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T13:53:15Z |
---
language:
- tsn
- en
license: afl-3.0
---
|
masakhane/afribyt5_en_tsn_news
|
masakhane
| 2022-09-24T15:05:46Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"tsn",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T13:52:35Z |
---
language:
- en
- tsn
license: afl-3.0
---
|
masakhane/mbart50_en_tsn_news
|
masakhane
| 2022-09-24T15:05:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"tsn",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:02:43Z |
---
language:
- en
- tsn
license: afl-3.0
---
|
masakhane/mbart50_tsn_en_news
|
masakhane
| 2022-09-24T15:05:44Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"tsn",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:02:58Z |
---
language:
- tsn
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_tsn_en_rel_news
|
masakhane
| 2022-09-24T15:05:42Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"tsn",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:23:28Z |
---
language:
- tsn
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_tsn_rel_news_ft
|
masakhane
| 2022-09-24T15:05:41Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"tsn",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:33:05Z |
---
language:
- en
- tsn
license: afl-3.0
---
|
masakhane/m2m100_418M_tsn_en_rel_ft
|
masakhane
| 2022-09-24T15:05:40Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"tsn",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:32:34Z |
---
language:
- tsn
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_tsn_rel_ft
|
masakhane
| 2022-09-24T15:05:40Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"tsn",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:32:08Z |
---
language:
- en
- tsn
license: afl-3.0
---
|
masakhane/m2m100_418M_en_tsn_rel
|
masakhane
| 2022-09-24T15:05:39Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"tsn",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:39:01Z |
---
language:
- en
- tsn
license: afl-3.0
---
|
masakhane/m2m100_418M_tsn_en_rel
|
masakhane
| 2022-09-24T15:05:39Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"tsn",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-10T14:38:31Z |
---
language:
- tsn
- en
license: afl-3.0
---
|
masakhane/afrimbart_en_twi_news
|
masakhane
| 2022-09-24T15:05:37Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"twi",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T08:53:52Z |
---
language:
- en
- twi
license: afl-3.0
---
|
masakhane/afrimt5_twi_en_news
|
masakhane
| 2022-09-24T15:05:37Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"twi",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T08:50:58Z |
---
language:
- twi
- en
license: afl-3.0
---
|
masakhane/afribyt5_twi_en_news
|
masakhane
| 2022-09-24T15:05:35Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"twi",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T08:56:34Z |
---
language:
- twi
- en
license: afl-3.0
---
|
masakhane/mt5_twi_en_news
|
masakhane
| 2022-09-24T15:05:33Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"twi",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:05:43Z |
---
language:
- twi
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_twi_en_news
|
masakhane
| 2022-09-24T15:05:31Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"twi",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:07:35Z |
---
language:
- twi
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_twi_rel_news
|
masakhane
| 2022-09-24T15:05:30Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"twi",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:10:15Z |
---
language:
- en
- twi
license: afl-3.0
---
|
masakhane/m2m100_418M_twi_en_rel_news_ft
|
masakhane
| 2022-09-24T15:05:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"twi",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:13:27Z |
---
language:
- twi
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_twi_en_rel_ft
|
masakhane
| 2022-09-24T15:05:27Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"twi",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:15:02Z |
---
language:
- twi
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_twi_en_rel
|
masakhane
| 2022-09-24T15:05:26Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"twi",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:17:38Z |
---
language:
- twi
- en
license: afl-3.0
---
|
masakhane/afrimbart_zul_en_news
|
masakhane
| 2022-09-24T15:05:22Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"zul",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T08:54:35Z |
---
language:
- zul
- en
license: afl-3.0
---
|
masakhane/afribyt5_en_zul_news
|
masakhane
| 2022-09-24T15:05:21Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"zul",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T08:57:13Z |
---
language:
- en
- zul
license: afl-3.0
---
|
masakhane/byt5_en_zul_news
|
masakhane
| 2022-09-24T15:05:20Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"zul",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:02:52Z |
---
language:
- en
- zul
license: afl-3.0
---
|
masakhane/mbart50_zul_en_news
|
masakhane
| 2022-09-24T15:05:19Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"zul",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:04:09Z |
---
language:
- zul
- en
license: afl-3.0
---
|
masakhane/mt5_zul_en_news
|
masakhane
| 2022-09-24T15:05:18Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"zul",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:06:24Z |
---
language:
- zul
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_zul_news
|
masakhane
| 2022-09-24T15:05:17Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"zul",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:08:08Z |
---
language:
- en
- zul
license: afl-3.0
---
|
masakhane/m2m100_418M_en_zul_rel_news
|
masakhane
| 2022-09-24T15:05:16Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"zul",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:09:23Z |
---
language:
- en
- zul
license: afl-3.0
---
|
masakhane/m2m100_418M_zul_en_news
|
masakhane
| 2022-09-24T15:05:16Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"zul",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:07:50Z |
---
language:
- zul
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_zul_en_rel_news
|
masakhane
| 2022-09-24T15:05:15Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"zul",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:09:44Z |
---
language:
- zul
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_zul_rel
|
masakhane
| 2022-09-24T15:05:12Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"zul",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-11T09:18:27Z |
---
language:
- en
- zul
license: afl-3.0
---
|
masakhane/m2m100_418M-FR-NEWS
|
masakhane
| 2022-09-24T15:05:11Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-02T22:22:31Z |
---
language: fr
license: afl-3.0
---
### Citation Information
```
@inproceedings{adelani-etal-2022-thousand,
title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
author = "Adelani, David and
Alabi, Jesujoba and
Fan, Angela and
Kreutzer, Julia and
Shen, Xiaoyu and
Reid, Machel and
Ruiter, Dana and
Klakow, Dietrich and
Nabende, Peter and
Chang, Ernie and
Gwadabe, Tajuddeen and
Sackey, Freshia and
Dossou, Bonaventure F. P. and
Emezue, Chris and
Leong, Colin and
Beukman, Michael and
Muhammad, Shamsuddeen and
Jarso, Guyo and
Yousuf, Oreen and
Niyongabo Rubungo, Andre and
Hacheme, Gilles and
Wairagala, Eric Peter and
Nasir, Muhammad Umair and
Ajibade, Benjamin and
Ajayi, Tunde and
Gitau, Yvonne and
Abbott, Jade and
Ahmed, Mohamed and
Ochieng, Millicent and
Aremu, Anuoluwapo and
Ogayo, Perez and
Mukiibi, Jonathan and
Ouoba Kabore, Fatoumata and
Kalipe, Godson and
Mbaye, Derguene and
Tapo, Allahsera Auguste and
Memdjokam Koagne, Victoire and
Munkoh-Buabeng, Edwin and
Wagner, Valencia and
Abdulmumin, Idris and
Awokoya, Ayodele and
Buzaaba, Happy and
Sibanda, Blessing and
Bukula, Andiswa and
Manthalu, Sam",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.223",
doi = "10.18653/v1/2022.naacl-main.223",
pages = "3053--3070",
abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.",
}
```
|
masakhane/m2m100_418M_en_amh_rel
|
masakhane
| 2022-09-24T15:05:10Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"amh",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-25T22:06:14Z |
---
language:
- en
- amh
license: cc-by-nc-4.0
---
|
masakhane/m2m100_418M_amh_en_rel
|
masakhane
| 2022-09-24T15:05:10Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"amh",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-25T22:05:47Z |
---
language:
- amh
- en
license: cc-by-nc-4.0
---
|
masakhane/m2m100_418M_en_xho_rel
|
masakhane
| 2022-09-24T15:05:06Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"xho",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-25T22:09:29Z |
---
language:
- en
- xho
license: cc-by-nc-4.0
---
|
CShorten/CORD-19-Title-Abstracts
|
CShorten
| 2022-09-24T14:43:13Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-11T19:30:32Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
dataset_tag: cord19
---
# CShorten/CORD-19-Title-Abstracts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('CShorten/CORD-19-Title-Abstracts')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=CShorten/CORD-19-Title-Abstracts)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5001 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
pranavkrishna/bert_amazon
|
pranavkrishna
| 2022-09-24T14:41:05Z | 86 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-24T14:40:15Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: pranavkrishna/bert_amazon
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pranavkrishna/bert_amazon
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.1854
- Validation Loss: 7.6542
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -981, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.1854 | 7.6542 | 0 |
### Framework versions
- Transformers 4.21.1
- TensorFlow 2.9.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
pere/pk-nb-t5x
|
pere
| 2022-09-24T14:38:59Z | 0 | 2 | null |
[
"region:us"
] | null | 2022-04-01T06:33:23Z |
Just a placeholder for a future model
|
sd-concepts-library/paolo-bonolis
|
sd-concepts-library
| 2022-09-24T14:36:08Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T13:56:26Z |
---
license: mit
---
### paolo bonolis on Stable Diffusion
This is the `<paolo-bonolis>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
gokuls/distilbert-emotion-intent
|
gokuls
| 2022-09-24T13:54:04Z | 123 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T13:44:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: distilbert-emotion-intent
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.937
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-emotion-intent
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1989
- Accuracy: 0.937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3939 | 1.0 | 1000 | 0.2123 | 0.9285 |
| 0.1539 | 2.0 | 2000 | 0.1635 | 0.936 |
| 0.1213 | 3.0 | 3000 | 0.1820 | 0.931 |
| 0.1016 | 4.0 | 4000 | 0.1989 | 0.937 |
| 0.0713 | 5.0 | 5000 | 0.2681 | 0.935 |
| 0.0462 | 6.0 | 6000 | 0.3034 | 0.9365 |
| 0.027 | 7.0 | 7000 | 0.3538 | 0.937 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Sebabrata/layoutlmv3-finetuned-cord_100
|
Sebabrata
| 2022-09-24T13:29:13Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-24T12:35:13Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.9385640266469282
- name: Recall
type: recall
value: 0.9491017964071856
- name: F1
type: f1
value: 0.9438034983252697
- name: Accuracy
type: accuracy
value: 0.9516129032258065
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2144
- Precision: 0.9386
- Recall: 0.9491
- F1: 0.9438
- Accuracy: 0.9516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0830 | 0.6854 | 0.7582 | 0.7200 | 0.7725 |
| 1.4266 | 3.12 | 500 | 0.5944 | 0.8379 | 0.8630 | 0.8503 | 0.8680 |
| 1.4266 | 4.69 | 750 | 0.3868 | 0.8828 | 0.9079 | 0.8952 | 0.9155 |
| 0.4084 | 6.25 | 1000 | 0.3146 | 0.9133 | 0.9304 | 0.9218 | 0.9338 |
| 0.4084 | 7.81 | 1250 | 0.2658 | 0.9240 | 0.9371 | 0.9305 | 0.9419 |
| 0.2139 | 9.38 | 1500 | 0.2432 | 0.9299 | 0.9439 | 0.9368 | 0.9474 |
| 0.2139 | 10.94 | 1750 | 0.2333 | 0.9291 | 0.9416 | 0.9353 | 0.9482 |
| 0.1478 | 12.5 | 2000 | 0.2098 | 0.9358 | 0.9491 | 0.9424 | 0.9529 |
| 0.1478 | 14.06 | 2250 | 0.2134 | 0.9379 | 0.9491 | 0.9435 | 0.9516 |
| 0.1124 | 15.62 | 2500 | 0.2144 | 0.9386 | 0.9491 | 0.9438 | 0.9516 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
RebekkaB/san_2409_1325
|
RebekkaB
| 2022-09-24T12:13:11Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T11:50:57Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: san_2409_1325
results: []
---
<!-- 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. -->
# san_2409_1325
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0992
- F1: 0.7727
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.91 | 5 | 1.9727 | 0.1939 |
| No log | 1.91 | 10 | 1.5642 | 0.3535 |
| No log | 2.91 | 15 | 1.2698 | 0.6818 |
| No log | 3.91 | 20 | 1.3642 | 0.6429 |
| No log | 4.91 | 25 | 1.3411 | 0.6818 |
| No log | 5.91 | 30 | 1.2627 | 0.6818 |
| No log | 6.91 | 35 | 1.1269 | 0.7727 |
| No log | 7.91 | 40 | 1.0719 | 0.7727 |
| No log | 8.91 | 45 | 1.0567 | 0.7727 |
| No log | 9.91 | 50 | 1.1256 | 0.7727 |
| No log | 10.91 | 55 | 0.7085 | 0.7727 |
| No log | 11.91 | 60 | 0.9290 | 0.7727 |
| No log | 12.91 | 65 | 1.0355 | 0.7727 |
| No log | 13.91 | 70 | 1.0866 | 0.7727 |
| No log | 14.91 | 75 | 1.0992 | 0.7727 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sd-concepts-library/dr-strange
|
sd-concepts-library
| 2022-09-24T12:11:20Z | 0 | 28 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T12:11:16Z |
---
license: mit
---
### <dr-strange> on Stable Diffusion
This is the `<dr-strange>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/yesdelete
|
sd-concepts-library
| 2022-09-24T09:46:05Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T09:46:01Z |
---
license: mit
---
### yesdelete on Stable Diffusion
This is the `<yesdelete>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
huggingtweets/cz_binance
|
huggingtweets
| 2022-09-24T09:16:00Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-05T21:10:34Z |
---
language: en
thumbnail: http://www.huggingtweets.com/cz_binance/1664010956441/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1572269909513478146/dfyw817W_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">CZ 🔶 Binance</div>
<div style="text-align: center; font-size: 14px;">@cz_binance</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from CZ 🔶 Binance.
| Data | CZ 🔶 Binance |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 149 |
| Short tweets | 473 |
| Tweets kept | 2624 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19171g9o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cz_binance's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ngvvhd8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ngvvhd8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/cz_binance')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/coop-himmelblau
|
sd-concepts-library
| 2022-09-24T09:06:36Z | 0 | 6 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T09:06:32Z |
---
license: mit
---
### coop himmelblau on Stable Diffusion
This is the `<coop himmelblau>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
aniketface/DialoGPT-product
|
aniketface
| 2022-09-24T09:05:12Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"convAI",
"conversational",
"facebook",
"en",
"dataset:blended_skill_talk",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-24T08:41:37Z |
---
language:
- en
thumbnail:
tags:
- convAI
- conversational
- facebook
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
|
mlyuya/ddpm-butterflies-128
|
mlyuya
| 2022-09-24T09:02:29Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-24T07:27:49Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/mlyuya/ddpm-butterflies-128/tensorboard?#scalars)
|
ScriptEdgeAI/MarathiSentiment-Bloom-560m
|
ScriptEdgeAI
| 2022-09-24T08:14:05Z | 102 | 5 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-classification",
"mr",
"Sentiment-Analysis",
"arxiv:2205.14728",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-23T07:25:34Z |
---
language:
- mr
tags:
- mr
- Sentiment-Analysis
license: cc-by-nc-4.0
widget:
- text: "मला तुम्ही आवडता. मी तुझ्यावर प्रेम करतो."
---
# Marathi-Bloom-560m is a Bloom fine-tuned model trained by ScriptEdge on MahaNLP tweets dataset from L3Cube-MahaNLP.
## Worked on by:
Trained by:
- Venkatesh Soni.
Assistance:
- Rayansh Srivastava.
Supervision:
- Akshay Ugale, Madhukar Alhat.
## Usage -
- It is intended for non-commercial usages.
## Model best metrics
| *Model* | *Data* | *Accuracy* |
|---------------------------------------------------|---------------------|-------------------|
| bigscience/bloom-560m | Validation | 34.7 |
| bigscience/bloom-560m | Test | **34.8** |
| ScriptEdgeAI/MarathiSentiment-Bloom-560m | Validation | 76.0 |
| ScriptEdgeAI/MarathiSentiment-Bloom-560m | Test | **77.0** |
Citation to L3CubePune by the dataset usage.
```
@article {joshi2022l3cube,
title= {L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library},
author= {Joshi, Raviraj},
journal= {arXiv preprint arXiv:2205.14728},
year= {2022}
}
```
|
huggingtweets/it_airmass
|
huggingtweets
| 2022-09-24T06:49:38Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-24T06:49:12Z |
---
language: en
thumbnail: http://www.huggingtweets.com/it_airmass/1664002173554/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529248676647944193/-N1UKgKg_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Airmass</div>
<div style="text-align: center; font-size: 14px;">@it_airmass</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Airmass.
| Data | Airmass |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 126 |
| Short tweets | 370 |
| Tweets kept | 2753 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2f99nys0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @it_airmass's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/nvbqf9p2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/nvbqf9p2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/it_airmass')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/inversebrah
|
huggingtweets
| 2022-09-24T06:29:34Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-28T20:05:27Z |
---
language: en
thumbnail: http://www.huggingtweets.com/inversebrah/1664000969650/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547362404061052928/WWnVS98w_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">smolting (wassie, verse)</div>
<div style="text-align: center; font-size: 14px;">@inversebrah</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from smolting (wassie, verse).
| Data | smolting (wassie, verse) |
| --- | --- |
| Tweets downloaded | 3217 |
| Retweets | 1592 |
| Short tweets | 865 |
| Tweets kept | 760 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mt8mw7j5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @inversebrah's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37fqg9kh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37fqg9kh/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/inversebrah')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/ransom
|
sd-concepts-library
| 2022-09-24T05:44:13Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T05:44:07Z |
---
license: mit
---
### ransom on Stable Diffusion
This is the `<ransom>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
nateraw/convnext-tiny-224-finetuned-eurosat-albumentations
|
nateraw
| 2022-09-24T01:57:26Z | 196 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-24T01:44:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned-eurosat-albumentations
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9814814814814815
---
<!-- 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. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0608
- Accuracy: 0.9815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1449 | 1.0 | 190 | 0.1327 | 0.9685 |
| 0.0766 | 2.0 | 380 | 0.0762 | 0.9774 |
| 0.0493 | 3.0 | 570 | 0.0608 | 0.9815 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
masakhane/m2m100_418M_xho_en_rel
|
masakhane
| 2022-09-24T00:22:27Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"xho",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-25T22:09:49Z |
---
language:
- xho
- en
license: cc-by-nc-4.0
---
|
neelmehta00/t5-small-finetuned-eli5-neel
|
neelmehta00
| 2022-09-23T23:44:40Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-23T22:36:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-finetuned-eli5-neel
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 9.613
---
<!-- 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. -->
# t5-small-finetuned-eli5-neel
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6887
- Rouge1: 9.613
- Rouge2: 1.7491
- Rougel: 8.8341
- Rougelsum: 9.3402
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.896 | 1.0 | 17040 | 3.6887 | 9.613 | 1.7491 | 8.8341 | 9.3402 | 19.0 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
carbon225/canine-s-wordseg-en
|
carbon225
| 2022-09-23T23:42:11Z | 98 | 1 |
transformers
|
[
"transformers",
"pytorch",
"canine",
"token-classification",
"en",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-22T03:58:10Z |
---
license: cc0-1.0
language: en
widget:
- text: "thismodelcanperformwordsegmentation"
- text: "sometimesitdoesntworkquitewell"
- text: "expertsexchange"
---
|
gokuls/bert-tiny-Massive-intent-KD-distilBERT
|
gokuls
| 2022-09-23T19:59:29Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:massive",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-23T19:36:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: bert-tiny-Massive-intent-KD-distilBERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.8396458435809149
---
<!-- 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-tiny-Massive-intent-KD-distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6612
- Accuracy: 0.8396
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 10.9795 | 1.0 | 720 | 9.3236 | 0.2917 |
| 9.4239 | 2.0 | 1440 | 7.9792 | 0.4092 |
| 8.2632 | 3.0 | 2160 | 6.9824 | 0.4811 |
| 7.3425 | 4.0 | 2880 | 6.1545 | 0.5514 |
| 6.56 | 5.0 | 3600 | 5.4829 | 0.6060 |
| 5.9032 | 6.0 | 4320 | 4.8994 | 0.6463 |
| 5.3078 | 7.0 | 5040 | 4.4129 | 0.6911 |
| 4.819 | 8.0 | 5760 | 4.0152 | 0.7073 |
| 4.3866 | 9.0 | 6480 | 3.6734 | 0.7324 |
| 3.9954 | 10.0 | 7200 | 3.3729 | 0.7516 |
| 3.6764 | 11.0 | 7920 | 3.1251 | 0.7600 |
| 3.3712 | 12.0 | 8640 | 2.9077 | 0.7752 |
| 3.1037 | 13.0 | 9360 | 2.7361 | 0.7787 |
| 2.8617 | 14.0 | 10080 | 2.5791 | 0.7860 |
| 2.6667 | 15.0 | 10800 | 2.4383 | 0.7944 |
| 2.476 | 16.0 | 11520 | 2.3301 | 0.7944 |
| 2.3203 | 17.0 | 12240 | 2.2099 | 0.8052 |
| 2.1698 | 18.0 | 12960 | 2.1351 | 0.8101 |
| 2.0563 | 19.0 | 13680 | 2.0554 | 0.8111 |
| 1.9294 | 20.0 | 14400 | 2.0100 | 0.8190 |
| 1.8304 | 21.0 | 15120 | 1.9566 | 0.8210 |
| 1.7315 | 22.0 | 15840 | 1.9076 | 0.8224 |
| 1.6587 | 23.0 | 16560 | 1.8511 | 0.8283 |
| 1.5876 | 24.0 | 17280 | 1.8230 | 0.8298 |
| 1.5173 | 25.0 | 18000 | 1.8002 | 0.8259 |
| 1.4676 | 26.0 | 18720 | 1.7667 | 0.8278 |
| 1.3956 | 27.0 | 19440 | 1.7512 | 0.8313 |
| 1.3436 | 28.0 | 20160 | 1.7233 | 0.8298 |
| 1.3031 | 29.0 | 20880 | 1.6802 | 0.8318 |
| 1.2584 | 30.0 | 21600 | 1.6768 | 0.8328 |
| 1.2233 | 31.0 | 22320 | 1.6612 | 0.8396 |
| 1.1884 | 32.0 | 23040 | 1.6608 | 0.8352 |
| 1.1374 | 33.0 | 23760 | 1.6195 | 0.8387 |
| 1.1299 | 34.0 | 24480 | 1.5969 | 0.8377 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
subtlegradient/distilbert-base-uncased-finetuned-cola
|
subtlegradient
| 2022-09-23T19:19:56Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-23T19:08:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5180
- eval_matthews_correlation: 0.4063
- eval_runtime: 0.8532
- eval_samples_per_second: 1222.419
- eval_steps_per_second: 77.353
- epoch: 1.0
- step: 535
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu116
- Datasets 2.5.1
- Tokenizers 0.12.1
|
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