librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-12 06:28:00	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 517 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-12 06:24:43	card stringlengths 11 1.01M
masakhane/mt5_pcm_en_news	masakhane	2022-09-24T15:06:19Z	104	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "pcm", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T08:53:01Z	--- language: - pcm - en 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_news	masakhane	2022-09-24T15:06:16Z	104	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "pcm", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T08:56:54Z	--- language: - en - pcm license: afl-3.0 ---
masakhane/m2m100_418M_en_pcm_rel_news_ft	masakhane	2022-09-24T15:06:15Z	106	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "pcm", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T08:57:27Z	--- language: - en - pcm license: afl-3.0 ---
masakhane/m2m100_418M_pcm_en_rel_ft	masakhane	2022-09-24T15:06:14Z	108	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "pcm", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T08:58:24Z	--- language: - pcm - en license: afl-3.0 ---
masakhane/m2m100_418M_pcm_en_rel	masakhane	2022-09-24T15:06:13Z	102	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "pcm", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T08:58:50Z	--- language: - pcm - en 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/afrimbart_yor_en_news	masakhane	2022-09-24T15:06:11Z	106	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "yor", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T12:11:53Z	--- 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/afrimbart_en_yor_news	masakhane	2022-09-24T15:06:10Z	111	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T12:12:10Z	--- language: - en - yor license: afl-3.0 ---
masakhane/byt5_en_yor_news	masakhane	2022-09-24T15:06:09Z	108	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T12:13:58Z	--- language: - en - yor 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/mt5_en_yor_news	masakhane	2022-09-24T15:06:07Z	108	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T12:14:50Z	--- language: - en - yor license: afl-3.0 ---
masakhane/mbart50_yor_en_news	masakhane	2022-09-24T15:06:06Z	95	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "yor", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T12:15:39Z	--- language: - yor - en license: afl-3.0 ---
masakhane/m2m100_418M_en_yor_news	masakhane	2022-09-24T15:06:05Z	108	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T12:19:30Z	--- language: - en - yor license: afl-3.0 ---
masakhane/m2m100_418M_yor_en_news	masakhane	2022-09-24T15:06:05Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "yor", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T12:19:46Z	--- language: - yor - en 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	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_en_swa_news	masakhane	2022-09-24T15:06:01Z	104	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "swa", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T09:01:16Z	--- language: - en - swa 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/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/mbart50_en_swa_news	masakhane	2022-09-24T15:05:56Z	119	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "swa", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T10:11:58Z	--- language: - en - swa license: afl-3.0 ---
masakhane/mbart50_swa_en_news	masakhane	2022-09-24T15:05:55Z	106	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "swa", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T10:12:16Z	--- language: - swa - en 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_rel_news	masakhane	2022-09-24T15:05:54Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "swa", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T10:13:13Z	--- language: - swa - en license: afl-3.0 ---
masakhane/m2m100_418M_en_swa_rel_news_ft	masakhane	2022-09-24T15:05:53Z	109	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "swa", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T10:13:52Z	--- 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_swa_en_rel_ft	masakhane	2022-09-24T15:05:52Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "swa", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T10:14:46Z	--- language: - swa - en 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_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/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/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/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/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/mt5_en_tsn_news	masakhane	2022-09-24T15:05:45Z	98	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "tsn", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T13:59:24Z	--- language: - en - tsn license: afl-3.0 ---
masakhane/mt5_tsn_en_news	masakhane	2022-09-24T15:05:44Z	108	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "tsn", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T13:59:09Z	--- language: - tsn - en license: afl-3.0 ---
masakhane/m2m100_418M_en_tsn_rel_news	masakhane	2022-09-24T15:05:42Z	107	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "tsn", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-10T14:23:45Z	--- language: - en - tsn 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	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/afrimt5_en_twi_news	masakhane	2022-09-24T15:05:38Z	106	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T08:50:40Z	--- 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/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/afrimbart_twi_en_news	masakhane	2022-09-24T15:05:36Z	118	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "twi", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T08:53:34Z	--- 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/byt5_en_twi_news	masakhane	2022-09-24T15:05:35Z	109	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:02:29Z	--- language: - en - twi license: afl-3.0 ---
masakhane/mbart50_en_twi_news	masakhane	2022-09-24T15:05:34Z	103	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:03:38Z	--- language: - en - twi license: afl-3.0 ---
masakhane/mt5_en_twi_news	masakhane	2022-09-24T15:05:32Z	103	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:06:00Z	--- language: - en - twi 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	masakhane	2022-09-24T15:05:30Z	106	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "twi", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:09:58Z	--- language: - twi - en license: afl-3.0 ---
masakhane/m2m100_418M_en_twi_rel_news_ft	masakhane	2022-09-24T15:05:29Z	103	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:12:51Z	--- language: - en - twi license: afl-3.0 ---
masakhane/m2m100_418M_en_twi_rel_ft	masakhane	2022-09-24T15:05:27Z	103	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:14:47Z	--- language: - en - twi 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/afrimt5_en_zul_news	masakhane	2022-09-24T15:05:24Z	84	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T08:52:03Z	--- language: - en - zul license: afl-3.0 ---
masakhane/afrimt5_zul_en_news	masakhane	2022-09-24T15:05:24Z	99	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T08:51:37Z	--- language: - zul - en license: afl-3.0 ---
masakhane/afribyt5_zul_en_news	masakhane	2022-09-24T15:05:21Z	106	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T08:57:29Z	--- language: - zul - en 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/byt5_zul_en_news	masakhane	2022-09-24T15:05:19Z	107	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:03:09Z	--- 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/mt5_en_zul_news	masakhane	2022-09-24T15:05:17Z	108	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:06:39Z	--- 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_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_rel	masakhane	2022-09-24T15:05:12Z	105	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-11T09:18:45Z	--- language: - zul - en 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-NEWS	masakhane	2022-09-24T15:05:11Z	109	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "dataset:masakhane/mafand", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-02T22:21:22Z	--- language: en license: afl-3.0 datasets: - masakhane/mafand --- ### 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_en_kin_rel	masakhane	2022-09-24T15:05:09Z	113	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "kin", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-25T22:07:12Z	--- language: - en - kin license: cc-by-nc-4.0 ---
masakhane/m2m100_418M_nya_en_rel	masakhane	2022-09-24T15:05:08Z	117	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "nya", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-25T22:08:07Z	--- language: - nya - en license: cc-by-nc-4.0 ---
masakhane/m2m100_418M_sna_en_rel	masakhane	2022-09-24T15:05:07Z	111	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "sna", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-25T22:09:06Z	--- language: - sna - 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 ---
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
sd-concepts-library/repeat	sd-concepts-library	2022-09-24T14:17:05Z	0	2	null	[ "license:mit", "region:us" ]	null	2022-09-24T14:16:59Z	--- license: mit --- ### REPEAT on Stable Diffusion This is the `<repeat>` 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`: ![<repeat> 0](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/3.jpeg) ![<repeat> 1](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/1.jpeg) ![<repeat> 2](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/0.jpeg) ![<repeat> 3](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/2.jpeg)
gokuls/BERT-tiny-emotion-intent	gokuls	2022-09-24T14:11:28Z	268	2	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T14:01:37Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: BERT-tiny-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: 0.91 --- <!-- 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-emotion-intent 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 emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3620 - Accuracy: 0.91 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.2603 \| 1.0 \| 1000 \| 0.7766 \| 0.7815 \| \| 0.5919 \| 2.0 \| 2000 \| 0.4117 \| 0.884 \| \| 0.367 \| 3.0 \| 3000 \| 0.3188 \| 0.8995 \| \| 0.2848 \| 4.0 \| 4000 \| 0.2928 \| 0.8985 \| \| 0.2395 \| 5.0 \| 5000 \| 0.2906 \| 0.898 \| \| 0.2094 \| 6.0 \| 6000 \| 0.2887 \| 0.907 \| \| 0.1884 \| 7.0 \| 7000 \| 0.2831 \| 0.9065 \| \| 0.1603 \| 8.0 \| 8000 \| 0.3044 \| 0.9065 \| \| 0.1519 \| 9.0 \| 9000 \| 0.3124 \| 0.9095 \| \| 0.1291 \| 10.0 \| 10000 \| 0.3256 \| 0.9065 \| \| 0.1179 \| 11.0 \| 11000 \| 0.3651 \| 0.9035 \| \| 0.1091 \| 12.0 \| 12000 \| 0.3620 \| 0.91 \| \| 0.0977 \| 13.0 \| 13000 \| 0.3992 \| 0.907 \| \| 0.0914 \| 14.0 \| 14000 \| 0.4285 \| 0.908 \| \| 0.0876 \| 15.0 \| 15000 \| 0.4268 \| 0.9055 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
gokuls/distilroberta-emotion-intent	gokuls	2022-09-24T13:36:17Z	105	1	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T13:26:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: distilroberta-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: 0.9435 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-emotion-intent This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1496 - Accuracy: 0.9435 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4501 \| 1.0 \| 1000 \| 0.2432 \| 0.924 \| \| 0.1947 \| 2.0 \| 2000 \| 0.1646 \| 0.934 \| \| 0.1497 \| 3.0 \| 3000 \| 0.1382 \| 0.9405 \| \| 0.1316 \| 4.0 \| 4000 \| 0.1496 \| 0.9435 \| \| 0.1145 \| 5.0 \| 5000 \| 0.1684 \| 0.9385 \| \| 0.1 \| 6.0 \| 6000 \| 0.2342 \| 0.943 \| \| 0.0828 \| 7.0 \| 7000 \| 0.2807 \| 0.939 \| ### 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/rlt_2409_1450	RebekkaB	2022-09-24T13:22:34Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T12:52:36Z	--- tags: - generated_from_trainer metrics: - f1 model-index: - name: rlt_2409_1450 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. --> # rlt_2409_1450 This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0518 - F1: 0.9826 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.99 \| 36 \| 0.5165 \| 0.8542 \| \| No log \| 1.99 \| 72 \| 0.1459 \| 0.9599 \| \| No log \| 2.99 \| 108 \| 0.0733 \| 0.9882 \| \| No log \| 3.99 \| 144 \| 0.1385 \| 0.9502 \| \| No log \| 4.99 \| 180 \| 0.0948 \| 0.9806 \| \| No log \| 5.99 \| 216 \| 0.0699 \| 0.9822 \| \| No log \| 6.99 \| 252 \| 0.0582 \| 0.9859 \| \| No log \| 7.99 \| 288 \| 0.0340 \| 0.9933 \| \| No log \| 8.99 \| 324 \| 0.0475 \| 0.9826 \| \| No log \| 9.99 \| 360 \| 0.0518 \| 0.9826 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
gokuls/bert-base-emotion-intent	gokuls	2022-09-24T13:18:17Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-24T13:05:47Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: bert-base-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: 0.9385 --- <!-- 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-emotion-intent This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1952 - Accuracy: 0.9385 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4058 \| 1.0 \| 1000 \| 0.2421 \| 0.9265 \| \| 0.1541 \| 2.0 \| 2000 \| 0.1952 \| 0.9385 \| \| 0.1279 \| 3.0 \| 3000 \| 0.1807 \| 0.9345 \| \| 0.1069 \| 4.0 \| 4000 \| 0.2292 \| 0.9365 \| \| 0.081 \| 5.0 \| 5000 \| 0.3315 \| 0.936 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
SaurabhKaushik/distilbert-base-uncased-finetuned-ner	SaurabhKaushik	2022-09-24T12:38:00Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-24T11:26:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9250386398763524 - name: Recall type: recall value: 0.9373531714956931 - name: F1 type: f1 value: 0.9311551925320887 - name: Accuracy type: accuracy value: 0.9839388692074285 --- <!-- 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-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0589 - Precision: 0.9250 - Recall: 0.9374 - F1: 0.9312 - Accuracy: 0.9839 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2343 \| 1.0 \| 878 \| 0.0674 \| 0.9177 \| 0.9233 \| 0.9205 \| 0.9818 \| \| 0.0525 \| 2.0 \| 1756 \| 0.0582 \| 0.9245 \| 0.9362 \| 0.9304 \| 0.9837 \| \| 0.0288 \| 3.0 \| 2634 \| 0.0589 \| 0.9250 \| 0.9374 \| 0.9312 \| 0.9839 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/hubris-oshri	sd-concepts-library	2022-09-24T12:35:06Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-24T12:35:02Z	--- license: mit --- ### Hubris-Oshri on Stable Diffusion This is the `<Hubris>` 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`: ![<Hubris> 0](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/3.jpeg) ![<Hubris> 1](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/1.jpeg) ![<Hubris> 2](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/4.jpeg) ![<Hubris> 3](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/0.jpeg) ![<Hubris> 4](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/2.jpeg)
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`: ![<dr-strange> 0](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/3.jpeg) ![<dr-strange> 1](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/1.jpeg) ![<dr-strange> 2](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/0.jpeg) ![<dr-strange> 3](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/2.jpeg)
sd-concepts-library/conway-pirate	sd-concepts-library	2022-09-24T10:44:50Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-24T10:44:44Z	--- license: mit --- ### Conway Pirate on Stable Diffusion This is the `<conway>` 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`: ![<conway> 0](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/3.jpeg) ![<conway> 1](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/1.jpeg) ![<conway> 2](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/4.jpeg) ![<conway> 3](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/0.jpeg) ![<conway> 4](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/2.jpeg)
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`: ![<yesdelete> 0](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/3.jpeg) ![<yesdelete> 1](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/1.jpeg) ![<yesdelete> 2](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/0.jpeg) ![<yesdelete> 3](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/2.jpeg)
huggingtweets/kingboiwabi	huggingtweets	2022-09-24T09:35:11Z	113	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T09:33:46Z	--- language: en thumbnail: http://www.huggingtweets.com/kingboiwabi/1664012106310/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/1381441602808385538/Sv6H8tsq_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">King Wabi The First</div> <div style="text-align: center; font-size: 14px;">@kingboiwabi</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 King Wabi The First. \| Data \| King Wabi The First \| \| --- \| --- \| \| Tweets downloaded \| 3240 \| \| Retweets \| 79 \| \| Short tweets \| 451 \| \| Tweets kept \| 2710 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lizz96v/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 @kingboiwabi's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1twunduv) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1twunduv/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/kingboiwabi') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](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`: ![<coop himmelblau> 0](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau> 1](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau> 2](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/4.jpeg) ![<coop himmelblau> 3](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/5.jpeg) ![<coop himmelblau> 4](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau> 5](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/2.jpeg)
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)
huggingtweets/beranewsnetwork	huggingtweets	2022-09-24T07:04:15Z	113	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T07:01:56Z	--- language: en thumbnail: http://www.huggingtweets.com/beranewsnetwork/1664003049616/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/1445950504102735872/bCnvrgeb_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">Bera News Network</div> <div style="text-align: center; font-size: 14px;">@beranewsnetwork</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Bera News Network. \| Data \| Bera News Network \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 1 \| \| Short tweets \| 579 \| \| Tweets kept \| 2670 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/254oa32x/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 @beranewsnetwork's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y/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/beranewsnetwork') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/marketsmeowmeow	huggingtweets	2022-09-24T06:43:25Z	111	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-24T06:42:56Z	--- language: en thumbnail: http://www.huggingtweets.com/marketsmeowmeow/1664001800470/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/1570418907575377921/1mTVqZQZ_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">RB</div> <div style="text-align: center; font-size: 14px;">@marketsmeowmeow</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 RB. \| Data \| RB \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 14 \| \| Short tweets \| 700 \| \| Tweets kept \| 2530 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a7yqyg23/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 @marketsmeowmeow's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87/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/marketsmeowmeow') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
sd-concepts-library/museum-by-coop-himmelblau	sd-concepts-library	2022-09-24T06:39:31Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-24T06:39:25Z	--- license: mit --- ### museum by coop himmelblau on Stable Diffusion This is the `<coop himmelblau museum>` 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`: ![<coop himmelblau museum> 0](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau museum> 1](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau museum> 2](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau museum> 3](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/2.jpeg)
BumblingOrange/GuraLv400	BumblingOrange	2022-09-24T05:56:03Z	0	10	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2022-09-24T04:58:13Z	--- license: bigscience-bloom-rail-1.0 --- Uses the Waifu Diffusion model as a base, linked here: https://huggingface.co/hakurei/waifu-diffusion Custom Dreambooth model based off of the likeness of Hololive Vtuber Gawr Gura. Dataset was 450 training images, and 900 regularization images. Trained for 3000 steps. To use the model, simply insert the name 'Gawr Gura' into your prompts.
sd-concepts-library/guttestreker	sd-concepts-library	2022-09-24T04:19:49Z	0	11	null	[ "license:mit", "region:us" ]	null	2022-09-24T04:19:26Z	--- license: mit --- ### guttestreker on Stable Diffusion This is the `<guttestreker>` 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`: ![<guttestreker> 0](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/9.jpeg) ![<guttestreker> 1](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/10.jpeg) ![<guttestreker> 2](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/3.jpeg) ![<guttestreker> 3](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/1.jpeg) ![<guttestreker> 4](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/4.jpeg) ![<guttestreker> 5](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/8.jpeg) ![<guttestreker> 6](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/11.jpeg) ![<guttestreker> 7](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/6.jpeg) ![<guttestreker> 8](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/5.jpeg) ![<guttestreker> 9](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/0.jpeg) ![<guttestreker> 10](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/2.jpeg) ![<guttestreker> 11](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/7.jpeg) ![<guttestreker> 12](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/12.jpeg)
sd-concepts-library/skyfalls	sd-concepts-library	2022-09-24T02:09:42Z	0	3	null	[ "license:mit", "region:us" ]	null	2022-09-19T05:48:35Z	--- license: mit --- ### SkyFalls on Stable Diffusion This is the `<SkyFalls>` 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`: ![<SkyFalls> 0](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/0.jpeg) ![<SkyFalls> 1](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/1.jpeg) ![<SkyFalls> 2](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/2.jpeg) ![<SkyFalls> 3](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/3.jpeg) ![<SkyFalls> 4](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/4.jpeg)
HumanCompatibleAI/ppo-AsteroidsNoFrameskip-v4	HumanCompatibleAI	2022-09-23T22:37:49Z	4	0	stable-baselines3	[ "stable-baselines3", "AsteroidsNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-09-23T22:35:00Z	--- library_name: stable-baselines3 tags: - AsteroidsNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 1666.00 +/- 472.83 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: AsteroidsNoFrameskip-v4 type: AsteroidsNoFrameskip-v4 --- # PPO Agent playing AsteroidsNoFrameskip-v4 This is a trained model of a PPO agent playing AsteroidsNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo ppo --env AsteroidsNoFrameskip-v4 -orga HumanCompatibleAI -f logs/ python enjoy.py --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.1'), ('ent_coef', 0.01), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('frame_stack', 4), ('learning_rate', 'lin_2.5e-4'), ('n_envs', 8), ('n_epochs', 4), ('n_steps', 128), ('n_timesteps', 10000000.0), ('policy', 'CnnPolicy'), ('vf_coef', 0.5), ('normalize', False)]) ```
philschmid/openai-whisper-endpoint	philschmid	2022-09-23T21:26:56Z	0	11	generic	[ "generic", "audio", "automatic-speech-recognition", "endpoints-template", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-23T20:27:44Z	--- license: mit tags: - audio - automatic-speech-recognition - endpoints-template library_name: generic inference: false --- # OpenAI [Whisper](https://github.com/openai/whisper) Inference Endpoint example > Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. For more information about the model, license and limitations check the original repository at [openai/whisper](https://github.com/openai/whisper). --- This repository implements a custom `handler` task for `automatic-speech-recognition` for 🤗 Inference Endpoints using OpenAIs new Whisper model. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/handler.py). There is also a [notebook](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py` ### Request The endpoint expects a binary audio file. Below is a cURL example and a Python example using the `requests` library. curl ```bash # load audio file wget https://cdn-media.huggingface.co/speech_samples/sample1.flac # run request curl --request POST \ --url https://{ENDPOINT}/ \ --header 'Content-Type: audio/x-flac' \ --header 'Authorization: Bearer {HF_TOKEN}' \ --data-binary '@sample1.flac' ``` Python ```python import json from typing import List import requests as r import base64 import mimetypes ENDPOINT_URL="" HF_TOKEN="" def predict(path_to_audio:str=None): # read audio file with open(path_to_audio, "rb") as i: b = i.read() # get mimetype content_type= mimetypes.guess_type(path_to_audio)[0] headers= { "Authorization": f"Bearer {HF_TOKEN}", "Content-Type": content_type } response = r.post(ENDPOINT_URL, headers=headers, data=b) return response.json() prediction = predict(path_to_audio="sample1.flac") prediction ``` expected output ```json {"text": " going along slushy country roads and speaking to damp audiences in draughty school rooms day after day for a fortnight. He'll have to put in an appearance at some place of worship on Sunday morning, and he can come to us immediately afterwards."} ```
affahrizain/roberta-base-finetuned-jigsaw-toxic	affahrizain	2022-09-23T21:10:22Z	113	1	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-29T03:46:08Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta-base-finetuned-jigsaw-toxic 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. --> # roberta-base-finetuned-jigsaw-toxic This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0859 - Accuracy: 0.9747 - F1: 0.9746 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.1179 \| 1.0 \| 2116 \| 0.0982 \| 0.9694 \| 0.9694 \| \| 0.0748 \| 2.0 \| 4232 \| 0.0859 \| 0.9747 \| 0.9746 \| \| 0.0582 \| 3.0 \| 6348 \| 0.0916 \| 0.9750 \| 0.9750 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
g30rv17ys/ddpm-geeve-cnv-1000-200ep	g30rv17ys	2022-09-23T19:10:42Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-23T15:29:54Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder 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-geeve-cnv-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` 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/geevegeorge/ddpm-geeve-cnv-1000-200ep/tensorboard?#scalars)

masakhane/mt5_pcm_en_news

masakhane

2022-09-24T15:06:19Z

104

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "pcm", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T08:53:01Z

--- language: - pcm - en 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_news

masakhane

2022-09-24T15:06:16Z

104

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "pcm", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T08:56:54Z

--- language: - en - pcm license: afl-3.0 ---

masakhane/m2m100_418M_en_pcm_rel_news_ft

masakhane

2022-09-24T15:06:15Z

106

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "pcm", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T08:57:27Z

--- language: - en - pcm license: afl-3.0 ---

masakhane/m2m100_418M_pcm_en_rel_ft

masakhane

2022-09-24T15:06:14Z

108

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "pcm", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T08:58:24Z

--- language: - pcm - en license: afl-3.0 ---

masakhane/m2m100_418M_pcm_en_rel

masakhane

2022-09-24T15:06:13Z

102

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "pcm", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T08:58:50Z

--- language: - pcm - en 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/afrimbart_yor_en_news

masakhane

2022-09-24T15:06:11Z

106

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "yor", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T12:11:53Z

--- 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/afrimbart_en_yor_news

masakhane

2022-09-24T15:06:10Z

111

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T12:12:10Z

--- language: - en - yor license: afl-3.0 ---

masakhane/byt5_en_yor_news

masakhane

2022-09-24T15:06:09Z

108

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T12:13:58Z

--- language: - en - yor 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/mt5_en_yor_news

masakhane

2022-09-24T15:06:07Z

108

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T12:14:50Z

--- language: - en - yor license: afl-3.0 ---

masakhane/mbart50_yor_en_news

masakhane

2022-09-24T15:06:06Z

95

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "yor", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T12:15:39Z

--- language: - yor - en license: afl-3.0 ---

masakhane/m2m100_418M_en_yor_news

masakhane

2022-09-24T15:06:05Z

108

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "yor", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T12:19:30Z

--- language: - en - yor license: afl-3.0 ---

masakhane/m2m100_418M_yor_en_news

masakhane

2022-09-24T15:06:05Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "yor", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T12:19:46Z

--- language: - yor - en 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

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_en_swa_news

masakhane

2022-09-24T15:06:01Z

104

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "swa", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T09:01:16Z

--- language: - en - swa 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/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/mbart50_en_swa_news

masakhane

2022-09-24T15:05:56Z

119

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "swa", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T10:11:58Z

--- language: - en - swa license: afl-3.0 ---

masakhane/mbart50_swa_en_news

masakhane

2022-09-24T15:05:55Z

106

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "swa", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T10:12:16Z

--- language: - swa - en 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_rel_news

masakhane

2022-09-24T15:05:54Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "swa", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T10:13:13Z

--- language: - swa - en license: afl-3.0 ---

masakhane/m2m100_418M_en_swa_rel_news_ft

masakhane

2022-09-24T15:05:53Z

109

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "swa", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T10:13:52Z

--- 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_swa_en_rel_ft

masakhane

2022-09-24T15:05:52Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "swa", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T10:14:46Z

--- language: - swa - en 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_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/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/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/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/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/mt5_en_tsn_news

masakhane

2022-09-24T15:05:45Z

98

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "tsn", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T13:59:24Z

--- language: - en - tsn license: afl-3.0 ---

masakhane/mt5_tsn_en_news

masakhane

2022-09-24T15:05:44Z

108

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "tsn", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T13:59:09Z

--- language: - tsn - en license: afl-3.0 ---

masakhane/m2m100_418M_en_tsn_rel_news

masakhane

2022-09-24T15:05:42Z

107

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "tsn", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-10T14:23:45Z

--- language: - en - tsn 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

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/afrimt5_en_twi_news

masakhane

2022-09-24T15:05:38Z

106

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T08:50:40Z

--- 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/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/afrimbart_twi_en_news

masakhane

2022-09-24T15:05:36Z

118

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "twi", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T08:53:34Z

--- 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/byt5_en_twi_news

masakhane

2022-09-24T15:05:35Z

109

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:02:29Z

--- language: - en - twi license: afl-3.0 ---

masakhane/mbart50_en_twi_news

masakhane

2022-09-24T15:05:34Z

103

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:03:38Z

--- language: - en - twi license: afl-3.0 ---

masakhane/mt5_en_twi_news

masakhane

2022-09-24T15:05:32Z

103

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:06:00Z

--- language: - en - twi 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

masakhane

2022-09-24T15:05:30Z

106

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "twi", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:09:58Z

--- language: - twi - en license: afl-3.0 ---

masakhane/m2m100_418M_en_twi_rel_news_ft

masakhane

2022-09-24T15:05:29Z

103

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:12:51Z

--- language: - en - twi license: afl-3.0 ---

masakhane/m2m100_418M_en_twi_rel_ft

masakhane

2022-09-24T15:05:27Z

103

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "twi", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:14:47Z

--- language: - en - twi 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/afrimt5_en_zul_news

masakhane

2022-09-24T15:05:24Z

84

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T08:52:03Z

--- language: - en - zul license: afl-3.0 ---

masakhane/afrimt5_zul_en_news

masakhane

2022-09-24T15:05:24Z

99

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T08:51:37Z

--- language: - zul - en license: afl-3.0 ---

masakhane/afribyt5_zul_en_news

masakhane

2022-09-24T15:05:21Z

106

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T08:57:29Z

--- language: - zul - en 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/byt5_zul_en_news

masakhane

2022-09-24T15:05:19Z

107

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:03:09Z

--- 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/mt5_en_zul_news

masakhane

2022-09-24T15:05:17Z

108

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "en", "zul", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:06:39Z

--- 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_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_rel

masakhane

2022-09-24T15:05:12Z

105

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "zul", "en", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-11T09:18:45Z

--- language: - zul - en 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-NEWS

masakhane

2022-09-24T15:05:11Z

109

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "dataset:masakhane/mafand", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-02T22:21:22Z

--- language: en license: afl-3.0 datasets: - masakhane/mafand --- ### 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_en_kin_rel

masakhane

2022-09-24T15:05:09Z

113

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "kin", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-25T22:07:12Z

--- language: - en - kin license: cc-by-nc-4.0 ---

masakhane/m2m100_418M_nya_en_rel

masakhane

2022-09-24T15:05:08Z

117

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "nya", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-25T22:08:07Z

--- language: - nya - en license: cc-by-nc-4.0 ---

masakhane/m2m100_418M_sna_en_rel

masakhane

2022-09-24T15:05:07Z

111

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "sna", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-25T22:09:06Z

--- language: - sna - 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 ---

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: [] ---  # 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

sd-concepts-library/repeat

sd-concepts-library

2022-09-24T14:17:05Z

0

2

null

[ "license:mit", "region:us" ]

null

2022-09-24T14:16:59Z

--- license: mit --- ### REPEAT on Stable Diffusion This is the `<repeat>` 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`: ![<repeat> 0](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/3.jpeg) ![<repeat> 1](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/1.jpeg) ![<repeat> 2](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/0.jpeg) ![<repeat> 3](https://huggingface.co/sd-concepts-library/repeat/resolve/main/concept_images/2.jpeg)

gokuls/BERT-tiny-emotion-intent

gokuls

2022-09-24T14:11:28Z

268

2

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T14:01:37Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: BERT-tiny-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: 0.91 ---  # BERT-tiny-emotion-intent 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 emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3620 - Accuracy: 0.91 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.2603 | 1.0 | 1000 | 0.7766 | 0.7815 | | 0.5919 | 2.0 | 2000 | 0.4117 | 0.884 | | 0.367 | 3.0 | 3000 | 0.3188 | 0.8995 | | 0.2848 | 4.0 | 4000 | 0.2928 | 0.8985 | | 0.2395 | 5.0 | 5000 | 0.2906 | 0.898 | | 0.2094 | 6.0 | 6000 | 0.2887 | 0.907 | | 0.1884 | 7.0 | 7000 | 0.2831 | 0.9065 | | 0.1603 | 8.0 | 8000 | 0.3044 | 0.9065 | | 0.1519 | 9.0 | 9000 | 0.3124 | 0.9095 | | 0.1291 | 10.0 | 10000 | 0.3256 | 0.9065 | | 0.1179 | 11.0 | 11000 | 0.3651 | 0.9035 | | 0.1091 | 12.0 | 12000 | 0.3620 | 0.91 | | 0.0977 | 13.0 | 13000 | 0.3992 | 0.907 | | 0.0914 | 14.0 | 14000 | 0.4285 | 0.908 | | 0.0876 | 15.0 | 15000 | 0.4268 | 0.9055 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

gokuls/distilroberta-emotion-intent

gokuls

2022-09-24T13:36:17Z

105

1

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T13:26:33Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: distilroberta-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: 0.9435 ---  # distilroberta-emotion-intent This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1496 - Accuracy: 0.9435 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4501 | 1.0 | 1000 | 0.2432 | 0.924 | | 0.1947 | 2.0 | 2000 | 0.1646 | 0.934 | | 0.1497 | 3.0 | 3000 | 0.1382 | 0.9405 | | 0.1316 | 4.0 | 4000 | 0.1496 | 0.9435 | | 0.1145 | 5.0 | 5000 | 0.1684 | 0.9385 | | 0.1 | 6.0 | 6000 | 0.2342 | 0.943 | | 0.0828 | 7.0 | 7000 | 0.2807 | 0.939 | ### 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 ---  # 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/rlt_2409_1450

RebekkaB

2022-09-24T13:22:34Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T12:52:36Z

--- tags: - generated_from_trainer metrics: - f1 model-index: - name: rlt_2409_1450 results: [] ---  # rlt_2409_1450 This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0518 - F1: 0.9826 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.99 | 36 | 0.5165 | 0.8542 | | No log | 1.99 | 72 | 0.1459 | 0.9599 | | No log | 2.99 | 108 | 0.0733 | 0.9882 | | No log | 3.99 | 144 | 0.1385 | 0.9502 | | No log | 4.99 | 180 | 0.0948 | 0.9806 | | No log | 5.99 | 216 | 0.0699 | 0.9822 | | No log | 6.99 | 252 | 0.0582 | 0.9859 | | No log | 7.99 | 288 | 0.0340 | 0.9933 | | No log | 8.99 | 324 | 0.0475 | 0.9826 | | No log | 9.99 | 360 | 0.0518 | 0.9826 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

gokuls/bert-base-emotion-intent

gokuls

2022-09-24T13:18:17Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-24T13:05:47Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: bert-base-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: 0.9385 ---  # bert-base-emotion-intent This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1952 - Accuracy: 0.9385 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4058 | 1.0 | 1000 | 0.2421 | 0.9265 | | 0.1541 | 2.0 | 2000 | 0.1952 | 0.9385 | | 0.1279 | 3.0 | 3000 | 0.1807 | 0.9345 | | 0.1069 | 4.0 | 4000 | 0.2292 | 0.9365 | | 0.081 | 5.0 | 5000 | 0.3315 | 0.936 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

SaurabhKaushik/distilbert-base-uncased-finetuned-ner

SaurabhKaushik

2022-09-24T12:38:00Z

105

0

transformers

[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-09-24T11:26:55Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9250386398763524 - name: Recall type: recall value: 0.9373531714956931 - name: F1 type: f1 value: 0.9311551925320887 - name: Accuracy type: accuracy value: 0.9839388692074285 ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0589 - Precision: 0.9250 - Recall: 0.9374 - F1: 0.9312 - Accuracy: 0.9839 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2343 | 1.0 | 878 | 0.0674 | 0.9177 | 0.9233 | 0.9205 | 0.9818 | | 0.0525 | 2.0 | 1756 | 0.0582 | 0.9245 | 0.9362 | 0.9304 | 0.9837 | | 0.0288 | 3.0 | 2634 | 0.0589 | 0.9250 | 0.9374 | 0.9312 | 0.9839 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.12.1

sd-concepts-library/hubris-oshri

sd-concepts-library

2022-09-24T12:35:06Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-24T12:35:02Z

--- license: mit --- ### Hubris-Oshri on Stable Diffusion This is the `<Hubris>` 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`: ![<Hubris> 0](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/3.jpeg) ![<Hubris> 1](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/1.jpeg) ![<Hubris> 2](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/4.jpeg) ![<Hubris> 3](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/0.jpeg) ![<Hubris> 4](https://huggingface.co/sd-concepts-library/hubris-oshri/resolve/main/concept_images/2.jpeg)

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: [] ---  # 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`: ![<dr-strange> 0](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/3.jpeg) ![<dr-strange> 1](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/1.jpeg) ![<dr-strange> 2](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/0.jpeg) ![<dr-strange> 3](https://huggingface.co/sd-concepts-library/dr-strange/resolve/main/concept_images/2.jpeg)

sd-concepts-library/conway-pirate

sd-concepts-library

2022-09-24T10:44:50Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-24T10:44:44Z

--- license: mit --- ### Conway Pirate on Stable Diffusion This is the `<conway>` 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`: ![<conway> 0](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/3.jpeg) ![<conway> 1](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/1.jpeg) ![<conway> 2](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/4.jpeg) ![<conway> 3](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/0.jpeg) ![<conway> 4](https://huggingface.co/sd-concepts-library/conway-pirate/resolve/main/concept_images/2.jpeg)

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`: ![<yesdelete> 0](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/3.jpeg) ![<yesdelete> 1](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/1.jpeg) ![<yesdelete> 2](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/0.jpeg) ![<yesdelete> 3](https://huggingface.co/sd-concepts-library/yesdelete/resolve/main/concept_images/2.jpeg)

huggingtweets/kingboiwabi

huggingtweets

2022-09-24T09:35:11Z

113

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T09:33:46Z

--- language: en thumbnail: http://www.huggingtweets.com/kingboiwabi/1664012106310/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/1381441602808385538/Sv6H8tsq_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">King Wabi The First</div> <div style="text-align: center; font-size: 14px;">@kingboiwabi</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 King Wabi The First. | Data | King Wabi The First | | --- | --- | | Tweets downloaded | 3240 | | Retweets | 79 | | Short tweets | 451 | | Tweets kept | 2710 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lizz96v/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 @kingboiwabi's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1twunduv) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1twunduv/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/kingboiwabi') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](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`: ![<coop himmelblau> 0](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau> 1](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau> 2](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/4.jpeg) ![<coop himmelblau> 3](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/5.jpeg) ![<coop himmelblau> 4](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau> 5](https://huggingface.co/sd-concepts-library/coop-himmelblau/resolve/main/concept_images/2.jpeg)

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: [] ---  # 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)

huggingtweets/beranewsnetwork

huggingtweets

2022-09-24T07:04:15Z

113

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T07:01:56Z

--- language: en thumbnail: http://www.huggingtweets.com/beranewsnetwork/1664003049616/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/1445950504102735872/bCnvrgeb_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">Bera News Network</div> <div style="text-align: center; font-size: 14px;">@beranewsnetwork</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Bera News Network. | Data | Bera News Network | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 1 | | Short tweets | 579 | | Tweets kept | 2670 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/254oa32x/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 @beranewsnetwork's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jqeuf1y/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/beranewsnetwork') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/marketsmeowmeow

huggingtweets

2022-09-24T06:43:25Z

111

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-24T06:42:56Z

--- language: en thumbnail: http://www.huggingtweets.com/marketsmeowmeow/1664001800470/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/1570418907575377921/1mTVqZQZ_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">RB</div> <div style="text-align: center; font-size: 14px;">@marketsmeowmeow</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 RB. | Data | RB | | --- | --- | | Tweets downloaded | 3244 | | Retweets | 14 | | Short tweets | 700 | | Tweets kept | 2530 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a7yqyg23/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 @marketsmeowmeow's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ou0r1v87/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/marketsmeowmeow') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

sd-concepts-library/museum-by-coop-himmelblau

sd-concepts-library

2022-09-24T06:39:31Z

0

null

[ "license:mit", "region:us" ]

null

2022-09-24T06:39:25Z

--- license: mit --- ### museum by coop himmelblau on Stable Diffusion This is the `<coop himmelblau museum>` 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`: ![<coop himmelblau museum> 0](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/3.jpeg) ![<coop himmelblau museum> 1](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/1.jpeg) ![<coop himmelblau museum> 2](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/0.jpeg) ![<coop himmelblau museum> 3](https://huggingface.co/sd-concepts-library/museum-by-coop-himmelblau/resolve/main/concept_images/2.jpeg)

BumblingOrange/GuraLv400

BumblingOrange

2022-09-24T05:56:03Z

0

10

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2022-09-24T04:58:13Z

--- license: bigscience-bloom-rail-1.0 --- Uses the Waifu Diffusion model as a base, linked here: https://huggingface.co/hakurei/waifu-diffusion Custom Dreambooth model based off of the likeness of Hololive Vtuber Gawr Gura. Dataset was 450 training images, and 900 regularization images. Trained for 3000 steps. To use the model, simply insert the name 'Gawr Gura' into your prompts.

sd-concepts-library/guttestreker

sd-concepts-library

2022-09-24T04:19:49Z

0

11

null

[ "license:mit", "region:us" ]

null

2022-09-24T04:19:26Z

--- license: mit --- ### guttestreker on Stable Diffusion This is the `<guttestreker>` 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`: ![<guttestreker> 0](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/9.jpeg) ![<guttestreker> 1](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/10.jpeg) ![<guttestreker> 2](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/3.jpeg) ![<guttestreker> 3](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/1.jpeg) ![<guttestreker> 4](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/4.jpeg) ![<guttestreker> 5](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/8.jpeg) ![<guttestreker> 6](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/11.jpeg) ![<guttestreker> 7](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/6.jpeg) ![<guttestreker> 8](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/5.jpeg) ![<guttestreker> 9](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/0.jpeg) ![<guttestreker> 10](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/2.jpeg) ![<guttestreker> 11](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/7.jpeg) ![<guttestreker> 12](https://huggingface.co/sd-concepts-library/guttestreker/resolve/main/concept_images/12.jpeg)

sd-concepts-library/skyfalls

sd-concepts-library

2022-09-24T02:09:42Z

0

3

null

[ "license:mit", "region:us" ]

null

2022-09-19T05:48:35Z

--- license: mit --- ### SkyFalls on Stable Diffusion This is the `<SkyFalls>` 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`: ![<SkyFalls> 0](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/0.jpeg) ![<SkyFalls> 1](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/1.jpeg) ![<SkyFalls> 2](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/2.jpeg) ![<SkyFalls> 3](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/3.jpeg) ![<SkyFalls> 4](https://huggingface.co/sd-concepts-library/skyfalls/resolve/main/concept_images/4.jpeg)

HumanCompatibleAI/ppo-AsteroidsNoFrameskip-v4

HumanCompatibleAI

2022-09-23T22:37:49Z

4

0

stable-baselines3

[ "stable-baselines3", "AsteroidsNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-09-23T22:35:00Z

--- library_name: stable-baselines3 tags: - AsteroidsNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 1666.00 +/- 472.83 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: AsteroidsNoFrameskip-v4 type: AsteroidsNoFrameskip-v4 --- # **PPO** Agent playing **AsteroidsNoFrameskip-v4** This is a trained model of a **PPO** agent playing **AsteroidsNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo ppo --env AsteroidsNoFrameskip-v4 -orga HumanCompatibleAI -f logs/ python enjoy.py --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.1'), ('ent_coef', 0.01), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('frame_stack', 4), ('learning_rate', 'lin_2.5e-4'), ('n_envs', 8), ('n_epochs', 4), ('n_steps', 128), ('n_timesteps', 10000000.0), ('policy', 'CnnPolicy'), ('vf_coef', 0.5), ('normalize', False)]) ```

philschmid/openai-whisper-endpoint

philschmid

2022-09-23T21:26:56Z

0

11

generic

[ "generic", "audio", "automatic-speech-recognition", "endpoints-template", "license:mit", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-09-23T20:27:44Z

--- license: mit tags: - audio - automatic-speech-recognition - endpoints-template library_name: generic inference: false --- # OpenAI [Whisper](https://github.com/openai/whisper) Inference Endpoint example > Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. For more information about the model, license and limitations check the original repository at [openai/whisper](https://github.com/openai/whisper). --- This repository implements a custom `handler` task for `automatic-speech-recognition` for 🤗 Inference Endpoints using OpenAIs new Whisper model. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/handler.py). There is also a [notebook](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py` ### Request The endpoint expects a binary audio file. Below is a cURL example and a Python example using the `requests` library. **curl** ```bash # load audio file wget https://cdn-media.huggingface.co/speech_samples/sample1.flac # run request curl --request POST \ --url https://{ENDPOINT}/ \ --header 'Content-Type: audio/x-flac' \ --header 'Authorization: Bearer {HF_TOKEN}' \ --data-binary '@sample1.flac' ``` **Python** ```python import json from typing import List import requests as r import base64 import mimetypes ENDPOINT_URL="" HF_TOKEN="" def predict(path_to_audio:str=None): # read audio file with open(path_to_audio, "rb") as i: b = i.read() # get mimetype content_type= mimetypes.guess_type(path_to_audio)[0] headers= { "Authorization": f"Bearer {HF_TOKEN}", "Content-Type": content_type } response = r.post(ENDPOINT_URL, headers=headers, data=b) return response.json() prediction = predict(path_to_audio="sample1.flac") prediction ``` expected output ```json {"text": " going along slushy country roads and speaking to damp audiences in draughty school rooms day after day for a fortnight. He'll have to put in an appearance at some place of worship on Sunday morning, and he can come to us immediately afterwards."} ```

affahrizain/roberta-base-finetuned-jigsaw-toxic

affahrizain

2022-09-23T21:10:22Z

113

1

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-07-29T03:46:08Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: roberta-base-finetuned-jigsaw-toxic results: [] ---  # roberta-base-finetuned-jigsaw-toxic This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0859 - Accuracy: 0.9747 - F1: 0.9746 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1179 | 1.0 | 2116 | 0.0982 | 0.9694 | 0.9694 | | 0.0748 | 2.0 | 4232 | 0.0859 | 0.9747 | 0.9746 | | 0.0582 | 3.0 | 6348 | 0.0916 | 0.9750 | 0.9750 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

g30rv17ys/ddpm-geeve-cnv-1000-200ep

g30rv17ys

2022-09-23T19:10:42Z

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-09-23T15:29:54Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-geeve-cnv-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` 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/geevegeorge/ddpm-geeve-cnv-1000-200ep/tensorboard?#scalars)