librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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sana-ngu/bart-base-finetuned-summarize-scientific-articles	sana-ngu	2023-05-12T20:07:11Z	101	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-12T19:30:00Z	# How to use ```python from transformers import pipeline summarizer = pipeline("summarization", model="sana-ngu/bart-base-finetuned-summarize-scientific-articles") article = "The novel Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus—2 (SARS-CoV-2), in Africa is characterised by a more substantial proportion of asymptomatic (or mildly symptomatic) individuals thought to be playing a role in the spread of the infection. The exact proportion and degree of infectiousness of asymptomatic individuals remains unclear. Studies however indicate that their management is crucial for control of SARS-CoV-2 transmission. We developed a simplified deterministic susceptible-exposed-infectious-removed (SEIR) mathematical model to assess the effect of active isolation of SARS-CoV-2 infected but asymptomatic individuals through blanket testing for control of the outbreak in Lusaka Province of Zambia. Here we modelled two scenarios; (1) assuming asymptomatic individuals comprised 70% of all COVID-19 cases and (2) asymptomatic individuals comprised only 50% of the cases. For contrast, the model was assessed first under the assumption that asymptomatic individuals are equally as infectious as symptomatic individuals and then secondly, and more likely, assuming asymptomatic individuals are only half as infectious as symptomatic individuals. For the model assuming 70% asymptomatic cases, a minimum sustained daily blanket testing rate of ≥ 7911 tests/100000 population was sufficient to control the outbreak if asymptomatic individuals are only half as infectious while if equal infectiousness was assumed then a testing rate of ≥ 10028 tests/ 100000 population would be required. For 50% asymptomatic, minimum blanket testing rates of ≥ 4540 tests/ 100000 population was sufficient to control the outbreak at both assumed levels of infectiousness for asymptomatic individuals relative to symptomatic individuals. Discussion and conclusion Our model predicts that active isolation of COVID-19 cases, including asymptomatic individuals, through blanket testing can be used as a possible measure for the control of the SARS-Cov-2 transmission in Lusaka, Zambia, but it would come at a high cost." summarizer(conversation) ```
sana-ngu/t5-large-finetuned-summarize-scientific-articles	sana-ngu	2023-05-12T20:05:19Z	94	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-12T19:21:03Z	# How to use ```python from transformers import pipeline summarizer = pipeline("summarization", model="sana-ngu/t5-large-finetuned-summarize-scientific-articles") article = "The novel Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus—2 (SARS-CoV-2), in Africa is characterised by a more substantial proportion of asymptomatic (or mildly symptomatic) individuals thought to be playing a role in the spread of the infection. The exact proportion and degree of infectiousness of asymptomatic individuals remains unclear. Studies however indicate that their management is crucial for control of SARS-CoV-2 transmission. We developed a simplified deterministic susceptible-exposed-infectious-removed (SEIR) mathematical model to assess the effect of active isolation of SARS-CoV-2 infected but asymptomatic individuals through blanket testing for control of the outbreak in Lusaka Province of Zambia. Here we modelled two scenarios; (1) assuming asymptomatic individuals comprised 70% of all COVID-19 cases and (2) asymptomatic individuals comprised only 50% of the cases. For contrast, the model was assessed first under the assumption that asymptomatic individuals are equally as infectious as symptomatic individuals and then secondly, and more likely, assuming asymptomatic individuals are only half as infectious as symptomatic individuals. For the model assuming 70% asymptomatic cases, a minimum sustained daily blanket testing rate of ≥ 7911 tests/100000 population was sufficient to control the outbreak if asymptomatic individuals are only half as infectious while if equal infectiousness was assumed then a testing rate of ≥ 10028 tests/ 100000 population would be required. For 50% asymptomatic, minimum blanket testing rates of ≥ 4540 tests/ 100000 population was sufficient to control the outbreak at both assumed levels of infectiousness for asymptomatic individuals relative to symptomatic individuals. Discussion and conclusion Our model predicts that active isolation of COVID-19 cases, including asymptomatic individuals, through blanket testing can be used as a possible measure for the control of the SARS-Cov-2 transmission in Lusaka, Zambia, but it would come at a high cost." summarizer(conversation) ```
sana-ngu/t5-small-finetuned-summarize-scientific-articles	sana-ngu	2023-05-12T20:02:49Z	10	2	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-12T19:14:02Z	# How to use ```python from transformers import pipeline summarizer = pipeline("summarization", model="sana-ngu/t5-small-finetuned-summarize-scientific-articles") article = "The novel Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus—2 (SARS-CoV-2), in Africa is characterised by a more substantial proportion of asymptomatic (or mildly symptomatic) individuals thought to be playing a role in the spread of the infection. The exact proportion and degree of infectiousness of asymptomatic individuals remains unclear. Studies however indicate that their management is crucial for control of SARS-CoV-2 transmission. We developed a simplified deterministic susceptible-exposed-infectious-removed (SEIR) mathematical model to assess the effect of active isolation of SARS-CoV-2 infected but asymptomatic individuals through blanket testing for control of the outbreak in Lusaka Province of Zambia. Here we modelled two scenarios; (1) assuming asymptomatic individuals comprised 70% of all COVID-19 cases and (2) asymptomatic individuals comprised only 50% of the cases. For contrast, the model was assessed first under the assumption that asymptomatic individuals are equally as infectious as symptomatic individuals and then secondly, and more likely, assuming asymptomatic individuals are only half as infectious as symptomatic individuals. For the model assuming 70% asymptomatic cases, a minimum sustained daily blanket testing rate of ≥ 7911 tests/100000 population was sufficient to control the outbreak if asymptomatic individuals are only half as infectious while if equal infectiousness was assumed then a testing rate of ≥ 10028 tests/ 100000 population would be required. For 50% asymptomatic, minimum blanket testing rates of ≥ 4540 tests/ 100000 population was sufficient to control the outbreak at both assumed levels of infectiousness for asymptomatic individuals relative to symptomatic individuals. Discussion and conclusion Our model predicts that active isolation of COVID-19 cases, including asymptomatic individuals, through blanket testing can be used as a possible measure for the control of the SARS-Cov-2 transmission in Lusaka, Zambia, but it would come at a high cost." summarizer(conversation) ```
hyoni/kogpt2-base-v2-finetuned-klue-ner	hyoni	2023-05-12T19:55:52Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "token-classification", "generated_from_trainer", "dataset:klue", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2023-05-12T19:42:52Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - klue metrics: - f1 model-index: - name: kogpt2-base-v2-finetuned-klue-ner results: - task: name: Token Classification type: token-classification dataset: name: klue type: klue config: ner split: validation args: ner metrics: - name: F1 type: f1 value: 0.37298165525403665 --- <!-- 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. --> # kogpt2-base-v2-finetuned-klue-ner This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.4076 - F1: 0.3730 ## 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: 24 - eval_batch_size: 24 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.6084 \| 1.0 \| 876 \| 0.5353 \| 0.2118 \| \| 0.3911 \| 2.0 \| 1752 \| 0.4691 \| 0.3041 \| \| 0.2855 \| 3.0 \| 2628 \| 0.4076 \| 0.3730 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Multi-Domain-Expert-Learning/merge-arxiv-freelaw-pubmed	Multi-Domain-Expert-Learning	2023-05-12T19:13:01Z	161	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "MDEL", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-12T19:10:40Z	--- tags: - MDEL --- # Model Name Multi-Domain-Expert-Layers/merge-arxiv-freelaw-pubmed # Model Description This model was generated by averaging the weights of the following models - [Multi-Domain-Expert-Layers/expert-pubmed_central](https://huggingface.co/Multi-Domain-Expert-Layers/expert-pubmed_central) - [Multi-Domain-Expert-Layers/expert-freelaw](https://huggingface.co/Multi-Domain-Expert-Layers/expert-freelaw) - [Multi-Domain-Expert-Layers/expert-arxiv](https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv)
guillaumekln/faster-whisper-large-v1	guillaumekln	2023-05-12T18:58:39Z	327	3	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]	automatic-speech-recognition	2023-03-28T12:33:41Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper large-v1 model for CTranslate2 This repository contains the conversion of [openai/whisper-large](https://huggingface.co/openai/whisper-large) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v1") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-large --output_dir faster-whisper-large-v1 \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-large).
guillaumekln/faster-whisper-large-v2	guillaumekln	2023-05-12T18:58:25Z	161,400	193	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]	automatic-speech-recognition	2023-03-23T10:36:06Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper large-v2 model for CTranslate2 This repository contains the conversion of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-large-v2 --output_dir faster-whisper-large-v2 \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-large-v2).
guillaumekln/faster-whisper-medium	guillaumekln	2023-05-12T18:58:10Z	15,872	32	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]	automatic-speech-recognition	2023-03-23T10:28:55Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper medium model for CTranslate2 This repository contains the conversion of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("medium") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-medium --output_dir faster-whisper-medium \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-medium).
guillaumekln/faster-whisper-small.en	guillaumekln	2023-05-12T18:57:44Z	110	1	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "license:mit", "region:us" ]	automatic-speech-recognition	2023-03-23T10:20:17Z	--- language: - en tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper small.en model for CTranslate2 This repository contains the conversion of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("small.en") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-small.en --output_dir faster-whisper-small.en \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-small.en).
seviladiguzel/roberta-base-finetuned-squad_roberta	seviladiguzel	2023-05-12T18:57:33Z	18	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T17:52:56Z	--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base-finetuned-squad_roberta 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-squad_roberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.8537 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.8917 \| 1.0 \| 5536 \| 0.8669 \| \| 0.6739 \| 2.0 \| 11072 \| 0.8537 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
guillaumekln/faster-whisper-base	guillaumekln	2023-05-12T18:57:32Z	110,649	9	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]	automatic-speech-recognition	2023-03-23T10:19:37Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper base model for CTranslate2 This repository contains the conversion of [openai/whisper-base](https://huggingface.co/openai/whisper-base) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("base") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-base --output_dir faster-whisper-base \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-base).
guillaumekln/faster-whisper-tiny	guillaumekln	2023-05-12T18:57:08Z	2,644	5	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]	automatic-speech-recognition	2023-03-23T10:14:28Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper tiny model for CTranslate2 This repository contains the conversion of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("tiny") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-tiny --output_dir faster-whisper-tiny \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-tiny).
guillaumekln/faster-whisper-tiny.en	guillaumekln	2023-05-12T18:56:53Z	378	2	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "license:mit", "region:us" ]	automatic-speech-recognition	2023-03-23T10:17:41Z	--- language: - en tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper tiny.en model for CTranslate2 This repository contains the conversion of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("tiny.en") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-tiny.en --output_dir faster-whisper-tiny.en \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-tiny.en).
Ktang2k/ppo-Huggy-1	Ktang2k	2023-05-12T18:54:20Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-05-12T18:54:13Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: Ktang2k/ppo-Huggy-1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
moisesmota/donut-base-sroie	moisesmota	2023-05-12T18:52:23Z	1	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-04-28T13:38:18Z	--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie 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. --> # donut-base-sroie This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.2
jainr3/sd-diffusiondb-pixelart-model-lora	jainr3	2023-05-12T18:51:50Z	3	2	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-05-12T18:39:45Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - jainr3/sd-diffusiondb-pixelart-model-lora These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the jainr3/diffusiondb-pixelart dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
Abdeldjalil21/djalil-base-sentiment-model-10k-samples	Abdeldjalil21	2023-05-12T18:29:32Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-09T23:59:57Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: djalil-base-sentiment-model-10k-samples 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. --> # djalil-base-sentiment-model-10k-samples This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4204 - Accuracy: 0.827 - F1: 0.8171 ## 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: 2 ### Training results ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
stillerman/MDEL-github-arxiv	stillerman	2023-05-12T18:27:15Z	4	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "MDEL", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-12T18:24:42Z	--- tags: - MDEL --- # Model Name stillerman/MDEL-github-arxiv # Model Description This model was generated by averaging the weights of the following models - [Multi-Domain-Expert-Layers/expert-github](https://huggingface.co/Multi-Domain-Expert-Layers/expert-github) - [Multi-Domain-Expert-Layers/expert-arxiv](https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv)
Guilherme34/Jennifer-lora-7bv3	Guilherme34	2023-05-12T18:25:36Z	0	1	null	[ "tensorboard", "pt", "region:us" ]	null	2023-05-12T17:32:20Z	--- language: - pt --- Esta é a terceira versão de uma inteligência artificial finetunada que fala em português do brasil, ela foi treinada em cima do llama 7b de decapoda, e foi treinada no LLaMA-LoRA Tuner de zetavg utilizando o dataset da cabrita lora e o alpaca cleaned Divirta-se!
stillerman/MDEL-pubmed-feelaw-github-arxiv	stillerman	2023-05-12T18:18:39Z	161	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "MDEL", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-12T18:11:40Z	--- tags: - MDEL --- # Model Name stillerman/MDEL-pubmed-feelaw-github-arxiv # Model Description This model was generated by averaging the weights of the following models - [Multi-Domain-Expert-Layers/expert-pubmed_central](https://huggingface.co/Multi-Domain-Expert-Layers/expert-pubmed_central) - [Multi-Domain-Expert-Layers/expert-freelaw](https://huggingface.co/Multi-Domain-Expert-Layers/expert-freelaw) - [Multi-Domain-Expert-Layers/expert-github](https://huggingface.co/Multi-Domain-Expert-Layers/expert-github) - [Multi-Domain-Expert-Layers/expert-arxiv](https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv)
aloutzidis/pegasus-large-cnn-quantized	aloutzidis	2023-05-12T18:08:53Z	4	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "en", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-11T20:55:16Z	--- tags: - generated_from_trainer model-index: - name: pegasus-large-cnn-quantized results: [] license: apache-2.0 datasets: - cnn_dailymail metrics: - bleu - rouge language: - en --- <!-- 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. --> # pegasus-large-cnn-quantized This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on CNN/DailyMail dataset. ![model image](./cover.jpeg) ## Model description Online news reading has become one of the most popular ways to consume the latest news. News aggregation websites such as Google News and Yahoo News have made it easy for users to find the latest news and provide thousands of news stories from hundreds of news publishers. As people have limited time, reading all news articles is not feasible. The success of zero-shot and few-shot prompting with models like GPT-3 has led to a paradigm shift in NLP. So, in the era of ChatGPT, we we conducted experiments using various large language models (LLMs) such as BART and PEGASUS to improve the quality and coherence of the generated news summaries. This is a quantized model finetuned on the CNN/DailyMail dataset. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Framework versions - Transformers 4.29.0 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
madhav-devrev/flan-t5-small-work-filters	madhav-devrev	2023-05-12T18:03:58Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-10T17:53:30Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: flan-t5-small-work-filters 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. --> # flan-t5-small-work-filters This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0346 - eval_rouge1: 46.2714 - eval_rouge2: 36.488 - eval_rougeL: 45.9709 - eval_rougeLsum: 45.9674 - eval_gen_len: 18.9753 - eval_runtime: 44.1148 - eval_samples_per_second: 23.87 - eval_steps_per_second: 2.992 - epoch: 5.0 - step: 660 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
millionhz/segformer-b0-finetuned-flame	millionhz	2023-05-12T17:55:49Z	8	0	transformers	[ "transformers", "pytorch", "safetensors", "segformer", "vision", "image-segmentation", "license:mit", "endpoints_compatible", "region:us" ]	image-segmentation	2023-05-12T14:59:06Z	--- license: mit tags: - vision - image-segmentation --- # SegFormer (b0-sized) model fine-tuned on FLAME The model was trained for a deep learning project titled [Forest Fire Detection](https://github.com/millionhz/forest-fire-detection). ## Model Description The model is intended to be used for fire detection through image segmentation. The provided pretrained model was finetuned on the [FLAME](https://dx.doi.org/10.21227/qad6-r683) dataset for 3 epochs with a learning rate of 1e-3 and was able to score an IOU score of 0.745 on the test examples. # How to use Here is how to use this model to segment an image: ```python from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation from PIL import Image import requests processor = AutoFeatureExtractor.from_pretrained("millionhz/segformer-b0-finetuned-flame") model = SegformerForSemanticSegmentation.from_pretrained("millionhz/segformer-b0-finetuned-flame") url = <add url here> image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) ``` ## License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).
eason0203/swin-tiny-patch4-window7-224-arty-bg-classifier	eason0203	2023-05-12T17:30:14Z	187	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-12T07:42:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: swin-tiny-patch4-window7-224-arty-bg-classifier 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. --> # swin-tiny-patch4-window7-224-arty-bg-classifier This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0086 - eval_accuracy: 0.9975 - eval_runtime: 102.2105 - eval_samples_per_second: 127.639 - eval_steps_per_second: 1.331 - epoch: 1.84 - step: 250 ## 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: 96 - eval_batch_size: 96 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 384 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Halcyonindo/an1kulora	Halcyonindo	2023-05-12T17:27:24Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T17:26:20Z	--- license: creativeml-openrail-m ---
hemagamal/mdeberta_Quran_qa	hemagamal	2023-05-12T17:24:14Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T17:07:25Z	--- license: mit tags: - generated_from_trainer model-index: - name: mdeberta_Quran_qa 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. --> # mdeberta_Quran_qa This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6248 ## 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: 8 - eval_batch_size: 10 - seed: 42 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 89 \| 2.2395 \| \| No log \| 2.0 \| 178 \| 2.3282 \| \| No log \| 3.0 \| 267 \| 2.4226 \| \| No log \| 4.0 \| 356 \| 2.6551 \| \| No log \| 5.0 \| 445 \| 2.9332 \| \| 1.0317 \| 6.0 \| 534 \| 3.2124 \| \| 1.0317 \| 7.0 \| 623 \| 3.2915 \| \| 1.0317 \| 8.0 \| 712 \| 3.5401 \| \| 1.0317 \| 9.0 \| 801 \| 3.6132 \| \| 1.0317 \| 10.0 \| 890 \| 3.6248 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
AustinCarthy/Base_10Kphish_benignFall_IL_10Krealphish	AustinCarthy	2023-05-12T17:22:27Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-12T16:10:35Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Base_10Kphish_benignFall_IL_10Krealphish_0.75 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. --> # Base_10Kphish_benignFall_IL_10Krealphish_0.75 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0551 - Accuracy: 0.9938 - F1: 0.9303 - Precision: 0.9982 - Recall: 0.871 - Roc Auc Score: 0.9355 - Tpr At Fpr 0.01: 0.8794 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc Score \| Tpr At Fpr 0.01 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------------:\|:---------------:\| \| 0.0079 \| 1.0 \| 6563 \| 0.0209 \| 0.9956 \| 0.9525 \| 0.9878 \| 0.9196 \| 0.9595 \| 0.862 \| \| 0.003 \| 2.0 \| 13126 \| 0.0338 \| 0.9949 \| 0.9438 \| 0.9940 \| 0.8984 \| 0.9491 \| 0.8796 \| \| 0.0024 \| 3.0 \| 19689 \| 0.0410 \| 0.9948 \| 0.9427 \| 0.9949 \| 0.8958 \| 0.9478 \| 0.8648 \| \| 0.0014 \| 4.0 \| 26252 \| 0.0493 \| 0.9941 \| 0.9342 \| 0.9982 \| 0.878 \| 0.9390 \| 0.881 \| \| 0.0003 \| 5.0 \| 32815 \| 0.0551 \| 0.9938 \| 0.9303 \| 0.9982 \| 0.871 \| 0.9355 \| 0.8794 \| ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
berluk/resnet50-fish-rec	berluk	2023-05-12T17:18:31Z	5	0	keras	[ "keras", "tf-keras", "image-classification", "region:us" ]	image-classification	2023-05-12T13:15:58Z	--- library_name: keras pipeline_tag: image-classification --- ## 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: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| learning_rate \| 0.001 \| \| decay \| 0.0 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \|
bohanhou14/abortion_news_model	bohanhou14	2023-05-12T17:09:43Z	161	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-12T07:20:21Z	--- license: apache-2.0 language: - en ---
asenella/mmnist_MoPoEconfig2_seed_1_ratio_0_c	asenella	2023-05-12T17:01:55Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-05-12T17:01:47Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
aprilzoo/distilbert-base-uncased-finetuned-imdb	aprilzoo	2023-05-12T16:53:13Z	61	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-05-06T18:51:57Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: aprilzoo/distilbert-base-uncased-finetuned-imdb 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. --> # aprilzoo/distilbert-base-uncased-finetuned-imdb 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: 4.2701 - Validation Loss: 2.6432 - 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': -997, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 4.2701 \| 2.6432 \| 0 \| ### Framework versions - Transformers 4.29.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
spectraldecomp/ppo-LunarLander-v2	spectraldecomp	2023-05-12T16:44:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T16:44:08Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.29 +/- 12.94 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
dian34323/melatijkt48	dian34323	2023-05-12T16:32:24Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T16:31:15Z	--- license: creativeml-openrail-m ---
muhammadravi251001/fine-tuned-DatasetQAS-TYDI-QA-ID-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05	muhammadravi251001	2023-05-12T16:25:57Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-05-05T04:56:01Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: fine-tuned-DatasetQAS-TYDI-QA-ID-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05 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. --> # fine-tuned-DatasetQAS-TYDI-QA-ID-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9402 - Exact Match: 69.3662 - F1: 82.0036 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - 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 \| Exact Match \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:-------:\| \| 6.2837 \| 0.5 \| 19 \| 3.6986 \| 8.4507 \| 17.7536 \| \| 6.2837 \| 0.99 \| 38 \| 2.5899 \| 18.4859 \| 29.7766 \| \| 3.6833 \| 1.5 \| 57 \| 1.7044 \| 42.6056 \| 56.8157 \| \| 3.6833 \| 1.99 \| 76 \| 1.2711 \| 53.3451 \| 70.2979 \| \| 3.6833 \| 2.5 \| 95 \| 1.1063 \| 62.3239 \| 75.7765 \| \| 1.5024 \| 2.99 \| 114 \| 1.0275 \| 64.2606 \| 78.0460 \| \| 1.5024 \| 3.5 \| 133 \| 0.9941 \| 65.8451 \| 79.1313 \| \| 1.0028 \| 3.99 \| 152 \| 0.9642 \| 67.4296 \| 80.6196 \| \| 1.0028 \| 4.5 \| 171 \| 0.9682 \| 69.0141 \| 82.4975 \| \| 1.0028 \| 4.99 \| 190 \| 0.9455 \| 67.9577 \| 81.0386 \| \| 0.7765 \| 5.5 \| 209 \| 0.9802 \| 67.7817 \| 81.0844 \| \| 0.7765 \| 5.99 \| 228 \| 0.9402 \| 69.3662 \| 82.0036 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.2.0 - Tokenizers 0.13.2
Amalq/roberta-large-schizophrenia-v12	Amalq	2023-05-12T16:18:11Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-05-12T15:54:41Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-large-schizophrenia-v12 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-large-schizophrenia-v12 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5260 ## 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: 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: 3.0 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
grenmon/pegasus-x-large-finetuned-summarization	grenmon	2023-05-12T16:06:30Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus_x", "text2text-generation", "summarization", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-05-12T15:25:46Z	--- tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: pegasus-x-large-finetuned-summarization 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. --> # pegasus-x-large-finetuned-summarization This model is a fine-tuned version of [google/pegasus-x-large](https://huggingface.co/google/pegasus-x-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9503 - Rouge1: 54.656 - Rouge2: 33.2773 - Rougel: 44.7797 - Rougelsum: 51.2888 ## 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: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 1.1821 \| 1.0 \| 308 \| 0.9389 \| 49.6848 \| 29.0753 \| 40.9828 \| 47.1619 \| \| 0.8932 \| 2.0 \| 616 \| 0.8955 \| 49.6176 \| 28.8588 \| 41.7149 \| 47.3719 \| \| 0.7433 \| 3.0 \| 924 \| 0.9202 \| 54.0016 \| 31.8254 \| 43.4441 \| 50.9312 \| \| 0.6495 \| 4.0 \| 1232 \| 0.9321 \| 52.6912 \| 31.6843 \| 43.8896 \| 49.8726 \| \| 0.587 \| 5.0 \| 1540 \| 0.9503 \| 54.656 \| 33.2773 \| 44.7797 \| 51.2888 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Mikepool117/q-FrozenLake-v1-4x4-noSlippery	Mikepool117	2023-05-12T16:00:55Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T16:00:23Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
henripett/ppo-Huggy	henripett	2023-05-12T15:59:31Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-05-12T15:12:33Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: henripett/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
saikatkumardey/my_awesome_qa_model	saikatkumardey	2023-05-12T15:46:32Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T15:12:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_model 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. --> # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.9972 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.5592 \| \| 2.909 \| 2.0 \| 500 \| 2.0550 \| \| 2.909 \| 3.0 \| 750 \| 1.9972 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
casellimarco/q-FrozenLake-v1-4x4-noSlippery-gamma_1	casellimarco	2023-05-12T15:33:36Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T15:31:22Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery-gamma_1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 0.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . With `gamma=1` the Q table is ``` array([[1., 1., 1., 1.], [1., 0., 1., 1.], [1., 1., 1., 1.], [1., 0., 1., 1.], [1., 1., 0., 1.], [0., 0., 0., 0.], [0., 1., 0., 1.], [0., 0., 0., 0.], [1., 0., 1., 1.], [1., 1., 1., 0.], [1., 1., 0., 1.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 1., 1., 1.], [1., 1., 1., 1.], [0., 0., 0., 0.]]) ``` ## Usage ```python model = load_from_hub(repo_id="casellimarco/q-FrozenLake-v1-4x4-noSlippery-gamma_1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
itsmeboris/jobbert-base-cased-ner	itsmeboris	2023-05-12T15:21:11Z	99	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-12T14:43:12Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: jobbert-base-cased-ner 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. --> # jobbert-base-cased-ner This model is a fine-tuned version of [jjzha/jobbert-base-cased](https://huggingface.co/jjzha/jobbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3789 - Job Title precision: 0.7916 - Job Title recall: 0.8721 - Job Title f1: 0.8299 - Loc precision: 0.8572 - Loc recall: 0.9506 - Loc f1: 0.9015 - Org precision: 0.6727 - Org recall: 0.7458 - Org f1: 0.7074 - Misc precision: 0.6893 - Misc recall: 0.6587 - Misc f1: 0.6736 - Precision: 0.7772 - Recall: 0.8551 - F1: 0.8143 - Accuracy: 0.8680 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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 \| Job Title precision \| Job Title recall \| Job Title f1 \| Loc precision \| Loc recall \| Loc f1 \| Org precision \| Org recall \| Org f1 \| Misc precision \| Misc recall \| Misc f1 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\|:----------------:\|:------------:\|:-------------:\|:----------:\|:------:\|:-------------:\|:----------:\|:------:\|:--------------:\|:-----------:\|:-------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 308 \| 0.3896 \| 0.7827 \| 0.8394 \| 0.8101 \| 0.8813 \| 0.8856 \| 0.8835 \| 0.6865 \| 0.7151 \| 0.7005 \| 0.6730 \| 0.6041 \| 0.6367 \| 0.7800 \| 0.8148 \| 0.7970 \| 0.8577 \| \| 0.4672 \| 2.0 \| 616 \| 0.3789 \| 0.7916 \| 0.8721 \| 0.8299 \| 0.8572 \| 0.9506 \| 0.9015 \| 0.6727 \| 0.7458 \| 0.7074 \| 0.6893 \| 0.6587 \| 0.6736 \| 0.7772 \| 0.8551 \| 0.8143 \| 0.8680 \| \| 0.4672 \| 3.0 \| 924 \| 0.4067 \| 0.7800 \| 0.8876 \| 0.8304 \| 0.8560 \| 0.9443 \| 0.8980 \| 0.6928 \| 0.7026 \| 0.6977 \| 0.6006 \| 0.7440 \| 0.6646 \| 0.7730 \| 0.8549 \| 0.8119 \| 0.8651 \| ### Framework versions - Transformers 4.28.1 - Pytorch 1.7.1+cu110 - Datasets 2.12.0 - Tokenizers 0.13.2
seviladiguzel/distilbert-base-uncased-finetuned-squad_v2	seviladiguzel	2023-05-12T15:21:02Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T13:20:15Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad_v2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1316 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2238 \| 1.0 \| 5533 \| 1.1654 \| \| 0.9823 \| 2.0 \| 11066 \| 1.1316 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
rickysk/videomae-base-finetuned-ucf101-subset	rickysk	2023-05-12T15:19:14Z	59	0	transformers	[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-05-12T05:24:04Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset 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. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3230 - Accuracy: 0.8968 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1382 \| 0.06 \| 75 \| 2.1056 \| 0.2571 \| \| 0.8185 \| 1.06 \| 150 \| 0.6270 \| 0.8 \| \| 0.5221 \| 2.06 \| 225 \| 0.4341 \| 0.8 \| \| 0.1069 \| 3.06 \| 300 \| 0.4390 \| 0.8714 \| \| 0.0195 \| 4.06 \| 375 \| 0.2938 \| 0.8571 \| \| 0.0097 \| 5.06 \| 450 \| 0.2114 \| 0.9 \| \| 0.0076 \| 6.06 \| 525 \| 0.1509 \| 0.9429 \| \| 0.1686 \| 7.06 \| 600 \| 0.2527 \| 0.9571 \| \| 0.0679 \| 8.06 \| 675 \| 0.0615 \| 0.9714 \| \| 0.0024 \| 9.06 \| 750 \| 0.1589 \| 0.9429 \| \| 0.1946 \| 10.06 \| 825 \| 0.4014 \| 0.9 \| \| 0.154 \| 11.06 \| 900 \| 0.1862 \| 0.9429 \| \| 0.0021 \| 12.06 \| 975 \| 0.0683 \| 0.9857 \| \| 0.0019 \| 13.06 \| 1050 \| 0.0541 \| 0.9857 \| \| 0.002 \| 14.06 \| 1125 \| 0.0473 \| 0.9857 \| \| 0.0018 \| 15.06 \| 1200 \| 0.0475 \| 0.9857 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
WALIDALI/bekiorangemixs	WALIDALI	2023-05-12T15:16:09Z	0	0	null	[ "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-05-12T15:03:42Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### bekiOrangeMixs Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
rishabhjain16/whisper_medium_en_to_myst_pf_ot100	rishabhjain16	2023-05-12T15:05:57Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-10T09:45:26Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: openai/whisper-medium.en results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_myst type: rishabhjain16/infer_myst config: en split: test metrics: - type: wer value: 12.3 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pfs type: rishabhjain16/infer_pfs config: en split: test metrics: - type: wer value: 3.28 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_cmu type: rishabhjain16/infer_cmu config: en split: test metrics: - type: wer value: 9.53 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/libritts_dev_clean type: rishabhjain16/libritts_dev_clean config: en split: test metrics: - type: wer value: 5.01 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_swedish type: rishabhjain16/infer_pf_swedish config: en split: test metrics: - type: wer value: 8.94 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_german type: rishabhjain16/infer_pf_german config: en split: test metrics: - type: wer value: 34.78 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_italian type: rishabhjain16/infer_pf_italian config: en split: test metrics: - type: wer value: 4.42 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_so_chinese type: rishabhjain16/infer_so_chinese config: en split: test metrics: - type: wer value: 14.87 name: WER --- <!-- 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. --> # openai/whisper-medium.en This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4158 - Wer: 10.8712 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.6148 \| 0.12 \| 500 \| 0.3107 \| 12.7838 \| \| 0.1877 \| 1.09 \| 1000 \| 0.2892 \| 11.2910 \| \| 0.0697 \| 2.05 \| 1500 \| 0.3146 \| 10.7857 \| \| 0.0748 \| 3.02 \| 2000 \| 0.3162 \| 11.5254 \| \| 0.0308 \| 3.14 \| 2500 \| 0.3450 \| 11.1111 \| \| 0.0192 \| 4.11 \| 3000 \| 0.3720 \| 10.9101 \| \| 0.0046 \| 5.07 \| 3500 \| 0.4155 \| 11.2344 \| \| 0.0096 \| 6.03 \| 4000 \| 0.4158 \| 10.8712 \| ### Framework versions - Transformers 4.29.0 - Pytorch 1.14.0a0+44dac51 - Datasets 2.12.0 - Tokenizers 0.13.3
tp-runport/bert-base-uncased-brands	tp-runport	2023-05-12T14:59:27Z	104	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-04-19T21:14:49Z	--- {} --- This is a BERT-based NER model trained to detect PERSON and BRAND entities in text.
alistvt/single-docalog	alistvt	2023-05-12T14:55:33Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:doc2dial", "endpoints_compatible", "region:us" ]	question-answering	2023-05-11T09:17:03Z	--- tags: - generated_from_trainer datasets: - doc2dial model-index: - name: single-docalog 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. --> # single-docalog This model is a fine-tuned version of [alistvt/single-docalog](https://huggingface.co/alistvt/single-docalog) on the doc2dial dataset. ## 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 30 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
grenmon/bart-large-finetuned-summarization	grenmon	2023-05-12T14:49:29Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-05-12T14:28:31Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: bart-large-finetuned-summarization 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. --> # bart-large-finetuned-summarization This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1841 - Rouge1: 32.6763 - Rouge2: 23.1598 - Rougel: 31.2322 - Rougelsum: 32.278 ## 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: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 1.7048 \| 1.0 \| 308 \| 1.1916 \| 32.0296 \| 21.6931 \| 30.2623 \| 31.1959 \| \| 1.1153 \| 2.0 \| 616 \| 1.2054 \| 30.7076 \| 21.7771 \| 29.3115 \| 29.9377 \| \| 0.78 \| 3.0 \| 924 \| 1.1096 \| 32.4164 \| 22.494 \| 31.0367 \| 31.8135 \| \| 0.5335 \| 4.0 \| 1232 \| 1.1547 \| 33.2561 \| 23.6119 \| 32.1371 \| 32.591 \| \| 0.361 \| 5.0 \| 1540 \| 1.1841 \| 32.6763 \| 23.1598 \| 31.2322 \| 32.278 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
arb9p4/rl_course_vizdoom_health_gathering_supreme	arb9p4	2023-05-12T14:43:29Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T13:42:41Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 13.07 +/- 5.50 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r arb9p4/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
itsmeboris/bert-base-cased-ner	itsmeboris	2023-05-12T14:30:54Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-12T14:02:52Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased-ner 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. --> # bert-base-cased-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3793 - Job Title precision: 0.8079 - Job Title recall: 0.8248 - Job Title f1: 0.8163 - Loc precision: 0.8911 - Loc recall: 0.9081 - Loc f1: 0.8995 - Org precision: 0.6484 - Org recall: 0.7620 - Org f1: 0.7006 - Misc precision: 0.6134 - Misc recall: 0.7201 - Misc f1: 0.6625 - Precision: 0.7800 - Recall: 0.8265 - F1: 0.8025 - Accuracy: 0.8606 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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 \| Job Title precision \| Job Title recall \| Job Title f1 \| Loc precision \| Loc recall \| Loc f1 \| Org precision \| Org recall \| Org f1 \| Misc precision \| Misc recall \| Misc f1 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\|:----------------:\|:------------:\|:-------------:\|:----------:\|:------:\|:-------------:\|:----------:\|:------:\|:--------------:\|:-----------:\|:-------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 308 \| 0.3793 \| 0.8079 \| 0.8248 \| 0.8163 \| 0.8911 \| 0.9081 \| 0.8995 \| 0.6484 \| 0.7620 \| 0.7006 \| 0.6134 \| 0.7201 \| 0.6625 \| 0.7800 \| 0.8265 \| 0.8025 \| 0.8606 \| \| 0.4249 \| 2.0 \| 616 \| 0.3866 \| 0.7911 \| 0.8728 \| 0.8299 \| 0.8676 \| 0.9541 \| 0.9088 \| 0.6551 \| 0.7886 \| 0.7157 \| 0.6623 \| 0.6962 \| 0.6789 \| 0.7719 \| 0.8669 \| 0.8167 \| 0.8685 \| ### Framework versions - Transformers 4.28.1 - Pytorch 1.7.1+cu110 - Datasets 2.12.0 - Tokenizers 0.13.2
zeeshan-sardar/Taxi-v3	zeeshan-sardar	2023-05-12T14:25:30Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T14:25:28Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.69 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="zeeshan-sardar/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ku-nlp/deberta-v2-base-japanese	ku-nlp	2023-05-12T14:13:03Z	56,613	28	transformers	[ "transformers", "pytorch", "safetensors", "deberta-v2", "fill-mask", "deberta", "ja", "dataset:wikipedia", "dataset:cc100", "dataset:oscar", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-01-05T08:04:14Z	--- language: ja license: cc-by-sa-4.0 library_name: transformers tags: - deberta - deberta-v2 - fill-mask datasets: - wikipedia - cc100 - oscar metrics: - accuracy mask_token: "[MASK]" widget: - text: "京都大学で自然言語処理を [MASK] する。" --- # Model Card for Japanese DeBERTa V2 base ## Model description This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-base-japanese') model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-base-japanese') sentence = '京都大学で自然言語処理を [MASK] する。' # input should be segmented into words by Juman++ in advance encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can also fine-tune this model on downstream tasks. ## Tokenization The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece). ## Training data We used the following corpora for pre-training: - Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents) - Japanese portion of CC-100 (85GB, 619M sentences, 66M documents) - Japanese portion of OSCAR (54GB, 326M sentences, 25M documents) Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR. Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB. ## Training procedure We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp). Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library. The training took three weeks using 8 NVIDIA A100-SXM4-40GB GPUs. The following hyperparameters were used during pre-training: - learning_rate: 2e-4 - per_device_train_batch_size: 44 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 6 - total_train_batch_size: 2,112 - max_seq_length: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear schedule with warmup - training_steps: 500,000 - warmup_steps: 10,000 The accuracy of the trained model on the masked language modeling task was 0.779. The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora. ## Fine-tuning on NLU tasks We fine-tuned the following models and evaluated them on the dev set of JGLUE. We tuned learning rate and training epochs for each model and task following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja). \| Model \| MARC-ja/acc \| JSTS/pearson \| JSTS/spearman \| JNLI/acc \| JSQuAD/EM \| JSQuAD/F1 \| JComQA/acc \| \|-------------------------------\|-------------\|--------------\|---------------\|----------\|-----------\|-----------\|------------\| \| Waseda RoBERTa base \| 0.965 \| 0.913 \| 0.876 \| 0.905 \| 0.853 \| 0.916 \| 0.853 \| \| Waseda RoBERTa large (seq512) \| 0.969 \| 0.925 \| 0.890 \| 0.928 \| 0.910 \| 0.955 \| 0.900 \| \| LUKE Japanese base* \| 0.965 \| 0.916 \| 0.877 \| 0.912 \| - \| - \| 0.842 \| \| LUKE Japanese large* \| 0.965 \| 0.932 \| 0.902 \| 0.927 \| - \| - \| 0.893 \| \| DeBERTaV2 base \| 0.970 \| 0.922 \| 0.886 \| 0.922 \| 0.899 \| 0.951 \| 0.873 \| \| DeBERTaV2 large \| 0.968 \| 0.925 \| 0.892 \| 0.924 \| 0.912 \| 0.959 \| 0.890 \| *The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke). ## Acknowledgments This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models". For training models, we used the mdx: a platform for the data-driven future.
ku-nlp/deberta-v2-large-japanese	ku-nlp	2023-05-12T14:10:35Z	36,402	9	transformers	[ "transformers", "pytorch", "safetensors", "deberta-v2", "fill-mask", "deberta", "ja", "dataset:wikipedia", "dataset:cc100", "dataset:oscar", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-01-07T07:45:25Z	--- language: ja license: cc-by-sa-4.0 library_name: transformers tags: - deberta - deberta-v2 - fill-mask datasets: - wikipedia - cc100 - oscar metrics: - accuracy mask_token: "[MASK]" widget: - text: "京都大学で自然言語処理を [MASK] する。" --- # Model Card for Japanese DeBERTa V2 large ## Model description This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese') model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese') sentence = '京都大学で自然言語処理を [MASK] する。' # input should be segmented into words by Juman++ in advance encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can also fine-tune this model on downstream tasks. ## Tokenization The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece). ## Training data We used the following corpora for pre-training: - Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents) - Japanese portion of CC-100 (85GB, 619M sentences, 66M documents) - Japanese portion of OSCAR (54GB, 326M sentences, 25M documents) Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR. Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB. ## Training procedure We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp). Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library. The training took 36 days using 8 NVIDIA A100-SXM4-40GB GPUs. The following hyperparameters were used during pre-training: - learning_rate: 1e-4 - per_device_train_batch_size: 18 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 2,304 - max_seq_length: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear schedule with warmup - training_steps: 300,000 - warmup_steps: 10,000 The accuracy of the trained model on the masked language modeling task was 0.799. The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora. ## Fine-tuning on NLU tasks We fine-tuned the following models and evaluated them on the dev set of JGLUE. We tuned learning rate and training epochs for each model and task following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja). \| Model \| MARC-ja/acc \| JSTS/pearson \| JSTS/spearman \| JNLI/acc \| JSQuAD/EM \| JSQuAD/F1 \| JComQA/acc \| \|-------------------------------\|-------------\|--------------\|---------------\|----------\|-----------\|-----------\|------------\| \| Waseda RoBERTa base \| 0.965 \| 0.913 \| 0.876 \| 0.905 \| 0.853 \| 0.916 \| 0.853 \| \| Waseda RoBERTa large (seq512) \| 0.969 \| 0.925 \| 0.890 \| 0.928 \| 0.910 \| 0.955 \| 0.900 \| \| LUKE Japanese base* \| 0.965 \| 0.916 \| 0.877 \| 0.912 \| - \| - \| 0.842 \| \| LUKE Japanese large* \| 0.965 \| 0.932 \| 0.902 \| 0.927 \| - \| - \| 0.893 \| \| DeBERTaV2 base \| 0.970 \| 0.922 \| 0.886 \| 0.922 \| 0.899 \| 0.951 \| 0.873 \| \| DeBERTaV2 large \| 0.968 \| 0.925 \| 0.892 \| 0.924 \| 0.912 \| 0.959 \| 0.890 \| *The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke). ## Acknowledgments This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures ( JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models". For training models, we used the mdx: a platform for the data-driven future.
dian34323/fionyjkt48	dian34323	2023-05-12T14:10:31Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T14:09:05Z	--- license: creativeml-openrail-m ---
seantyh/mpt-1b-rp200b-dolly	seantyh	2023-05-12T14:10:13Z	14	0	transformers	[ "transformers", "pytorch", "mosaic_gpt", "text-generation", "custom_code", "dataset:togethercomputer/RedPajama-Data-1T", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2108.12409", "license:cc-by-sa-3.0", "autotrain_compatible", "region:us" ]	text-generation	2023-05-12T12:49:07Z	--- license: cc-by-sa-3.0 datasets: - togethercomputer/RedPajama-Data-1T --- [This is a experimental fork of mosaicml/mpt-1b-redpajama-200b-dolly] # MPT-1b-RedPajama-200b-dolly MPT-1b-RedPajama-200b-dolly is a 1.3 billion parameter decoder-only transformer pre-trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and subsequently fine-tuned on the [Databricks Dolly](https://github.com/databrickslabs/dolly/tree/master/data) instruction dataset. The model was pre-trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971). This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. This model is an instruction fine-tuned version of [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). In other words, the pre-trained version of this model is [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). ## Model Date April 20, 2023 ## How to Use Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture `MosaicGPT` that is not yet part of the `transformers` package. `MosaicGPT` includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALIBI](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True) ``` To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so: ```python model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True, attn_impl='triton') model.to(device='cuda:0', dtype=torch.bfloat16) ``` ## Model Description This model uses the MosaicML LLM codebase, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm). The architecture is a modification of a standard decoder-only transformer. The transformer has 24 layers, 16 attention heads, and width 2048. The model has been modified from a standard transformer in the following ways: * It uses ALiBi and does not use positional embeddings. * It uses QK LayerNorm. * It does not use biases. ## Training Data ### Pre-Training The model was pre-trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix: * 67% RedPajama Common Crawl * 15% [C4](https://huggingface.co/datasets/c4) * 4.5% RedPajama GitHub * 4.5% RedPajama Wikipedia * 4.5% RedPajama Books * 2.5% RedPajama Arxiv * 2% RedPajama StackExchange This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971). Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above. The examples were shuffled within each dataset. Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ### Fine-Tuning We fine tuned this model on the [databricks-dolly-15k dataset](https://github.com/databrickslabs/dolly/tree/master/data) released by Databricks, following the same hyperparameters found in their [train_dolly.py](https://github.com/databrickslabs/dolly/blob/master/train_dolly.py) script. ## Training Configuration This model was pre-trained on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was pre-trained with sharded data parallelism using FSDP. ## Acknowledgements This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models. We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work. This model also builds on the work of [Databricks](https://www.databricks.com/), which created the Dolly instruction fine-tuning dataset. We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.
asenella/reproducing_mopoe_seed_4	asenella	2023-05-12T14:00:49Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-05-12T14:00:42Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
madmaxxed/gpt-work-filter-auto-complete	madmaxxed	2023-05-12T13:59:23Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-12T09:15:35Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-work-filter-auto-complete 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. --> # gpt-work-filter-auto-complete This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0939 ## 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 22 \| 0.6395 \| \| No log \| 2.0 \| 44 \| 0.6235 \| \| No log \| 3.0 \| 66 \| 0.6035 \| \| No log \| 4.0 \| 88 \| 0.5845 \| \| No log \| 5.0 \| 110 \| 0.5645 \| \| No log \| 6.0 \| 132 \| 0.5442 \| \| No log \| 7.0 \| 154 \| 0.5346 \| \| No log \| 8.0 \| 176 \| 0.5167 \| \| No log \| 9.0 \| 198 \| 0.5009 \| \| No log \| 10.0 \| 220 \| 0.4893 \| \| No log \| 11.0 \| 242 \| 0.4676 \| \| No log \| 12.0 \| 264 \| 0.4498 \| \| No log \| 13.0 \| 286 \| 0.4382 \| \| No log \| 14.0 \| 308 \| 0.4276 \| \| No log \| 15.0 \| 330 \| 0.4132 \| \| No log \| 16.0 \| 352 \| 0.4075 \| \| No log \| 17.0 \| 374 \| 0.3952 \| \| No log \| 18.0 \| 396 \| 0.3822 \| \| No log \| 19.0 \| 418 \| 0.3677 \| \| No log \| 20.0 \| 440 \| 0.3563 \| \| No log \| 21.0 \| 462 \| 0.3495 \| \| No log \| 22.0 \| 484 \| 0.3455 \| \| 0.6366 \| 23.0 \| 506 \| 0.3316 \| \| 0.6366 \| 24.0 \| 528 \| 0.3126 \| \| 0.6366 \| 25.0 \| 550 \| 0.3118 \| \| 0.6366 \| 26.0 \| 572 \| 0.3021 \| \| 0.6366 \| 27.0 \| 594 \| 0.2944 \| \| 0.6366 \| 28.0 \| 616 \| 0.2878 \| \| 0.6366 \| 29.0 \| 638 \| 0.2772 \| \| 0.6366 \| 30.0 \| 660 \| 0.2701 \| \| 0.6366 \| 31.0 \| 682 \| 0.2643 \| \| 0.6366 \| 32.0 \| 704 \| 0.2576 \| \| 0.6366 \| 33.0 \| 726 \| 0.2514 \| \| 0.6366 \| 34.0 \| 748 \| 0.2467 \| \| 0.6366 \| 35.0 \| 770 \| 0.2359 \| \| 0.6366 \| 36.0 \| 792 \| 0.2326 \| \| 0.6366 \| 37.0 \| 814 \| 0.2205 \| \| 0.6366 \| 38.0 \| 836 \| 0.2182 \| \| 0.6366 \| 39.0 \| 858 \| 0.2137 \| \| 0.6366 \| 40.0 \| 880 \| 0.2086 \| \| 0.6366 \| 41.0 \| 902 \| 0.2058 \| \| 0.6366 \| 42.0 \| 924 \| 0.1979 \| \| 0.6366 \| 43.0 \| 946 \| 0.1930 \| \| 0.6366 \| 44.0 \| 968 \| 0.1922 \| \| 0.6366 \| 45.0 \| 990 \| 0.1853 \| \| 0.4122 \| 46.0 \| 1012 \| 0.1800 \| \| 0.4122 \| 47.0 \| 1034 \| 0.1787 \| \| 0.4122 \| 48.0 \| 1056 \| 0.1738 \| \| 0.4122 \| 49.0 \| 1078 \| 0.1689 \| \| 0.4122 \| 50.0 \| 1100 \| 0.1670 \| \| 0.4122 \| 51.0 \| 1122 \| 0.1583 \| \| 0.4122 \| 52.0 \| 1144 \| 0.1560 \| \| 0.4122 \| 53.0 \| 1166 \| 0.1540 \| \| 0.4122 \| 54.0 \| 1188 \| 0.1507 \| \| 0.4122 \| 55.0 \| 1210 \| 0.1475 \| \| 0.4122 \| 56.0 \| 1232 \| 0.1452 \| \| 0.4122 \| 57.0 \| 1254 \| 0.1458 \| \| 0.4122 \| 58.0 \| 1276 \| 0.1425 \| \| 0.4122 \| 59.0 \| 1298 \| 0.1377 \| \| 0.4122 \| 60.0 \| 1320 \| 0.1338 \| \| 0.4122 \| 61.0 \| 1342 \| 0.1365 \| \| 0.4122 \| 62.0 \| 1364 \| 0.1278 \| \| 0.4122 \| 63.0 \| 1386 \| 0.1272 \| \| 0.4122 \| 64.0 \| 1408 \| 0.1253 \| \| 0.4122 \| 65.0 \| 1430 \| 0.1251 \| \| 0.4122 \| 66.0 \| 1452 \| 0.1217 \| \| 0.4122 \| 67.0 \| 1474 \| 0.1219 \| \| 0.4122 \| 68.0 \| 1496 \| 0.1177 \| \| 0.3005 \| 69.0 \| 1518 \| 0.1174 \| \| 0.3005 \| 70.0 \| 1540 \| 0.1155 \| \| 0.3005 \| 71.0 \| 1562 \| 0.1144 \| \| 0.3005 \| 72.0 \| 1584 \| 0.1127 \| \| 0.3005 \| 73.0 \| 1606 \| 0.1106 \| \| 0.3005 \| 74.0 \| 1628 \| 0.1098 \| \| 0.3005 \| 75.0 \| 1650 \| 0.1092 \| \| 0.3005 \| 76.0 \| 1672 \| 0.1067 \| \| 0.3005 \| 77.0 \| 1694 \| 0.1086 \| \| 0.3005 \| 78.0 \| 1716 \| 0.1042 \| \| 0.3005 \| 79.0 \| 1738 \| 0.1051 \| \| 0.3005 \| 80.0 \| 1760 \| 0.1038 \| \| 0.3005 \| 81.0 \| 1782 \| 0.1022 \| \| 0.3005 \| 82.0 \| 1804 \| 0.1015 \| \| 0.3005 \| 83.0 \| 1826 \| 0.1004 \| \| 0.3005 \| 84.0 \| 1848 \| 0.1003 \| \| 0.3005 \| 85.0 \| 1870 \| 0.0978 \| \| 0.3005 \| 86.0 \| 1892 \| 0.0987 \| \| 0.3005 \| 87.0 \| 1914 \| 0.0974 \| \| 0.3005 \| 88.0 \| 1936 \| 0.0975 \| \| 0.3005 \| 89.0 \| 1958 \| 0.0965 \| \| 0.3005 \| 90.0 \| 1980 \| 0.0960 \| \| 0.2455 \| 91.0 \| 2002 \| 0.0958 \| \| 0.2455 \| 92.0 \| 2024 \| 0.0952 \| \| 0.2455 \| 93.0 \| 2046 \| 0.0952 \| \| 0.2455 \| 94.0 \| 2068 \| 0.0944 \| \| 0.2455 \| 95.0 \| 2090 \| 0.0943 \| \| 0.2455 \| 96.0 \| 2112 \| 0.0940 \| \| 0.2455 \| 97.0 \| 2134 \| 0.0942 \| \| 0.2455 \| 98.0 \| 2156 \| 0.0940 \| \| 0.2455 \| 99.0 \| 2178 \| 0.0939 \| \| 0.2455 \| 100.0 \| 2200 \| 0.0939 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
AustinCarthy/Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75	AustinCarthy	2023-05-12T13:54:37Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-12T12:53:56Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75 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. --> # Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0237 - Accuracy: 0.9975 - F1: 0.9731 - Precision: 0.9983 - Recall: 0.9492 - Roc Auc Score: 0.9746 - Tpr At Fpr 0.01: 0.9508 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc Score \| Tpr At Fpr 0.01 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------------:\|:---------------:\| \| 0.0042 \| 1.0 \| 6563 \| 0.0276 \| 0.9966 \| 0.9628 \| 0.9983 \| 0.9298 \| 0.9649 \| 0.9308 \| \| 0.0024 \| 2.0 \| 13126 \| 0.0242 \| 0.9972 \| 0.9698 \| 0.9973 \| 0.9438 \| 0.9718 \| 0.927 \| \| 0.0026 \| 3.0 \| 19689 \| 0.0244 \| 0.9970 \| 0.9679 \| 0.9987 \| 0.939 \| 0.9695 \| 0.9514 \| \| 0.0003 \| 4.0 \| 26252 \| 0.0293 \| 0.9968 \| 0.9657 \| 0.9989 \| 0.9346 \| 0.9673 \| 0.9472 \| \| 0.0007 \| 5.0 \| 32815 \| 0.0237 \| 0.9975 \| 0.9731 \| 0.9983 \| 0.9492 \| 0.9746 \| 0.9508 \| ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
Amalq/roberta-large-schizophrenia-v2	Amalq	2023-05-12T13:45:51Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-05-12T12:54:45Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-large-schizophrenia-v2 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-large-schizophrenia-v2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5239 ## 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: 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: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 248 \| 1.5340 \| \| No log \| 2.0 \| 496 \| 1.5273 \| \| 1.6401 \| 3.0 \| 744 \| 1.5209 \| \| 1.6401 \| 4.0 \| 992 \| 1.5218 \| \| 1.5704 \| 5.0 \| 1240 \| 1.5167 \| \| 1.5704 \| 6.0 \| 1488 \| 1.5245 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
luigisaetta/whisper-atcosim2	luigisaetta	2023-05-12T13:26:03Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-12T11:26:48Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-atcosim2 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. --> # whisper-atcosim2 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0524 - Wer: 0.0304 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 300 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5702 \| 0.2 \| 50 \| 0.2557 \| 0.1007 \| \| 0.1181 \| 0.39 \| 100 \| 0.1144 \| 0.0775 \| \| 0.1084 \| 0.59 \| 150 \| 0.0747 \| 0.0482 \| \| 0.0737 \| 0.79 \| 200 \| 0.0616 \| 0.0369 \| \| 0.064 \| 0.98 \| 250 \| 0.0556 \| 0.0440 \| \| 0.0313 \| 1.18 \| 300 \| 0.0524 \| 0.0304 \| ### Framework versions - Transformers 4.29.0 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.11.0
LarryAIDraw/asuna1-000004	LarryAIDraw	2023-05-12T13:26:00Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T13:18:28Z	--- license: creativeml-openrail-m --- https://civitai.com/models/63637/ichinose-asuna-blue-archive-or-character-lora-1630
LarryAIDraw/towerofgod_androssi_zahard	LarryAIDraw	2023-05-12T13:25:16Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T13:17:26Z	--- license: creativeml-openrail-m --- https://civitai.com/models/63523/androssi-zahard-endorsi-or-tower-of-god
LarryAIDraw/nanami_mami_v1	LarryAIDraw	2023-05-12T13:23:44Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T13:14:51Z	--- license: creativeml-openrail-m --- https://civitai.com/models/64071/nanami-mami-rent-a-girlfriend
LarryAIDraw/yami-v1	LarryAIDraw	2023-05-12T13:23:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T13:14:31Z	--- license: creativeml-openrail-m --- https://civitai.com/models/63759/yami-3-in-one-to-love-ru-darkness-tolove
LarryAIDraw/Amano_Hina	LarryAIDraw	2023-05-12T13:23:17Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T13:14:06Z	--- license: creativeml-openrail-m --- https://civitai.com/models/63559/amano-hina-tenki-no-ko-weathering-with-you
piperunner/ppo-LunarLander-v2	piperunner	2023-05-12T13:15:46Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T13:15:28Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.13 +/- 21.48 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
zetavg/zh-tw-pythia-1b-a12k-f84566-embeddings-gcp-a100-trans-t3-d2ad	zetavg	2023-05-12T13:07:34Z	16	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "dataset:zetavg/zh-tw-pythia-te01-zh-tw-pythia-ta12k-f84566-embeddings-tr-ec3f26-c2048", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-12T13:04:21Z	--- datasets: - zetavg/zh-tw-pythia-te01-zh-tw-pythia-ta12k-f84566-embeddings-tr-ec3f26-c2048 --- # zh-tw-pythia-1b-a12k-f84566-embeddings-gcp-a100-trans-t3-d2ad This model is a part of the `zh-tw-pythia` project. * Base model: `EleutherAI/pythia-1b` * Tokenizer: `zh-tw-pythia-tokenizer-a12k-f84566` * Vocab size: `62232` * Train pass: `embeddings` * Dataset used: `zh-tw-pythia-te01-zh-tw-pythia-ta12k-f84566-embeddings-tr-ec3f26-c2048` * Full config: ```json {"project_name": "zh-tw-pythia", "group_name": "te01", "base_tokenizer_name": "EleutherAI/pythia-70m", "base_model_name": "EleutherAI/pythia-1b", "tokenizer_name": "zh-tw-pythia-tokenizer-a12k-f84566", "hf_user_or_org_name": "zetavg", "tokenizer": {"build_with": "word_frequency_list", "tokens_to_add": 12000, "word_frequency_list_settings": {"word_frequency_list_name": "zetavg/tw-sinica-corpus-word-frequency", "include_words": ["。", "，", "、", "？", "！", "；", "：", "……", "～", "「", "」", "『", "』", "【", "】", "〖", "〗", "（", "）", "〔", "〕", "［", "］", "｛", "｝", "《", "》", "〈", "〉", "——", "──", "－", "−", "＿", "・", "．", "·", "／", "＼", "｜", "＜", "＞"], "replace_rules": [{"match": {"regex": "�"}, "replace": null}, {"match": {"pos": ["Nb", "FW", null]}, "replace": null, "except": ["奧運", "中共", "國民黨", "民進黨", "新黨", "共產黨", "媽祖", "耶穌"]}, {"match": {"regex": ["^[Ａ-Ｚａ-ｚ０-９﹒• ]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]{2,}$", "^（[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]+）$", "^[第數][零一二兩三四五六七八九十百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+分之[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+[多餘來幾成次年月日天時分點世代歲起段樓％]$", "^[零一二三四五六七八九十廿卅０-９]+(月份\|年代?\|世紀\|學?年度\|年級)$", "^(星期\|週\|周)[一二三四五六日]$"]}, "replace": null, "except": ["十分", "一起", "一點", "一時", "千萬", "兩三", "百分之百"]}, {"match": {"pos": "VHC", "regex": "^(.{2,})化$"}, "sub": "\\1"}, {"match": "高爾夫球場", "replace": "高爾夫"}, {"match": {"regex": "^(.+球)場$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})園區$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[鄉鎮縣市區]$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[界院部會署局館系所]$"}, "sub": "\\1", "except": ["委員會", "研究所", "中研院", "國科會", "資策會", "經建會", "工研院", "電信總局", "鎮公所", "事務所", "交易所", "農委會", "鄉公所", "地檢署", "警分局", "派出所", "托兒所", "消基會", "文建會", "兩廳院", "陸委會", "市議會"]}, {"match": {"pos": "Na", "regex": "^(.{2,})人$"}, "sub": "\\1", "except": ["年輕人", "負責人", "投資人", "候選人", "一家人", "當地人", "製作人"]}, {"match": {"pos": "Na", "regex": "^(.{2,3})學?家$"}, "sub": "\\1", "except": ["女人家", "婦人家", "新儒家", "窮人家", "縱橫家", "老人家", "老東家", "闊人家", "大戶人家", "婦道人家", "小戶人家", "水上人家", "諸子百家"]}, {"match": {"pos": "Na", "regex": "^副?總?([^副總]{2,})師$"}, "sub": "\\1", "except": ["中醫師", "囝仔師", "正機師", "準教師", "獸醫師", "班導師", "練馬師", "總舖師", "老像師", "新三十師", "至聖先師", "音樂大師"]}, {"match": {"pos": "Na", "regex": "^[原前]?(?:代\|代理)?副?總?([^前代副總議警里首院部署局廳司處科組課股]{2,})[院部署局廳司處科組課股]?次?長$"}, "sub": "\\1", "except": ["董事長", "理事長", "秘書長", "執行長", "分局長", "縣市長", "一技之長", "省市長", "負成長", "高成長", "大家長", "小組長", "區組長", "低成長", "偵一組長", "停管隊長", "考選部長", "年增長", "正成長", "支店長", "公賣局長", "中宣部長", "小市長"]}, {"match": {"pos": "Na", "regex": "^副?總?正?([^副總正議委人隊]{2,})[委人隊]?員$"}, "sub": "\\1", "except": ["主跑員", "乘務員", "佐理員", "共黨員", "外務員", "從業員", "特派員", "義服員", "銜道員", "啦啦隊員", "指服團員"]}, {"match": {"pos": "Na", "regex": "^副(.{2,})$"}, "sub": "\\1", "except": ["副作用"]}, {"match": "一剎那", "replace": "剎那"}, {"match": "不能夠", "replace": "能夠"}, {"match": "光碟機", "replace": "光碟"}, {"match": "共和國", "replace": "共和"}, {"match": "原住民", "replace": "住民"}, {"match": "吸引力", "replace": "吸引"}, {"match": "國際性", "replace": "國際"}, {"match": "垃圾場", "replace": "垃圾"}, {"match": "大規模", "replace": "規模"}, {"match": "廢棄物", "replace": "廢棄"}, {"match": "愛滋病", "replace": "愛滋"}, {"match": "成交量", "replace": "成交"}, {"match": "接觸到", "replace": "接觸"}, {"match": "掩埋場", "replace": "掩埋"}, {"match": "正確率", "replace": "正確"}, {"match": "清華園", "replace": "清華"}, {"match": "聯誼會", "replace": "聯誼"}, {"match": "調查站", "replace": "調查"}, {"match": "轉換成", "replace": "轉換"}, {"match": "開放式", "replace": "開放"}, {"match": "開玩笑", "replace": "玩笑"}, {"match": "陽明山", "replace": "陽明"}, {"match": "雜貨店", "replace": "雜貨"}, {"match": "電視機", "replace": "電視"}, {"match": "高品質", "replace": "品質"}, {"match": "鬆弛法", "replace": "鬆弛"}, {"match": "共產主義", "replace": "共產"}, {"match": "資本主義", "replace": "資本"}, {"match": "微處理器", "replace": "處理器"}, {"match": "有線電視", "replace": "電視"}, {"match": "隨選視訊", "replace": "視訊"}, {"match": "電信總局", "replace": "總局"}, {"match": "進一步", "replace": ["一步", "進一步"]}, {"match": "差不多", "replace": ["不多", "差不多"]}, {"match": "忍不住", "replace": ["不住", "忍不住"]}, {"match": "不見得", "replace": ["見得", "不見得"]}, {"match": "有助於", "replace": ["助於", "有助於"]}, {"match": "舊金山", "replace": ["金山", "舊金山"]}, {"match": "大躍進", "replace": ["躍進", "大躍進"]}, {"match": "半導體", "replace": ["導體", "半導體"]}, {"match": "總幹事", "replace": ["幹事", "總幹事"]}, {"match": "兩廳院", "replace": ["廳院", "兩廳院"]}]}}, "training": {"embeddings": {"run_suffix": "gcp-a100-trans-t3", "max_training_text_length": 2048, "dataset": {"build_with": "translations", "preview_length": 64, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\\nChinese: {lang_2}", "Chinese: {lang_2}\\nEnglish: {lang_1}"]}}, "only_train_parameters_matching": ["embed"], "training_arguments": {"num_train_epochs": 1, "per_device_train_batch_size": 16, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "save_steps": 1000, "save_total_limit": 8}}}, "push_outputs_to_hf": true, "report_to_wandb": true, "wandb_project": "zh-tw-llm"} ```
Amalq/roberta-large-schizophrenia-v3	Amalq	2023-05-12T12:59:02Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-05-12T11:19:09Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-large-schizophrenia-v3 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-large-schizophrenia-v3 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5296 ## 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: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 248 \| 1.5515 \| \| No log \| 2.0 \| 496 \| 1.5410 \| \| 1.6456 \| 3.0 \| 744 \| 1.5340 \| \| 1.6456 \| 4.0 \| 992 \| 1.5326 \| \| 1.5589 \| 5.0 \| 1240 \| 1.5248 \| \| 1.5589 \| 6.0 \| 1488 \| 1.5308 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
jccj/q-Taxi-v3	jccj	2023-05-12T12:56:24Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-05T14:51:41Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="jccj/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
PhDmath/distilbert-base-uncased-finetuned-emotion	PhDmath	2023-05-12T12:52:33Z	106	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-12T11:33:07Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9295 - name: F1 type: f1 value: 0.9293576247301535 --- <!-- 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2169 - Accuracy: 0.9295 - F1: 0.9294 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8632 \| 1.0 \| 250 \| 0.3270 \| 0.904 \| 0.9008 \| \| 0.253 \| 2.0 \| 500 \| 0.2169 \| 0.9295 \| 0.9294 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
choohan/finetuned-opt-squad-covidqa-dataset	choohan	2023-05-12T12:45:25Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T12:41:54Z	--- license: other tags: - generated_from_trainer model-index: - name: finetuned-opt-squad-covidqa-dataset 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. --> # finetuned-opt-squad-covidqa-dataset This model is a fine-tuned version of [choohan/finetuned-opt-squad-dataset-3](https://huggingface.co/choohan/finetuned-opt-squad-dataset-3) on the None dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
choohan/finetuned-opt-covidqa-dataset	choohan	2023-05-12T12:41:16Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T12:37:42Z	--- license: other tags: - generated_from_trainer model-index: - name: finetuned-opt-covidqa-dataset 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. --> # finetuned-opt-covidqa-dataset This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
NabeelUppel/ppo-LunaLander-v2	NabeelUppel	2023-05-12T12:36:49Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T12:36:30Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 275.91 +/- 21.43 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
apcl/jam	apcl	2023-05-12T12:24:47Z	0	2	null	[ "dataset:apcl/jm52m", "license:bigscience-openrail-m", "region:us" ]	null	2023-04-29T21:16:07Z	--- license: bigscience-openrail-m datasets: - apcl/jm52m --- # Jam Jam is a GPT2-like model for research in fine-grained Java analysis. It is intended for fine-grained analysis of Java source code at the level of methods, statements, and variables, as a foundation for downstream tasks like code completion, comment generation, and automated bug repair. --- ## Jam Training Details - We trained the jam model using the training procedures from Daniel Grittner's [NanoGPT-LoRA](https://github.com/danielgrittner/nanoGPT-LoRA) - The dataset used to train our model is our own dataset [jm52m dataset](https://huggingface.co/datasets/apcl/jm52m), which consists of the processed source code of 52 million Java methods. - We train the model on [training set](https://huggingface.co/datasets/apcl/jm52m/blob/main/train.bin) for 1 epoch, roughly 300,000 training iterations. - Our [GitHub repo](https://github.com/apcl-research/jam/blob/main) contains the code for re-training using the [raw data](https://huggingface.co/datasets/apcl/jm52m/blob/main/fundats-j1.pkl) \| Hyperparameter \| Description \| Value \| \| ----------- \| ----------- \|------------\| \|e \| embedding dimensions \| 1024 \| \|L \| number of layers \| 24 \| \|h \| attention heads \| 16 \| \|c \| block size / context length \| 256 \| \|b \| batch size \| 4 \| \|a \| accumulation steps \| 32 \| \|d \| dropout \| 0.20 \| \|r \| learning rate \| 3e-5 \| \|y \| weight decay \| 1e-1 \| We train our models using a single NVidia A5000 GPU. --- ## Jam Projects Current projects using the JAM pre-trained model can be found at our Github repository: https://github.com/apcl-research/jam
eachadea/ggml-gpt4-x-vicuna-13b	eachadea	2023-05-12T12:20:07Z	0	13	null	[ "conversational", "region:us" ]	text-generation	2023-05-12T11:07:21Z	--- pipeline_tag: conversational --- ggml version (post-PR #1405) As a base model used https://huggingface.co/eachadea/vicuna-13b-1.1 Finetuned on Teknium's GPTeacher dataset, unreleased Roleplay v2 dataset, GPT-4-LLM dataset, and Nous Research Instruct Dataset Approx 180k instructions, all from GPT-4, all cleaned of any OpenAI censorship/"As an AI Language Model" etc. Base model still has OpenAI censorship. Soon, a new version will be released with cleaned vicuna from https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltere Trained on 8 A100-80GB GPUs for 5 epochs following Alpaca deepspeed training code. Nous Research Instruct Dataset will be released soon. GPTeacher, Roleplay v2 by https://huggingface.co/teknium Wizard LM by https://github.com/nlpxucan Nous Research Instruct Dataset by https://huggingface.co/karan4d and https://huggingface.co/huemin Compute provided by our project sponsor https://redmond.ai/
trustvare/TrustVare-OST-Duplicate-Remover	trustvare	2023-05-12T12:15:47Z	0	0	null	[ "region:us" ]	null	2023-05-12T11:51:57Z	TrustVare OST Duplicate Remover is a software application that can be used to find and remove duplicate items from an offline Outlook data file (.ost). OST files are created by Microsoft Outlook when it is configured to work in offline mode. They store email messages, contacts, calendars, and other data that are not currently synchronized with the Microsoft Exchange server. Removing duplicate items from an OST file can improve the performance of Microsoft Outlook by reducing the amount of data that the application has to process. Removing duplicate items from an OST file can improve the performance of Microsoft Outlook by reducing the amount of data that the application has to process.The software is easy to use and has a number of features that make it a valuable tool for Microsoft Outlook users. Read More:- https://www.trustvare.com/duplicate-remover/ost/
OliAiart/Stable-diffusion	OliAiart	2023-05-12T12:11:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T11:20:47Z	--- license: creativeml-openrail-m ---
choohan/finetuned-opt-squad-dataset-4	choohan	2023-05-12T12:05:01Z	95	0	transformers	[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T11:40:39Z	--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: finetuned-opt-squad-dataset-4 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. --> # finetuned-opt-squad-dataset-4 This model is a fine-tuned version of [choohan/finetuned-opt-squad-dataset-3](https://huggingface.co/choohan/finetuned-opt-squad-dataset-3) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
farmerinatechstack/PPO-LunarLander-v2	farmerinatechstack	2023-05-12T12:01:22Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T12:01:04Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.58 +/- 15.29 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
henripett/ppo_lunar_lander_v2	henripett	2023-05-12T11:53:01Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T11:34:47Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 126.66 +/- 105.78 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
dian34323/cynthiajkt48	dian34323	2023-05-12T11:52:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T11:50:47Z	--- license: creativeml-openrail-m ---
OliAiart/models	OliAiart	2023-05-12T11:22:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T01:04:25Z	--- license: creativeml-openrail-m ---
choohan/finetuned-opt-squad-dataset-2	choohan	2023-05-12T11:08:16Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T10:42:34Z	--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: finetuned-opt-squad-dataset-2 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. --> # finetuned-opt-squad-dataset-2 This model is a fine-tuned version of [choohan/finetuned-opt-squad-dataset](https://huggingface.co/choohan/finetuned-opt-squad-dataset) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Bennet1996/donut-base-sroie8	Bennet1996	2023-05-12T11:03:16Z	46	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-05-11T16:41:30Z	--- tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie8 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. --> # donut-base-sroie8 This model is a fine-tuned version of [Bennet1996/donut-base-sroie6](https://huggingface.co/Bennet1996/donut-base-sroie6) on the imagefolder dataset. ## 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: 3e-05 - train_batch_size: 11 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Gayathri142214002/t5-paraphrase	Gayathri142214002	2023-05-12T10:42:57Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-12T06:01:40Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-paraphrase 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. --> # t5-paraphrase This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.3873 - eval_runtime: 785.1467 - eval_samples_per_second: 19.012 - eval_steps_per_second: 19.012 - epoch: 0.0 - step: 20 ## 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
AXX1995/anastasianadav1	AXX1995	2023-05-12T10:40:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-12T10:38:10Z	--- license: creativeml-openrail-m ---
iamjoy/ppo-Pyramids	iamjoy	2023-05-12T10:40:16Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-05-12T10:40:11Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: iamjoy/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
seanghay/xlm-roberta-base-imdb	seanghay	2023-05-12T10:38:06Z	451	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-12T09:56:06Z	--- license: mit tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: xlm-roberta-base-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93936 --- <!-- 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. --> # xlm-roberta-base-imdb This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2223 - Accuracy: 0.9394 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2345 \| 1.0 \| 1563 \| 0.1808 \| 0.9306 \| \| 0.1612 \| 2.0 \| 3126 \| 0.2223 \| 0.9394 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
choohan/finetuned-opt-squad-dataset	choohan	2023-05-12T10:34:15Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T10:08:35Z	--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: finetuned-opt-squad-dataset 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. --> # finetuned-opt-squad-dataset This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
shivansh-ka/Multilingual-Toxic-Comment-Roberta-best	shivansh-ka	2023-05-12T10:15:00Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2023-05-12T10:13:13Z	--- library_name: keras --- ## 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: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| 1e-06 \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| False \| \| is_legacy_optimizer \| False \| \| learning_rate \| 9.999999747378752e-06 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \|
choohan/opt-finetuned-squad-dataset	choohan	2023-05-12T10:03:18Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-05-12T06:52:21Z	--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: opt-finetuned-squad-dataset 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. --> # opt-finetuned-squad-dataset This model is a fine-tuned version of [choohan/opt-finetuned-squad-dataset](https://huggingface.co/choohan/opt-finetuned-squad-dataset) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
casellimarco/Taxi	casellimarco	2023-05-12T09:32:12Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T09:32:11Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="casellimarco/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
xkirinx/bart-large-mnli-rotten-tomatoes	xkirinx	2023-05-12T09:30:26Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text-classification", "generated_from_trainer", "dataset:rotten_tomatoes", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-12T08:18:32Z	--- license: mit tags: - generated_from_trainer datasets: - rotten_tomatoes model-index: - name: bart-large-mnli-rotten-tomatoes 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. --> # bart-large-mnli-rotten-tomatoes This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.6032 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1965 \| 1.0 \| 1067 \| 0.6041 \| \| 0.1604 \| 2.0 \| 2134 \| 0.6165 \| \| 0.0796 \| 3.0 \| 3201 \| 0.6032 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
LinaSaba/BertDisasterTweets	LinaSaba	2023-05-12T09:28:32Z	0	0	sklearn	[ "sklearn", "clim", "text-classification", "license:afl-3.0", "region:us" ]	text-classification	2023-05-12T08:39:25Z	--- license: afl-3.0 metrics: - accuracy - code_eval library_name: sklearn pipeline_tag: text-classification tags: - clim --- # bert-model-disaster-tweets-classification This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Natural-Language-Processing-with-Disaster-Tweets dataset. It achieves the following results on the evaluation set: - Accuracy: 0.82 - F1 Score: 0.82 ## Model description Load BertForSequenceClassification, the pretrained BERT model with a single linear classification layer on top, using an optimizer : incorporates weight decay, which is a regularization technique that helps prevent overfitting during training. ## Intended uses & limitations Use to classify if a tweet represents a disaster or not. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with epsilon = 1e-8. - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Epoch \| Average training loss \| Training epoch \| Accuracy \| F1 \| \|:-----:\|:---------------------:\|:---------------:\|:--------:\|:----:\| \| 1.0 \| 0.47 \| 0:00:49 \| 0.82 \| 0.82 \| \| 2.0 \| 0.36 \| 0:00:36 \| 0.82 \| 0.82 \| \| 3.0 \| 0.29 \| 0:00:51 \| 0.82 \| 0.82 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
jakubgajski/PyramydsRND	jakubgajski	2023-05-12T09:15:44Z	23	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-05-12T06:45:28Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: jakubgajski/PyramydsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
CatherineGeng/vit-base-patch16-224-in21k-euroSat	CatherineGeng	2023-05-12T09:01:57Z	63	0	transformers	[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-12T01:29:25Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: CatherineGeng/vit-base-patch16-224-in21k-euroSat 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. --> # CatherineGeng/vit-base-patch16-224-in21k-euroSat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4854 - Train Accuracy: 0.9415 - Train Top-3-accuracy: 0.9856 - Validation Loss: 0.1574 - Validation Accuracy: 0.9817 - Validation Top-3-accuracy: 0.9988 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3590, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 0.4854 \| 0.9415 \| 0.9856 \| 0.1574 \| 0.9817 \| 0.9988 \| 0 \| ### Framework versions - Transformers 4.29.1 - TensorFlow 2.4.0 - Datasets 2.12.0 - Tokenizers 0.13.3
debin/Taxi-v3	debin	2023-05-12T09:01:37Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T09:01:35Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.75 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="debin/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
jesse0506/test	jesse0506	2023-05-12T09:00:43Z	0	0	null	[ "region:us" ]	null	2023-05-12T08:58:59Z	import gradio as gr def process(image): #image[:,:,0] red image[:,:,1]=0 image[:,:,2]=0 return image gr.Interface(process,"image","image").launch(share=True)
debin/q-FrozenLake-v1-4x4-noSlippery	debin	2023-05-12T08:58:15Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-12T08:58:12Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="debin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
xqchq/TextClassificationTHUCNews	xqchq	2023-05-12T08:52:32Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:thuc_news", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-12T07:24:31Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - thuc_news model-index: - name: TextClassificationTHUCNews 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. --> # TextClassificationTHUCNews This model is a fine-tuned version of [hfl/minirbt-h256](https://huggingface.co/hfl/minirbt-h256) on the thuc_news dataset. ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

sana-ngu/bart-base-finetuned-summarize-scientific-articles

sana-ngu

2023-05-12T20:07:11Z

101

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-12T19:30:00Z

# How to use ```python from transformers import pipeline summarizer = pipeline("summarization", model="sana-ngu/bart-base-finetuned-summarize-scientific-articles") article = "The novel Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus—2 (SARS-CoV-2), in Africa is characterised by a more substantial proportion of asymptomatic (or mildly symptomatic) individuals thought to be playing a role in the spread of the infection. The exact proportion and degree of infectiousness of asymptomatic individuals remains unclear. Studies however indicate that their management is crucial for control of SARS-CoV-2 transmission. We developed a simplified deterministic susceptible-exposed-infectious-removed (SEIR) mathematical model to assess the effect of active isolation of SARS-CoV-2 infected but asymptomatic individuals through blanket testing for control of the outbreak in Lusaka Province of Zambia. Here we modelled two scenarios; (1) assuming asymptomatic individuals comprised 70% of all COVID-19 cases and (2) asymptomatic individuals comprised only 50% of the cases. For contrast, the model was assessed first under the assumption that asymptomatic individuals are equally as infectious as symptomatic individuals and then secondly, and more likely, assuming asymptomatic individuals are only half as infectious as symptomatic individuals. For the model assuming 70% asymptomatic cases, a minimum sustained daily blanket testing rate of ≥ 7911 tests/100000 population was sufficient to control the outbreak if asymptomatic individuals are only half as infectious while if equal infectiousness was assumed then a testing rate of ≥ 10028 tests/ 100000 population would be required. For 50% asymptomatic, minimum blanket testing rates of ≥ 4540 tests/ 100000 population was sufficient to control the outbreak at both assumed levels of infectiousness for asymptomatic individuals relative to symptomatic individuals. Discussion and conclusion Our model predicts that active isolation of COVID-19 cases, including asymptomatic individuals, through blanket testing can be used as a possible measure for the control of the SARS-Cov-2 transmission in Lusaka, Zambia, but it would come at a high cost." summarizer(conversation) ```

sana-ngu/t5-large-finetuned-summarize-scientific-articles

sana-ngu

2023-05-12T20:05:19Z

94

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-12T19:21:03Z

# How to use ```python from transformers import pipeline summarizer = pipeline("summarization", model="sana-ngu/t5-large-finetuned-summarize-scientific-articles") article = "The novel Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus—2 (SARS-CoV-2), in Africa is characterised by a more substantial proportion of asymptomatic (or mildly symptomatic) individuals thought to be playing a role in the spread of the infection. The exact proportion and degree of infectiousness of asymptomatic individuals remains unclear. Studies however indicate that their management is crucial for control of SARS-CoV-2 transmission. We developed a simplified deterministic susceptible-exposed-infectious-removed (SEIR) mathematical model to assess the effect of active isolation of SARS-CoV-2 infected but asymptomatic individuals through blanket testing for control of the outbreak in Lusaka Province of Zambia. Here we modelled two scenarios; (1) assuming asymptomatic individuals comprised 70% of all COVID-19 cases and (2) asymptomatic individuals comprised only 50% of the cases. For contrast, the model was assessed first under the assumption that asymptomatic individuals are equally as infectious as symptomatic individuals and then secondly, and more likely, assuming asymptomatic individuals are only half as infectious as symptomatic individuals. For the model assuming 70% asymptomatic cases, a minimum sustained daily blanket testing rate of ≥ 7911 tests/100000 population was sufficient to control the outbreak if asymptomatic individuals are only half as infectious while if equal infectiousness was assumed then a testing rate of ≥ 10028 tests/ 100000 population would be required. For 50% asymptomatic, minimum blanket testing rates of ≥ 4540 tests/ 100000 population was sufficient to control the outbreak at both assumed levels of infectiousness for asymptomatic individuals relative to symptomatic individuals. Discussion and conclusion Our model predicts that active isolation of COVID-19 cases, including asymptomatic individuals, through blanket testing can be used as a possible measure for the control of the SARS-Cov-2 transmission in Lusaka, Zambia, but it would come at a high cost." summarizer(conversation) ```

sana-ngu/t5-small-finetuned-summarize-scientific-articles

sana-ngu

2023-05-12T20:02:49Z

10

2

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-12T19:14:02Z

# How to use ```python from transformers import pipeline summarizer = pipeline("summarization", model="sana-ngu/t5-small-finetuned-summarize-scientific-articles") article = "The novel Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus—2 (SARS-CoV-2), in Africa is characterised by a more substantial proportion of asymptomatic (or mildly symptomatic) individuals thought to be playing a role in the spread of the infection. The exact proportion and degree of infectiousness of asymptomatic individuals remains unclear. Studies however indicate that their management is crucial for control of SARS-CoV-2 transmission. We developed a simplified deterministic susceptible-exposed-infectious-removed (SEIR) mathematical model to assess the effect of active isolation of SARS-CoV-2 infected but asymptomatic individuals through blanket testing for control of the outbreak in Lusaka Province of Zambia. Here we modelled two scenarios; (1) assuming asymptomatic individuals comprised 70% of all COVID-19 cases and (2) asymptomatic individuals comprised only 50% of the cases. For contrast, the model was assessed first under the assumption that asymptomatic individuals are equally as infectious as symptomatic individuals and then secondly, and more likely, assuming asymptomatic individuals are only half as infectious as symptomatic individuals. For the model assuming 70% asymptomatic cases, a minimum sustained daily blanket testing rate of ≥ 7911 tests/100000 population was sufficient to control the outbreak if asymptomatic individuals are only half as infectious while if equal infectiousness was assumed then a testing rate of ≥ 10028 tests/ 100000 population would be required. For 50% asymptomatic, minimum blanket testing rates of ≥ 4540 tests/ 100000 population was sufficient to control the outbreak at both assumed levels of infectiousness for asymptomatic individuals relative to symptomatic individuals. Discussion and conclusion Our model predicts that active isolation of COVID-19 cases, including asymptomatic individuals, through blanket testing can be used as a possible measure for the control of the SARS-Cov-2 transmission in Lusaka, Zambia, but it would come at a high cost." summarizer(conversation) ```

hyoni/kogpt2-base-v2-finetuned-klue-ner

hyoni

2023-05-12T19:55:52Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "token-classification", "generated_from_trainer", "dataset:klue", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

token-classification

2023-05-12T19:42:52Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - klue metrics: - f1 model-index: - name: kogpt2-base-v2-finetuned-klue-ner results: - task: name: Token Classification type: token-classification dataset: name: klue type: klue config: ner split: validation args: ner metrics: - name: F1 type: f1 value: 0.37298165525403665 ---  # kogpt2-base-v2-finetuned-klue-ner This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.4076 - F1: 0.3730 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6084 | 1.0 | 876 | 0.5353 | 0.2118 | | 0.3911 | 2.0 | 1752 | 0.4691 | 0.3041 | | 0.2855 | 3.0 | 2628 | 0.4076 | 0.3730 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Multi-Domain-Expert-Learning/merge-arxiv-freelaw-pubmed

Multi-Domain-Expert-Learning

2023-05-12T19:13:01Z

161

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "MDEL", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-12T19:10:40Z

--- tags: - MDEL --- # Model Name Multi-Domain-Expert-Layers/merge-arxiv-freelaw-pubmed # Model Description This model was generated by averaging the weights of the following models - [Multi-Domain-Expert-Layers/expert-pubmed_central](https://huggingface.co/Multi-Domain-Expert-Layers/expert-pubmed_central) - [Multi-Domain-Expert-Layers/expert-freelaw](https://huggingface.co/Multi-Domain-Expert-Layers/expert-freelaw) - [Multi-Domain-Expert-Layers/expert-arxiv](https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv)

guillaumekln/faster-whisper-large-v1

guillaumekln

2023-05-12T18:58:39Z

327

3

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]

automatic-speech-recognition

2023-03-28T12:33:41Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper large-v1 model for CTranslate2 This repository contains the conversion of [openai/whisper-large](https://huggingface.co/openai/whisper-large) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v1") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-large --output_dir faster-whisper-large-v1 \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-large).**

guillaumekln/faster-whisper-large-v2

guillaumekln

2023-05-12T18:58:25Z

161,400

193

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]

automatic-speech-recognition

2023-03-23T10:36:06Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper large-v2 model for CTranslate2 This repository contains the conversion of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-large-v2 --output_dir faster-whisper-large-v2 \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-large-v2).**

guillaumekln/faster-whisper-medium

guillaumekln

2023-05-12T18:58:10Z

15,872

32

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]

automatic-speech-recognition

2023-03-23T10:28:55Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper medium model for CTranslate2 This repository contains the conversion of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("medium") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-medium --output_dir faster-whisper-medium \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-medium).**

guillaumekln/faster-whisper-small.en

guillaumekln

2023-05-12T18:57:44Z

110

1

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "license:mit", "region:us" ]

automatic-speech-recognition

2023-03-23T10:20:17Z

--- language: - en tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper small.en model for CTranslate2 This repository contains the conversion of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("small.en") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-small.en --output_dir faster-whisper-small.en \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-small.en).**

seviladiguzel/roberta-base-finetuned-squad_roberta

seviladiguzel

2023-05-12T18:57:33Z

18

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T17:52:56Z

--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base-finetuned-squad_roberta results: [] ---  # roberta-base-finetuned-squad_roberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.8537 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8917 | 1.0 | 5536 | 0.8669 | | 0.6739 | 2.0 | 11072 | 0.8537 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

guillaumekln/faster-whisper-base

guillaumekln

2023-05-12T18:57:32Z

110,649

9

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]

automatic-speech-recognition

2023-03-23T10:19:37Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper base model for CTranslate2 This repository contains the conversion of [openai/whisper-base](https://huggingface.co/openai/whisper-base) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("base") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-base --output_dir faster-whisper-base \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-base).**

guillaumekln/faster-whisper-tiny

guillaumekln

2023-05-12T18:57:08Z

2,644

5

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]

automatic-speech-recognition

2023-03-23T10:14:28Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper tiny model for CTranslate2 This repository contains the conversion of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("tiny") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-tiny --output_dir faster-whisper-tiny \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-tiny).**

guillaumekln/faster-whisper-tiny.en

guillaumekln

2023-05-12T18:56:53Z

378

2

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "license:mit", "region:us" ]

automatic-speech-recognition

2023-03-23T10:17:41Z

--- language: - en tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper tiny.en model for CTranslate2 This repository contains the conversion of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("tiny.en") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-tiny.en --output_dir faster-whisper-tiny.en \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-tiny.en).**

Ktang2k/ppo-Huggy-1

Ktang2k

2023-05-12T18:54:20Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-05-12T18:54:13Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: Ktang2k/ppo-Huggy-1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

moisesmota/donut-base-sroie

moisesmota

2023-05-12T18:52:23Z

1

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-04-28T13:38:18Z

--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie results: [] ---  # donut-base-sroie This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.2

jainr3/sd-diffusiondb-pixelart-model-lora

jainr3

2023-05-12T18:51:50Z

3

2

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-05-12T18:39:45Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - jainr3/sd-diffusiondb-pixelart-model-lora These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the jainr3/diffusiondb-pixelart dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

Abdeldjalil21/djalil-base-sentiment-model-10k-samples

Abdeldjalil21

2023-05-12T18:29:32Z

5

0

transformers

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

text-classification

2023-05-09T23:59:57Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: djalil-base-sentiment-model-10k-samples results: [] ---  # djalil-base-sentiment-model-10k-samples This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4204 - Accuracy: 0.827 - F1: 0.8171 ## 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: 2 ### Training results ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

stillerman/MDEL-github-arxiv

stillerman

2023-05-12T18:27:15Z

4

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "MDEL", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-12T18:24:42Z

--- tags: - MDEL --- # Model Name stillerman/MDEL-github-arxiv # Model Description This model was generated by averaging the weights of the following models - [Multi-Domain-Expert-Layers/expert-github](https://huggingface.co/Multi-Domain-Expert-Layers/expert-github) - [Multi-Domain-Expert-Layers/expert-arxiv](https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv)

Guilherme34/Jennifer-lora-7bv3

Guilherme34

2023-05-12T18:25:36Z

0

1

null

[ "tensorboard", "pt", "region:us" ]

null

2023-05-12T17:32:20Z

--- language: - pt --- Esta é a terceira versão de uma inteligência artificial finetunada que fala em português do brasil, ela foi treinada em cima do llama 7b de decapoda, e foi treinada no LLaMA-LoRA Tuner de zetavg utilizando o dataset da cabrita lora e o alpaca cleaned Divirta-se!

stillerman/MDEL-pubmed-feelaw-github-arxiv

stillerman

2023-05-12T18:18:39Z

161

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "MDEL", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-12T18:11:40Z

--- tags: - MDEL --- # Model Name stillerman/MDEL-pubmed-feelaw-github-arxiv # Model Description This model was generated by averaging the weights of the following models - [Multi-Domain-Expert-Layers/expert-pubmed_central](https://huggingface.co/Multi-Domain-Expert-Layers/expert-pubmed_central) - [Multi-Domain-Expert-Layers/expert-freelaw](https://huggingface.co/Multi-Domain-Expert-Layers/expert-freelaw) - [Multi-Domain-Expert-Layers/expert-github](https://huggingface.co/Multi-Domain-Expert-Layers/expert-github) - [Multi-Domain-Expert-Layers/expert-arxiv](https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv)

aloutzidis/pegasus-large-cnn-quantized

aloutzidis

2023-05-12T18:08:53Z

4

0

transformers

[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "en", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-11T20:55:16Z

--- tags: - generated_from_trainer model-index: - name: pegasus-large-cnn-quantized results: [] license: apache-2.0 datasets: - cnn_dailymail metrics: - bleu - rouge language: - en ---  # pegasus-large-cnn-quantized This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on CNN/DailyMail dataset. ![model image](./cover.jpeg) ## Model description Online news reading has become one of the most popular ways to consume the latest news. News aggregation websites such as Google News and Yahoo News have made it easy for users to find the latest news and provide thousands of news stories from hundreds of news publishers. As people have limited time, reading all news articles is not feasible. The success of zero-shot and few-shot prompting with models like GPT-3 has led to a paradigm shift in NLP. So, in the era of ChatGPT, we we conducted experiments using various large language models (LLMs) such as BART and PEGASUS to improve the quality and coherence of the generated news summaries. This is a quantized model finetuned on the CNN/DailyMail dataset. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 123 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Framework versions - Transformers 4.29.0 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

madhav-devrev/flan-t5-small-work-filters

madhav-devrev

2023-05-12T18:03:58Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-10T17:53:30Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: flan-t5-small-work-filters results: [] ---  # flan-t5-small-work-filters This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0346 - eval_rouge1: 46.2714 - eval_rouge2: 36.488 - eval_rougeL: 45.9709 - eval_rougeLsum: 45.9674 - eval_gen_len: 18.9753 - eval_runtime: 44.1148 - eval_samples_per_second: 23.87 - eval_steps_per_second: 2.992 - epoch: 5.0 - step: 660 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

millionhz/segformer-b0-finetuned-flame

millionhz

2023-05-12T17:55:49Z

8

0

transformers

[ "transformers", "pytorch", "safetensors", "segformer", "vision", "image-segmentation", "license:mit", "endpoints_compatible", "region:us" ]

image-segmentation

2023-05-12T14:59:06Z

--- license: mit tags: - vision - image-segmentation --- # SegFormer (b0-sized) model fine-tuned on FLAME The model was trained for a deep learning project titled [Forest Fire Detection](https://github.com/millionhz/forest-fire-detection). ## Model Description The model is intended to be used for fire detection through image segmentation. The provided pretrained model was finetuned on the [FLAME](https://dx.doi.org/10.21227/qad6-r683) dataset for 3 epochs with a learning rate of 1e-3 and was able to score an IOU score of **0.745** on the test examples. # How to use Here is how to use this model to segment an image: ```python from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation from PIL import Image import requests processor = AutoFeatureExtractor.from_pretrained("millionhz/segformer-b0-finetuned-flame") model = SegformerForSemanticSegmentation.from_pretrained("millionhz/segformer-b0-finetuned-flame") url = <add url here> image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) ``` ## License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).

eason0203/swin-tiny-patch4-window7-224-arty-bg-classifier

eason0203

2023-05-12T17:30:14Z

187

0

transformers

[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-05-12T07:42:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: swin-tiny-patch4-window7-224-arty-bg-classifier results: [] ---  # swin-tiny-patch4-window7-224-arty-bg-classifier This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0086 - eval_accuracy: 0.9975 - eval_runtime: 102.2105 - eval_samples_per_second: 127.639 - eval_steps_per_second: 1.331 - epoch: 1.84 - step: 250 ## 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: 96 - eval_batch_size: 96 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 384 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Halcyonindo/an1kulora

Halcyonindo

2023-05-12T17:27:24Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T17:26:20Z

--- license: creativeml-openrail-m ---

hemagamal/mdeberta_Quran_qa

hemagamal

2023-05-12T17:24:14Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "deberta-v2", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T17:07:25Z

--- license: mit tags: - generated_from_trainer model-index: - name: mdeberta_Quran_qa results: [] ---  # mdeberta_Quran_qa This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6248 ## 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: 8 - eval_batch_size: 10 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 89 | 2.2395 | | No log | 2.0 | 178 | 2.3282 | | No log | 3.0 | 267 | 2.4226 | | No log | 4.0 | 356 | 2.6551 | | No log | 5.0 | 445 | 2.9332 | | 1.0317 | 6.0 | 534 | 3.2124 | | 1.0317 | 7.0 | 623 | 3.2915 | | 1.0317 | 8.0 | 712 | 3.5401 | | 1.0317 | 9.0 | 801 | 3.6132 | | 1.0317 | 10.0 | 890 | 3.6248 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AustinCarthy/Base_10Kphish_benignFall_IL_10Krealphish

AustinCarthy

2023-05-12T17:22:27Z

161

0

transformers

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

text-classification

2023-05-12T16:10:35Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Base_10Kphish_benignFall_IL_10Krealphish_0.75 results: [] ---  # Base_10Kphish_benignFall_IL_10Krealphish_0.75 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0551 - Accuracy: 0.9938 - F1: 0.9303 - Precision: 0.9982 - Recall: 0.871 - Roc Auc Score: 0.9355 - Tpr At Fpr 0.01: 0.8794 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0079 | 1.0 | 6563 | 0.0209 | 0.9956 | 0.9525 | 0.9878 | 0.9196 | 0.9595 | 0.862 | | 0.003 | 2.0 | 13126 | 0.0338 | 0.9949 | 0.9438 | 0.9940 | 0.8984 | 0.9491 | 0.8796 | | 0.0024 | 3.0 | 19689 | 0.0410 | 0.9948 | 0.9427 | 0.9949 | 0.8958 | 0.9478 | 0.8648 | | 0.0014 | 4.0 | 26252 | 0.0493 | 0.9941 | 0.9342 | 0.9982 | 0.878 | 0.9390 | 0.881 | | 0.0003 | 5.0 | 32815 | 0.0551 | 0.9938 | 0.9303 | 0.9982 | 0.871 | 0.9355 | 0.8794 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2

berluk/resnet50-fish-rec

berluk

2023-05-12T17:18:31Z

5

0

keras

[ "keras", "tf-keras", "image-classification", "region:us" ]

image-classification

2023-05-12T13:15:58Z

bohanhou14/abortion_news_model

bohanhou14

2023-05-12T17:09:43Z

161

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-12T07:20:21Z

--- license: apache-2.0 language: - en ---

asenella/mmnist_MoPoEconfig2_seed_1_ratio_0_c

asenella

2023-05-12T17:01:55Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-05-12T17:01:47Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

aprilzoo/distilbert-base-uncased-finetuned-imdb

aprilzoo

2023-05-12T16:53:13Z

61

0

transformers

[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-05-06T18:51:57Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: aprilzoo/distilbert-base-uncased-finetuned-imdb results: [] ---  # aprilzoo/distilbert-base-uncased-finetuned-imdb 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: 4.2701 - Validation Loss: 2.6432 - 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': -997, '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 | |:----------:|:---------------:|:-----:| | 4.2701 | 2.6432 | 0 | ### Framework versions - Transformers 4.29.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

spectraldecomp/ppo-LunarLander-v2

spectraldecomp

2023-05-12T16:44:30Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T16:44:08Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.29 +/- 12.94 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

dian34323/melatijkt48

dian34323

2023-05-12T16:32:24Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T16:31:15Z

--- license: creativeml-openrail-m ---

muhammadravi251001/fine-tuned-DatasetQAS-TYDI-QA-ID-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05

muhammadravi251001

2023-05-12T16:25:57Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-05-05T04:56:01Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: fine-tuned-DatasetQAS-TYDI-QA-ID-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05 results: [] ---  # fine-tuned-DatasetQAS-TYDI-QA-ID-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9402 - Exact Match: 69.3662 - F1: 82.0036 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - 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 | Exact Match | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:| | 6.2837 | 0.5 | 19 | 3.6986 | 8.4507 | 17.7536 | | 6.2837 | 0.99 | 38 | 2.5899 | 18.4859 | 29.7766 | | 3.6833 | 1.5 | 57 | 1.7044 | 42.6056 | 56.8157 | | 3.6833 | 1.99 | 76 | 1.2711 | 53.3451 | 70.2979 | | 3.6833 | 2.5 | 95 | 1.1063 | 62.3239 | 75.7765 | | 1.5024 | 2.99 | 114 | 1.0275 | 64.2606 | 78.0460 | | 1.5024 | 3.5 | 133 | 0.9941 | 65.8451 | 79.1313 | | 1.0028 | 3.99 | 152 | 0.9642 | 67.4296 | 80.6196 | | 1.0028 | 4.5 | 171 | 0.9682 | 69.0141 | 82.4975 | | 1.0028 | 4.99 | 190 | 0.9455 | 67.9577 | 81.0386 | | 0.7765 | 5.5 | 209 | 0.9802 | 67.7817 | 81.0844 | | 0.7765 | 5.99 | 228 | 0.9402 | 69.3662 | 82.0036 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.2.0 - Tokenizers 0.13.2

Amalq/roberta-large-schizophrenia-v12

Amalq

2023-05-12T16:18:11Z

163

0

transformers

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

fill-mask

2023-05-12T15:54:41Z

--- license: mit tags: - generated_from_trainer model-index: - name: roberta-large-schizophrenia-v12 results: [] ---  # roberta-large-schizophrenia-v12 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5260 ## 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: 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: 3.0 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

grenmon/pegasus-x-large-finetuned-summarization

grenmon

2023-05-12T16:06:30Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "pegasus_x", "text2text-generation", "summarization", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-05-12T15:25:46Z

--- tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: pegasus-x-large-finetuned-summarization results: [] ---  # pegasus-x-large-finetuned-summarization This model is a fine-tuned version of [google/pegasus-x-large](https://huggingface.co/google/pegasus-x-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9503 - Rouge1: 54.656 - Rouge2: 33.2773 - Rougel: 44.7797 - Rougelsum: 51.2888 ## 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: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.1821 | 1.0 | 308 | 0.9389 | 49.6848 | 29.0753 | 40.9828 | 47.1619 | | 0.8932 | 2.0 | 616 | 0.8955 | 49.6176 | 28.8588 | 41.7149 | 47.3719 | | 0.7433 | 3.0 | 924 | 0.9202 | 54.0016 | 31.8254 | 43.4441 | 50.9312 | | 0.6495 | 4.0 | 1232 | 0.9321 | 52.6912 | 31.6843 | 43.8896 | 49.8726 | | 0.587 | 5.0 | 1540 | 0.9503 | 54.656 | 33.2773 | 44.7797 | 51.2888 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Mikepool117/q-FrozenLake-v1-4x4-noSlippery

Mikepool117

2023-05-12T16:00:55Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T16:00:23Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .

henripett/ppo-Huggy

henripett

2023-05-12T15:59:31Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-05-12T15:12:33Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: henripett/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

saikatkumardey/my_awesome_qa_model

saikatkumardey

2023-05-12T15:46:32Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T15:12:29Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_model results: [] ---  # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.9972 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.5592 | | 2.909 | 2.0 | 500 | 2.0550 | | 2.909 | 3.0 | 750 | 1.9972 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

casellimarco/q-FrozenLake-v1-4x4-noSlippery-gamma_1

casellimarco

2023-05-12T15:33:36Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T15:31:22Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery-gamma_1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 0.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . With `gamma=1` the Q table is ``` array([[1., 1., 1., 1.], [1., 0., 1., 1.], [1., 1., 1., 1.], [1., 0., 1., 1.], [1., 1., 0., 1.], [0., 0., 0., 0.], [0., 1., 0., 1.], [0., 0., 0., 0.], [1., 0., 1., 1.], [1., 1., 1., 0.], [1., 1., 0., 1.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 1., 1., 1.], [1., 1., 1., 1.], [0., 0., 0., 0.]]) ``` ## Usage ```python model = load_from_hub(repo_id="casellimarco/q-FrozenLake-v1-4x4-noSlippery-gamma_1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

itsmeboris/jobbert-base-cased-ner

itsmeboris

2023-05-12T15:21:11Z

99

1

transformers

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

token-classification

2023-05-12T14:43:12Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: jobbert-base-cased-ner results: [] ---  # jobbert-base-cased-ner This model is a fine-tuned version of [jjzha/jobbert-base-cased](https://huggingface.co/jjzha/jobbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3789 - Job Title precision: 0.7916 - Job Title recall: 0.8721 - Job Title f1: 0.8299 - Loc precision: 0.8572 - Loc recall: 0.9506 - Loc f1: 0.9015 - Org precision: 0.6727 - Org recall: 0.7458 - Org f1: 0.7074 - Misc precision: 0.6893 - Misc recall: 0.6587 - Misc f1: 0.6736 - Precision: 0.7772 - Recall: 0.8551 - F1: 0.8143 - Accuracy: 0.8680 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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 | Job Title precision | Job Title recall | Job Title f1 | Loc precision | Loc recall | Loc f1 | Org precision | Org recall | Org f1 | Misc precision | Misc recall | Misc f1 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 308 | 0.3896 | 0.7827 | 0.8394 | 0.8101 | 0.8813 | 0.8856 | 0.8835 | 0.6865 | 0.7151 | 0.7005 | 0.6730 | 0.6041 | 0.6367 | 0.7800 | 0.8148 | 0.7970 | 0.8577 | | 0.4672 | 2.0 | 616 | 0.3789 | 0.7916 | 0.8721 | 0.8299 | 0.8572 | 0.9506 | 0.9015 | 0.6727 | 0.7458 | 0.7074 | 0.6893 | 0.6587 | 0.6736 | 0.7772 | 0.8551 | 0.8143 | 0.8680 | | 0.4672 | 3.0 | 924 | 0.4067 | 0.7800 | 0.8876 | 0.8304 | 0.8560 | 0.9443 | 0.8980 | 0.6928 | 0.7026 | 0.6977 | 0.6006 | 0.7440 | 0.6646 | 0.7730 | 0.8549 | 0.8119 | 0.8651 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.7.1+cu110 - Datasets 2.12.0 - Tokenizers 0.13.2

seviladiguzel/distilbert-base-uncased-finetuned-squad_v2

seviladiguzel

2023-05-12T15:21:02Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T13:20:15Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad_v2 results: [] ---  # distilbert-base-uncased-finetuned-squad_v2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1316 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2238 | 1.0 | 5533 | 1.1654 | | 0.9823 | 2.0 | 11066 | 1.1316 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

rickysk/videomae-base-finetuned-ucf101-subset

rickysk

2023-05-12T15:19:14Z

59

0

transformers

[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-05-12T05:24:04Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] ---  # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3230 - Accuracy: 0.8968 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1382 | 0.06 | 75 | 2.1056 | 0.2571 | | 0.8185 | 1.06 | 150 | 0.6270 | 0.8 | | 0.5221 | 2.06 | 225 | 0.4341 | 0.8 | | 0.1069 | 3.06 | 300 | 0.4390 | 0.8714 | | 0.0195 | 4.06 | 375 | 0.2938 | 0.8571 | | 0.0097 | 5.06 | 450 | 0.2114 | 0.9 | | 0.0076 | 6.06 | 525 | 0.1509 | 0.9429 | | 0.1686 | 7.06 | 600 | 0.2527 | 0.9571 | | 0.0679 | 8.06 | 675 | 0.0615 | 0.9714 | | 0.0024 | 9.06 | 750 | 0.1589 | 0.9429 | | 0.1946 | 10.06 | 825 | 0.4014 | 0.9 | | 0.154 | 11.06 | 900 | 0.1862 | 0.9429 | | 0.0021 | 12.06 | 975 | 0.0683 | 0.9857 | | 0.0019 | 13.06 | 1050 | 0.0541 | 0.9857 | | 0.002 | 14.06 | 1125 | 0.0473 | 0.9857 | | 0.0018 | 15.06 | 1200 | 0.0475 | 0.9857 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

WALIDALI/bekiorangemixs

WALIDALI

2023-05-12T15:16:09Z

0

null

[ "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-05-12T15:03:42Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### bekiOrangeMixs Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

rishabhjain16/whisper_medium_en_to_myst_pf_ot100

rishabhjain16

2023-05-12T15:05:57Z

10

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-05-10T09:45:26Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: openai/whisper-medium.en results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_myst type: rishabhjain16/infer_myst config: en split: test metrics: - type: wer value: 12.3 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pfs type: rishabhjain16/infer_pfs config: en split: test metrics: - type: wer value: 3.28 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_cmu type: rishabhjain16/infer_cmu config: en split: test metrics: - type: wer value: 9.53 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/libritts_dev_clean type: rishabhjain16/libritts_dev_clean config: en split: test metrics: - type: wer value: 5.01 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_swedish type: rishabhjain16/infer_pf_swedish config: en split: test metrics: - type: wer value: 8.94 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_german type: rishabhjain16/infer_pf_german config: en split: test metrics: - type: wer value: 34.78 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_italian type: rishabhjain16/infer_pf_italian config: en split: test metrics: - type: wer value: 4.42 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_so_chinese type: rishabhjain16/infer_so_chinese config: en split: test metrics: - type: wer value: 14.87 name: WER ---  # openai/whisper-medium.en This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4158 - Wer: 10.8712 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6148 | 0.12 | 500 | 0.3107 | 12.7838 | | 0.1877 | 1.09 | 1000 | 0.2892 | 11.2910 | | 0.0697 | 2.05 | 1500 | 0.3146 | 10.7857 | | 0.0748 | 3.02 | 2000 | 0.3162 | 11.5254 | | 0.0308 | 3.14 | 2500 | 0.3450 | 11.1111 | | 0.0192 | 4.11 | 3000 | 0.3720 | 10.9101 | | 0.0046 | 5.07 | 3500 | 0.4155 | 11.2344 | | 0.0096 | 6.03 | 4000 | 0.4158 | 10.8712 | ### Framework versions - Transformers 4.29.0 - Pytorch 1.14.0a0+44dac51 - Datasets 2.12.0 - Tokenizers 0.13.3

tp-runport/bert-base-uncased-brands

tp-runport

2023-05-12T14:59:27Z

104

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-04-19T21:14:49Z

--- {} --- This is a BERT-based NER model trained to detect PERSON and BRAND entities in text.

alistvt/single-docalog

alistvt

2023-05-12T14:55:33Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:doc2dial", "endpoints_compatible", "region:us" ]

question-answering

2023-05-11T09:17:03Z

--- tags: - generated_from_trainer datasets: - doc2dial model-index: - name: single-docalog results: [] ---  # single-docalog This model is a fine-tuned version of [alistvt/single-docalog](https://huggingface.co/alistvt/single-docalog) on the doc2dial dataset. ## 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 30 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

grenmon/bart-large-finetuned-summarization

grenmon

2023-05-12T14:49:29Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-05-12T14:28:31Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: bart-large-finetuned-summarization results: [] ---  # bart-large-finetuned-summarization This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1841 - Rouge1: 32.6763 - Rouge2: 23.1598 - Rougel: 31.2322 - Rougelsum: 32.278 ## 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: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.7048 | 1.0 | 308 | 1.1916 | 32.0296 | 21.6931 | 30.2623 | 31.1959 | | 1.1153 | 2.0 | 616 | 1.2054 | 30.7076 | 21.7771 | 29.3115 | 29.9377 | | 0.78 | 3.0 | 924 | 1.1096 | 32.4164 | 22.494 | 31.0367 | 31.8135 | | 0.5335 | 4.0 | 1232 | 1.1547 | 33.2561 | 23.6119 | 32.1371 | 32.591 | | 0.361 | 5.0 | 1540 | 1.1841 | 32.6763 | 23.1598 | 31.2322 | 32.278 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

arb9p4/rl_course_vizdoom_health_gathering_supreme

arb9p4

2023-05-12T14:43:29Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T13:42:41Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 13.07 +/- 5.50 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r arb9p4/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

itsmeboris/bert-base-cased-ner

itsmeboris

2023-05-12T14:30:54Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-05-12T14:02:52Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased-ner results: [] ---  # bert-base-cased-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3793 - Job Title precision: 0.8079 - Job Title recall: 0.8248 - Job Title f1: 0.8163 - Loc precision: 0.8911 - Loc recall: 0.9081 - Loc f1: 0.8995 - Org precision: 0.6484 - Org recall: 0.7620 - Org f1: 0.7006 - Misc precision: 0.6134 - Misc recall: 0.7201 - Misc f1: 0.6625 - Precision: 0.7800 - Recall: 0.8265 - F1: 0.8025 - Accuracy: 0.8606 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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 | Job Title precision | Job Title recall | Job Title f1 | Loc precision | Loc recall | Loc f1 | Org precision | Org recall | Org f1 | Misc precision | Misc recall | Misc f1 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 308 | 0.3793 | 0.8079 | 0.8248 | 0.8163 | 0.8911 | 0.9081 | 0.8995 | 0.6484 | 0.7620 | 0.7006 | 0.6134 | 0.7201 | 0.6625 | 0.7800 | 0.8265 | 0.8025 | 0.8606 | | 0.4249 | 2.0 | 616 | 0.3866 | 0.7911 | 0.8728 | 0.8299 | 0.8676 | 0.9541 | 0.9088 | 0.6551 | 0.7886 | 0.7157 | 0.6623 | 0.6962 | 0.6789 | 0.7719 | 0.8669 | 0.8167 | 0.8685 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.7.1+cu110 - Datasets 2.12.0 - Tokenizers 0.13.2

zeeshan-sardar/Taxi-v3

zeeshan-sardar

2023-05-12T14:25:30Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T14:25:28Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.69 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="zeeshan-sardar/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

ku-nlp/deberta-v2-base-japanese

ku-nlp

2023-05-12T14:13:03Z

56,613

28

transformers

[ "transformers", "pytorch", "safetensors", "deberta-v2", "fill-mask", "deberta", "ja", "dataset:wikipedia", "dataset:cc100", "dataset:oscar", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-01-05T08:04:14Z

--- language: ja license: cc-by-sa-4.0 library_name: transformers tags: - deberta - deberta-v2 - fill-mask datasets: - wikipedia - cc100 - oscar metrics: - accuracy mask_token: "[MASK]" widget: - text: "京都大学で自然言語処理を [MASK] する。" --- # Model Card for Japanese DeBERTa V2 base ## Model description This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-base-japanese') model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-base-japanese') sentence = '京都大学で自然言語処理を [MASK] する。' # input should be segmented into words by Juman++ in advance encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can also fine-tune this model on downstream tasks. ## Tokenization The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece). ## Training data We used the following corpora for pre-training: - Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents) - Japanese portion of CC-100 (85GB, 619M sentences, 66M documents) - Japanese portion of OSCAR (54GB, 326M sentences, 25M documents) Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR. Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB. ## Training procedure We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp). Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library. The training took three weeks using 8 NVIDIA A100-SXM4-40GB GPUs. The following hyperparameters were used during pre-training: - learning_rate: 2e-4 - per_device_train_batch_size: 44 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 6 - total_train_batch_size: 2,112 - max_seq_length: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear schedule with warmup - training_steps: 500,000 - warmup_steps: 10,000 The accuracy of the trained model on the masked language modeling task was 0.779. The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora. ## Fine-tuning on NLU tasks We fine-tuned the following models and evaluated them on the dev set of JGLUE. We tuned learning rate and training epochs for each model and task following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja). | Model | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc | |-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------| | Waseda RoBERTa base | 0.965 | 0.913 | 0.876 | 0.905 | 0.853 | 0.916 | 0.853 | | Waseda RoBERTa large (seq512) | 0.969 | 0.925 | 0.890 | 0.928 | 0.910 | 0.955 | 0.900 | | LUKE Japanese base* | 0.965 | 0.916 | 0.877 | 0.912 | - | - | 0.842 | | LUKE Japanese large* | 0.965 | 0.932 | 0.902 | 0.927 | - | - | 0.893 | | DeBERTaV2 base | 0.970 | 0.922 | 0.886 | 0.922 | 0.899 | 0.951 | 0.873 | | DeBERTaV2 large | 0.968 | 0.925 | 0.892 | 0.924 | 0.912 | 0.959 | 0.890 | *The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke). ## Acknowledgments This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models". For training models, we used the mdx: a platform for the data-driven future.

ku-nlp/deberta-v2-large-japanese

ku-nlp

2023-05-12T14:10:35Z

36,402

9

transformers

[ "transformers", "pytorch", "safetensors", "deberta-v2", "fill-mask", "deberta", "ja", "dataset:wikipedia", "dataset:cc100", "dataset:oscar", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-01-07T07:45:25Z

--- language: ja license: cc-by-sa-4.0 library_name: transformers tags: - deberta - deberta-v2 - fill-mask datasets: - wikipedia - cc100 - oscar metrics: - accuracy mask_token: "[MASK]" widget: - text: "京都大学で自然言語処理を [MASK] する。" --- # Model Card for Japanese DeBERTa V2 large ## Model description This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese') model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese') sentence = '京都大学で自然言語処理を [MASK] する。' # input should be segmented into words by Juman++ in advance encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can also fine-tune this model on downstream tasks. ## Tokenization The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece). ## Training data We used the following corpora for pre-training: - Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents) - Japanese portion of CC-100 (85GB, 619M sentences, 66M documents) - Japanese portion of OSCAR (54GB, 326M sentences, 25M documents) Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR. Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB. ## Training procedure We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp). Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library. The training took 36 days using 8 NVIDIA A100-SXM4-40GB GPUs. The following hyperparameters were used during pre-training: - learning_rate: 1e-4 - per_device_train_batch_size: 18 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 2,304 - max_seq_length: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear schedule with warmup - training_steps: 300,000 - warmup_steps: 10,000 The accuracy of the trained model on the masked language modeling task was 0.799. The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora. ## Fine-tuning on NLU tasks We fine-tuned the following models and evaluated them on the dev set of JGLUE. We tuned learning rate and training epochs for each model and task following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja). | Model | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc | |-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------| | Waseda RoBERTa base | 0.965 | 0.913 | 0.876 | 0.905 | 0.853 | 0.916 | 0.853 | | Waseda RoBERTa large (seq512) | 0.969 | 0.925 | 0.890 | 0.928 | 0.910 | 0.955 | 0.900 | | LUKE Japanese base* | 0.965 | 0.916 | 0.877 | 0.912 | - | - | 0.842 | | LUKE Japanese large* | 0.965 | 0.932 | 0.902 | 0.927 | - | - | 0.893 | | DeBERTaV2 base | 0.970 | 0.922 | 0.886 | 0.922 | 0.899 | 0.951 | 0.873 | | DeBERTaV2 large | 0.968 | 0.925 | 0.892 | 0.924 | 0.912 | 0.959 | 0.890 | *The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke). ## Acknowledgments This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures ( JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models". For training models, we used the mdx: a platform for the data-driven future.

dian34323/fionyjkt48

dian34323

2023-05-12T14:10:31Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T14:09:05Z

--- license: creativeml-openrail-m ---

seantyh/mpt-1b-rp200b-dolly

seantyh

2023-05-12T14:10:13Z

14

0

transformers

[ "transformers", "pytorch", "mosaic_gpt", "text-generation", "custom_code", "dataset:togethercomputer/RedPajama-Data-1T", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2108.12409", "license:cc-by-sa-3.0", "autotrain_compatible", "region:us" ]

text-generation

2023-05-12T12:49:07Z

--- license: cc-by-sa-3.0 datasets: - togethercomputer/RedPajama-Data-1T --- [This is a experimental fork of mosaicml/mpt-1b-redpajama-200b-dolly] # MPT-1b-RedPajama-200b-dolly MPT-1b-RedPajama-200b-dolly is a 1.3 billion parameter decoder-only transformer pre-trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and subsequently fine-tuned on the [Databricks Dolly](https://github.com/databrickslabs/dolly/tree/master/data) instruction dataset. The model was pre-trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971). This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. This model is an instruction fine-tuned version of [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). In other words, the pre-trained version of this model is [mpt-1b-redpajama-200b](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b). ## Model Date April 20, 2023 ## How to Use Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture `MosaicGPT` that is not yet part of the `transformers` package. `MosaicGPT` includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALIBI](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True) ``` To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so: ```python model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True, attn_impl='triton') model.to(device='cuda:0', dtype=torch.bfloat16) ``` ## Model Description This model uses the MosaicML LLM codebase, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm). The architecture is a modification of a standard decoder-only transformer. The transformer has 24 layers, 16 attention heads, and width 2048. The model has been modified from a standard transformer in the following ways: * It uses ALiBi and does not use positional embeddings. * It uses QK LayerNorm. * It does not use biases. ## Training Data ### Pre-Training The model was pre-trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix: * 67% RedPajama Common Crawl * 15% [C4](https://huggingface.co/datasets/c4) * 4.5% RedPajama GitHub * 4.5% RedPajama Wikipedia * 4.5% RedPajama Books * 2.5% RedPajama Arxiv * 2% RedPajama StackExchange This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971). Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above. The examples were shuffled within each dataset. Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ### Fine-Tuning We fine tuned this model on the [databricks-dolly-15k dataset](https://github.com/databrickslabs/dolly/tree/master/data) released by Databricks, following the same hyperparameters found in their [train_dolly.py](https://github.com/databrickslabs/dolly/blob/master/train_dolly.py) script. ## Training Configuration This model was pre-trained on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was pre-trained with sharded data parallelism using FSDP. ## Acknowledgements This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models. We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work. This model also builds on the work of [Databricks](https://www.databricks.com/), which created the Dolly instruction fine-tuning dataset. We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.

asenella/reproducing_mopoe_seed_4

asenella

2023-05-12T14:00:49Z

0

null

[ "multivae", "en", "license:apache-2.0", "region:us" ]

null

2023-05-12T14:00:42Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

madmaxxed/gpt-work-filter-auto-complete

madmaxxed

2023-05-12T13:59:23Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-12T09:15:35Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-work-filter-auto-complete results: [] ---  # gpt-work-filter-auto-complete This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0939 ## 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 22 | 0.6395 | | No log | 2.0 | 44 | 0.6235 | | No log | 3.0 | 66 | 0.6035 | | No log | 4.0 | 88 | 0.5845 | | No log | 5.0 | 110 | 0.5645 | | No log | 6.0 | 132 | 0.5442 | | No log | 7.0 | 154 | 0.5346 | | No log | 8.0 | 176 | 0.5167 | | No log | 9.0 | 198 | 0.5009 | | No log | 10.0 | 220 | 0.4893 | | No log | 11.0 | 242 | 0.4676 | | No log | 12.0 | 264 | 0.4498 | | No log | 13.0 | 286 | 0.4382 | | No log | 14.0 | 308 | 0.4276 | | No log | 15.0 | 330 | 0.4132 | | No log | 16.0 | 352 | 0.4075 | | No log | 17.0 | 374 | 0.3952 | | No log | 18.0 | 396 | 0.3822 | | No log | 19.0 | 418 | 0.3677 | | No log | 20.0 | 440 | 0.3563 | | No log | 21.0 | 462 | 0.3495 | | No log | 22.0 | 484 | 0.3455 | | 0.6366 | 23.0 | 506 | 0.3316 | | 0.6366 | 24.0 | 528 | 0.3126 | | 0.6366 | 25.0 | 550 | 0.3118 | | 0.6366 | 26.0 | 572 | 0.3021 | | 0.6366 | 27.0 | 594 | 0.2944 | | 0.6366 | 28.0 | 616 | 0.2878 | | 0.6366 | 29.0 | 638 | 0.2772 | | 0.6366 | 30.0 | 660 | 0.2701 | | 0.6366 | 31.0 | 682 | 0.2643 | | 0.6366 | 32.0 | 704 | 0.2576 | | 0.6366 | 33.0 | 726 | 0.2514 | | 0.6366 | 34.0 | 748 | 0.2467 | | 0.6366 | 35.0 | 770 | 0.2359 | | 0.6366 | 36.0 | 792 | 0.2326 | | 0.6366 | 37.0 | 814 | 0.2205 | | 0.6366 | 38.0 | 836 | 0.2182 | | 0.6366 | 39.0 | 858 | 0.2137 | | 0.6366 | 40.0 | 880 | 0.2086 | | 0.6366 | 41.0 | 902 | 0.2058 | | 0.6366 | 42.0 | 924 | 0.1979 | | 0.6366 | 43.0 | 946 | 0.1930 | | 0.6366 | 44.0 | 968 | 0.1922 | | 0.6366 | 45.0 | 990 | 0.1853 | | 0.4122 | 46.0 | 1012 | 0.1800 | | 0.4122 | 47.0 | 1034 | 0.1787 | | 0.4122 | 48.0 | 1056 | 0.1738 | | 0.4122 | 49.0 | 1078 | 0.1689 | | 0.4122 | 50.0 | 1100 | 0.1670 | | 0.4122 | 51.0 | 1122 | 0.1583 | | 0.4122 | 52.0 | 1144 | 0.1560 | | 0.4122 | 53.0 | 1166 | 0.1540 | | 0.4122 | 54.0 | 1188 | 0.1507 | | 0.4122 | 55.0 | 1210 | 0.1475 | | 0.4122 | 56.0 | 1232 | 0.1452 | | 0.4122 | 57.0 | 1254 | 0.1458 | | 0.4122 | 58.0 | 1276 | 0.1425 | | 0.4122 | 59.0 | 1298 | 0.1377 | | 0.4122 | 60.0 | 1320 | 0.1338 | | 0.4122 | 61.0 | 1342 | 0.1365 | | 0.4122 | 62.0 | 1364 | 0.1278 | | 0.4122 | 63.0 | 1386 | 0.1272 | | 0.4122 | 64.0 | 1408 | 0.1253 | | 0.4122 | 65.0 | 1430 | 0.1251 | | 0.4122 | 66.0 | 1452 | 0.1217 | | 0.4122 | 67.0 | 1474 | 0.1219 | | 0.4122 | 68.0 | 1496 | 0.1177 | | 0.3005 | 69.0 | 1518 | 0.1174 | | 0.3005 | 70.0 | 1540 | 0.1155 | | 0.3005 | 71.0 | 1562 | 0.1144 | | 0.3005 | 72.0 | 1584 | 0.1127 | | 0.3005 | 73.0 | 1606 | 0.1106 | | 0.3005 | 74.0 | 1628 | 0.1098 | | 0.3005 | 75.0 | 1650 | 0.1092 | | 0.3005 | 76.0 | 1672 | 0.1067 | | 0.3005 | 77.0 | 1694 | 0.1086 | | 0.3005 | 78.0 | 1716 | 0.1042 | | 0.3005 | 79.0 | 1738 | 0.1051 | | 0.3005 | 80.0 | 1760 | 0.1038 | | 0.3005 | 81.0 | 1782 | 0.1022 | | 0.3005 | 82.0 | 1804 | 0.1015 | | 0.3005 | 83.0 | 1826 | 0.1004 | | 0.3005 | 84.0 | 1848 | 0.1003 | | 0.3005 | 85.0 | 1870 | 0.0978 | | 0.3005 | 86.0 | 1892 | 0.0987 | | 0.3005 | 87.0 | 1914 | 0.0974 | | 0.3005 | 88.0 | 1936 | 0.0975 | | 0.3005 | 89.0 | 1958 | 0.0965 | | 0.3005 | 90.0 | 1980 | 0.0960 | | 0.2455 | 91.0 | 2002 | 0.0958 | | 0.2455 | 92.0 | 2024 | 0.0952 | | 0.2455 | 93.0 | 2046 | 0.0952 | | 0.2455 | 94.0 | 2068 | 0.0944 | | 0.2455 | 95.0 | 2090 | 0.0943 | | 0.2455 | 96.0 | 2112 | 0.0940 | | 0.2455 | 97.0 | 2134 | 0.0942 | | 0.2455 | 98.0 | 2156 | 0.0940 | | 0.2455 | 99.0 | 2178 | 0.0939 | | 0.2455 | 100.0 | 2200 | 0.0939 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AustinCarthy/Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75

AustinCarthy

2023-05-12T13:54:37Z

161

0

transformers

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

text-classification

2023-05-12T12:53:56Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75 results: [] ---  # Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0237 - Accuracy: 0.9975 - F1: 0.9731 - Precision: 0.9983 - Recall: 0.9492 - Roc Auc Score: 0.9746 - Tpr At Fpr 0.01: 0.9508 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0042 | 1.0 | 6563 | 0.0276 | 0.9966 | 0.9628 | 0.9983 | 0.9298 | 0.9649 | 0.9308 | | 0.0024 | 2.0 | 13126 | 0.0242 | 0.9972 | 0.9698 | 0.9973 | 0.9438 | 0.9718 | 0.927 | | 0.0026 | 3.0 | 19689 | 0.0244 | 0.9970 | 0.9679 | 0.9987 | 0.939 | 0.9695 | 0.9514 | | 0.0003 | 4.0 | 26252 | 0.0293 | 0.9968 | 0.9657 | 0.9989 | 0.9346 | 0.9673 | 0.9472 | | 0.0007 | 5.0 | 32815 | 0.0237 | 0.9975 | 0.9731 | 0.9983 | 0.9492 | 0.9746 | 0.9508 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2

Amalq/roberta-large-schizophrenia-v2

Amalq

2023-05-12T13:45:51Z

4

0

transformers

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

fill-mask

2023-05-12T12:54:45Z

--- license: mit tags: - generated_from_trainer model-index: - name: roberta-large-schizophrenia-v2 results: [] ---  # roberta-large-schizophrenia-v2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5239 ## 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: 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: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 248 | 1.5340 | | No log | 2.0 | 496 | 1.5273 | | 1.6401 | 3.0 | 744 | 1.5209 | | 1.6401 | 4.0 | 992 | 1.5218 | | 1.5704 | 5.0 | 1240 | 1.5167 | | 1.5704 | 6.0 | 1488 | 1.5245 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

luigisaetta/whisper-atcosim2

luigisaetta

2023-05-12T13:26:03Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-05-12T11:26:48Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-atcosim2 results: [] ---  # whisper-atcosim2 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0524 - Wer: 0.0304 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 300 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5702 | 0.2 | 50 | 0.2557 | 0.1007 | | 0.1181 | 0.39 | 100 | 0.1144 | 0.0775 | | 0.1084 | 0.59 | 150 | 0.0747 | 0.0482 | | 0.0737 | 0.79 | 200 | 0.0616 | 0.0369 | | 0.064 | 0.98 | 250 | 0.0556 | 0.0440 | | 0.0313 | 1.18 | 300 | 0.0524 | 0.0304 | ### Framework versions - Transformers 4.29.0 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.11.0

LarryAIDraw/asuna1-000004

LarryAIDraw

2023-05-12T13:26:00Z

0

1

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T13:18:28Z

--- license: creativeml-openrail-m --- https://civitai.com/models/63637/ichinose-asuna-blue-archive-or-character-lora-1630

LarryAIDraw/towerofgod_androssi_zahard

LarryAIDraw

2023-05-12T13:25:16Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T13:17:26Z

--- license: creativeml-openrail-m --- https://civitai.com/models/63523/androssi-zahard-endorsi-or-tower-of-god

LarryAIDraw/nanami_mami_v1

LarryAIDraw

2023-05-12T13:23:44Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T13:14:51Z

--- license: creativeml-openrail-m --- https://civitai.com/models/64071/nanami-mami-rent-a-girlfriend

LarryAIDraw/yami-v1

LarryAIDraw

2023-05-12T13:23:29Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T13:14:31Z

--- license: creativeml-openrail-m --- https://civitai.com/models/63759/yami-3-in-one-to-love-ru-darkness-tolove

LarryAIDraw/Amano_Hina

LarryAIDraw

2023-05-12T13:23:17Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T13:14:06Z

--- license: creativeml-openrail-m --- https://civitai.com/models/63559/amano-hina-tenki-no-ko-weathering-with-you

piperunner/ppo-LunarLander-v2

piperunner

2023-05-12T13:15:46Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T13:15:28Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.13 +/- 21.48 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

zetavg/zh-tw-pythia-1b-a12k-f84566-embeddings-gcp-a100-trans-t3-d2ad

zetavg

2023-05-12T13:07:34Z

16

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "dataset:zetavg/zh-tw-pythia-te01-zh-tw-pythia-ta12k-f84566-embeddings-tr-ec3f26-c2048", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-12T13:04:21Z

--- datasets: - zetavg/zh-tw-pythia-te01-zh-tw-pythia-ta12k-f84566-embeddings-tr-ec3f26-c2048 --- # zh-tw-pythia-1b-a12k-f84566-embeddings-gcp-a100-trans-t3-d2ad This model is a part of the `zh-tw-pythia` project. * Base model: `EleutherAI/pythia-1b` * Tokenizer: `zh-tw-pythia-tokenizer-a12k-f84566` * Vocab size: `62232` * Train pass: `embeddings` * Dataset used: `zh-tw-pythia-te01-zh-tw-pythia-ta12k-f84566-embeddings-tr-ec3f26-c2048` * Full config: ```json {"project_name": "zh-tw-pythia", "group_name": "te01", "base_tokenizer_name": "EleutherAI/pythia-70m", "base_model_name": "EleutherAI/pythia-1b", "tokenizer_name": "zh-tw-pythia-tokenizer-a12k-f84566", "hf_user_or_org_name": "zetavg", "tokenizer": {"build_with": "word_frequency_list", "tokens_to_add": 12000, "word_frequency_list_settings": {"word_frequency_list_name": "zetavg/tw-sinica-corpus-word-frequency", "include_words": ["。", "，", "、", "？", "！", "；", "：", "……", "～", "「", "」", "『", "』", "【", "】", "〖", "〗", "（", "）", "〔", "〕", "［", "］", "｛", "｝", "《", "》", "〈", "〉", "——", "──", "－", "−", "＿", "・", "．", "·", "／", "＼", "｜", "＜", "＞"], "replace_rules": [{"match": {"regex": "�"}, "replace": null}, {"match": {"pos": ["Nb", "FW", null]}, "replace": null, "except": ["奧運", "中共", "國民黨", "民進黨", "新黨", "共產黨", "媽祖", "耶穌"]}, {"match": {"regex": ["^[Ａ-Ｚａ-ｚ０-９﹒• ]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]{2,}$", "^（[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]+）$", "^[第數][零一二兩三四五六七八九十百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+分之[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+[多餘來幾成次年月日天時分點世代歲起段樓％]$", "^[零一二三四五六七八九十廿卅０-９]+(月份|年代?|世紀|學?年度|年級)$", "^(星期|週|周)[一二三四五六日]$"]}, "replace": null, "except": ["十分", "一起", "一點", "一時", "千萬", "兩三", "百分之百"]}, {"match": {"pos": "VHC", "regex": "^(.{2,})化$"}, "sub": "\\1"}, {"match": "高爾夫球場", "replace": "高爾夫"}, {"match": {"regex": "^(.+球)場$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})園區$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[鄉鎮縣市區]$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[界院部會署局館系所]$"}, "sub": "\\1", "except": ["委員會", "研究所", "中研院", "國科會", "資策會", "經建會", "工研院", "電信總局", "鎮公所", "事務所", "交易所", "農委會", "鄉公所", "地檢署", "警分局", "派出所", "托兒所", "消基會", "文建會", "兩廳院", "陸委會", "市議會"]}, {"match": {"pos": "Na", "regex": "^(.{2,})人$"}, "sub": "\\1", "except": ["年輕人", "負責人", "投資人", "候選人", "一家人", "當地人", "製作人"]}, {"match": {"pos": "Na", "regex": "^(.{2,3})學?家$"}, "sub": "\\1", "except": ["女人家", "婦人家", "新儒家", "窮人家", "縱橫家", "老人家", "老東家", "闊人家", "大戶人家", "婦道人家", "小戶人家", "水上人家", "諸子百家"]}, {"match": {"pos": "Na", "regex": "^副?總?([^副總]{2,})師$"}, "sub": "\\1", "except": ["中醫師", "囝仔師", "正機師", "準教師", "獸醫師", "班導師", "練馬師", "總舖師", "老像師", "新三十師", "至聖先師", "音樂大師"]}, {"match": {"pos": "Na", "regex": "^[原前]?(?:代|代理)?副?總?([^前代副總議警里首院部署局廳司處科組課股]{2,})[院部署局廳司處科組課股]?次?長$"}, "sub": "\\1", "except": ["董事長", "理事長", "秘書長", "執行長", "分局長", "縣市長", "一技之長", "省市長", "負成長", "高成長", "大家長", "小組長", "區組長", "低成長", "偵一組長", "停管隊長", "考選部長", "年增長", "正成長", "支店長", "公賣局長", "中宣部長", "小市長"]}, {"match": {"pos": "Na", "regex": "^副?總?正?([^副總正議委人隊]{2,})[委人隊]?員$"}, "sub": "\\1", "except": ["主跑員", "乘務員", "佐理員", "共黨員", "外務員", "從業員", "特派員", "義服員", "銜道員", "啦啦隊員", "指服團員"]}, {"match": {"pos": "Na", "regex": "^副(.{2,})$"}, "sub": "\\1", "except": ["副作用"]}, {"match": "一剎那", "replace": "剎那"}, {"match": "不能夠", "replace": "能夠"}, {"match": "光碟機", "replace": "光碟"}, {"match": "共和國", "replace": "共和"}, {"match": "原住民", "replace": "住民"}, {"match": "吸引力", "replace": "吸引"}, {"match": "國際性", "replace": "國際"}, {"match": "垃圾場", "replace": "垃圾"}, {"match": "大規模", "replace": "規模"}, {"match": "廢棄物", "replace": "廢棄"}, {"match": "愛滋病", "replace": "愛滋"}, {"match": "成交量", "replace": "成交"}, {"match": "接觸到", "replace": "接觸"}, {"match": "掩埋場", "replace": "掩埋"}, {"match": "正確率", "replace": "正確"}, {"match": "清華園", "replace": "清華"}, {"match": "聯誼會", "replace": "聯誼"}, {"match": "調查站", "replace": "調查"}, {"match": "轉換成", "replace": "轉換"}, {"match": "開放式", "replace": "開放"}, {"match": "開玩笑", "replace": "玩笑"}, {"match": "陽明山", "replace": "陽明"}, {"match": "雜貨店", "replace": "雜貨"}, {"match": "電視機", "replace": "電視"}, {"match": "高品質", "replace": "品質"}, {"match": "鬆弛法", "replace": "鬆弛"}, {"match": "共產主義", "replace": "共產"}, {"match": "資本主義", "replace": "資本"}, {"match": "微處理器", "replace": "處理器"}, {"match": "有線電視", "replace": "電視"}, {"match": "隨選視訊", "replace": "視訊"}, {"match": "電信總局", "replace": "總局"}, {"match": "進一步", "replace": ["一步", "進一步"]}, {"match": "差不多", "replace": ["不多", "差不多"]}, {"match": "忍不住", "replace": ["不住", "忍不住"]}, {"match": "不見得", "replace": ["見得", "不見得"]}, {"match": "有助於", "replace": ["助於", "有助於"]}, {"match": "舊金山", "replace": ["金山", "舊金山"]}, {"match": "大躍進", "replace": ["躍進", "大躍進"]}, {"match": "半導體", "replace": ["導體", "半導體"]}, {"match": "總幹事", "replace": ["幹事", "總幹事"]}, {"match": "兩廳院", "replace": ["廳院", "兩廳院"]}]}}, "training": {"embeddings": {"run_suffix": "gcp-a100-trans-t3", "max_training_text_length": 2048, "dataset": {"build_with": "translations", "preview_length": 64, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\\nChinese: {lang_2}", "Chinese: {lang_2}\\nEnglish: {lang_1}"]}}, "only_train_parameters_matching": ["embed"], "training_arguments": {"num_train_epochs": 1, "per_device_train_batch_size": 16, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "save_steps": 1000, "save_total_limit": 8}}}, "push_outputs_to_hf": true, "report_to_wandb": true, "wandb_project": "zh-tw-llm"} ```

Amalq/roberta-large-schizophrenia-v3

Amalq

2023-05-12T12:59:02Z

4

0

transformers

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

fill-mask

2023-05-12T11:19:09Z

--- license: mit tags: - generated_from_trainer model-index: - name: roberta-large-schizophrenia-v3 results: [] ---  # roberta-large-schizophrenia-v3 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5296 ## 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: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 248 | 1.5515 | | No log | 2.0 | 496 | 1.5410 | | 1.6456 | 3.0 | 744 | 1.5340 | | 1.6456 | 4.0 | 992 | 1.5326 | | 1.5589 | 5.0 | 1240 | 1.5248 | | 1.5589 | 6.0 | 1488 | 1.5308 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

jccj/q-Taxi-v3

jccj

2023-05-12T12:56:24Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-05-05T14:51:41Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="jccj/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

PhDmath/distilbert-base-uncased-finetuned-emotion

PhDmath

2023-05-12T12:52:33Z

106

0

transformers

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

text-classification

2023-05-12T11:33:07Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9295 - name: F1 type: f1 value: 0.9293576247301535 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2169 - Accuracy: 0.9295 - F1: 0.9294 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8632 | 1.0 | 250 | 0.3270 | 0.904 | 0.9008 | | 0.253 | 2.0 | 500 | 0.2169 | 0.9295 | 0.9294 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3

choohan/finetuned-opt-squad-covidqa-dataset

choohan

2023-05-12T12:45:25Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T12:41:54Z

--- license: other tags: - generated_from_trainer model-index: - name: finetuned-opt-squad-covidqa-dataset results: [] ---  # finetuned-opt-squad-covidqa-dataset This model is a fine-tuned version of [choohan/finetuned-opt-squad-dataset-3](https://huggingface.co/choohan/finetuned-opt-squad-dataset-3) on the None dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

choohan/finetuned-opt-covidqa-dataset

choohan

2023-05-12T12:41:16Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T12:37:42Z

--- license: other tags: - generated_from_trainer model-index: - name: finetuned-opt-covidqa-dataset results: [] ---  # finetuned-opt-covidqa-dataset This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

NabeelUppel/ppo-LunaLander-v2

NabeelUppel

2023-05-12T12:36:49Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T12:36:30Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 275.91 +/- 21.43 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

apcl/jam

apcl

2023-05-12T12:24:47Z

0

2

null

[ "dataset:apcl/jm52m", "license:bigscience-openrail-m", "region:us" ]

null

2023-04-29T21:16:07Z

--- license: bigscience-openrail-m datasets: - apcl/jm52m --- # Jam Jam is a GPT2-like model for research in fine-grained Java analysis. It is intended for fine-grained analysis of Java source code at the level of methods, statements, and variables, as a foundation for downstream tasks like code completion, comment generation, and automated bug repair. --- ## Jam Training Details - We trained the jam model using the training procedures from Daniel Grittner's [NanoGPT-LoRA](https://github.com/danielgrittner/nanoGPT-LoRA) - The dataset used to train our model is our own dataset [jm52m dataset](https://huggingface.co/datasets/apcl/jm52m), which consists of the processed source code of 52 million Java methods. - We train the model on [training set](https://huggingface.co/datasets/apcl/jm52m/blob/main/train.bin) for 1 epoch, roughly 300,000 training iterations. - Our [GitHub repo](https://github.com/apcl-research/jam/blob/main) contains the code for re-training using the [raw data](https://huggingface.co/datasets/apcl/jm52m/blob/main/fundats-j1.pkl) | Hyperparameter | Description | Value | | ----------- | ----------- |------------| |e | embedding dimensions | 1024 | |L | number of layers | 24 | |h | attention heads | 16 | |c | block size / context length | 256 | |b | batch size | 4 | |a | accumulation steps | 32 | |d | dropout | 0.20 | |r | learning rate | 3e-5 | |y | weight decay | 1e-1 | We train our models using a single NVidia A5000 GPU. --- ## Jam Projects Current projects using the JAM pre-trained model can be found at our Github repository: https://github.com/apcl-research/jam

eachadea/ggml-gpt4-x-vicuna-13b

eachadea

2023-05-12T12:20:07Z

0

13

null

[ "conversational", "region:us" ]

text-generation

2023-05-12T11:07:21Z

--- pipeline_tag: conversational --- ggml version (post-PR #1405) As a base model used https://huggingface.co/eachadea/vicuna-13b-1.1 Finetuned on Teknium's GPTeacher dataset, unreleased Roleplay v2 dataset, GPT-4-LLM dataset, and Nous Research Instruct Dataset Approx 180k instructions, all from GPT-4, all cleaned of any OpenAI censorship/"As an AI Language Model" etc. Base model still has OpenAI censorship. Soon, a new version will be released with cleaned vicuna from https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltere Trained on 8 A100-80GB GPUs for 5 epochs following Alpaca deepspeed training code. Nous Research Instruct Dataset will be released soon. GPTeacher, Roleplay v2 by https://huggingface.co/teknium Wizard LM by https://github.com/nlpxucan Nous Research Instruct Dataset by https://huggingface.co/karan4d and https://huggingface.co/huemin Compute provided by our project sponsor https://redmond.ai/

trustvare/TrustVare-OST-Duplicate-Remover

trustvare

2023-05-12T12:15:47Z

0

null

[ "region:us" ]

null

2023-05-12T11:51:57Z

TrustVare OST Duplicate Remover is a software application that can be used to find and remove duplicate items from an offline Outlook data file (.ost). OST files are created by Microsoft Outlook when it is configured to work in offline mode. They store email messages, contacts, calendars, and other data that are not currently synchronized with the Microsoft Exchange server. Removing duplicate items from an OST file can improve the performance of Microsoft Outlook by reducing the amount of data that the application has to process. Removing duplicate items from an OST file can improve the performance of Microsoft Outlook by reducing the amount of data that the application has to process.The software is easy to use and has a number of features that make it a valuable tool for Microsoft Outlook users. Read More:- https://www.trustvare.com/duplicate-remover/ost/

OliAiart/Stable-diffusion

OliAiart

2023-05-12T12:11:14Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T11:20:47Z

--- license: creativeml-openrail-m ---

choohan/finetuned-opt-squad-dataset-4

choohan

2023-05-12T12:05:01Z

95

0

transformers

[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T11:40:39Z

--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: finetuned-opt-squad-dataset-4 results: [] ---  # finetuned-opt-squad-dataset-4 This model is a fine-tuned version of [choohan/finetuned-opt-squad-dataset-3](https://huggingface.co/choohan/finetuned-opt-squad-dataset-3) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

farmerinatechstack/PPO-LunarLander-v2

farmerinatechstack

2023-05-12T12:01:22Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T12:01:04Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.58 +/- 15.29 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

henripett/ppo_lunar_lander_v2

henripett

2023-05-12T11:53:01Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T11:34:47Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 126.66 +/- 105.78 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

dian34323/cynthiajkt48

dian34323

2023-05-12T11:52:20Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T11:50:47Z

--- license: creativeml-openrail-m ---

OliAiart/models

OliAiart

2023-05-12T11:22:07Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T01:04:25Z

--- license: creativeml-openrail-m ---

choohan/finetuned-opt-squad-dataset-2

choohan

2023-05-12T11:08:16Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T10:42:34Z

--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: finetuned-opt-squad-dataset-2 results: [] ---  # finetuned-opt-squad-dataset-2 This model is a fine-tuned version of [choohan/finetuned-opt-squad-dataset](https://huggingface.co/choohan/finetuned-opt-squad-dataset) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Bennet1996/donut-base-sroie8

Bennet1996

2023-05-12T11:03:16Z

46

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-05-11T16:41:30Z

--- tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie8 results: [] ---  # donut-base-sroie8 This model is a fine-tuned version of [Bennet1996/donut-base-sroie6](https://huggingface.co/Bennet1996/donut-base-sroie6) on the imagefolder dataset. ## 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: 3e-05 - train_batch_size: 11 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Gayathri142214002/t5-paraphrase

Gayathri142214002

2023-05-12T10:42:57Z

3

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-12T06:01:40Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-paraphrase results: [] ---  # t5-paraphrase This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.3873 - eval_runtime: 785.1467 - eval_samples_per_second: 19.012 - eval_steps_per_second: 19.012 - epoch: 0.0 - step: 20 ## 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AXX1995/anastasianadav1

AXX1995

2023-05-12T10:40:59Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-05-12T10:38:10Z

--- license: creativeml-openrail-m ---

iamjoy/ppo-Pyramids

iamjoy

2023-05-12T10:40:16Z

2

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-05-12T10:40:11Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: iamjoy/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

seanghay/xlm-roberta-base-imdb

seanghay

2023-05-12T10:38:06Z

451

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-12T09:56:06Z

--- license: mit tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: xlm-roberta-base-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93936 ---  # xlm-roberta-base-imdb This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2223 - Accuracy: 0.9394 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2345 | 1.0 | 1563 | 0.1808 | 0.9306 | | 0.1612 | 2.0 | 3126 | 0.2223 | 0.9394 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

choohan/finetuned-opt-squad-dataset

choohan

2023-05-12T10:34:15Z

12

0

transformers

[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T10:08:35Z

--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: finetuned-opt-squad-dataset results: [] ---  # finetuned-opt-squad-dataset This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

shivansh-ka/Multilingual-Toxic-Comment-Roberta-best

shivansh-ka

2023-05-12T10:15:00Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2023-05-12T10:13:13Z

choohan/opt-finetuned-squad-dataset

choohan

2023-05-12T10:03:18Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "opt", "question-answering", "generated_from_trainer", "dataset:squad", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2023-05-12T06:52:21Z

--- license: other tags: - generated_from_trainer datasets: - squad model-index: - name: opt-finetuned-squad-dataset results: [] ---  # opt-finetuned-squad-dataset This model is a fine-tuned version of [choohan/opt-finetuned-squad-dataset](https://huggingface.co/choohan/opt-finetuned-squad-dataset) on the squad dataset. ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

casellimarco/Taxi

casellimarco

2023-05-12T09:32:12Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T09:32:11Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="casellimarco/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

xkirinx/bart-large-mnli-rotten-tomatoes

xkirinx

2023-05-12T09:30:26Z

6

0

transformers

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

text-classification

2023-05-12T08:18:32Z

--- license: mit tags: - generated_from_trainer datasets: - rotten_tomatoes model-index: - name: bart-large-mnli-rotten-tomatoes results: [] ---  # bart-large-mnli-rotten-tomatoes This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.6032 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1965 | 1.0 | 1067 | 0.6041 | | 0.1604 | 2.0 | 2134 | 0.6165 | | 0.0796 | 3.0 | 3201 | 0.6032 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

LinaSaba/BertDisasterTweets

LinaSaba

2023-05-12T09:28:32Z

0

sklearn

[ "sklearn", "clim", "text-classification", "license:afl-3.0", "region:us" ]

text-classification

2023-05-12T08:39:25Z

--- license: afl-3.0 metrics: - accuracy - code_eval library_name: sklearn pipeline_tag: text-classification tags: - clim --- # bert-model-disaster-tweets-classification This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Natural-Language-Processing-with-Disaster-Tweets dataset. It achieves the following results on the evaluation set: - Accuracy: 0.82 - F1 Score: 0.82 ## Model description Load BertForSequenceClassification, the pretrained BERT model with a single linear classification layer on top, using an optimizer : incorporates weight decay, which is a regularization technique that helps prevent overfitting during training. ## Intended uses & limitations Use to classify if a tweet represents a disaster or not. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with epsilon = 1e-8. - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Epoch | Average training loss | Training epoch | Accuracy | F1 | |:-----:|:---------------------:|:---------------:|:--------:|:----:| | 1.0 | 0.47 | 0:00:49 | 0.82 | 0.82 | | 2.0 | 0.36 | 0:00:36 | 0.82 | 0.82 | | 3.0 | 0.29 | 0:00:51 | 0.82 | 0.82 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

jakubgajski/PyramydsRND

jakubgajski

2023-05-12T09:15:44Z

23

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-05-12T06:45:28Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: jakubgajski/PyramydsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

CatherineGeng/vit-base-patch16-224-in21k-euroSat

CatherineGeng

2023-05-12T09:01:57Z

63

0

transformers

[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-05-12T01:29:25Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: CatherineGeng/vit-base-patch16-224-in21k-euroSat results: [] ---  # CatherineGeng/vit-base-patch16-224-in21k-euroSat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4854 - Train Accuracy: 0.9415 - Train Top-3-accuracy: 0.9856 - Validation Loss: 0.1574 - Validation Accuracy: 0.9817 - Validation Top-3-accuracy: 0.9988 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3590, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 0.4854 | 0.9415 | 0.9856 | 0.1574 | 0.9817 | 0.9988 | 0 | ### Framework versions - Transformers 4.29.1 - TensorFlow 2.4.0 - Datasets 2.12.0 - Tokenizers 0.13.3

debin/Taxi-v3

debin

2023-05-12T09:01:37Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T09:01:35Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.75 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="debin/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

jesse0506/test

jesse0506

2023-05-12T09:00:43Z

0

null

[ "region:us" ]

null

2023-05-12T08:58:59Z

import gradio as gr def process(image): #image[:,:,0] red image[:,:,1]=0 image[:,:,2]=0 return image gr.Interface(process,"image","image").launch(share=True)

debin/q-FrozenLake-v1-4x4-noSlippery

debin

2023-05-12T08:58:15Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-05-12T08:58:12Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="debin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

xqchq/TextClassificationTHUCNews

xqchq

2023-05-12T08:52:32Z

7

0

transformers

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

text-classification

2023-05-12T07:24:31Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - thuc_news model-index: - name: TextClassificationTHUCNews results: [] ---  # TextClassificationTHUCNews This model is a fine-tuned version of [hfl/minirbt-h256](https://huggingface.co/hfl/minirbt-h256) on the thuc_news dataset. ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3