librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
smilton/mt5-large-qasrl-es-p2-question	smilton	2022-11-29T04:36:00Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T03:55:16Z	--- language: - es license: apache-2.0 tags: - generated_from_trainer model-index: - name: mt5-large-qasrl-es-p2-question 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. --> # mt5-large-qasrl-es-p2-question This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7515 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.11.0 - Datasets 2.7.1 - Tokenizers 0.11.0
renatanerenata/bart-paraphrase1-finetuned-in-to-fo	renatanerenata	2022-11-29T04:35:59Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T00:54:14Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-paraphrase1-finetuned-in-to-fo 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-paraphrase1-finetuned-in-to-fo This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) 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: 0.002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Zhaohui/finetuning-misinfo-model-700-Zhaohui-1_misinfo	Zhaohui	2022-11-29T04:10:10Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T03:57:58Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-misinfo-model-700-Zhaohui-1_misinfo 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. --> # finetuning-misinfo-model-700-Zhaohui-1_misinfo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5343 - Accuracy: 0.8571 - F1: 0.8571 ## 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: 20 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
NSandra/distilbert-base-uncased-finetuned-ner	NSandra	2022-11-29T04:09:17Z	125	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-29T03:55:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-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. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2393 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 1 \| 1.5491 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 2.0 \| 2 \| 1.3278 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 3.0 \| 3 \| 1.2393 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
ryvalenza/sd-class-butterflies-32	ryvalenza	2022-11-29T04:00:32Z	34	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T04:00:01Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(ryvalenza/sd-class-butterflies-32) image = pipeline().images[0] image ```
jeraldflowers/vit_model	jeraldflowers	2022-11-29T03:51:31Z	188	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-27T05:06:17Z	--- license: apache-2.0 tags: - image-classification - generated_from_trainer datasets: - beans metrics: - accuracy widget: - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/healthy.jpeg example_title: Healthy - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/bean_rust.jpeg example_title: Bean Rust model-index: - name: vit_model results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- 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. --> # vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0095 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1526 \| 3.85 \| 500 \| 0.0095 \| 1.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
UCSYNLP/MyanBERTa	UCSYNLP	2022-11-29T03:35:58Z	297	3	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "MyanBERTa", "Myanmar", "BERT", "RoBERTa", "my", "dataset:MyCorpus", "dataset:Web", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-25T06:57:10Z	--- language: my tags: - MyanBERTa - Myanmar - BERT - RoBERTa license: apache-2.0 datasets: - MyCorpus - Web --- ## Model description This model is a BERT based Myanmar pre-trained language model. MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words). As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is applied. Cite this work as: ``` Aye Mya Hlaing, Win Pa Pa, "MyanBERTa: A Pre-trained Language Model For Myanmar", In Proceedings of 2022 International Conference on Communication and Computer Research (ICCR2022), November 2022, Seoul, Republic of Korea ``` [Download Paper](https://journal-home.s3.ap-northeast-2.amazonaws.com/site/iccr2022/abs/QOHFI-0004.pdf)
jeraldflowers/distilroberts-base-mrpc-glue-jeraldflowers	jeraldflowers	2022-11-29T02:57:36Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T05:30:00Z	--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: ["Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."] example_title: Not Equivalent - text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.", "With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."] example_title: Equivalent model-index: - name: distilroberts-base-mrpc-glue-jeraldflowers results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8431372549019608 - name: F1 type: f1 value: 0.8814814814814815 --- <!-- 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. --> # distilroberts-base-mrpc-glue-jeraldflowers This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.4990 - Accuracy: 0.8431 - F1: 0.8815 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5289 \| 1.09 \| 500 \| 0.5668 \| 0.8211 \| 0.8689 \| \| 0.3675 \| 2.18 \| 1000 \| 0.4990 \| 0.8431 \| 0.8815 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
neulab/omnitab-large-128shot-finetuned-wtq-128shot	neulab	2022-11-29T02:55:31Z	47	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-11-29T02:54:00Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-128shot-finetuned-wtq-128shot` (based on BART architecture) is initialized with `neulab/omnitab-large-128shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 128-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
neulab/omnitab-large-1024shot-finetuned-wtq-1024shot	neulab	2022-11-29T02:45:55Z	51	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-11-29T02:44:57Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-1024shot-finetuned-wtq-1024shot` (based on BART architecture) is initialized with `neulab/omnitab-large-1024shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 1024-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
npark/asr-conformer-ksponspeech	npark	2022-11-29T02:25:40Z	5	1	null	[ "region:us" ]	null	2022-11-29T01:26:29Z	# KsponSpeech ASR with Transformers This repository provides pretrained end-to-end ASR models on KsponSpeech with Speechbrain v0.5.13. Model files in this repository trained using the files is below URL, but in Speechbrain version 0.5.13. https://github.com/speechbrain/speechbrain/tree/develop/recipes/KsponSpeech/ASR/transformer language: - "ko" - ko datasets: - KsponSpeech ## About SpeechBrain * Website: https://speechbrain.github.io/ * Code: https://github.com/speechbrain/speechbrain/ * HuggingFace: https://huggingface.co/speechbrain/
huggingtweets/elonmusk-lexfridman	huggingtweets	2022-11-29T01:35:11Z	118	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/956331551435960322/OaqR8pAB_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Lex Fridman</div> <div style="text-align: center; font-size: 14px;">@elonmusk-lexfridman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Lex Fridman. \| Data \| Elon Musk \| Lex Fridman \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3198 \| 2410 \| \| Retweets \| 126 \| 253 \| \| Short tweets \| 968 \| 49 \| \| Tweets kept \| 2104 \| 2108 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18nt3c0k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-lexfridman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-lexfridman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
akmoyu/whisper-medium-mn	akmoyu	2022-11-29T01:27:26Z	3	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "mn", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-27T12:12:01Z	--- language: - mn license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Medium Mn - akmoyu results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 42.52948885976409 --- <!-- 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 Medium Mn - akmoyu This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7233 - Wer: 42.5295 ## 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: 16 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0182 \| 7.94 \| 1000 \| 0.5995 \| 46.5269 \| \| 0.0027 \| 15.87 \| 2000 \| 0.6499 \| 44.2169 \| \| 0.0002 \| 23.81 \| 3000 \| 0.7057 \| 42.5623 \| \| 0.0001 \| 31.75 \| 4000 \| 0.7233 \| 42.5295 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2
dlwh/legal-xlm-base_128k	dlwh	2022-11-29T00:48:35Z	4	2	transformers	[ "transformers", "roberta", "fill-mask", "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-29T00:41:54Z	--- license: apache-2.0 language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv dataset: - joelito/MultiLegalPile_Wikipedia_Filtered --- Huggingface thinks this is a model, but it's just a tokenizer. Trained on https://huggingface.co/datasets/joelito/MultiLegalPile_Wikipedia_Filtered
matan-diamond/sd-class-butterflies-32	matan-diamond	2022-11-29T00:47:21Z	36	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T00:46:35Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(matan-diamond/sd-class-butterflies-32) image = pipeline().images[0] image ```
adrien-alloreview/whisper-small-fr	adrien-alloreview	2022-11-29T00:13:29Z	83	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-28T22:32:23Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Sanchit Gandhi 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 Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2226 - eval_wer: 10.0023 - eval_runtime: 65.2041 - eval_samples_per_second: 1.748 - eval_steps_per_second: 0.23 - epoch: 19.51 - step: 800 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
joweyel/sd-class-butterflies-32	joweyel	2022-11-28T23:54:45Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T23:51:15Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of (more or less) cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(datboi223/sd-class-butterflies-32) image = pipeline().images[0] image ```
Serhio/sd-fine-tune-v2	Serhio	2022-11-28T23:43:18Z	34	0	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T23:41:46Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### sd-fine-tune-v2 on Stable Diffusion via Dreambooth #### model by Serhio This your the Stable Diffusion model fine-tuned the sd-fine-tune-v2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: Bashkov Sergey You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
pig4431/TweetEval_BERT_5E	pig4431	2022-11-28T23:38:03Z	102	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T23:31:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_BERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9266666666666666 --- <!-- 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. --> # TweetEval_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5419 - Accuracy: 0.9267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6264 \| 0.04 \| 50 \| 0.5266 \| 0.74 \| \| 0.5054 \| 0.08 \| 100 \| 0.5959 \| 0.6333 \| \| 0.4732 \| 0.12 \| 150 \| 0.3524 \| 0.86 \| \| 0.3916 \| 0.16 \| 200 \| 0.3195 \| 0.8667 \| \| 0.3477 \| 0.2 \| 250 \| 0.2878 \| 0.8867 \| \| 0.3116 \| 0.24 \| 300 \| 0.2903 \| 0.92 \| \| 0.3039 \| 0.28 \| 350 \| 0.2488 \| 0.8933 \| \| 0.2633 \| 0.32 \| 400 \| 0.2530 \| 0.92 \| \| 0.2667 \| 0.37 \| 450 \| 0.2125 \| 0.9267 \| \| 0.2604 \| 0.41 \| 500 \| 0.2628 \| 0.8867 \| \| 0.278 \| 0.45 \| 550 \| 0.2322 \| 0.8867 \| \| 0.2625 \| 0.49 \| 600 \| 0.1903 \| 0.92 \| \| 0.2808 \| 0.53 \| 650 \| 0.2400 \| 0.8933 \| \| 0.2396 \| 0.57 \| 700 \| 0.2184 \| 0.9067 \| \| 0.2571 \| 0.61 \| 750 \| 0.1906 \| 0.9133 \| \| 0.2676 \| 0.65 \| 800 \| 0.2467 \| 0.9067 \| \| 0.2288 \| 0.69 \| 850 \| 0.2038 \| 0.9133 \| \| 0.2959 \| 0.73 \| 900 \| 0.1941 \| 0.9 \| \| 0.2619 \| 0.77 \| 950 \| 0.2100 \| 0.9333 \| \| 0.2504 \| 0.81 \| 1000 \| 0.1523 \| 0.9333 \| \| 0.2338 \| 0.85 \| 1050 \| 0.1429 \| 0.94 \| \| 0.2529 \| 0.89 \| 1100 \| 0.1269 \| 0.94 \| \| 0.2238 \| 0.93 \| 1150 \| 0.1722 \| 0.9333 \| \| 0.2295 \| 0.97 \| 1200 \| 0.1874 \| 0.94 \| \| 0.2089 \| 1.01 \| 1250 \| 0.2214 \| 0.9067 \| \| 0.1406 \| 1.06 \| 1300 \| 0.3410 \| 0.9133 \| \| 0.1587 \| 1.1 \| 1350 \| 0.3330 \| 0.9133 \| \| 0.1732 \| 1.14 \| 1400 \| 0.2716 \| 0.9133 \| \| 0.195 \| 1.18 \| 1450 \| 0.3726 \| 0.92 \| \| 0.1777 \| 1.22 \| 1500 \| 0.2430 \| 0.9267 \| \| 0.1433 \| 1.26 \| 1550 \| 0.3011 \| 0.9267 \| \| 0.1333 \| 1.3 \| 1600 \| 0.2489 \| 0.9333 \| \| 0.1516 \| 1.34 \| 1650 \| 0.3340 \| 0.9267 \| \| 0.1774 \| 1.38 \| 1700 \| 0.2497 \| 0.8933 \| \| 0.1608 \| 1.42 \| 1750 \| 0.3234 \| 0.9 \| \| 0.1534 \| 1.46 \| 1800 \| 0.3383 \| 0.9133 \| \| 0.1287 \| 1.5 \| 1850 \| 0.3134 \| 0.9133 \| \| 0.1422 \| 1.54 \| 1900 \| 0.3330 \| 0.9 \| \| 0.1578 \| 1.58 \| 1950 \| 0.3281 \| 0.9133 \| \| 0.1786 \| 1.62 \| 2000 \| 0.2939 \| 0.9267 \| \| 0.2019 \| 1.66 \| 2050 \| 0.3535 \| 0.9 \| \| 0.1995 \| 1.7 \| 2100 \| 0.3032 \| 0.9067 \| \| 0.159 \| 1.75 \| 2150 \| 0.2598 \| 0.9267 \| \| 0.1493 \| 1.79 \| 2200 \| 0.2391 \| 0.9267 \| \| 0.1748 \| 1.83 \| 2250 \| 0.2258 \| 0.92 \| \| 0.1783 \| 1.87 \| 2300 \| 0.2749 \| 0.9133 \| \| 0.1619 \| 1.91 \| 2350 \| 0.2699 \| 0.92 \| \| 0.1378 \| 1.95 \| 2400 \| 0.2776 \| 0.9067 \| \| 0.1529 \| 1.99 \| 2450 \| 0.2235 \| 0.9333 \| \| 0.1071 \| 2.03 \| 2500 \| 0.2841 \| 0.9267 \| \| 0.0812 \| 2.07 \| 2550 \| 0.3178 \| 0.9267 \| \| 0.0464 \| 2.11 \| 2600 \| 0.3567 \| 0.92 \| \| 0.1108 \| 2.15 \| 2650 \| 0.2723 \| 0.92 \| \| 0.0845 \| 2.19 \| 2700 \| 0.2774 \| 0.9267 \| \| 0.0795 \| 2.23 \| 2750 \| 0.3027 \| 0.9267 \| \| 0.0403 \| 2.27 \| 2800 \| 0.3566 \| 0.9267 \| \| 0.0664 \| 2.31 \| 2850 \| 0.4015 \| 0.92 \| \| 0.0659 \| 2.35 \| 2900 \| 0.4298 \| 0.9067 \| \| 0.1059 \| 2.39 \| 2950 \| 0.4028 \| 0.92 \| \| 0.105 \| 2.44 \| 3000 \| 0.3701 \| 0.92 \| \| 0.0808 \| 2.48 \| 3050 \| 0.3206 \| 0.9267 \| \| 0.0811 \| 2.52 \| 3100 \| 0.3644 \| 0.9133 \| \| 0.0458 \| 2.56 \| 3150 \| 0.3781 \| 0.9267 \| \| 0.0764 \| 2.6 \| 3200 \| 0.3749 \| 0.9267 \| \| 0.0567 \| 2.64 \| 3250 \| 0.3995 \| 0.92 \| \| 0.0971 \| 2.68 \| 3300 \| 0.3455 \| 0.92 \| \| 0.0579 \| 2.72 \| 3350 \| 0.4508 \| 0.92 \| \| 0.0853 \| 2.76 \| 3400 \| 0.4350 \| 0.92 \| \| 0.0577 \| 2.8 \| 3450 \| 0.3804 \| 0.9333 \| \| 0.0732 \| 2.84 \| 3500 \| 0.4387 \| 0.92 \| \| 0.0874 \| 2.88 \| 3550 \| 0.3885 \| 0.9333 \| \| 0.1031 \| 2.92 \| 3600 \| 0.3937 \| 0.92 \| \| 0.0335 \| 2.96 \| 3650 \| 0.4963 \| 0.8933 \| \| 0.0913 \| 3.0 \| 3700 \| 0.3827 \| 0.9333 \| \| 0.047 \| 3.04 \| 3750 \| 0.4136 \| 0.92 \| \| 0.0531 \| 3.08 \| 3800 \| 0.4362 \| 0.92 \| \| 0.0265 \| 3.12 \| 3850 \| 0.4857 \| 0.92 \| \| 0.038 \| 3.17 \| 3900 \| 0.4425 \| 0.92 \| \| 0.0294 \| 3.21 \| 3950 \| 0.4347 \| 0.92 \| \| 0.0367 \| 3.25 \| 4000 \| 0.4291 \| 0.9333 \| \| 0.0102 \| 3.29 \| 4050 \| 0.5178 \| 0.9267 \| \| 0.0311 \| 3.33 \| 4100 \| 0.4784 \| 0.9267 \| \| 0.0274 \| 3.37 \| 4150 \| 0.5421 \| 0.9267 \| \| 0.0275 \| 3.41 \| 4200 \| 0.5194 \| 0.92 \| \| 0.0795 \| 3.45 \| 4250 \| 0.4788 \| 0.92 \| \| 0.0413 \| 3.49 \| 4300 \| 0.4393 \| 0.9267 \| \| 0.0373 \| 3.53 \| 4350 \| 0.4965 \| 0.92 \| \| 0.0303 \| 3.57 \| 4400 \| 0.4284 \| 0.9267 \| \| 0.0248 \| 3.61 \| 4450 \| 0.4476 \| 0.9267 \| \| 0.0557 \| 3.65 \| 4500 \| 0.4690 \| 0.92 \| \| 0.0358 \| 3.69 \| 4550 \| 0.4774 \| 0.9133 \| \| 0.0194 \| 3.73 \| 4600 \| 0.4755 \| 0.92 \| \| 0.0473 \| 3.77 \| 4650 \| 0.4637 \| 0.92 \| \| 0.0133 \| 3.81 \| 4700 \| 0.4868 \| 0.92 \| \| 0.0204 \| 3.86 \| 4750 \| 0.4886 \| 0.9267 \| \| 0.0338 \| 3.9 \| 4800 \| 0.5101 \| 0.9267 \| \| 0.0424 \| 3.94 \| 4850 \| 0.4812 \| 0.9267 \| \| 0.0237 \| 3.98 \| 4900 \| 0.4837 \| 0.9267 \| \| 0.0372 \| 4.02 \| 4950 \| 0.5000 \| 0.9267 \| \| 0.0254 \| 4.06 \| 5000 \| 0.5210 \| 0.92 \| \| 0.024 \| 4.1 \| 5050 \| 0.5272 \| 0.92 \| \| 0.0117 \| 4.14 \| 5100 \| 0.5447 \| 0.92 \| \| 0.018 \| 4.18 \| 5150 \| 0.5353 \| 0.92 \| \| 0.0097 \| 4.22 \| 5200 \| 0.5415 \| 0.9267 \| \| 0.0151 \| 4.26 \| 5250 \| 0.5447 \| 0.9267 \| \| 0.0118 \| 4.3 \| 5300 \| 0.5285 \| 0.9267 \| \| 0.0004 \| 4.34 \| 5350 \| 0.5399 \| 0.9267 \| \| 0.0102 \| 4.38 \| 5400 \| 0.5552 \| 0.9267 \| \| 0.0012 \| 4.42 \| 5450 \| 0.5689 \| 0.92 \| \| 0.02 \| 4.46 \| 5500 \| 0.5619 \| 0.9267 \| \| 0.0056 \| 4.5 \| 5550 \| 0.5784 \| 0.92 \| \| 0.0271 \| 4.55 \| 5600 \| 0.5766 \| 0.92 \| \| 0.0191 \| 4.59 \| 5650 \| 0.5662 \| 0.92 \| \| 0.0311 \| 4.63 \| 5700 \| 0.5514 \| 0.9267 \| \| 0.0167 \| 4.67 \| 5750 \| 0.5510 \| 0.9267 \| \| 0.0293 \| 4.71 \| 5800 \| 0.5571 \| 0.9267 \| \| 0.0304 \| 4.75 \| 5850 \| 0.5494 \| 0.92 \| \| 0.0161 \| 4.79 \| 5900 \| 0.5469 \| 0.9267 \| \| 0.0017 \| 4.83 \| 5950 \| 0.5468 \| 0.9267 \| \| 0.0176 \| 4.87 \| 6000 \| 0.5426 \| 0.9267 \| \| 0.0094 \| 4.91 \| 6050 \| 0.5402 \| 0.9267 \| \| 0.0041 \| 4.95 \| 6100 \| 0.5416 \| 0.9267 \| \| 0.0281 \| 4.99 \| 6150 \| 0.5419 \| 0.9267 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2
jiping/whisper-small-jsun2-hi	jiping	2022-11-28T22:38:58Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-24T21:04:14Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Jsun Hi - Jiping results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 31.761618555828324 --- <!-- 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 Small Jsun Hi - Jiping This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2775 - Wer: 31.7616 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2092 \| 0.61 \| 1000 \| 0.3201 \| 38.7666 \| \| 0.1106 \| 1.22 \| 2000 \| 0.2810 \| 34.1023 \| \| 0.1049 \| 1.83 \| 3000 \| 0.2660 \| 32.4812 \| \| 0.052 \| 2.45 \| 4000 \| 0.2775 \| 31.7616 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
rahul77/t5-small-finetuned-rahul-summariza	rahul77	2022-11-28T22:11:14Z	106	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	2022-11-28T22:03:46Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-rahul-summariza 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-small-finetuned-rahul-summariza This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7002 - Rouge1: 29.5043 - Rouge2: 23.832 - Rougel: 27.5786 - Rougelsum: 28.404 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.123 \| 1.0 \| 16 \| 0.8258 \| 27.2788 \| 21.3634 \| 25.7114 \| 26.7324 \| 19.0 \| \| 0.9067 \| 2.0 \| 32 \| 0.7539 \| 28.873 \| 23.5401 \| 27.2337 \| 27.939 \| 19.0 \| \| 0.8137 \| 3.0 \| 48 \| 0.7280 \| 29.1767 \| 23.6599 \| 27.7065 \| 28.3569 \| 19.0 \| \| 0.7872 \| 4.0 \| 64 \| 0.7230 \| 29.0451 \| 23.4597 \| 27.2762 \| 28.1324 \| 19.0 \| \| 0.7338 \| 5.0 \| 80 \| 0.7133 \| 29.4821 \| 23.8113 \| 27.4912 \| 28.326 \| 19.0 \| \| 0.6913 \| 6.0 \| 96 \| 0.7101 \| 29.4237 \| 23.8523 \| 27.4109 \| 28.2418 \| 19.0 \| \| 0.6679 \| 7.0 \| 112 \| 0.7097 \| 29.4237 \| 23.8523 \| 27.4109 \| 28.2418 \| 19.0 \| \| 0.6963 \| 8.0 \| 128 \| 0.7046 \| 29.4237 \| 23.8523 \| 27.4109 \| 28.2418 \| 19.0 \| \| 0.6223 \| 9.0 \| 144 \| 0.7052 \| 29.4237 \| 23.7633 \| 27.493 \| 28.3362 \| 19.0 \| \| 0.6494 \| 10.0 \| 160 \| 0.7019 \| 29.4237 \| 23.7633 \| 27.493 \| 28.3362 \| 19.0 \| \| 0.616 \| 11.0 \| 176 \| 0.7010 \| 29.4237 \| 23.7633 \| 27.493 \| 28.3362 \| 19.0 \| \| 0.6058 \| 12.0 \| 192 \| 0.7028 \| 29.4237 \| 23.7633 \| 27.493 \| 28.3362 \| 19.0 \| \| 0.5964 \| 13.0 \| 208 \| 0.6996 \| 29.4237 \| 23.7633 \| 27.493 \| 28.3362 \| 19.0 \| \| 0.5958 \| 14.0 \| 224 \| 0.6997 \| 29.4237 \| 23.7633 \| 27.493 \| 28.3362 \| 19.0 \| \| 0.57 \| 15.0 \| 240 \| 0.6996 \| 29.5043 \| 23.832 \| 27.5786 \| 28.404 \| 19.0 \| \| 0.5714 \| 16.0 \| 256 \| 0.6998 \| 29.5043 \| 23.832 \| 27.5786 \| 28.404 \| 19.0 \| \| 0.5648 \| 17.0 \| 272 \| 0.6999 \| 29.5043 \| 23.832 \| 27.5786 \| 28.404 \| 19.0 \| \| 0.5258 \| 18.0 \| 288 \| 0.7005 \| 29.5043 \| 23.832 \| 27.5786 \| 28.404 \| 19.0 \| \| 0.5692 \| 19.0 \| 304 \| 0.7001 \| 29.5043 \| 23.832 \| 27.5786 \| 28.404 \| 19.0 \| \| 0.5708 \| 20.0 \| 320 \| 0.7002 \| 29.5043 \| 23.832 \| 27.5786 \| 28.404 \| 19.0 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
ThomasSimonini/ML-Agents-SnowballFight-1vs1-model	ThomasSimonini	2022-11-28T22:07:31Z	6	0	ml-agents	[ "ml-agents", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Snowballfight-1vs1", "region:us" ]	reinforcement-learning	2022-11-28T21:26:07Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Snowballfight-1vs1 library_name: ml-agents ---
alryan1478/gpt-neo-125M-wikitext2	alryan1478	2022-11-28T21:57:47Z	4	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-22T20:55:28Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-wikitext2 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-neo-125M-wikitext2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0325 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 259 \| 6.4308 \| \| 6.8563 \| 2.0 \| 518 \| 6.0898 \| \| 6.8563 \| 3.0 \| 777 \| 6.0325 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
michaelmayo704/sd-class-butterflies-64	michaelmayo704	2022-11-28T21:39:43Z	34	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T21:38:51Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(michaelmayo704/sd-class-butterflies-64) image = pipeline().images[0] image ```
pig4431/TUF_ALBERT_5E	pig4431	2022-11-28T21:34:30Z	105	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:32:18Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_ALBERT_5E 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. --> # TUF_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2389 - Accuracy: 0.9533 ## 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: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5099 \| 0.1 \| 50 \| 0.3861 \| 0.8533 \| \| 0.2985 \| 0.2 \| 100 \| 0.2961 \| 0.8933 \| \| 0.2972 \| 0.3 \| 150 \| 0.2335 \| 0.9333 \| \| 0.2835 \| 0.4 \| 200 \| 0.1872 \| 0.94 \| \| 0.26 \| 0.5 \| 250 \| 0.4147 \| 0.9133 \| \| 0.2986 \| 0.59 \| 300 \| 0.2080 \| 0.9267 \| \| 0.2554 \| 0.69 \| 350 \| 0.3984 \| 0.9133 \| \| 0.2306 \| 0.79 \| 400 \| 0.2136 \| 0.9333 \| \| 0.2218 \| 0.89 \| 450 \| 0.4455 \| 0.8867 \| \| 0.2113 \| 0.99 \| 500 \| 0.2205 \| 0.94 \| \| 0.2541 \| 1.09 \| 550 \| 0.1705 \| 0.9333 \| \| 0.1947 \| 1.19 \| 600 \| 0.3264 \| 0.8933 \| \| 0.2409 \| 1.29 \| 650 \| 0.2084 \| 0.92 \| \| 0.1968 \| 1.39 \| 700 \| 0.2550 \| 0.9267 \| \| 0.172 \| 1.49 \| 750 \| 0.2238 \| 0.9467 \| \| 0.1478 \| 1.58 \| 800 \| 0.2501 \| 0.9533 \| \| 0.2199 \| 1.68 \| 850 \| 0.2618 \| 0.9133 \| \| 0.1792 \| 1.78 \| 900 \| 0.2109 \| 0.9267 \| \| 0.1831 \| 1.88 \| 950 \| 0.2641 \| 0.92 \| \| 0.1534 \| 1.98 \| 1000 \| 0.1924 \| 0.94 \| \| 0.1208 \| 2.08 \| 1050 \| 0.2990 \| 0.9333 \| \| 0.1118 \| 2.18 \| 1100 \| 0.4952 \| 0.9 \| \| 0.158 \| 2.28 \| 1150 \| 0.1706 \| 0.9533 \| \| 0.1163 \| 2.38 \| 1200 \| 0.1238 \| 0.9733 \| \| 0.1738 \| 2.48 \| 1250 \| 0.1989 \| 0.9467 \| \| 0.1305 \| 2.57 \| 1300 \| 0.4354 \| 0.9067 \| \| 0.1668 \| 2.67 \| 1350 \| 0.1276 \| 0.9667 \| \| 0.1195 \| 2.77 \| 1400 \| 0.2170 \| 0.9533 \| \| 0.1057 \| 2.87 \| 1450 \| 0.2882 \| 0.9333 \| \| 0.1172 \| 2.97 \| 1500 \| 0.1435 \| 0.9667 \| \| 0.0893 \| 3.07 \| 1550 \| 0.1754 \| 0.96 \| \| 0.0582 \| 3.17 \| 1600 \| 0.1858 \| 0.96 \| \| 0.0887 \| 3.27 \| 1650 \| 0.4954 \| 0.92 \| \| 0.1166 \| 3.37 \| 1700 \| 0.2356 \| 0.9467 \| \| 0.0518 \| 3.47 \| 1750 \| 0.1910 \| 0.96 \| \| 0.0741 \| 3.56 \| 1800 \| 0.1328 \| 0.9733 \| \| 0.072 \| 3.66 \| 1850 \| 0.2769 \| 0.9467 \| \| 0.0534 \| 3.76 \| 1900 \| 0.3501 \| 0.94 \| \| 0.0776 \| 3.86 \| 1950 \| 0.3171 \| 0.94 \| \| 0.0537 \| 3.96 \| 2000 \| 0.2138 \| 0.9533 \| \| 0.0683 \| 4.06 \| 2050 \| 0.2934 \| 0.94 \| \| 0.015 \| 4.16 \| 2100 \| 0.2233 \| 0.9533 \| \| 0.0236 \| 4.26 \| 2150 \| 0.2673 \| 0.9533 \| \| 0.0357 \| 4.36 \| 2200 \| 0.2279 \| 0.96 \| \| 0.0298 \| 4.46 \| 2250 \| 0.3017 \| 0.9467 \| \| 0.0357 \| 4.55 \| 2300 \| 0.2910 \| 0.9467 \| \| 0.0208 \| 4.65 \| 2350 \| 0.2498 \| 0.9533 \| \| 0.0345 \| 4.75 \| 2400 \| 0.2259 \| 0.9667 \| \| 0.0174 \| 4.85 \| 2450 \| 0.2274 \| 0.9667 \| \| 0.0393 \| 4.95 \| 2500 \| 0.2389 \| 0.9533 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
anikethjr/PromoGen_K562_2080Ti_restart	anikethjr	2022-11-28T21:24:36Z	91	0	transformers	[ "transformers", "pytorch", "tensorboard", "prophetnet", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-27T05:27:24Z	--- tags: - generated_from_trainer model-index: - name: PromoGen_K562_2080Ti_restart 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. --> # PromoGen_K562_2080Ti_restart This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 ## 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: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.7676 \| 0.49 \| 2500 \| 0.7383 \| \| 0.7121 \| 0.97 \| 5000 \| 0.6867 \| \| 0.6914 \| 1.46 \| 7500 \| 0.6705 \| \| 0.6837 \| 1.95 \| 10000 \| 0.6622 \| \| 0.6778 \| 2.44 \| 12500 \| 0.6558 \| \| 0.6748 \| 2.92 \| 15000 \| 0.6517 \| \| 0.6676 \| 3.41 \| 17500 \| 0.6433 \| \| 0.6593 \| 3.9 \| 20000 \| 0.6358 \| \| 0.6584 \| 4.38 \| 22500 \| 0.6320 \| \| 0.6557 \| 4.87 \| 25000 \| 0.6301 \| \| 0.6523 \| 5.36 \| 27500 \| 0.6257 \| \| 0.6478 \| 5.84 \| 30000 \| 0.6236 \| \| 0.6393 \| 6.33 \| 32500 \| 0.6145 \| \| 0.6039 \| 6.82 \| 35000 \| 0.5658 \| \| 0.5616 \| 7.31 \| 37500 \| 0.5376 \| \| 0.5518 \| 7.79 \| 40000 \| 0.5310 \| \| 0.5509 \| 8.28 \| 42500 \| 0.5273 \| \| 0.5487 \| 8.77 \| 45000 \| 0.5261 \| \| 0.5479 \| 9.25 \| 47500 \| 0.5249 \| \| 0.546 \| 9.74 \| 50000 \| 0.5242 \| \| 0.5447 \| 10.23 \| 52500 \| 0.5229 \| \| 0.5439 \| 10.71 \| 55000 \| 0.5220 \| \| 0.5433 \| 11.2 \| 57500 \| 0.5209 \| \| 0.5394 \| 11.69 \| 60000 \| 0.5162 \| \| 0.5153 \| 12.18 \| 62500 \| 0.4944 \| \| 0.5137 \| 12.66 \| 65000 \| 0.4932 \| \| 0.514 \| 13.15 \| 67500 \| 0.4924 \| \| 0.5131 \| 13.64 \| 70000 \| 0.4919 \| \| 0.5104 \| 14.12 \| 72500 \| 0.4914 \| \| 0.5122 \| 14.61 \| 75000 \| 0.4906 \| \| 0.5089 \| 15.1 \| 77500 \| 0.4901 \| \| 0.5076 \| 15.59 \| 80000 \| 0.4891 \| \| 0.4986 \| 16.07 \| 82500 \| 0.4721 \| \| 0.4875 \| 16.56 \| 85000 \| 0.4672 \| \| 0.4887 \| 17.05 \| 87500 \| 0.4669 \| \| 0.4839 \| 17.53 \| 90000 \| 0.4661 \| \| 0.4849 \| 18.02 \| 92500 \| 0.4654 \| \| 0.4848 \| 18.51 \| 95000 \| 0.4649 \| \| 0.4831 \| 18.99 \| 97500 \| 0.4646 \| \| 0.4816 \| 19.48 \| 100000 \| 0.4644 \| \| 0.4808 \| 19.97 \| 102500 \| 0.4637 \| \| 0.4812 \| 20.46 \| 105000 \| 0.4634 \| \| 0.4813 \| 20.94 \| 107500 \| 0.4633 \| \| 0.4818 \| 21.43 \| 110000 \| 0.4631 \| \| 0.4813 \| 21.92 \| 112500 \| 0.4629 \| \| 0.4782 \| 22.4 \| 115000 \| 0.4628 \| \| 0.4804 \| 22.89 \| 117500 \| 0.4626 \| \| 0.4815 \| 23.38 \| 120000 \| 0.4625 \| \| 0.4812 \| 23.87 \| 122500 \| 0.4625 \| \| 0.4785 \| 24.35 \| 125000 \| 0.4624 \| \| 0.4795 \| 24.84 \| 127500 \| 0.4624 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.0 - Tokenizers 0.13.0.dev0
Inayat/Fine_tune_whisper_small	Inayat	2022-11-28T21:14:32Z	79	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-14T19:18:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Fine_tune_whisper_small 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_tune_whisper_small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8238 - Wer: 42.9362 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 900 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2994 \| 3.92 \| 200 \| 0.6607 \| 44.0797 \| \| 0.0201 \| 7.84 \| 400 \| 0.7371 \| 42.6042 \| \| 0.002 \| 11.76 \| 600 \| 0.8027 \| 42.5304 \| \| 0.0011 \| 15.69 \| 800 \| 0.8238 \| 42.9362 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
pig4431/TweetEval_DistilBERT_5E	pig4431	2022-11-28T21:09:36Z	103	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:03:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_DistilBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9133333333333333 --- <!-- 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. --> # TweetEval_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.4043 - Accuracy: 0.9133 ## 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: 8 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5747 \| 0.04 \| 50 \| 0.4843 \| 0.7333 \| \| 0.4336 \| 0.08 \| 100 \| 0.2888 \| 0.8667 \| \| 0.3437 \| 0.12 \| 150 \| 0.2895 \| 0.8667 \| \| 0.3375 \| 0.16 \| 200 \| 0.2864 \| 0.8733 \| \| 0.3072 \| 0.2 \| 250 \| 0.2577 \| 0.8867 \| \| 0.3019 \| 0.24 \| 300 \| 0.2574 \| 0.8933 \| \| 0.2662 \| 0.28 \| 350 \| 0.2621 \| 0.8867 \| \| 0.283 \| 0.32 \| 400 \| 0.2340 \| 0.92 \| \| 0.2949 \| 0.37 \| 450 \| 0.2482 \| 0.8933 \| \| 0.3066 \| 0.41 \| 500 \| 0.2537 \| 0.9 \| \| 0.2457 \| 0.45 \| 550 \| 0.2473 \| 0.9 \| \| 0.295 \| 0.49 \| 600 \| 0.2177 \| 0.9133 \| \| 0.2862 \| 0.53 \| 650 \| 0.2215 \| 0.9133 \| \| 0.2603 \| 0.57 \| 700 \| 0.2272 \| 0.9133 \| \| 0.2976 \| 0.61 \| 750 \| 0.2298 \| 0.9067 \| \| 0.2823 \| 0.65 \| 800 \| 0.2451 \| 0.8933 \| \| 0.2583 \| 0.69 \| 850 \| 0.2645 \| 0.8933 \| \| 0.2694 \| 0.73 \| 900 \| 0.2352 \| 0.9 \| \| 0.2433 \| 0.77 \| 950 \| 0.2322 \| 0.9133 \| \| 0.2598 \| 0.81 \| 1000 \| 0.2300 \| 0.9 \| \| 0.2701 \| 0.85 \| 1050 \| 0.2162 \| 0.9 \| \| 0.2227 \| 0.89 \| 1100 \| 0.2135 \| 0.8933 \| \| 0.2045 \| 0.93 \| 1150 \| 0.2233 \| 0.9133 \| \| 0.2821 \| 0.97 \| 1200 \| 0.2194 \| 0.9 \| \| 0.2342 \| 1.01 \| 1250 \| 0.2488 \| 0.88 \| \| 0.2028 \| 1.06 \| 1300 \| 0.2451 \| 0.8867 \| \| 0.1509 \| 1.1 \| 1350 \| 0.3174 \| 0.88 \| \| 0.1888 \| 1.14 \| 1400 \| 0.2537 \| 0.9133 \| \| 0.1825 \| 1.18 \| 1450 \| 0.2559 \| 0.9067 \| \| 0.1721 \| 1.22 \| 1500 \| 0.2511 \| 0.92 \| \| 0.2137 \| 1.26 \| 1550 \| 0.2963 \| 0.9133 \| \| 0.2153 \| 1.3 \| 1600 \| 0.2210 \| 0.92 \| \| 0.1989 \| 1.34 \| 1650 \| 0.2231 \| 0.9133 \| \| 0.2155 \| 1.38 \| 1700 \| 0.1991 \| 0.9133 \| \| 0.1912 \| 1.42 \| 1750 \| 0.2146 \| 0.92 \| \| 0.1623 \| 1.46 \| 1800 \| 0.2721 \| 0.9 \| \| 0.2236 \| 1.5 \| 1850 \| 0.2301 \| 0.9267 \| \| 0.1907 \| 1.54 \| 1900 \| 0.1988 \| 0.92 \| \| 0.1286 \| 1.58 \| 1950 \| 0.2326 \| 0.9 \| \| 0.2147 \| 1.62 \| 2000 \| 0.2432 \| 0.9267 \| \| 0.2018 \| 1.66 \| 2050 \| 0.2162 \| 0.9067 \| \| 0.2073 \| 1.7 \| 2100 \| 0.2153 \| 0.9133 \| \| 0.1498 \| 1.75 \| 2150 \| 0.2335 \| 0.92 \| \| 0.1812 \| 1.79 \| 2200 \| 0.2275 \| 0.9267 \| \| 0.1482 \| 1.83 \| 2250 \| 0.2734 \| 0.9 \| \| 0.2233 \| 1.87 \| 2300 \| 0.2454 \| 0.9 \| \| 0.1673 \| 1.91 \| 2350 \| 0.2394 \| 0.92 \| \| 0.1555 \| 1.95 \| 2400 \| 0.2725 \| 0.92 \| \| 0.2082 \| 1.99 \| 2450 \| 0.2684 \| 0.9133 \| \| 0.1545 \| 2.03 \| 2500 \| 0.3049 \| 0.9067 \| \| 0.1384 \| 2.07 \| 2550 \| 0.2960 \| 0.9133 \| \| 0.1201 \| 2.11 \| 2600 \| 0.3259 \| 0.9 \| \| 0.1348 \| 2.15 \| 2650 \| 0.3091 \| 0.9133 \| \| 0.1046 \| 2.19 \| 2700 \| 0.2916 \| 0.9267 \| \| 0.1506 \| 2.23 \| 2750 \| 0.2910 \| 0.9133 \| \| 0.1481 \| 2.27 \| 2800 \| 0.2855 \| 0.9067 \| \| 0.1318 \| 2.31 \| 2850 \| 0.3075 \| 0.9 \| \| 0.1204 \| 2.35 \| 2900 \| 0.3169 \| 0.8933 \| \| 0.1669 \| 2.39 \| 2950 \| 0.3050 \| 0.9067 \| \| 0.1725 \| 2.44 \| 3000 \| 0.2970 \| 0.9133 \| \| 0.1305 \| 2.48 \| 3050 \| 0.3065 \| 0.9 \| \| 0.1508 \| 2.52 \| 3100 \| 0.3079 \| 0.9133 \| \| 0.184 \| 2.56 \| 3150 \| 0.3482 \| 0.9067 \| \| 0.1263 \| 2.6 \| 3200 \| 0.3310 \| 0.9 \| \| 0.1282 \| 2.64 \| 3250 \| 0.3520 \| 0.8933 \| \| 0.1217 \| 2.68 \| 3300 \| 0.3158 \| 0.9067 \| \| 0.1203 \| 2.72 \| 3350 \| 0.3351 \| 0.92 \| \| 0.1068 \| 2.76 \| 3400 \| 0.3239 \| 0.92 \| \| 0.1517 \| 2.8 \| 3450 \| 0.3247 \| 0.92 \| \| 0.113 \| 2.84 \| 3500 \| 0.3269 \| 0.9133 \| \| 0.1276 \| 2.88 \| 3550 \| 0.3162 \| 0.92 \| \| 0.1548 \| 2.92 \| 3600 \| 0.3196 \| 0.9133 \| \| 0.1305 \| 2.96 \| 3650 \| 0.3163 \| 0.92 \| \| 0.149 \| 3.0 \| 3700 \| 0.3013 \| 0.92 \| \| 0.0816 \| 3.04 \| 3750 \| 0.3097 \| 0.9267 \| \| 0.0884 \| 3.08 \| 3800 \| 0.3028 \| 0.92 \| \| 0.0727 \| 3.12 \| 3850 \| 0.3487 \| 0.9133 \| \| 0.1018 \| 3.17 \| 3900 \| 0.3447 \| 0.92 \| \| 0.1266 \| 3.21 \| 3950 \| 0.3589 \| 0.9133 \| \| 0.1216 \| 3.25 \| 4000 \| 0.3464 \| 0.92 \| \| 0.091 \| 3.29 \| 4050 \| 0.3454 \| 0.92 \| \| 0.0829 \| 3.33 \| 4100 \| 0.3450 \| 0.92 \| \| 0.1084 \| 3.37 \| 4150 \| 0.3670 \| 0.92 \| \| 0.0754 \| 3.41 \| 4200 \| 0.3661 \| 0.92 \| \| 0.094 \| 3.45 \| 4250 \| 0.3588 \| 0.9067 \| \| 0.0641 \| 3.49 \| 4300 \| 0.3936 \| 0.92 \| \| 0.1138 \| 3.53 \| 4350 \| 0.3616 \| 0.92 \| \| 0.0744 \| 3.57 \| 4400 \| 0.3562 \| 0.92 \| \| 0.0697 \| 3.61 \| 4450 \| 0.3532 \| 0.9267 \| \| 0.1083 \| 3.65 \| 4500 \| 0.3451 \| 0.9267 \| \| 0.0701 \| 3.69 \| 4550 \| 0.3307 \| 0.92 \| \| 0.0849 \| 3.73 \| 4600 \| 0.3797 \| 0.92 \| \| 0.09 \| 3.77 \| 4650 \| 0.3746 \| 0.9267 \| \| 0.0799 \| 3.81 \| 4700 \| 0.3799 \| 0.92 \| \| 0.0589 \| 3.86 \| 4750 \| 0.3805 \| 0.92 \| \| 0.0578 \| 3.9 \| 4800 \| 0.3910 \| 0.9133 \| \| 0.0816 \| 3.94 \| 4850 \| 0.3856 \| 0.9133 \| \| 0.1366 \| 3.98 \| 4900 \| 0.3707 \| 0.92 \| \| 0.0846 \| 4.02 \| 4950 \| 0.3802 \| 0.92 \| \| 0.0401 \| 4.06 \| 5000 \| 0.3842 \| 0.92 \| \| 0.0851 \| 4.1 \| 5050 \| 0.3773 \| 0.9267 \| \| 0.0514 \| 4.14 \| 5100 \| 0.3922 \| 0.9133 \| \| 0.0909 \| 4.18 \| 5150 \| 0.3893 \| 0.92 \| \| 0.0764 \| 4.22 \| 5200 \| 0.3818 \| 0.9133 \| \| 0.1208 \| 4.26 \| 5250 \| 0.4096 \| 0.92 \| \| 0.0689 \| 4.3 \| 5300 \| 0.3940 \| 0.9133 \| \| 0.0524 \| 4.34 \| 5350 \| 0.4020 \| 0.9133 \| \| 0.0733 \| 4.38 \| 5400 \| 0.4002 \| 0.9133 \| \| 0.0699 \| 4.42 \| 5450 \| 0.4013 \| 0.9133 \| \| 0.0712 \| 4.46 \| 5500 \| 0.4037 \| 0.9067 \| \| 0.0557 \| 4.5 \| 5550 \| 0.4121 \| 0.92 \| \| 0.0679 \| 4.55 \| 5600 \| 0.4067 \| 0.9133 \| \| 0.0651 \| 4.59 \| 5650 \| 0.4194 \| 0.9133 \| \| 0.0607 \| 4.63 \| 5700 \| 0.4007 \| 0.9133 \| \| 0.0676 \| 4.67 \| 5750 \| 0.4013 \| 0.9133 \| \| 0.0303 \| 4.71 \| 5800 \| 0.3984 \| 0.9133 \| \| 0.0674 \| 4.75 \| 5850 \| 0.4037 \| 0.9133 \| \| 0.0842 \| 4.79 \| 5900 \| 0.4072 \| 0.9133 \| \| 0.0516 \| 4.83 \| 5950 \| 0.4096 \| 0.9133 \| \| 0.0556 \| 4.87 \| 6000 \| 0.4111 \| 0.92 \| \| 0.0277 \| 4.91 \| 6050 \| 0.4079 \| 0.9133 \| \| 0.0629 \| 4.95 \| 6100 \| 0.4053 \| 0.9133 \| \| 0.0426 \| 4.99 \| 6150 \| 0.4043 \| 0.9133 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2
michaelmayo704/sd-class-butterflies-32	michaelmayo704	2022-11-28T21:00:24Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T20:59:37Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(michaelmayo704/sd-class-butterflies-32) image = pipeline().images[0] image ```
pig4431/YELP_roBERTa_5E	pig4431	2022-11-28T20:50:36Z	108	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:yelp_review_full", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T20:34:22Z	--- license: mit tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_roBERTa_5E results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: train args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.9866666666666667 --- <!-- 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. --> # YELP_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.0995 - Accuracy: 0.9867 ## 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: 8 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5721 \| 0.03 \| 50 \| 0.3248 \| 0.88 \| \| 0.2836 \| 0.06 \| 100 \| 0.1190 \| 0.9733 \| \| 0.1793 \| 0.1 \| 150 \| 0.1707 \| 0.96 \| \| 0.2196 \| 0.13 \| 200 \| 0.0841 \| 0.9733 \| \| 0.2102 \| 0.16 \| 250 \| 0.0634 \| 0.9867 \| \| 0.2197 \| 0.19 \| 300 \| 0.0763 \| 0.98 \| \| 0.1866 \| 0.22 \| 350 \| 0.0640 \| 0.9867 \| \| 0.1717 \| 0.26 \| 400 \| 0.0612 \| 0.9867 \| \| 0.1443 \| 0.29 \| 450 \| 0.0844 \| 0.9733 \| \| 0.1669 \| 0.32 \| 500 \| 0.1297 \| 0.9667 \| \| 0.2005 \| 0.35 \| 550 \| 0.0644 \| 0.9867 \| \| 0.1543 \| 0.38 \| 600 \| 0.0874 \| 0.9867 \| \| 0.1345 \| 0.42 \| 650 \| 0.1853 \| 0.96 \| \| 0.1664 \| 0.45 \| 700 \| 0.1157 \| 0.9667 \| \| 0.1876 \| 0.48 \| 750 \| 0.0474 \| 0.9733 \| \| 0.111 \| 0.51 \| 800 \| 0.0645 \| 0.98 \| \| 0.1511 \| 0.54 \| 850 \| 0.0432 \| 0.9933 \| \| 0.1846 \| 0.58 \| 900 \| 0.0505 \| 0.9867 \| \| 0.151 \| 0.61 \| 950 \| 0.0452 \| 0.98 \| \| 0.1338 \| 0.64 \| 1000 \| 0.1007 \| 0.98 \| \| 0.1175 \| 0.67 \| 1050 \| 0.0747 \| 0.9867 \| \| 0.1818 \| 0.7 \| 1100 \| 0.0852 \| 0.98 \| \| 0.1557 \| 0.74 \| 1150 \| 0.0255 \| 0.9933 \| \| 0.1487 \| 0.77 \| 1200 \| 0.1266 \| 0.9733 \| \| 0.1315 \| 0.8 \| 1250 \| 0.0593 \| 0.9867 \| \| 0.1059 \| 0.83 \| 1300 \| 0.0697 \| 0.9867 \| \| 0.108 \| 0.86 \| 1350 \| 0.0459 \| 0.9933 \| \| 0.1525 \| 0.9 \| 1400 \| 0.0446 \| 0.9933 \| \| 0.1185 \| 0.93 \| 1450 \| 0.0528 \| 0.9867 \| \| 0.1611 \| 0.96 \| 1500 \| 0.0582 \| 0.9867 \| \| 0.1556 \| 0.99 \| 1550 \| 0.0726 \| 0.98 \| \| 0.0902 \| 1.02 \| 1600 \| 0.0466 \| 0.9867 \| \| 0.1535 \| 1.06 \| 1650 \| 0.0850 \| 0.9733 \| \| 0.0787 \| 1.09 \| 1700 \| 0.0869 \| 0.9867 \| \| 0.1019 \| 1.12 \| 1750 \| 0.0984 \| 0.98 \| \| 0.1234 \| 1.15 \| 1800 \| 0.0358 \| 0.9933 \| \| 0.0884 \| 1.18 \| 1850 \| 0.0621 \| 0.9867 \| \| 0.0785 \| 1.22 \| 1900 \| 0.0507 \| 0.9867 \| \| 0.1454 \| 1.25 \| 1950 \| 0.0793 \| 0.98 \| \| 0.1035 \| 1.28 \| 2000 \| 0.0501 \| 0.9867 \| \| 0.0579 \| 1.31 \| 2050 \| 0.0935 \| 0.9867 \| \| 0.1215 \| 1.34 \| 2100 \| 0.0079 \| 1.0 \| \| 0.0958 \| 1.38 \| 2150 \| 0.0673 \| 0.9867 \| \| 0.106 \| 1.41 \| 2200 \| 0.0875 \| 0.9867 \| \| 0.095 \| 1.44 \| 2250 \| 0.0745 \| 0.9867 \| \| 0.0958 \| 1.47 \| 2300 \| 0.0715 \| 0.9867 \| \| 0.085 \| 1.5 \| 2350 \| 0.0742 \| 0.9867 \| \| 0.082 \| 1.54 \| 2400 \| 0.1053 \| 0.9733 \| \| 0.1202 \| 1.57 \| 2450 \| 0.0711 \| 0.9867 \| \| 0.1041 \| 1.6 \| 2500 \| 0.0723 \| 0.9867 \| \| 0.1145 \| 1.63 \| 2550 \| 0.0361 \| 0.9867 \| \| 0.0909 \| 1.66 \| 2600 \| 0.0868 \| 0.9867 \| \| 0.1029 \| 1.7 \| 2650 \| 0.0680 \| 0.9867 \| \| 0.1083 \| 1.73 \| 2700 \| 0.0599 \| 0.9867 \| \| 0.0871 \| 1.76 \| 2750 \| 0.0452 \| 0.9867 \| \| 0.1506 \| 1.79 \| 2800 \| 0.0344 \| 0.9933 \| \| 0.0778 \| 1.82 \| 2850 \| 0.0380 \| 0.9933 \| \| 0.0982 \| 1.86 \| 2900 \| 0.0349 \| 0.9933 \| \| 0.1296 \| 1.89 \| 2950 \| 0.0713 \| 0.9867 \| \| 0.0836 \| 1.92 \| 3000 \| 0.0693 \| 0.9867 \| \| 0.0699 \| 1.95 \| 3050 \| 0.1023 \| 0.98 \| \| 0.0631 \| 1.98 \| 3100 \| 0.0852 \| 0.98 \| \| 0.0724 \| 2.02 \| 3150 \| 0.0835 \| 0.9867 \| \| 0.0898 \| 2.05 \| 3200 \| 0.0872 \| 0.9867 \| \| 0.0642 \| 2.08 \| 3250 \| 0.0427 \| 0.9933 \| \| 0.0524 \| 2.11 \| 3300 \| 0.0731 \| 0.9867 \| \| 0.0415 \| 2.14 \| 3350 \| 0.0632 \| 0.9867 \| \| 0.0604 \| 2.18 \| 3400 \| 0.0428 \| 0.9867 \| \| 0.0701 \| 2.21 \| 3450 \| 0.0671 \| 0.9867 \| \| 0.0668 \| 2.24 \| 3500 \| 0.0360 \| 0.9933 \| \| 0.0442 \| 2.27 \| 3550 \| 0.0454 \| 0.9933 \| \| 0.0677 \| 2.3 \| 3600 \| 0.0517 \| 0.9867 \| \| 0.0965 \| 2.34 \| 3650 \| 0.0659 \| 0.98 \| \| 0.0781 \| 2.37 \| 3700 \| 0.0732 \| 0.9867 \| \| 0.0421 \| 2.4 \| 3750 \| 0.0855 \| 0.9867 \| \| 0.0674 \| 2.43 \| 3800 \| 0.0813 \| 0.9867 \| \| 0.0613 \| 2.46 \| 3850 \| 0.0859 \| 0.98 \| \| 0.0679 \| 2.5 \| 3900 \| 0.0721 \| 0.9867 \| \| 0.0417 \| 2.53 \| 3950 \| 0.0977 \| 0.9867 \| \| 0.0616 \| 2.56 \| 4000 \| 0.0789 \| 0.9867 \| \| 0.0678 \| 2.59 \| 4050 \| 0.0804 \| 0.9867 \| \| 0.0651 \| 2.62 \| 4100 \| 0.0994 \| 0.98 \| \| 0.0714 \| 2.66 \| 4150 \| 0.0744 \| 0.98 \| \| 0.034 \| 2.69 \| 4200 \| 0.0679 \| 0.9867 \| \| 0.0356 \| 2.72 \| 4250 \| 0.0432 \| 0.9933 \| \| 0.0813 \| 2.75 \| 4300 \| 0.0483 \| 0.9933 \| \| 0.052 \| 2.78 \| 4350 \| 0.0689 \| 0.9867 \| \| 0.0611 \| 2.82 \| 4400 \| 0.0474 \| 0.9867 \| \| 0.0615 \| 2.85 \| 4450 \| 0.0557 \| 0.9867 \| \| 0.0569 \| 2.88 \| 4500 \| 0.1056 \| 0.98 \| \| 0.0352 \| 2.91 \| 4550 \| 0.0443 \| 0.9933 \| \| 0.0312 \| 2.94 \| 4600 \| 0.1026 \| 0.98 \| \| 0.0662 \| 2.98 \| 4650 \| 0.0677 \| 0.9867 \| \| 0.0694 \| 3.01 \| 4700 \| 0.0368 \| 0.9933 \| \| 0.0144 \| 3.04 \| 4750 \| 0.0647 \| 0.9867 \| \| 0.0378 \| 3.07 \| 4800 \| 0.0893 \| 0.9867 \| \| 0.0393 \| 3.1 \| 4850 \| 0.0841 \| 0.9867 \| \| 0.0598 \| 3.13 \| 4900 \| 0.0594 \| 0.9867 \| \| 0.0329 \| 3.17 \| 4950 \| 0.0933 \| 0.9867 \| \| 0.036 \| 3.2 \| 5000 \| 0.0974 \| 0.9867 \| \| 0.0166 \| 3.23 \| 5050 \| 0.0962 \| 0.9867 \| \| 0.0189 \| 3.26 \| 5100 \| 0.0827 \| 0.9867 \| \| 0.0482 \| 3.29 \| 5150 \| 0.0955 \| 0.9867 \| \| 0.0105 \| 3.33 \| 5200 \| 0.0745 \| 0.9867 \| \| 0.0447 \| 3.36 \| 5250 \| 0.1038 \| 0.9867 \| \| 0.0495 \| 3.39 \| 5300 \| 0.0684 \| 0.9867 \| \| 0.0445 \| 3.42 \| 5350 \| 0.0815 \| 0.9867 \| \| 0.0006 \| 3.45 \| 5400 \| 0.1012 \| 0.9867 \| \| 0.0214 \| 3.49 \| 5450 \| 0.0707 \| 0.9867 \| \| 0.0289 \| 3.52 \| 5500 \| 0.1000 \| 0.9867 \| \| 0.0304 \| 3.55 \| 5550 \| 0.1069 \| 0.9867 \| \| 0.0339 \| 3.58 \| 5600 \| 0.1079 \| 0.9867 \| \| 0.0227 \| 3.61 \| 5650 \| 0.1032 \| 0.9867 \| \| 0.0626 \| 3.65 \| 5700 \| 0.0978 \| 0.9867 \| \| 0.04 \| 3.68 \| 5750 \| 0.0965 \| 0.9867 \| \| 0.0358 \| 3.71 \| 5800 \| 0.1048 \| 0.9867 \| \| 0.0287 \| 3.74 \| 5850 \| 0.0921 \| 0.9867 \| \| 0.049 \| 3.77 \| 5900 \| 0.1108 \| 0.98 \| \| 0.0497 \| 3.81 \| 5950 \| 0.0795 \| 0.9867 \| \| 0.0047 \| 3.84 \| 6000 \| 0.0979 \| 0.9867 \| \| 0.0252 \| 3.87 \| 6050 \| 0.1071 \| 0.9867 \| \| 0.0691 \| 3.9 \| 6100 \| 0.0821 \| 0.9867 \| \| 0.0419 \| 3.93 \| 6150 \| 0.0896 \| 0.9867 \| \| 0.0197 \| 3.97 \| 6200 \| 0.0943 \| 0.9867 \| \| 0.0281 \| 4.0 \| 6250 \| 0.0901 \| 0.9867 \| \| 0.0118 \| 4.03 \| 6300 \| 0.0950 \| 0.9867 \| \| 0.0057 \| 4.06 \| 6350 \| 0.1031 \| 0.9867 \| \| 0.0335 \| 4.09 \| 6400 \| 0.0896 \| 0.9867 \| \| 0.0095 \| 4.13 \| 6450 \| 0.0966 \| 0.9867 \| \| 0.05 \| 4.16 \| 6500 \| 0.0977 \| 0.9867 \| \| 0.0142 \| 4.19 \| 6550 \| 0.1174 \| 0.98 \| \| 0.018 \| 4.22 \| 6600 \| 0.0963 \| 0.9867 \| \| 0.0274 \| 4.25 \| 6650 \| 0.0953 \| 0.9867 \| \| 0.0199 \| 4.29 \| 6700 \| 0.0968 \| 0.9867 \| \| 0.0171 \| 4.32 \| 6750 \| 0.0963 \| 0.9867 \| \| 0.0195 \| 4.35 \| 6800 \| 0.0916 \| 0.9867 \| \| 0.0091 \| 4.38 \| 6850 \| 0.0954 \| 0.9867 \| \| 0.0115 \| 4.41 \| 6900 \| 0.0974 \| 0.9867 \| \| 0.0299 \| 4.45 \| 6950 \| 0.0971 \| 0.9867 \| \| 0.0338 \| 4.48 \| 7000 \| 0.0922 \| 0.9867 \| \| 0.0107 \| 4.51 \| 7050 \| 0.0964 \| 0.9867 \| \| 0.0063 \| 4.54 \| 7100 \| 0.0921 \| 0.9867 \| \| 0.0099 \| 4.57 \| 7150 \| 0.0923 \| 0.9867 \| \| 0.0101 \| 4.61 \| 7200 \| 0.0971 \| 0.9867 \| \| 0.0262 \| 4.64 \| 7250 \| 0.1008 \| 0.9867 \| \| 0.0097 \| 4.67 \| 7300 \| 0.0999 \| 0.9867 \| \| 0.0302 \| 4.7 \| 7350 \| 0.0980 \| 0.9867 \| \| 0.0225 \| 4.73 \| 7400 \| 0.0976 \| 0.9867 \| \| 0.0235 \| 4.77 \| 7450 \| 0.1016 \| 0.9867 \| \| 0.0106 \| 4.8 \| 7500 \| 0.1034 \| 0.9867 \| \| 0.0495 \| 4.83 \| 7550 \| 0.1135 \| 0.98 \| \| 0.0228 \| 4.86 \| 7600 \| 0.1034 \| 0.9867 \| \| 0.0229 \| 4.89 \| 7650 \| 0.0990 \| 0.9867 \| \| 0.0206 \| 4.93 \| 7700 \| 0.0993 \| 0.9867 \| \| 0.0188 \| 4.96 \| 7750 \| 0.0993 \| 0.9867 \| \| 0.0189 \| 4.99 \| 7800 \| 0.0995 \| 0.9867 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2
SwePalm/sd-class-butterflies-64	SwePalm	2022-11-28T20:42:14Z	34	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T20:41:56Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(SwePalm/sd-class-butterflies-64) image = pipeline().images[0] image ```
pig4431/TUF_XLNET_5E	pig4431	2022-11-28T20:40:06Z	89	0	transformers	[ "transformers", "pytorch", "xlnet", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T20:20:56Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_XLNET_5E 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. --> # TUF_XLNET_5E This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2725 - Accuracy: 0.9533 ## 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: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4817 \| 0.1 \| 50 \| 0.2602 \| 0.8733 \| \| 0.2405 \| 0.2 \| 100 \| 0.5818 \| 0.88 \| \| 0.2172 \| 0.3 \| 150 \| 0.1851 \| 0.9533 \| \| 0.2697 \| 0.4 \| 200 \| 0.1692 \| 0.9267 \| \| 0.2313 \| 0.5 \| 250 \| 0.1086 \| 0.9467 \| \| 0.2245 \| 0.59 \| 300 \| 0.2031 \| 0.9267 \| \| 0.1805 \| 0.69 \| 350 \| 0.1414 \| 0.9467 \| \| 0.1896 \| 0.79 \| 400 \| 0.0824 \| 0.9733 \| \| 0.1969 \| 0.89 \| 450 \| 0.1499 \| 0.9533 \| \| 0.1745 \| 0.99 \| 500 \| 0.1827 \| 0.9267 \| \| 0.1143 \| 1.09 \| 550 \| 0.1923 \| 0.9533 \| \| 0.1478 \| 1.19 \| 600 \| 0.1718 \| 0.94 \| \| 0.1368 \| 1.29 \| 650 \| 0.1170 \| 0.9733 \| \| 0.1288 \| 1.39 \| 700 \| 0.1418 \| 0.9667 \| \| 0.1689 \| 1.49 \| 750 \| 0.1173 \| 0.9733 \| \| 0.1078 \| 1.58 \| 800 \| 0.2784 \| 0.9333 \| \| 0.1343 \| 1.68 \| 850 \| 0.1555 \| 0.9533 \| \| 0.1104 \| 1.78 \| 900 \| 0.1361 \| 0.9533 \| \| 0.1267 \| 1.88 \| 950 \| 0.1936 \| 0.9267 \| \| 0.0928 \| 1.98 \| 1000 \| 0.3070 \| 0.94 \| \| 0.0949 \| 2.08 \| 1050 \| 0.1905 \| 0.94 \| \| 0.0329 \| 2.18 \| 1100 \| 0.2296 \| 0.9533 \| \| 0.0406 \| 2.28 \| 1150 \| 0.3202 \| 0.94 \| \| 0.0983 \| 2.38 \| 1200 \| 0.4515 \| 0.9267 \| \| 0.0533 \| 2.48 \| 1250 \| 0.2152 \| 0.9533 \| \| 0.0878 \| 2.57 \| 1300 \| 0.1573 \| 0.9533 \| \| 0.0595 \| 2.67 \| 1350 \| 0.1699 \| 0.96 \| \| 0.0937 \| 2.77 \| 1400 \| 0.2825 \| 0.9333 \| \| 0.0817 \| 2.87 \| 1450 \| 0.2325 \| 0.9467 \| \| 0.0845 \| 2.97 \| 1500 \| 0.1918 \| 0.9533 \| \| 0.0711 \| 3.07 \| 1550 \| 0.3186 \| 0.94 \| \| 0.033 \| 3.17 \| 1600 \| 0.2571 \| 0.94 \| \| 0.0134 \| 3.27 \| 1650 \| 0.2733 \| 0.94 \| \| 0.0546 \| 3.37 \| 1700 \| 0.1934 \| 0.9533 \| \| 0.0277 \| 3.47 \| 1750 \| 0.2731 \| 0.94 \| \| 0.0081 \| 3.56 \| 1800 \| 0.2531 \| 0.9467 \| \| 0.0387 \| 3.66 \| 1850 \| 0.2121 \| 0.96 \| \| 0.0014 \| 3.76 \| 1900 \| 0.2601 \| 0.96 \| \| 0.0379 \| 3.86 \| 1950 \| 0.2501 \| 0.9467 \| \| 0.0271 \| 3.96 \| 2000 \| 0.2899 \| 0.94 \| \| 0.0182 \| 4.06 \| 2050 \| 0.2197 \| 0.9533 \| \| 0.0263 \| 4.16 \| 2100 \| 0.2374 \| 0.9533 \| \| 0.0079 \| 4.26 \| 2150 \| 0.3192 \| 0.94 \| \| 0.0239 \| 4.36 \| 2200 \| 0.3755 \| 0.9333 \| \| 0.02 \| 4.46 \| 2250 \| 0.2702 \| 0.9467 \| \| 0.0072 \| 4.55 \| 2300 \| 0.2055 \| 0.9533 \| \| 0.0124 \| 4.65 \| 2350 \| 0.2299 \| 0.9533 \| \| 0.0072 \| 4.75 \| 2400 \| 0.2813 \| 0.9533 \| \| 0.0125 \| 4.85 \| 2450 \| 0.2696 \| 0.9533 \| \| 0.0205 \| 4.95 \| 2500 \| 0.2725 \| 0.9533 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
rmartinshort/sd-class-butterflies-64	rmartinshort	2022-11-28T20:32:13Z	36	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T20:31:54Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(rmartinshort/sd-class-butterflies-64) image = pipeline().images[0] image ```
CyantifiCQ/noisy_butterflied_diffusion	CyantifiCQ	2022-11-28T20:23:45Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T20:22:34Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(CyantifiCQ/noisy_butterflied_diffusion) image = pipeline().images[0] image ```
futuredatascience/from-classifier-v1	futuredatascience	2022-11-28T20:07:27Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-28T20:07:15Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 53 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 530, "warmup_steps": 53, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
SwePalm/sd-class-butterflies-32	SwePalm	2022-11-28T20:01:43Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T20:00:51Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of (not so?) cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(SwePalm/sd-class-butterflies-32) image = pipeline().images[0] image ```
reubenjohn/stack-overflow-open-status-classifier-pt	reubenjohn	2022-11-28T20:01:21Z	4	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-16T03:44:14Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: stack-overflow-open-status-classifier-pt 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. --> # stack-overflow-open-status-classifier-pt This model is a fine-tuned version of [reubenjohn/stack-overflow-open-status-classifier-pt](https://huggingface.co/reubenjohn/stack-overflow-open-status-classifier-pt) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9448 - eval_runtime: 3.554 - eval_samples_per_second: 28.137 - eval_steps_per_second: 0.563 - epoch: 0.01 - step: 60 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 1 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
UKP-SQuARE/tweac_16	UKP-SQuARE	2022-11-28T19:43:48Z	102	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "QA", "en", "dataset:BoolQ", "dataset:CommonSenseQA", "dataset:DROP", "dataset:DuoRC", "dataset:HellaSWAG", "dataset:HotpotQA", "dataset:HybridQA", "dataset:NarrativeQA", "dataset:NaturalQuestionsShort", "dataset:NewsQA", "dataset:QAMR", "dataset:RACE", "dataset:SearchQA", "dataset:SIQA", "dataset:SQuAD", "dataset:TriviaQA-web", "arxiv:2104.07081", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-09T18:34:07Z	--- language: - en tags: - QA license: cc-by-4.0 datasets: - BoolQ - CommonSenseQA - DROP - DuoRC - HellaSWAG - HotpotQA - HybridQA - NarrativeQA - NaturalQuestionsShort - NewsQA - QAMR - RACE - SearchQA - SIQA - SQuAD - TriviaQA-web metrics: - Accuracy - Precision - Recall - F1 - MRR - R@3 - R@5 --- BERT for Sequence Classification trained on QA Dataset prediction task. - Input: question. - Output: dataset from where that question comes from. Original paper: TWEAC: Transformer with Extendable QA Agent Classifiers https://arxiv.org/abs/2104.07081 Datasets used for training: ``` list_datasets = ['BoolQ','CommonSenseQA','DROP','DuoRC','HellaSWAG','HotpotQA','HybridQA','NarrativeQA','NaturalQuestionsShort','NewsQA','QAMR','RACE','SearchQA','SIQA','SQuAD','TriviaQA-web'] ``` Results for all datasets: - Accuracy: 0.7919096825783123 - Precision: 0.731586272892176 - Recall: 0.7919096825783123 - F1: 0.7494425609552463 - MRR: 0.8720871733637521 - R@3: 0.9438690810655046 - R@5: 0.9745318608004427 - Queries/second: 6052.33538824659 Results per dataset: ``` "BoolQ": { "accuracy": 0.998776758409786, "mrr": 0.999388379204893, "r@3": 1.0, "r@5": 1.0, "query_per_second": 6978.947907596168, "precision": 0.8649364406779662, "recall": 0.998776758409786, "f1": 0.9270508089696281 }, "CommonSenseQA": { "accuracy": 0.9247135842880524, "mrr": 0.9476358338878795, "r@3": 0.9705400981996727, "r@5": 0.9705400981996727, "query_per_second": 5823.984138936813, "precision": 0.442443226311668, "recall": 0.9247135842880524, "f1": 0.5985169491525425 }, "DROP": { "accuracy": 0.9075083892617449, "mrr": 0.9378200367399193, "r@3": 0.9609899328859061, "r@5": 0.9786073825503355, "query_per_second": 6440.988897129248, "precision": 0.8636726546906187, "recall": 0.9075083892617449, "f1": 0.8850480670893842 }, "DuoRC": { "accuracy": 0.5555803405457654, "mrr": 0.7368963429107307, "r@3": 0.9092125808610305, "r@5": 0.9596996059186557, "query_per_second": 6853.643198794893, "precision": 0.646814404432133, "recall": 0.5555803405457654, "f1": 0.5977360905563778 }, "HellaSWAG": { "accuracy": 0.998406691894045, "mrr": 0.9990705702715262, "r@3": 1.0, "r@5": 1.0, "query_per_second": 3091.5012960785157, "precision": 0.9974134500596896, "recall": 0.998406691894045, "f1": 0.9979098238280083 }, "HotpotQA": { "accuracy": 0.7414435784479837, "mrr": 0.8435804344945315, "r@3": 0.9325652321247034, "r@5": 0.973568281938326, "query_per_second": 4972.668019223381, "precision": 0.7352150537634409, "recall": 0.7414435784479837, "f1": 0.7383161801923401 }, "HybridQA": { "accuracy": 0.7934218118869013, "mrr": 0.8806947764680021, "r@3": 0.964800923254472, "r@5": 0.9930755914598961, "query_per_second": 4886.494046259562, "precision": 0.7198952879581152, "recall": 0.7934218118869013, "f1": 0.7548723579467472 }, "NarrativeQA": { "accuracy": 0.5623756749076442, "mrr": 0.7416681781060867, "r@3": 0.9011082693947144, "r@5": 0.9580373212086767, "query_per_second": 7081.067049796865, "precision": 0.5623224095472628, "recall": 0.5623756749076442, "f1": 0.5623490409661377 }, "NaturalQuestionsShort": { "accuracy": 0.7985353692739171, "mrr": 0.8743599435345307, "r@3": 0.9439077594266126, "r@5": 0.9774072919912745, "query_per_second": 7136.590426649795, "precision": 0.7963020509633313, "recall": 0.7985353692739171, "f1": 0.7974171464135678 }, "NewsQA": { "accuracy": 0.5375118708452041, "mrr": 0.71192075967717, "r@3": 0.855650522317189, "r@5": 0.939696106362773, "query_per_second": 7193.851409052092, "precision": 0.18757249378624688, "recall": 0.5375118708452041, "f1": 0.2780985136961061 }, "QAMR": { "accuracy": 0.6658497602557272, "mrr": 0.7969741223377345, "r@3": 0.9207778369738945, "r@5": 0.973361747469366, "query_per_second": 7321.775044800525, "precision": 0.8654525309881587, "recall": 0.6658497602557272, "f1": 0.7526421968624852 }, "RACE": { "accuracy": 0.8771538617474154, "mrr": 0.917901778042666, "r@3": 0.9489154672613015, "r@5": 0.9693898236367322, "query_per_second": 6952.225120744351, "precision": 0.8767983789260385, "recall": 0.8771538617474154, "f1": 0.8769760843129306 }, "SearchQA": { "accuracy": 0.9762073027090695, "mrr": 0.9865069592101393, "r@3": 0.9972909305064782, "r@5": 0.9984687868080094, "query_per_second": 4031.0193826035634, "precision": 0.9870191735143503, "recall": 0.9762073027090695, "f1": 0.9815834665719192 }, "SIQA": { "accuracy": 0.9969293756397134, "mrr": 0.9977823268509042, "r@3": 0.9979529170931423, "r@5": 1.0, "query_per_second": 6711.547709005977, "precision": 0.9329501915708812, "recall": 0.9969293756397134, "f1": 0.9638792676892627 }, "SQuAD": { "accuracy": 0.550628092881614, "mrr": 0.7164538452390565, "r@3": 0.8660068519223448, "r@5": 0.9366197183098591, "query_per_second": 7033.420124363291, "precision": 0.48613678373382624, "recall": 0.550628092881614, "f1": 0.5163766175814368 }, "TriviaQA-web": { "accuracy": 0.7855124582584125, "mrr": 0.8647404868442627, "r@3": 0.9321859748266119, "r@5": 0.9640380169535063, "query_per_second": 4327.642440910395, "precision": 0.7404358353510896, "recall": 0.7855124582584125, "f1": 0.7623083634550667 }, ```
essayproj/syntax	essayproj	2022-11-28T19:15:52Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T18:59:34Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: syntax 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. --> # syntax This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1395 - Accuracy: 0.6111 - F1: 0.4596 ## 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: 8 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
huggingtweets/ttunguz	huggingtweets	2022-11-28T19:09:57Z	115	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-24T01:44:13Z	--- language: en thumbnail: http://www.huggingtweets.com/ttunguz/1669662593098/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/901542400559992832/yDp0b2Al_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tomasz Tunguz</div> <div style="text-align: center; font-size: 14px;">@ttunguz</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tomasz Tunguz. \| Data \| Tomasz Tunguz \| \| --- \| --- \| \| Tweets downloaded \| 3239 \| \| Retweets \| 590 \| \| Short tweets \| 50 \| \| Tweets kept \| 2599 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vxbo3iui/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ttunguz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/190cyogq) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/190cyogq/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ttunguz') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
essayproj/roberta-base-essay	essayproj	2022-11-28T19:08:54Z	59	0	transformers	[ "transformers", "tf", "roberta", "feature-extraction", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-28T19:08:03Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: roberta-base-essay 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. --> # roberta-base-essay This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Tokenizers 0.13.2
leonrafael29/bert2bert_uncased_english_to_spanish	leonrafael29	2022-11-28T18:52:56Z	13	0	transformers	[ "transformers", "encoder-decoder", "text2text-generation", "translation", "en", "es", "dataset:news_commentary", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-11-28T17:32:46Z	--- language: - en - es tags: - translation datasets: - news_commentary metrics: - bleurt ---
FrancoisDongier/sd-class-butterflies-32	FrancoisDongier	2022-11-28T18:19:31Z	34	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T18:16:21Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(FrancoisDongier/sd-class-butterflies-32) image = pipeline().images[0] image ```
ashu1318/lilt-en-funsd	ashu1318	2022-11-28T18:17:59Z	80	0	transformers	[ "transformers", "pytorch", "tensorboard", "lilt", "token-classification", "generated_from_trainer", "dataset:funsd-layoutlmv3", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-28T17:49:59Z	--- license: mit tags: - generated_from_trainer datasets: - funsd-layoutlmv3 model-index: - name: lilt-en-funsd 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. --> # lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.8731 - Answer: {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817} - Header: {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119} - Question: {'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077} - Overall Precision: 0.8792 - Overall Recall: 0.8857 - Overall F1: 0.8825 - Overall Accuracy: 0.7976 ## 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 - training_steps: 2500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Answer \| Header \| Question \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------------------------------------------------------------------------------------------------:\|:----------------------------------------------------------------------------------------------------------:\|:---------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.4323 \| 10.53 \| 200 \| 1.0423 \| {'precision': 0.8369195922989807, 'recall': 0.9045287637698899, 'f1': 0.8694117647058823, 'number': 817} \| {'precision': 0.5405405405405406, 'recall': 0.5042016806722689, 'f1': 0.5217391304347826, 'number': 119} \| {'precision': 0.8869323447636701, 'recall': 0.8885793871866295, 'f1': 0.8877551020408162, 'number': 1077} \| 0.8471 \| 0.8723 \| 0.8595 \| 0.7981 \| \| 0.045 \| 21.05 \| 400 \| 1.2757 \| {'precision': 0.8435374149659864, 'recall': 0.9106487148102815, 'f1': 0.8758092995879929, 'number': 817} \| {'precision': 0.5795454545454546, 'recall': 0.42857142857142855, 'f1': 0.49275362318840576, 'number': 119} \| {'precision': 0.8626943005181347, 'recall': 0.9275766016713092, 'f1': 0.8939597315436242, 'number': 1077} \| 0.8430 \| 0.8912 \| 0.8665 \| 0.8026 \| \| 0.0133 \| 31.58 \| 600 \| 1.4887 \| {'precision': 0.8632075471698113, 'recall': 0.8959608323133414, 'f1': 0.8792792792792793, 'number': 817} \| {'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119} \| {'precision': 0.8791887125220459, 'recall': 0.9257195914577531, 'f1': 0.9018543645409318, 'number': 1077} \| 0.8596 \| 0.8882 \| 0.8737 \| 0.7983 \| \| 0.0051 \| 42.11 \| 800 \| 1.7382 \| {'precision': 0.8601645123384254, 'recall': 0.8959608323133414, 'f1': 0.8776978417266187, 'number': 817} \| {'precision': 0.5636363636363636, 'recall': 0.5210084033613446, 'f1': 0.5414847161572053, 'number': 119} \| {'precision': 0.9032558139534884, 'recall': 0.9015784586815228, 'f1': 0.9024163568773235, 'number': 1077} \| 0.8669 \| 0.8768 \| 0.8718 \| 0.7925 \| \| 0.004 \| 52.63 \| 1000 \| 1.7599 \| {'precision': 0.8307349665924276, 'recall': 0.9130966952264382, 'f1': 0.8699708454810495, 'number': 817} \| {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119} \| {'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077} \| 0.8530 \| 0.8907 \| 0.8714 \| 0.7941 \| \| 0.002 \| 63.16 \| 1200 \| 1.8409 \| {'precision': 0.8312985571587126, 'recall': 0.9167686658506732, 'f1': 0.8719441210710128, 'number': 817} \| {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} \| {'precision': 0.8814949863263446, 'recall': 0.8978644382544104, 'f1': 0.8896044158233671, 'number': 1077} \| 0.8461 \| 0.8847 \| 0.8650 \| 0.7876 \| \| 0.0013 \| 73.68 \| 1400 \| 1.7795 \| {'precision': 0.81445523193096, 'recall': 0.9241126070991432, 'f1': 0.8658256880733943, 'number': 817} \| {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119} \| {'precision': 0.888785046728972, 'recall': 0.883008356545961, 'f1': 0.8858872845831393, 'number': 1077} \| 0.8432 \| 0.8788 \| 0.8606 \| 0.7934 \| \| 0.0011 \| 84.21 \| 1600 \| 1.8386 \| {'precision': 0.8338833883388339, 'recall': 0.9277845777233782, 'f1': 0.8783314020857474, 'number': 817} \| {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119} \| {'precision': 0.8943985307621671, 'recall': 0.904363974001857, 'f1': 0.8993536472760849, 'number': 1077} \| 0.8573 \| 0.8922 \| 0.8744 \| 0.7945 \| \| 0.0048 \| 94.74 \| 1800 \| 1.8664 \| {'precision': 0.8589595375722543, 'recall': 0.9094247246022031, 'f1': 0.8834720570749108, 'number': 817} \| {'precision': 0.6504854368932039, 'recall': 0.5630252100840336, 'f1': 0.6036036036036037, 'number': 119} \| {'precision': 0.9003656307129799, 'recall': 0.914577530176416, 'f1': 0.9074159373560571, 'number': 1077} \| 0.8705 \| 0.8917 \| 0.8810 \| 0.7927 \| \| 0.0004 \| 105.26 \| 2000 \| 1.8672 \| {'precision': 0.8634772462077013, 'recall': 0.9057527539779682, 'f1': 0.8841099163679809, 'number': 817} \| {'precision': 0.7093023255813954, 'recall': 0.5126050420168067, 'f1': 0.5951219512195123, 'number': 119} \| {'precision': 0.8923076923076924, 'recall': 0.9155060352831941, 'f1': 0.9037580201649862, 'number': 1077} \| 0.8726 \| 0.8877 \| 0.8801 \| 0.7953 \| \| 0.0005 \| 115.79 \| 2200 \| 1.8731 \| {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817} \| {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119} \| {'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077} \| 0.8792 \| 0.8857 \| 0.8825 \| 0.7976 \| \| 0.0002 \| 126.32 \| 2400 \| 1.9408 \| {'precision': 0.8408071748878924, 'recall': 0.9179926560587516, 'f1': 0.8777062609713283, 'number': 817} \| {'precision': 0.6310679611650486, 'recall': 0.5462184873949579, 'f1': 0.5855855855855856, 'number': 119} \| {'precision': 0.9091760299625468, 'recall': 0.9015784586815228, 'f1': 0.9053613053613054, 'number': 1077} \| 0.8657 \| 0.8872 \| 0.8763 \| 0.7935 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
kejian/final-filter-again	kejian	2022-11-28T17:39:16Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-28T01:33:32Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-filter-again 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. --> # kejian/final-filter-again This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 64 - 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_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-filter-again', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/25z4zfy3
akmmsr/mt5-small-finetuned-amazon-en-es_akmmsr	akmmsr	2022-11-28T17:15:10Z	61	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-28T16:23:28Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: akmmsr/mt5-small-finetuned-amazon-en-es_akmmsr 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. --> # akmmsr/mt5-small-finetuned-amazon-en-es_akmmsr This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0336 - Validation Loss: 3.3393 - Epoch: 7 ## 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, '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} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 9.6397 \| 4.2364 \| 0 \| \| 5.8621 \| 3.7162 \| 1 \| \| 5.0948 \| 3.5552 \| 2 \| \| 4.6724 \| 3.4873 \| 3 \| \| 4.4007 \| 3.4245 \| 4 \| \| 4.2162 \| 3.3792 \| 5 \| \| 4.0985 \| 3.3499 \| 6 \| \| 4.0336 \| 3.3393 \| 7 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
wa3dbk/whisper-small-ar	wa3dbk	2022-11-28T17:11:32Z	113	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-25T18:33:06Z	## whisper-small-ar This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset (language=Arabic).
antgrutta/sd-class-butterflies-32	antgrutta	2022-11-28T16:59:10Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T16:58:32Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(antgrutta/sd-class-butterflies-32) image = pipeline().images[0] image ```
EmnaBou/bert-finetuned-DT	EmnaBou	2022-11-28T16:49:12Z	123	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-28T15:20:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-DT 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-finetuned-DT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6697 - Precision: 0.2381 - Recall: 0.0321 - F1: 0.0565 - Accuracy: 0.8179 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 99 \| 0.7505 \| 0.0 \| 0.0 \| 0.0 \| 0.8196 \| \| No log \| 2.0 \| 198 \| 0.7033 \| 0.0 \| 0.0 \| 0.0 \| 0.8196 \| \| No log \| 3.0 \| 297 \| 0.6697 \| 0.2381 \| 0.0321 \| 0.0565 \| 0.8179 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
luisgasco/distilbert-base-uncased-finetuned-emotion	luisgasco	2022-11-28T16:17:49Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T16:03:30Z	--- 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 args: default metrics: - name: Accuracy type: accuracy value: 0.892 - name: F1 type: f1 value: 0.8873822002431591 --- <!-- 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: 0.3693 - Accuracy: 0.892 - F1: 0.8874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.5715 \| 0.8275 \| 0.8047 \| \| 0.7552 \| 2.0 \| 250 \| 0.3693 \| 0.892 \| 0.8874 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
tomekkorbak/awesome_ride	tomekkorbak	2022-11-28T16:12:40Z	0	0	null	[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]	null	2022-11-28T16:12:19Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: awesome_ride 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. --> # awesome_ride This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'awesome_ride', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3m98rnwq
alexziweiwang/pure-start-epoch2	alexziweiwang	2022-11-28T16:08:48Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-28T15:52:06Z	--- tags: - generated_from_trainer model-index: - name: pure-start-epoch2 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. --> # pure-start-epoch2 This model is a fine-tuned version of [alexziweiwang/pure-start-epoch1](https://huggingface.co/alexziweiwang/pure-start-epoch1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.7447 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Acc \| Wer \| Correct \| Total \| Strlen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----:\|:---:\|:-------:\|:-----:\|:------:\| \| No log \| 0.01 \| 2 \| 20.4002 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| No log \| 0.02 \| 4 \| 19.9080 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| No log \| 0.03 \| 6 \| 19.4711 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| No log \| 0.03 \| 8 \| 19.1535 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.04 \| 10 \| 18.6684 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.05 \| 12 \| 18.1640 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.06 \| 14 \| 17.6937 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.07 \| 16 \| 17.2710 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.08 \| 18 \| 16.8469 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.08 \| 20 \| 16.4418 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.09 \| 22 \| 16.0409 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.1 \| 24 \| 15.6677 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.11 \| 26 \| 15.3291 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.12 \| 28 \| 15.0097 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.13 \| 30 \| 14.6776 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.13 \| 32 \| 14.3788 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.14 \| 34 \| 14.0924 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.15 \| 36 \| 13.8133 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.16 \| 38 \| 13.5539 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 34.4057 \| 0.17 \| 40 \| 13.3095 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 34.4057 \| 0.18 \| 42 \| 13.0804 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 34.4057 \| 0.19 \| 44 \| 12.8580 \| 0.105 \| 1.0 \| 21 \| 200 \| 200 \| \| 34.4057 \| 0.19 \| 46 \| 12.6532 \| 0.115 \| 1.0 \| 23 \| 200 \| 200 \| \| 34.4057 \| 0.2 \| 48 \| 12.4532 \| 0.13 \| 1.0 \| 26 \| 200 \| 200 \| \| 33.2759 \| 0.21 \| 50 \| 12.2452 \| 0.14 \| 1.0 \| 28 \| 200 \| 200 \| \| 33.2759 \| 0.22 \| 52 \| 12.0666 \| 0.13 \| 1.0 \| 26 \| 200 \| 200 \| \| 33.2759 \| 0.23 \| 54 \| 11.8976 \| 0.165 \| 1.0 \| 33 \| 200 \| 200 \| \| 33.2759 \| 0.24 \| 56 \| 11.7373 \| 0.175 \| 1.0 \| 35 \| 200 \| 200 \| \| 33.2759 \| 0.24 \| 58 \| 11.5933 \| 0.17 \| 1.0 \| 34 \| 200 \| 200 \| \| 29.8129 \| 0.25 \| 60 \| 11.4281 \| 0.15 \| 1.0 \| 30 \| 200 \| 200 \| \| 29.8129 \| 0.26 \| 62 \| 11.2665 \| 0.14 \| 1.0 \| 28 \| 200 \| 200 \| \| 29.8129 \| 0.27 \| 64 \| 11.1158 \| 0.145 \| 1.0 \| 29 \| 200 \| 200 \| \| 29.8129 \| 0.28 \| 66 \| 10.9840 \| 0.135 \| 1.0 \| 27 \| 200 \| 200 \| \| 29.8129 \| 0.29 \| 68 \| 10.8502 \| 0.15 \| 1.0 \| 30 \| 200 \| 200 \| \| 38.792 \| 0.3 \| 70 \| 10.7341 \| 0.15 \| 1.0 \| 30 \| 200 \| 200 \| \| 38.792 \| 0.3 \| 72 \| 10.6082 \| 0.165 \| 1.0 \| 33 \| 200 \| 200 \| \| 38.792 \| 0.31 \| 74 \| 10.4944 \| 0.18 \| 1.0 \| 36 \| 200 \| 200 \| \| 38.792 \| 0.32 \| 76 \| 10.3818 \| 0.21 \| 1.0 \| 42 \| 200 \| 200 \| \| 38.792 \| 0.33 \| 78 \| 10.2719 \| 0.235 \| 1.0 \| 47 \| 200 \| 200 \| \| 28.0092 \| 0.34 \| 80 \| 10.1636 \| 0.235 \| 1.0 \| 47 \| 200 \| 200 \| \| 28.0092 \| 0.35 \| 82 \| 10.0709 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 28.0092 \| 0.35 \| 84 \| 9.9797 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 28.0092 \| 0.36 \| 86 \| 9.8958 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 28.0092 \| 0.37 \| 88 \| 9.7977 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.38 \| 90 \| 9.7015 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.39 \| 92 \| 9.6150 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.4 \| 94 \| 9.5304 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.4 \| 96 \| 9.4521 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.41 \| 98 \| 9.3832 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.42 \| 100 \| 9.3148 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.43 \| 102 \| 9.2563 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.44 \| 104 \| 9.1944 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.45 \| 106 \| 9.1323 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.46 \| 108 \| 9.0717 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.46 \| 110 \| 9.0245 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.47 \| 112 \| 8.9772 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.48 \| 114 \| 8.9390 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.49 \| 116 \| 8.9013 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.5 \| 118 \| 8.8605 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.51 \| 120 \| 8.8126 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.51 \| 122 \| 8.7503 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.52 \| 124 \| 8.6921 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.53 \| 126 \| 8.6378 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.54 \| 128 \| 8.5927 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.55 \| 130 \| 8.5520 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.56 \| 132 \| 8.5126 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.56 \| 134 \| 8.4743 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.57 \| 136 \| 8.4369 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.58 \| 138 \| 8.3993 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.59 \| 140 \| 8.3636 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.6 \| 142 \| 8.3311 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.61 \| 144 \| 8.2983 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.62 \| 146 \| 8.2652 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.62 \| 148 \| 8.2345 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.63 \| 150 \| 8.2064 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.64 \| 152 \| 8.1818 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.65 \| 154 \| 8.1603 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.66 \| 156 \| 8.1403 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.67 \| 158 \| 8.1180 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.67 \| 160 \| 8.0997 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.68 \| 162 \| 8.0791 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.69 \| 164 \| 8.0563 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.7 \| 166 \| 8.0342 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.71 \| 168 \| 8.0130 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.72 \| 170 \| 7.9936 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.72 \| 172 \| 7.9756 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.73 \| 174 \| 7.9594 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.74 \| 176 \| 7.9439 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.75 \| 178 \| 7.9298 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.76 \| 180 \| 7.9157 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.77 \| 182 \| 7.9021 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.78 \| 184 \| 7.8899 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.78 \| 186 \| 7.8796 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.79 \| 188 \| 7.8697 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.8 \| 190 \| 7.8598 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.81 \| 192 \| 7.8490 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.82 \| 194 \| 7.8390 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.83 \| 196 \| 7.8293 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.83 \| 198 \| 7.8211 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.84 \| 200 \| 7.8135 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.85 \| 202 \| 7.8064 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.86 \| 204 \| 7.7991 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.87 \| 206 \| 7.7924 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.88 \| 208 \| 7.7862 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.89 \| 210 \| 7.7803 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.89 \| 212 \| 7.7749 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.9 \| 214 \| 7.7701 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.91 \| 216 \| 7.7657 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.92 \| 218 \| 7.7628 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.93 \| 220 \| 7.7595 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.94 \| 222 \| 7.7567 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.94 \| 224 \| 7.7541 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.95 \| 226 \| 7.7518 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.96 \| 228 \| 7.7497 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.97 \| 230 \| 7.7479 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.98 \| 232 \| 7.7463 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.99 \| 234 \| 7.7453 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.99 \| 236 \| 7.7447 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
SYH99999/autotrain-translator-2261971987	SYH99999	2022-11-28T15:30:31Z	104	0	transformers	[ "transformers", "pytorch", "autotrain", "translation", "ja", "en", "dataset:SYH99999/autotrain-data-translator-3c03831c-5fcf2e86-839aa322-a7658498-cb30b55a-eefc0458", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	translation	2022-11-28T11:53:31Z	--- tags: - autotrain - translation language: - ja - en datasets: - SYH99999/autotrain-data-translator-3c03831c-5fcf2e86-839aa322-a7658498-cb30b55a-eefc0458 co2_eq_emissions: emissions: 234.5986254372695 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 2261971987 - CO2 Emissions (in grams): 234.5986 ## Validation Metrics - Loss: 4.237 - SacreBLEU: 0.697 - Gen len: 256.387
fathyshalab/all-roberta-large-v1-banking-2-2-1	fathyshalab	2022-11-28T15:28:40Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T15:27:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-2-2-1 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. --> # all-roberta-large-v1-banking-2-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6817 - Accuracy: 0.1022 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.653 \| 1.0 \| 5 \| 2.6817 \| 0.1022 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
ConvLab/ddpt-policy-sgd_0.01multiwoz21	ConvLab	2022-11-28T15:24:29Z	0	0	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]	null	2022-11-28T15:21:11Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd_0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) and afterwards on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
ConvLab/ddpt-policy-0.01multiwoz21	ConvLab	2022-11-28T15:20:35Z	0	0	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]	null	2022-11-28T15:18:28Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
fathyshalab/all-roberta-large-v1-banking-1-2-1	fathyshalab	2022-11-28T15:12:05Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T15:10:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1-2-1 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. --> # all-roberta-large-v1-banking-1-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6235 - Accuracy: 0.2578 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.6542 \| 1.0 \| 3 \| 2.6235 \| 0.2578 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
ConvLab/mle-policy-multiwoz21	ConvLab	2022-11-28T15:11:19Z	0	0	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/multiwoz21", "license:apache-2.0", "region:us" ]	null	2022-11-28T15:07:50Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/multiwoz21 --- # mle-policy-multiwoz21 This is a MLE model trained on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - seed: 0 - optimizer: Adam - num_epochs: 24 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
ConvLab/ddpt-policy-sgd	ConvLab	2022-11-28T15:01:15Z	0	1	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]	null	2022-11-28T13:21:09Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 1 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
regel-corpus/hunflair-tfbs	regel-corpus	2022-11-28T14:37:52Z	3	0	flair	[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]	token-classification	2022-03-29T11:26:41Z	--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "It contains a functional GCGGCGGCG Egr-1-binding site" --- ## HunFlair model for Transcription Factor Binding Site (TFBS) [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for TFBS entity. Predicts 1 tag: \| tag \| meaning \| \|---------------------------------\|-----------\| \| Tfbs \| DNA region bound by transcription factor \| --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-tfbs") text = "We found that Egr-1 specifically binds to the PTEN 5' untranslated region, which contains a functional GCGGCGGCG Egr-1-binding site." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [19,20,21]: "GCGGCGGCG Egr-1-binding site" [− Labels: Tfbs (0.9631)] ``` So, the entity "GCGGCGGCG Egr-1-binding site" is found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ``` ---
regel-corpus/hunflair-enhancer	regel-corpus	2022-11-28T14:37:03Z	4	0	flair	[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]	token-classification	2022-03-29T09:09:18Z	--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "Isolate an enhancer element located between -89 and -50 bp in PAI-1" --- ## HunFlair model for ENHANCER [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for enhancer entity. Predicts 1 tag: \| tag \| meaning \| \|---------------------------------\|-----------\| \| Enhancer \| DNA enhancer region \| --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-promoter") text = "An upstream activator of the mitogen-activated protein (MAP) kinase pathways was used to isolate an enhancer element located between -89 and -50 bp in PAI-1 promoter that was activated by MEKK-1." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [18,19,20,21,22,23,24,25,26,27,28,29,30]: "enhancer element located between - 89 and - 50 bp in PAI-1 promoter" [− Labels: Enhancer (0.992)] ``` So, the entity "enhancer element located between - 89 and - 50 bp in PAI-1" (labeled as a enhancer) is found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ``` ---
regel-corpus/hunflair-promoter	regel-corpus	2022-11-28T14:36:20Z	7	0	flair	[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]	token-classification	2022-03-29T11:22:27Z	--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "Two putative extended promoters consensus sequences (p1 and p2)." --- ## HunFlair model for PROMOTER [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for promoter entity. Predicts 1 tag: \| tag \| meaning \| \|---------------------------------\|-----------\| \| Promoter \| DNA promoter region \| --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-promoter") text = "The upstream region of the glnA gene contained two putative extended promoter consensus sequences (p1 and p2)." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [16]: "p1" [− Labels: Promoter (0.9878)] Span [18]: "p2" [− Labels: Promoter (0.9216)] ``` So, the entities "p1" and "p2" (labeled as a promoter) are found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ```
fathyshalab/all-roberta-large-v1-banking-1	fathyshalab	2022-11-28T14:25:57Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T14:24:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1 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. --> # all-roberta-large-v1-banking-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6515 - Accuracy: 0.1644 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.5795 \| 1.0 \| 3 \| 2.6515 \| 0.1644 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
fathyshalab/bert-uncased-massive-intent-classification-banking-1	fathyshalab	2022-11-28T14:15:57Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T14:11:34Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification-banking-1 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-uncased-massive-intent-classification-banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7010 - Accuracy: 0.1289 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.6675 \| 1.0 \| 3 \| 2.7010 \| 0.1289 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
Fabiuas/test	Fabiuas	2022-11-28T14:01:56Z	187	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-28T13:42:00Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: test results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # test Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### cat ![cat](images/cat.jpg) #### dog ![dog](images/dog.jpg)
fathyshalab/bert-uncased-massive-intent-classification_banking-1	fathyshalab	2022-11-28T13:48:29Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T13:40:09Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification_banking-1 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-uncased-massive-intent-classification_banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6770 - Accuracy: 0.1378 ## 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: 6 - eval_batch_size: 6 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.8977 \| 1.0 \| 3 \| 2.7353 \| 0.0622 \| \| 2.5889 \| 2.0 \| 6 \| 2.7109 \| 0.0933 \| \| 2.4362 \| 3.0 \| 9 \| 2.6940 \| 0.1111 \| \| 2.3175 \| 4.0 \| 12 \| 2.6817 \| 0.1333 \| \| 2.2524 \| 5.0 \| 15 \| 2.6770 \| 0.1378 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
fathyshalab/bert-uncased-massive-intent-classification-finetuned-banking	fathyshalab	2022-11-28T12:54:50Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T11:50:48Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification-finetuned-banking 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-uncased-massive-intent-classification-finetuned-banking This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5965 - Accuracy: 0.12 ## 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: 6 - eval_batch_size: 6 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.731 \| 1.0 \| 3 \| 2.6423 \| 0.1067 \| \| 2.4424 \| 2.0 \| 6 \| 2.6178 \| 0.1067 \| \| 2.2005 \| 3.0 \| 9 \| 2.6028 \| 0.1111 \| \| 2.1954 \| 4.0 \| 12 \| 2.5965 \| 0.12 \| \| 2.0599 \| 5.0 \| 15 \| 2.5935 \| 0.12 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
minhtoan/t5-small-vietnamese-news	minhtoan	2022-11-28T12:52:14Z	122	4	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "summarization", "vi", "dataset:Wikilingua", "dataset:Vietnews", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-11-24T08:01:28Z	--- language: vi datasets: - Wikilingua - Vietnews tags: - summarization license: mit widget: - text: 'VKS cáo buộc ông Nguyễn Thế Hiệp có sai phạm trong vụ cháy gần Bệnh viện Nhi trung ương khiến 2 người chết, thiệt hại 1,9 tỷ đồng song bị cáo khẳng định vô tội. Mức án đề nghị 9-10 năm tù với bị cáo 73 tuổi được đại diện VKSND quận Ba Đình đưa ra chiều 28/11, quy buộc phạm tội Vi phạm quy định về phòng cháy chữa cháy, theo Điều 313 Bộ luật Hình sự. VKS nhận định ông Hiệp có lỗi trong việc vận hành nhà trọ không phép, không đủ điều kiện an toàn phòng cháy chữa cháy, gây thiệt hại về tài sản và khiến hai người chết. Tuy nhiên, bị cáo chưa bồi thường. Bản luận tội nêu, tại phiên tòa hôm nay ông Hiệp "chưa tỏ thái độ ăn năn hối hận, có nhân thân đặc biệt xấu". Từ hàng chục năm trước, ông từng 11 lần bị lập danh chỉ bản về hành vi trộm cắp, năm 1985 lại nhận 18 năm tù về các tội cướp tài sản, hiếp dâm, đưa hối lộ...' inference: parameters: max_length: 150 --- # Text summarization for Vietnamese Language State-of-the-art lightweights pretrained Transformer-based encoder-decoder model for Vietnamese. Model trained on dataset Vietnamese News with input length = 512, output length = 150 ## How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Example test data on VNExpress: https://vnexpress.net/ong-hiep-khung-khong-nhan-toi-trong-vu-chay-gan-benh-vien-nhi-4541483.html tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-small-vietnamese-news") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-small-vietnamese-news") model.cuda() src = 'VKS cáo buộc ông Nguyễn Thế Hiệp có sai phạm trong vụ cháy gần Bệnh viện Nhi trung ương khiến 2 người chết, thiệt hại 1,9 tỷ đồng song bị cáo khẳng định vô tội. Mức án đề nghị 9-10 năm tù với bị cáo 73 tuổi được đại diện VKSND quận Ba Đình đưa ra chiều 28/11, quy buộc phạm tội Vi phạm quy định về phòng cháy chữa cháy, theo Điều 313 Bộ luật Hình sự. VKS nhận định ông Hiệp có lỗi trong việc vận hành nhà trọ không phép, không đủ điều kiện an toàn phòng cháy chữa cháy, gây thiệt hại về tài sản và khiến hai người chết. Tuy nhiên, bị cáo chưa bồi thường. Bản luận tội nêu, tại phiên tòa hôm nay ông Hiệp "chưa tỏ thái độ ăn năn hối hận, có nhân thân đặc biệt xấu". Từ hàng chục năm trước, ông từng 11 lần bị lập danh chỉ bản về hành vi trộm cắp, năm 1985 lại nhận 18 năm tù về các tội cướp tài sản, hiếp dâm, đưa hối lộ...' tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda() model.eval() summary_ids = model.generate(tokenized_text, max_length=150) output = tokenizer.decode(summary_ids[0], skip_special_tokens=True) output ``` ## Author ` Phan Minh Toan `
team-nave/distilbert-base-uncased-distilled-clinc	team-nave	2022-11-28T12:14:29Z	108	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T12:06:14Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9367741935483871 --- <!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.4175 - Accuracy: 0.9368 ## 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: 96 - eval_batch_size: 96 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 159 \| 3.3516 \| 0.6652 \| \| 3.4274 \| 2.0 \| 318 \| 2.2866 \| 0.7848 \| \| 3.4274 \| 3.0 \| 477 \| 1.5064 \| 0.8545 \| \| 1.6307 \| 4.0 \| 636 \| 1.0204 \| 0.8971 \| \| 1.6307 \| 5.0 \| 795 \| 0.7421 \| 0.9177 \| \| 0.7641 \| 6.0 \| 954 \| 0.5838 \| 0.9258 \| \| 0.7641 \| 7.0 \| 1113 \| 0.4986 \| 0.9306 \| \| 0.4482 \| 8.0 \| 1272 \| 0.4489 \| 0.9365 \| \| 0.4482 \| 9.0 \| 1431 \| 0.4258 \| 0.9368 \| \| 0.3442 \| 10.0 \| 1590 \| 0.4175 \| 0.9368 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3
tomekkorbak/zealous_almeida	tomekkorbak	2022-11-28T12:04:20Z	0	0	null	[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]	null	2022-11-28T12:04:13Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: zealous_almeida 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. --> # zealous_almeida This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'zealous_almeida', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/llhbsik2
cardiffnlp/twitter-roberta-base-offensive	cardiffnlp	2022-11-28T11:36:23Z	35,866	27	transformers	[ "transformers", "pytorch", "tf", "jax", "roberta", "text-classification", "arxiv:2010.12421", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# Twitter-roBERTa-base for Offensive Language Identification This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark. - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='offensive' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) not-offensive 0.9073 2) offensive 0.0927 ```
biu-nlp/f-coref	biu-nlp	2022-11-28T11:35:52Z	88,201	18	transformers	[ "transformers", "pytorch", "roberta", "fast", "coreference-resolution", "en", "dataset:multi_news", "dataset:ontonotes", "arxiv:2209.04280", "arxiv:2205.12644", "arxiv:1907.10529", "arxiv:2101.00434", "arxiv:2109.04127", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	null	2022-08-19T12:01:10Z	--- language: - en tags: - fast - coreference-resolution license: mit datasets: - multi_news - ontonotes metrics: - CoNLL task_categories: - coreference-resolution model-index: - name: biu-nlp/f-coref results: - task: type: coreference-resolution name: coreference-resolution dataset: name: ontonotes type: coreference metrics: - name: Avg. F1 type: CoNLL value: 78.5 --- ## F-Coref: Fast, Accurate and Easy to Use Coreference Resolution [F-Coref](https://arxiv.org/abs/2209.04280) allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the [LingMess](https://arxiv.org/abs/2205.12644) model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy. The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover Please check the [official repository](https://github.com/shon-otmazgin/fastcoref) for more details and updates. #### Experiments \| Model \| Runtime \| Memory \| \|-----------------------\|---------\|---------\| \| [Joshi et al. (2020)](https://arxiv.org/abs/1907.10529) \| 12:06 \| 27.4 \| \| [Otmazgin et al. (2022)](https://arxiv.org/abs/2205.12644) \| 06:43 \| 4.6 \| \| + Batching \| 06:00 \| 6.6 \| \| [Kirstain et al. (2021)](https://arxiv.org/abs/2101.00434) \| 04:37 \| 4.4 \| \| [Dobrovolskii (2021)](https://arxiv.org/abs/2109.04127) \| 03:49 \| 3.5 \| \| [F-Coref](https://arxiv.org/abs/2209.04280) \| 00:45 \| 3.3 \| \| + Batching \| 00:35 \| 4.5 \| \| + Leftovers batching \| 00:25 \| 4.0 \| The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2. ### Citation ``` @inproceedings{Otmazgin2022FcorefFA, title={F-coref: Fast, Accurate and Easy to Use Coreference Resolution}, author={Shon Otmazgin and Arie Cattan and Yoav Goldberg}, booktitle={AACL}, year={2022} } ``` [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://aclanthology.org/2022.aacl-demo.6) (Otmazgin et al., AACL-IJCNLP 2022)
projecte-aina/roberta-base-ca-v2-cased-wikicat-ca	projecte-aina	2022-11-28T11:03:27Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "catalan", "text classification", "WikiCAT_ca", "CaText", "Catalan Textual Corpus", "ca", "dataset:projecte-aina/WikiCAT_ca", "arxiv:1907.11692", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-11T12:00:58Z	--- language: - ca license: apache-2.0 tags: - "catalan" - "text classification" - "WikiCAT_ca" - "CaText" - "Catalan Textual Corpus" datasets: - "projecte-aina/WikiCAT_ca" metrics: - f1 model-index: - name: roberta-base-ca-v2-cased-wikicat-ca results: - task: type: text-classification dataset: type: projecte-aina/WikiCAT_ca name: WikiCAT_ca metrics: - name: F1 type: f1 value: 77.823 widget: - text: "La ressonància magnètica és una prova diagnòstica clau per a moltes malalties." - text: "Les tres idees bàsiques del noümen són l'ànima, el món i Déu, i és una continuació de les tres substàncies de Descartes (tot i que el francès anomenava jo o ment l'ànima)." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Viquipedia-based Text Classification. ## Table of Contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-uses-and-limitations) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Disclaimer](#disclaimer) </details> ## Model description The roberta-base-ca-v2-cased-wikicat-ca is a Text Classification model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). Dataset used is https://huggingface.co/datasets/projecte-aina/WikiCAT_ca, automatically created from Wikipedia and Wikidata sources ## Intended uses and limitations roberta-base-ca-v2-cased-wikicat-ca model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="roberta-base-ca-v2-cased-wikicat-ca") example = "La ressonància magnètica és una prova diagnòstica clau per a moltes malalties." tc_results = nlp(example) pprint(tc_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data We used the TC dataset in Catalan called [WikiCAT_ca](https://huggingface.co/datasets/projecte-aina/WikiCAT_ca) for training and evaluation. ### Training procedure The model was trained with a batch size of 16 and three learning rates (1e-5, 3e-5, 5e-5) for 10 epochs. We then selected the best learning rate (3e-5) and checkpoint (epoch 3, step 1857) using the downstream task metric in the corresponding development set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 (weighted) score. ### Evaluation results We evaluated the _roberta-base-ca-v2-cased-wikicat-ca_ on the WikiCAT_ca dev set: \| Model \| WikiCAT_ca (F1)\| \| ------------\|:-------------\| \| roberta-base-ca-v2-cased-wikicat-ca \| 77.823 \| For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca\|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
projecte-aina/roberta-base-ca-v2-cased-tc	projecte-aina	2022-11-28T11:02:09Z	110	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "catalan", "text classification", "tecla", "CaText", "Catalan Textual Corpus", "ca", "dataset:projecte-aina/tecla", "arxiv:1907.11692", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-30T07:55:23Z	--- language: - ca tags: - "catalan" - "text classification" - "tecla" - "CaText" - "Catalan Textual Corpus" datasets: - "projecte-aina/tecla" metrics: - accuracy model-index: - name: roberta-base-ca-v2-cased-tc results: - task: type: text-classification dataset: name: TeCla type: projecte-aina/tecla metrics: - name: Accuracy type: accuracy value: 0.8034 widget: - text: "Els Pets presenten el seu nou treball al Palau Sant Jordi." - text: "Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la pandèmia." - text: "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." - text: "Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la vacuna covid els propers dies." - text: "Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification. ## Table of Contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Tokenization](#tokenization) - [Hyperparameters](#hyperparameters) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description The roberta-base-ca-v2-cased-tc is a Text Classification (TC) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). The previous version of this model, which was trained on the old TeCla dataset (v1), can still be accessed through the "v1" tag. ## Intended uses and limitations roberta-base-ca-v2-cased-tc model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-tc") example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." tc_results = nlp(example) pprint(tc_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data We used the TC dataset in Catalan called [TeCla](https://huggingface.co/datasets/projecte-aina/tecla) for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training. ### Training procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 (weighted). ## Evaluation results We evaluated the _roberta-base-ca-v2-cased-tc_ on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1'). \| Model \| TeCla - label1 (Accuracy) \| TeCla - label2 (Accuracy) \| \| ------------\|:-------------\|:-------------\| \| roberta-base-ca-v2 \| 96.31 \| 80.34 \| \| roberta-large-ca-v2 \| 96.51 \| 80.68 \| \| mBERT \| 95.72 \| 78.47 \| \| XLM-RoBERTa \| 95.66 \| 78.01 \| For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca\|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Citation Information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` ### Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
JapaNLP/t5-efficient-xl-nl6-japanese	JapaNLP	2022-11-28T10:09:07Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-28T09:58:01Z	--- license: afl-3.0 --- # Overview `t5-efficient-xl-nl6-ja` is a Japanese version of [`google/t5-efficient-xl-nl6`](https://huggingface.co/google/t5-efficient-xl-nl6). # Results - Under construction - If you get some experimental results of this model on downstream tasks, please feel free to make Pull Requests. ## Question Answering ## Others # Acknowledgement Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
mn367/radio-mlm	mn367	2022-11-28T09:52:57Z	61	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-28T09:42:20Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mn367/radio-mlm 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. --> # mn367/radio-mlm 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: 4.6630 - Validation Loss: 4.6014 - 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': 39000, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 4.6630 \| 4.6014 \| 0 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
pkachhad/t5-small-finetuned-parth	pkachhad	2022-11-28T09:19:48Z	105	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	2022-11-28T07:51:00Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-parth 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-small-finetuned-parth This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9468 - Rouge1: 26.5826 - Rouge2: 21.7867 - Rougel: 25.1629 - Rougelsum: 26.2364 - Gen Len: 16.9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 4 \| 3.3692 \| 25.2983 \| 20.639 \| 24.0087 \| 25.0732 \| 16.2 \| \| No log \| 2.0 \| 8 \| 3.1818 \| 25.4926 \| 20.9783 \| 24.0651 \| 25.2635 \| 16.3 \| \| No log \| 3.0 \| 12 \| 3.0498 \| 26.2652 \| 21.5076 \| 24.8077 \| 25.9478 \| 16.65 \| \| No log \| 4.0 \| 16 \| 2.9742 \| 26.5826 \| 21.7867 \| 25.1629 \| 26.2364 \| 16.9 \| \| No log \| 5.0 \| 20 \| 2.9468 \| 26.5826 \| 21.7867 \| 25.1629 \| 26.2364 \| 16.9 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
huggingtweets/bobkerns	huggingtweets	2022-11-28T08:14:20Z	115	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-28T08:14:12Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/3653376550/f40f9602f2e8e185eb7ddce332157ffe_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bob (Moderna #5) Kerns</div> <div style="text-align: center; font-size: 14px;">@bobkerns</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Bob (Moderna #5) Kerns. \| Data \| Bob (Moderna #5) Kerns \| \| --- \| --- \| \| Tweets downloaded \| 3234 \| \| Retweets \| 315 \| \| Short tweets \| 42 \| \| Tweets kept \| 2877 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/390ksfue/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bobkerns's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/bobkerns') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
pere/whisper-NST2-unfreeze-constanti-low-lr	pere	2022-11-28T07:41:42Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-23T10:34:53Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-NST2-unfreeze-constanti-low-lr 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-NST2-unfreeze-constanti-low-lr This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3562 - Wer: 8.5519 ## 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: 96 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.1901 \| 0.05 \| 1000 \| 0.3069 \| 14.8233 \| \| 0.1323 \| 0.1 \| 2000 \| 0.2687 \| 11.2885 \| \| 0.1137 \| 0.15 \| 3000 \| 0.2620 \| 10.8324 \| \| 0.1022 \| 0.2 \| 4000 \| 0.2976 \| 9.0080 \| \| 0.0937 \| 0.25 \| 5000 \| 0.2584 \| 9.5781 \| \| 0.0875 \| 0.3 \| 6000 \| 0.2704 \| 20.2965 \| \| 0.0592 \| 1.05 \| 7000 \| 0.2751 \| 9.0080 \| \| 0.0488 \| 1.1 \| 8000 \| 0.2778 \| 8.6659 \| \| 0.0475 \| 1.15 \| 9000 \| 0.2792 \| 9.4641 \| \| 0.0439 \| 1.2 \| 10000 \| 0.2880 \| 8.3238 \| \| 0.0425 \| 1.25 \| 11000 \| 0.2954 \| 8.5519 \| \| 0.0416 \| 1.3 \| 12000 \| 0.2896 \| 20.2965 \| \| 0.0289 \| 2.05 \| 13000 \| 0.2990 \| 7.9818 \| \| 0.0229 \| 2.1 \| 14000 \| 0.3027 \| 7.4116 \| \| 0.0248 \| 2.15 \| 15000 \| 0.2968 \| 8.6659 \| \| 0.0225 \| 2.2 \| 16000 \| 0.3100 \| 8.5519 \| \| 0.0222 \| 2.25 \| 17000 \| 0.3132 \| 9.3501 \| \| 0.0219 \| 2.3 \| 18000 \| 0.3230 \| 7.6397 \| \| 0.0162 \| 3.04 \| 19000 \| 0.3380 \| 9.8062 \| \| 0.0132 \| 3.09 \| 20000 \| 0.3562 \| 8.5519 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1
linfuyou/bert-squad-training	linfuyou	2022-11-28T07:41:14Z	117	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "endpoints_compatible", "region:us" ]	question-answering	2022-11-15T09:15:55Z	bert-base-cased-squadv1.1-training
Shubham09/whispertestlocal	Shubham09	2022-11-28T06:40:40Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-25T09:13:41Z	--- tags: - generated_from_trainer metrics: - wer model-index: - name: whispertestlocal 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. --> # whispertestlocal This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4481 - Wer: 46.1754 ## 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: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1886 \| 1.12 \| 100 \| 0.4481 \| 46.1754 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2	amagzari	2022-11-28T05:20:40Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-28T03:55:08Z	--- tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: pegasus-cnn_dailymail-finetuned-samsum-v2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 45.3045 --- <!-- 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-cnn_dailymail-finetuned-samsum-v2 This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.5218 - Rouge1: 45.3045 - Rouge2: 21.7601 - Rougel: 35.8643 - Rougelsum: 41.6595 - Gen Len: 35.4425 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.6997 \| 1.0 \| 1841 \| 1.5218 \| 45.3045 \| 21.7601 \| 35.8643 \| 41.6595 \| 35.4425 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
inkoziev/sbert_pq	inkoziev	2022-11-28T04:45:46Z	309	16	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "ru", "license:unlicense", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-17T13:27:40Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: ru license: unlicense widget: - source_sentence: "Кошка ловит мышку." sentences: ["Кто ловит мышку?", "Где живет кошка?", "Как мышку зовут?"] --- # SBERT_PQ Это [sentence-transformers](https://www.SBERT.net) модель, предназначенная для определения релевантности короткого текста (преимущественно одно предложение длиной до 10-15 слов) и вопроса. Модель вычисляет для текста и вопроса векторы размерностью 312. Косинус угла между этими векторами дает оценку того, содержит ли текст ответ на заданный вопрос. В [проекте диалоговой системы](https://github.com/Koziev/chatbot) она используется для семантического поиска записей в базе фактов по заданному собеседником вопросу. # Скорость и точность Модель основана на [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2). Она имеет очень небольшой размер и быстро выполняет инференс даже на CPU. Максимальное значение метрики cossim_f1 на тестовой выборке (10% датасета) равно 0.986. При использовании модели sberbank-ai/ruBert-base в качестве базовой, максимум cossim_f1 составляет 0.992. ## Использование с библиотекой (Sentence-Transformers) Необходимо установить [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` Чтобы определить релевантность в одной паре "текст-вопрос", можно использовать такой код: ``` import sentence_transformers sentences = ["Кошка ловит мышку.", "Чем занята кошка?"] model = sentence_transformers.SentenceTransformer('inkoziev/sbert_pq') embeddings = model.encode(sentences) s = sentence_transformers.util.cos_sim(a=embeddings[0], b=embeddings[1]) print('text={} question={} cossim={}'.format(sentences[0], sentences[1], s)) ``` ## Контакты и цитирование ``` @MISC{rugpt_chitchat, author = {Ilya Koziev}, title = {Texts & Questions Relevancy Model}, url = {https://huggingface.co/inkoziev/sbert_pq}, year = 2022 } ```
cavitcakir/swin-tiny-patch4-window7-224-finetuned-eurosat	cavitcakir	2022-11-28T04:30:00Z	206	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-28T04:24:46Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat 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-finetuned-eurosat 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.5373 - Accuracy: 0.7639 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6855 \| 0.98 \| 10 \| 0.6436 \| 0.625 \| \| 0.6499 \| 1.98 \| 20 \| 0.5745 \| 0.7083 \| \| 0.6021 \| 2.98 \| 30 \| 0.5373 \| 0.7639 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
speedrunner/atitanstrawberry	speedrunner	2022-11-28T03:30:00Z	0	4	null	[ "region:us" ]	null	2022-11-28T00:55:15Z	not my work - all credit to original author!
thisisHJLee/wav2vec2-large-xls-r-1b-korean-sample2	thisisHJLee	2022-11-28T02:25:48Z	18	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-25T04:56:38Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-1b-korean-sample2 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. --> # wav2vec2-large-xls-r-1b-korean-sample2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1283 - Cer: 0.0294 ## 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: 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_steps: 1000 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.3415 \| 1.0 \| 11471 \| 0.2666 \| 0.0750 \| \| 0.1997 \| 2.0 \| 22942 \| 0.1617 \| 0.0415 \| \| 0.1153 \| 3.0 \| 34413 \| 0.1283 \| 0.0294 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.11.0
minimaxir/midjourney_sd_2_0	minimaxir	2022-11-28T02:13:38Z	0	12	null	[ "license:mit", "region:us" ]	null	2022-11-28T02:04:00Z	--- license: mit --- ### Midjourney Style for Stable Diffusion 2.0 A textual inversion embedding for the `<midjourney>` token, adapted for Stable Diffusion 2.0 from [sd-concepts-library/midjourney-style](https://huggingface.co/sd-concepts-library/midjourney-style). It's recommended to use the following as an addition to a prompt: ```txt in the style of <midjourney> ```
huggingtweets/tarunchitra	huggingtweets	2022-11-28T02:11:02Z	115	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-28T02:09:42Z	--- language: en thumbnail: http://www.huggingtweets.com/tarunchitra/1669601459083/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1587539091444432897/Z6_nmrCB_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tarun Chitra</div> <div style="text-align: center; font-size: 14px;">@tarunchitra</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tarun Chitra. \| Data \| Tarun Chitra \| \| --- \| --- \| \| Tweets downloaded \| 3234 \| \| Retweets \| 439 \| \| Short tweets \| 362 \| \| Tweets kept \| 2433 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ex37piz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tarunchitra's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12p1kbwc) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12p1kbwc/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/tarunchitra') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lewispons/large-email-classifier	lewispons	2022-11-28T01:56:52Z	2	1	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-26T22:47:23Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{lewispons/large-email-classifier}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 752 with parameters: ``` {'batch_size': 50, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 2256, "warmup_steps": 226, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
fanpu/final_model_output_subreddit-wallstreetbets_3	fanpu	2022-11-28T01:42:49Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-27T19:02:43Z	--- tags: - generated_from_trainer model-index: - name: final_model_output_subreddit-wallstreetbets_3 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. --> # final_model_output_subreddit-wallstreetbets_3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6824 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.2588 \| 1.25 \| 5000 \| 3.6824 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
erkanxyzalaca/turkishReviews-ds-mini	erkanxyzalaca	2022-11-28T01:38:07Z	61	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-27T22:00:36Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: turkishReviews-ds-mini 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. --> # turkishReviews-ds-mini This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.3867 - Validation Loss: 8.3741 - Epoch: 2 ## 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': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -765, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 10.2149 \| 9.6891 \| 0 \| \| 9.0695 \| 8.7610 \| 1 \| \| 8.3867 \| 8.3741 \| 2 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.10.1 - Datasets 2.7.1 - Tokenizers 0.13.2
ohrenn/lorepass	ohrenn	2022-11-28T00:28:39Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2022-11-28T00:28:39Z	--- license: bigscience-bloom-rail-1.0 ---
Tara2301/PPO-LunarLander-v22	Tara2301	2022-11-27T23:31:17Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-27T22:02:04Z	--- 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: 253.24 +/- 19.64 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 ... ```
wnordmann/klaus_weights	wnordmann	2022-11-27T23:23:06Z	0	0	null	[ "license:openrail", "region:us" ]	null	2022-11-27T18:39:46Z	--- license: openrail --- # Klaus the Cat This is trained on 30+ picture of my Cat Klaus. Klaus has Manx Syndrom which means he has no tail and limited feeling in his legs. He's a super cute yellow kitten my family loves ## Prompt `nord klaus`
jasoneden/BLOOM-560-QuestionAnswering-CDC-Covid19-Tuned	jasoneden	2022-11-27T23:16:23Z	48	1	transformers	[ "transformers", "pytorch", "bloom", "question-answering", "generated_from_trainer", "dataset:dataset", "license:bigscience-bloom-rail-1.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2022-11-24T04:56:03Z	--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer datasets: - dataset model-index: - name: cdcmodel_train02 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. --> # cdcmodel_train02 This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the dataset dataset. Currently will not load. ## 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: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
Ueumol/Utapri_Style	Ueumol	2022-11-27T22:09:28Z	0	0	null	[ "region:us" ]	null	2022-11-27T21:19:16Z	Need to use prompt - Utapristyle
cmudrc/wave-energy-analysis	cmudrc	2022-11-27T22:08:42Z	12	1	tf-keras	[ "tf-keras", "mechanical-engineering", "simulation", "hydrodynamics", "en", "dataset:cmudrc/wave-energy", "license:mit", "region:us" ]	null	2022-11-27T04:33:25Z	--- license: mit language: en datasets: - cmudrc/wave-energy tags: - mechanical-engineering - simulation - hydrodynamics ---
cmudrc/wave-energy-synthesis	cmudrc	2022-11-27T21:30:39Z	3	1	tf-keras	[ "tf-keras", "en", "dataset:cmudrc/wave-energy", "license:mit", "region:us" ]	null	2022-11-27T04:33:15Z	--- license: mit language: en datasets: - cmudrc/wave-energy ---
ProGamerGov/Object-Taped-To-Wall-Diffusion-V1	ProGamerGov	2022-11-27T21:17:56Z	0	15	null	[ "stable-diffusion", "text-to-image", "dataset:ProGamerGov/StableDiffusion-v1-5-Regularization-Images", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-11-24T01:24:34Z	--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image datasets: - ProGamerGov/StableDiffusion-v1-5-Regularization-Images --- Object-Taped-To-Wall-Diffusion This fine-tuned Stable Diffusion v1.5 model was trained for 2000 iterations with a batch size of 4, on a selection of photos of things taped to a wall. Training was performed using [ShivamShrirao/diffusers](https://github.com/ShivamShrirao/diffusers) with full precision, prior-preservation loss, the train-text-encoder feature, and the new [1.5 MSE VAE from Stability AI](https://huggingface.co/stabilityai/sd-vae-ft-mse). A total of 2100 regularization / class images were used from [here](https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images). Regularization images were generated using the prompt "artwork style" with 50 DPM++ 2S a Karras steps and a CFG of 7, using the MSE VAE. A negative prompt of "text" was also used for this dataset. Use the tokens ttw style in your prompts for the effect. Note that the effect also appears to occur at a much weaker strength on prompts that steer the output towards specific artistic styles. This model will likely not perform well on taping objects that are not traditionally able to be taped to walls. <div align="center"> <img src="https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png) Example images were generated with the v1 2000 iteration model using DPM++ 2S a Karras: ``` ttw style, <object> taped to wall ``` This model was inspired by the 2019 art piece [Comedian by Italian artist Maurizio Cattelan](https://en.wikipedia.org/wiki/Comedian_(artwork\)), where a banana was duct taped to a wall.
Davimartins/Farias123	Davimartins	2022-11-27T20:50:51Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-11-27T20:50:50Z	--- license: bigscience-openrail-m ---

smilton/mt5-large-qasrl-es-p2-question

smilton

2022-11-29T04:36:00Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T03:55:16Z

--- language: - es license: apache-2.0 tags: - generated_from_trainer model-index: - name: mt5-large-qasrl-es-p2-question results: [] ---  # mt5-large-qasrl-es-p2-question This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7515 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.11.0 - Datasets 2.7.1 - Tokenizers 0.11.0

renatanerenata/bart-paraphrase1-finetuned-in-to-fo

renatanerenata

2022-11-29T04:35:59Z

105

0

transformers

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

text2text-generation

2022-11-29T00:54:14Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-paraphrase1-finetuned-in-to-fo results: [] ---  # bart-paraphrase1-finetuned-in-to-fo This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) 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: 0.002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Zhaohui/finetuning-misinfo-model-700-Zhaohui-1_misinfo

Zhaohui

2022-11-29T04:10:10Z

106

0

transformers

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

text-classification

2022-11-29T03:57:58Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-misinfo-model-700-Zhaohui-1_misinfo results: [] ---  # finetuning-misinfo-model-700-Zhaohui-1_misinfo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5343 - Accuracy: 0.8571 - F1: 0.8571 ## 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: 20 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

NSandra/distilbert-base-uncased-finetuned-ner

NSandra

2022-11-29T04:09:17Z

125

0

transformers

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

token-classification

2022-11-29T03:55:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2393 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 1 | 1.5491 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 2.0 | 2 | 1.3278 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 3.0 | 3 | 1.2393 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

ryvalenza/sd-class-butterflies-32

ryvalenza

2022-11-29T04:00:32Z

34

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T04:00:01Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(ryvalenza/sd-class-butterflies-32) image = pipeline().images[0] image ```

jeraldflowers/vit_model

jeraldflowers

2022-11-29T03:51:31Z

188

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-27T05:06:17Z

--- license: apache-2.0 tags: - image-classification - generated_from_trainer datasets: - beans metrics: - accuracy widget: - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/healthy.jpeg example_title: Healthy - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/bean_rust.jpeg example_title: Bean Rust model-index: - name: vit_model results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 ---  # vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0095 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1526 | 3.85 | 500 | 0.0095 | 1.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

UCSYNLP/MyanBERTa

UCSYNLP

2022-11-29T03:35:58Z

297

3

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "MyanBERTa", "Myanmar", "BERT", "RoBERTa", "my", "dataset:MyCorpus", "dataset:Web", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-07-25T06:57:10Z

--- language: my tags: - MyanBERTa - Myanmar - BERT - RoBERTa license: apache-2.0 datasets: - MyCorpus - Web --- ## Model description This model is a BERT based Myanmar pre-trained language model. MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words). As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is applied. Cite this work as: ``` Aye Mya Hlaing, Win Pa Pa, "MyanBERTa: A Pre-trained Language Model For Myanmar", In Proceedings of 2022 International Conference on Communication and Computer Research (ICCR2022), November 2022, Seoul, Republic of Korea ``` [Download Paper](https://journal-home.s3.ap-northeast-2.amazonaws.com/site/iccr2022/abs/QOHFI-0004.pdf)

jeraldflowers/distilroberts-base-mrpc-glue-jeraldflowers

jeraldflowers

2022-11-29T02:57:36Z

108

0

transformers

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

text-classification

2022-11-28T05:30:00Z

--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: ["Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."] example_title: Not Equivalent - text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.", "With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."] example_title: Equivalent model-index: - name: distilroberts-base-mrpc-glue-jeraldflowers results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8431372549019608 - name: F1 type: f1 value: 0.8814814814814815 ---  # distilroberts-base-mrpc-glue-jeraldflowers This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.4990 - Accuracy: 0.8431 - F1: 0.8815 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5289 | 1.09 | 500 | 0.5668 | 0.8211 | 0.8689 | | 0.3675 | 2.18 | 1000 | 0.4990 | 0.8431 | 0.8815 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

neulab/omnitab-large-128shot-finetuned-wtq-128shot

neulab

2022-11-29T02:55:31Z

47

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-11-29T02:54:00Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-128shot-finetuned-wtq-128shot` (based on BART architecture) is initialized with `neulab/omnitab-large-128shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 128-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

neulab/omnitab-large-1024shot-finetuned-wtq-1024shot

neulab

2022-11-29T02:45:55Z

51

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-11-29T02:44:57Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-1024shot-finetuned-wtq-1024shot` (based on BART architecture) is initialized with `neulab/omnitab-large-1024shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 1024-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

npark/asr-conformer-ksponspeech

npark

2022-11-29T02:25:40Z

5

1

null

[ "region:us" ]

null

2022-11-29T01:26:29Z

# KsponSpeech ASR with Transformers This repository provides pretrained end-to-end ASR models on KsponSpeech with Speechbrain v0.5.13. Model files in this repository trained using the files is below URL, but in Speechbrain version 0.5.13. https://github.com/speechbrain/speechbrain/tree/develop/recipes/KsponSpeech/ASR/transformer language: - "ko" - ko datasets: - KsponSpeech ## About SpeechBrain * Website: https://speechbrain.github.io/ * Code: https://github.com/speechbrain/speechbrain/ * HuggingFace: https://huggingface.co/speechbrain/

huggingtweets/elonmusk-lexfridman

huggingtweets

2022-11-29T01:35:11Z

118

1

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/956331551435960322/OaqR8pAB_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Lex Fridman</div> <div style="text-align: center; font-size: 14px;">@elonmusk-lexfridman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Lex Fridman. | Data | Elon Musk | Lex Fridman | | --- | --- | --- | | Tweets downloaded | 3198 | 2410 | | Retweets | 126 | 253 | | Short tweets | 968 | 49 | | Tweets kept | 2104 | 2108 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18nt3c0k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-lexfridman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-lexfridman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

akmoyu/whisper-medium-mn

akmoyu

2022-11-29T01:27:26Z

3

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "mn", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-27T12:12:01Z

--- language: - mn license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Medium Mn - akmoyu results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 42.52948885976409 ---  # Whisper Medium Mn - akmoyu This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7233 - Wer: 42.5295 ## 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: 16 - 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0182 | 7.94 | 1000 | 0.5995 | 46.5269 | | 0.0027 | 15.87 | 2000 | 0.6499 | 44.2169 | | 0.0002 | 23.81 | 3000 | 0.7057 | 42.5623 | | 0.0001 | 31.75 | 4000 | 0.7233 | 42.5295 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2

dlwh/legal-xlm-base_128k

dlwh

2022-11-29T00:48:35Z

4

2

transformers

[ "transformers", "roberta", "fill-mask", "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-29T00:41:54Z

--- license: apache-2.0 language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv dataset: - joelito/MultiLegalPile_Wikipedia_Filtered --- Huggingface thinks this is a model, but it's just a tokenizer. Trained on https://huggingface.co/datasets/joelito/MultiLegalPile_Wikipedia_Filtered

matan-diamond/sd-class-butterflies-32

matan-diamond

2022-11-29T00:47:21Z

36

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T00:46:35Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(matan-diamond/sd-class-butterflies-32) image = pipeline().images[0] image ```

adrien-alloreview/whisper-small-fr

adrien-alloreview

2022-11-29T00:13:29Z

83

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-28T22:32:23Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Sanchit Gandhi results: [] ---  # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2226 - eval_wer: 10.0023 - eval_runtime: 65.2041 - eval_samples_per_second: 1.748 - eval_steps_per_second: 0.23 - epoch: 19.51 - step: 800 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

joweyel/sd-class-butterflies-32

joweyel

2022-11-28T23:54:45Z

37

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T23:51:15Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of (more or less) cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(datboi223/sd-class-butterflies-32) image = pipeline().images[0] image ```

Serhio/sd-fine-tune-v2

Serhio

2022-11-28T23:43:18Z

34

0

diffusers

[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-28T23:41:46Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### sd-fine-tune-v2 on Stable Diffusion via Dreambooth #### model by Serhio This your the Stable Diffusion model fine-tuned the sd-fine-tune-v2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **Bashkov Sergey** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)

pig4431/TweetEval_BERT_5E

pig4431

2022-11-28T23:38:03Z

102

0

transformers

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

text-classification

2022-11-28T23:31:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_BERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9266666666666666 ---  # TweetEval_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5419 - Accuracy: 0.9267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6264 | 0.04 | 50 | 0.5266 | 0.74 | | 0.5054 | 0.08 | 100 | 0.5959 | 0.6333 | | 0.4732 | 0.12 | 150 | 0.3524 | 0.86 | | 0.3916 | 0.16 | 200 | 0.3195 | 0.8667 | | 0.3477 | 0.2 | 250 | 0.2878 | 0.8867 | | 0.3116 | 0.24 | 300 | 0.2903 | 0.92 | | 0.3039 | 0.28 | 350 | 0.2488 | 0.8933 | | 0.2633 | 0.32 | 400 | 0.2530 | 0.92 | | 0.2667 | 0.37 | 450 | 0.2125 | 0.9267 | | 0.2604 | 0.41 | 500 | 0.2628 | 0.8867 | | 0.278 | 0.45 | 550 | 0.2322 | 0.8867 | | 0.2625 | 0.49 | 600 | 0.1903 | 0.92 | | 0.2808 | 0.53 | 650 | 0.2400 | 0.8933 | | 0.2396 | 0.57 | 700 | 0.2184 | 0.9067 | | 0.2571 | 0.61 | 750 | 0.1906 | 0.9133 | | 0.2676 | 0.65 | 800 | 0.2467 | 0.9067 | | 0.2288 | 0.69 | 850 | 0.2038 | 0.9133 | | 0.2959 | 0.73 | 900 | 0.1941 | 0.9 | | 0.2619 | 0.77 | 950 | 0.2100 | 0.9333 | | 0.2504 | 0.81 | 1000 | 0.1523 | 0.9333 | | 0.2338 | 0.85 | 1050 | 0.1429 | 0.94 | | 0.2529 | 0.89 | 1100 | 0.1269 | 0.94 | | 0.2238 | 0.93 | 1150 | 0.1722 | 0.9333 | | 0.2295 | 0.97 | 1200 | 0.1874 | 0.94 | | 0.2089 | 1.01 | 1250 | 0.2214 | 0.9067 | | 0.1406 | 1.06 | 1300 | 0.3410 | 0.9133 | | 0.1587 | 1.1 | 1350 | 0.3330 | 0.9133 | | 0.1732 | 1.14 | 1400 | 0.2716 | 0.9133 | | 0.195 | 1.18 | 1450 | 0.3726 | 0.92 | | 0.1777 | 1.22 | 1500 | 0.2430 | 0.9267 | | 0.1433 | 1.26 | 1550 | 0.3011 | 0.9267 | | 0.1333 | 1.3 | 1600 | 0.2489 | 0.9333 | | 0.1516 | 1.34 | 1650 | 0.3340 | 0.9267 | | 0.1774 | 1.38 | 1700 | 0.2497 | 0.8933 | | 0.1608 | 1.42 | 1750 | 0.3234 | 0.9 | | 0.1534 | 1.46 | 1800 | 0.3383 | 0.9133 | | 0.1287 | 1.5 | 1850 | 0.3134 | 0.9133 | | 0.1422 | 1.54 | 1900 | 0.3330 | 0.9 | | 0.1578 | 1.58 | 1950 | 0.3281 | 0.9133 | | 0.1786 | 1.62 | 2000 | 0.2939 | 0.9267 | | 0.2019 | 1.66 | 2050 | 0.3535 | 0.9 | | 0.1995 | 1.7 | 2100 | 0.3032 | 0.9067 | | 0.159 | 1.75 | 2150 | 0.2598 | 0.9267 | | 0.1493 | 1.79 | 2200 | 0.2391 | 0.9267 | | 0.1748 | 1.83 | 2250 | 0.2258 | 0.92 | | 0.1783 | 1.87 | 2300 | 0.2749 | 0.9133 | | 0.1619 | 1.91 | 2350 | 0.2699 | 0.92 | | 0.1378 | 1.95 | 2400 | 0.2776 | 0.9067 | | 0.1529 | 1.99 | 2450 | 0.2235 | 0.9333 | | 0.1071 | 2.03 | 2500 | 0.2841 | 0.9267 | | 0.0812 | 2.07 | 2550 | 0.3178 | 0.9267 | | 0.0464 | 2.11 | 2600 | 0.3567 | 0.92 | | 0.1108 | 2.15 | 2650 | 0.2723 | 0.92 | | 0.0845 | 2.19 | 2700 | 0.2774 | 0.9267 | | 0.0795 | 2.23 | 2750 | 0.3027 | 0.9267 | | 0.0403 | 2.27 | 2800 | 0.3566 | 0.9267 | | 0.0664 | 2.31 | 2850 | 0.4015 | 0.92 | | 0.0659 | 2.35 | 2900 | 0.4298 | 0.9067 | | 0.1059 | 2.39 | 2950 | 0.4028 | 0.92 | | 0.105 | 2.44 | 3000 | 0.3701 | 0.92 | | 0.0808 | 2.48 | 3050 | 0.3206 | 0.9267 | | 0.0811 | 2.52 | 3100 | 0.3644 | 0.9133 | | 0.0458 | 2.56 | 3150 | 0.3781 | 0.9267 | | 0.0764 | 2.6 | 3200 | 0.3749 | 0.9267 | | 0.0567 | 2.64 | 3250 | 0.3995 | 0.92 | | 0.0971 | 2.68 | 3300 | 0.3455 | 0.92 | | 0.0579 | 2.72 | 3350 | 0.4508 | 0.92 | | 0.0853 | 2.76 | 3400 | 0.4350 | 0.92 | | 0.0577 | 2.8 | 3450 | 0.3804 | 0.9333 | | 0.0732 | 2.84 | 3500 | 0.4387 | 0.92 | | 0.0874 | 2.88 | 3550 | 0.3885 | 0.9333 | | 0.1031 | 2.92 | 3600 | 0.3937 | 0.92 | | 0.0335 | 2.96 | 3650 | 0.4963 | 0.8933 | | 0.0913 | 3.0 | 3700 | 0.3827 | 0.9333 | | 0.047 | 3.04 | 3750 | 0.4136 | 0.92 | | 0.0531 | 3.08 | 3800 | 0.4362 | 0.92 | | 0.0265 | 3.12 | 3850 | 0.4857 | 0.92 | | 0.038 | 3.17 | 3900 | 0.4425 | 0.92 | | 0.0294 | 3.21 | 3950 | 0.4347 | 0.92 | | 0.0367 | 3.25 | 4000 | 0.4291 | 0.9333 | | 0.0102 | 3.29 | 4050 | 0.5178 | 0.9267 | | 0.0311 | 3.33 | 4100 | 0.4784 | 0.9267 | | 0.0274 | 3.37 | 4150 | 0.5421 | 0.9267 | | 0.0275 | 3.41 | 4200 | 0.5194 | 0.92 | | 0.0795 | 3.45 | 4250 | 0.4788 | 0.92 | | 0.0413 | 3.49 | 4300 | 0.4393 | 0.9267 | | 0.0373 | 3.53 | 4350 | 0.4965 | 0.92 | | 0.0303 | 3.57 | 4400 | 0.4284 | 0.9267 | | 0.0248 | 3.61 | 4450 | 0.4476 | 0.9267 | | 0.0557 | 3.65 | 4500 | 0.4690 | 0.92 | | 0.0358 | 3.69 | 4550 | 0.4774 | 0.9133 | | 0.0194 | 3.73 | 4600 | 0.4755 | 0.92 | | 0.0473 | 3.77 | 4650 | 0.4637 | 0.92 | | 0.0133 | 3.81 | 4700 | 0.4868 | 0.92 | | 0.0204 | 3.86 | 4750 | 0.4886 | 0.9267 | | 0.0338 | 3.9 | 4800 | 0.5101 | 0.9267 | | 0.0424 | 3.94 | 4850 | 0.4812 | 0.9267 | | 0.0237 | 3.98 | 4900 | 0.4837 | 0.9267 | | 0.0372 | 4.02 | 4950 | 0.5000 | 0.9267 | | 0.0254 | 4.06 | 5000 | 0.5210 | 0.92 | | 0.024 | 4.1 | 5050 | 0.5272 | 0.92 | | 0.0117 | 4.14 | 5100 | 0.5447 | 0.92 | | 0.018 | 4.18 | 5150 | 0.5353 | 0.92 | | 0.0097 | 4.22 | 5200 | 0.5415 | 0.9267 | | 0.0151 | 4.26 | 5250 | 0.5447 | 0.9267 | | 0.0118 | 4.3 | 5300 | 0.5285 | 0.9267 | | 0.0004 | 4.34 | 5350 | 0.5399 | 0.9267 | | 0.0102 | 4.38 | 5400 | 0.5552 | 0.9267 | | 0.0012 | 4.42 | 5450 | 0.5689 | 0.92 | | 0.02 | 4.46 | 5500 | 0.5619 | 0.9267 | | 0.0056 | 4.5 | 5550 | 0.5784 | 0.92 | | 0.0271 | 4.55 | 5600 | 0.5766 | 0.92 | | 0.0191 | 4.59 | 5650 | 0.5662 | 0.92 | | 0.0311 | 4.63 | 5700 | 0.5514 | 0.9267 | | 0.0167 | 4.67 | 5750 | 0.5510 | 0.9267 | | 0.0293 | 4.71 | 5800 | 0.5571 | 0.9267 | | 0.0304 | 4.75 | 5850 | 0.5494 | 0.92 | | 0.0161 | 4.79 | 5900 | 0.5469 | 0.9267 | | 0.0017 | 4.83 | 5950 | 0.5468 | 0.9267 | | 0.0176 | 4.87 | 6000 | 0.5426 | 0.9267 | | 0.0094 | 4.91 | 6050 | 0.5402 | 0.9267 | | 0.0041 | 4.95 | 6100 | 0.5416 | 0.9267 | | 0.0281 | 4.99 | 6150 | 0.5419 | 0.9267 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2

jiping/whisper-small-jsun2-hi

jiping

2022-11-28T22:38:58Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-24T21:04:14Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Jsun Hi - Jiping results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 31.761618555828324 ---  # Whisper Small Jsun Hi - Jiping This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2775 - Wer: 31.7616 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2092 | 0.61 | 1000 | 0.3201 | 38.7666 | | 0.1106 | 1.22 | 2000 | 0.2810 | 34.1023 | | 0.1049 | 1.83 | 3000 | 0.2660 | 32.4812 | | 0.052 | 2.45 | 4000 | 0.2775 | 31.7616 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

rahul77/t5-small-finetuned-rahul-summariza

rahul77

2022-11-28T22:11:14Z

106

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

2022-11-28T22:03:46Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-rahul-summariza results: [] ---  # t5-small-finetuned-rahul-summariza This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7002 - Rouge1: 29.5043 - Rouge2: 23.832 - Rougel: 27.5786 - Rougelsum: 28.404 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.123 | 1.0 | 16 | 0.8258 | 27.2788 | 21.3634 | 25.7114 | 26.7324 | 19.0 | | 0.9067 | 2.0 | 32 | 0.7539 | 28.873 | 23.5401 | 27.2337 | 27.939 | 19.0 | | 0.8137 | 3.0 | 48 | 0.7280 | 29.1767 | 23.6599 | 27.7065 | 28.3569 | 19.0 | | 0.7872 | 4.0 | 64 | 0.7230 | 29.0451 | 23.4597 | 27.2762 | 28.1324 | 19.0 | | 0.7338 | 5.0 | 80 | 0.7133 | 29.4821 | 23.8113 | 27.4912 | 28.326 | 19.0 | | 0.6913 | 6.0 | 96 | 0.7101 | 29.4237 | 23.8523 | 27.4109 | 28.2418 | 19.0 | | 0.6679 | 7.0 | 112 | 0.7097 | 29.4237 | 23.8523 | 27.4109 | 28.2418 | 19.0 | | 0.6963 | 8.0 | 128 | 0.7046 | 29.4237 | 23.8523 | 27.4109 | 28.2418 | 19.0 | | 0.6223 | 9.0 | 144 | 0.7052 | 29.4237 | 23.7633 | 27.493 | 28.3362 | 19.0 | | 0.6494 | 10.0 | 160 | 0.7019 | 29.4237 | 23.7633 | 27.493 | 28.3362 | 19.0 | | 0.616 | 11.0 | 176 | 0.7010 | 29.4237 | 23.7633 | 27.493 | 28.3362 | 19.0 | | 0.6058 | 12.0 | 192 | 0.7028 | 29.4237 | 23.7633 | 27.493 | 28.3362 | 19.0 | | 0.5964 | 13.0 | 208 | 0.6996 | 29.4237 | 23.7633 | 27.493 | 28.3362 | 19.0 | | 0.5958 | 14.0 | 224 | 0.6997 | 29.4237 | 23.7633 | 27.493 | 28.3362 | 19.0 | | 0.57 | 15.0 | 240 | 0.6996 | 29.5043 | 23.832 | 27.5786 | 28.404 | 19.0 | | 0.5714 | 16.0 | 256 | 0.6998 | 29.5043 | 23.832 | 27.5786 | 28.404 | 19.0 | | 0.5648 | 17.0 | 272 | 0.6999 | 29.5043 | 23.832 | 27.5786 | 28.404 | 19.0 | | 0.5258 | 18.0 | 288 | 0.7005 | 29.5043 | 23.832 | 27.5786 | 28.404 | 19.0 | | 0.5692 | 19.0 | 304 | 0.7001 | 29.5043 | 23.832 | 27.5786 | 28.404 | 19.0 | | 0.5708 | 20.0 | 320 | 0.7002 | 29.5043 | 23.832 | 27.5786 | 28.404 | 19.0 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

ThomasSimonini/ML-Agents-SnowballFight-1vs1-model

ThomasSimonini

2022-11-28T22:07:31Z

6

0

ml-agents

[ "ml-agents", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Snowballfight-1vs1", "region:us" ]

reinforcement-learning

2022-11-28T21:26:07Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Snowballfight-1vs1 library_name: ml-agents ---

alryan1478/gpt-neo-125M-wikitext2

alryan1478

2022-11-28T21:57:47Z

4

0

transformers

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

text-generation

2022-11-22T20:55:28Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-wikitext2 results: [] ---  # gpt-neo-125M-wikitext2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0325 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 259 | 6.4308 | | 6.8563 | 2.0 | 518 | 6.0898 | | 6.8563 | 3.0 | 777 | 6.0325 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

michaelmayo704/sd-class-butterflies-64

michaelmayo704

2022-11-28T21:39:43Z

34

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T21:38:51Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(michaelmayo704/sd-class-butterflies-64) image = pipeline().images[0] image ```

pig4431/TUF_ALBERT_5E

pig4431

2022-11-28T21:34:30Z

105

0

transformers

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

text-classification

2022-11-28T21:32:18Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_ALBERT_5E results: [] ---  # TUF_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2389 - Accuracy: 0.9533 ## 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: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5099 | 0.1 | 50 | 0.3861 | 0.8533 | | 0.2985 | 0.2 | 100 | 0.2961 | 0.8933 | | 0.2972 | 0.3 | 150 | 0.2335 | 0.9333 | | 0.2835 | 0.4 | 200 | 0.1872 | 0.94 | | 0.26 | 0.5 | 250 | 0.4147 | 0.9133 | | 0.2986 | 0.59 | 300 | 0.2080 | 0.9267 | | 0.2554 | 0.69 | 350 | 0.3984 | 0.9133 | | 0.2306 | 0.79 | 400 | 0.2136 | 0.9333 | | 0.2218 | 0.89 | 450 | 0.4455 | 0.8867 | | 0.2113 | 0.99 | 500 | 0.2205 | 0.94 | | 0.2541 | 1.09 | 550 | 0.1705 | 0.9333 | | 0.1947 | 1.19 | 600 | 0.3264 | 0.8933 | | 0.2409 | 1.29 | 650 | 0.2084 | 0.92 | | 0.1968 | 1.39 | 700 | 0.2550 | 0.9267 | | 0.172 | 1.49 | 750 | 0.2238 | 0.9467 | | 0.1478 | 1.58 | 800 | 0.2501 | 0.9533 | | 0.2199 | 1.68 | 850 | 0.2618 | 0.9133 | | 0.1792 | 1.78 | 900 | 0.2109 | 0.9267 | | 0.1831 | 1.88 | 950 | 0.2641 | 0.92 | | 0.1534 | 1.98 | 1000 | 0.1924 | 0.94 | | 0.1208 | 2.08 | 1050 | 0.2990 | 0.9333 | | 0.1118 | 2.18 | 1100 | 0.4952 | 0.9 | | 0.158 | 2.28 | 1150 | 0.1706 | 0.9533 | | 0.1163 | 2.38 | 1200 | 0.1238 | 0.9733 | | 0.1738 | 2.48 | 1250 | 0.1989 | 0.9467 | | 0.1305 | 2.57 | 1300 | 0.4354 | 0.9067 | | 0.1668 | 2.67 | 1350 | 0.1276 | 0.9667 | | 0.1195 | 2.77 | 1400 | 0.2170 | 0.9533 | | 0.1057 | 2.87 | 1450 | 0.2882 | 0.9333 | | 0.1172 | 2.97 | 1500 | 0.1435 | 0.9667 | | 0.0893 | 3.07 | 1550 | 0.1754 | 0.96 | | 0.0582 | 3.17 | 1600 | 0.1858 | 0.96 | | 0.0887 | 3.27 | 1650 | 0.4954 | 0.92 | | 0.1166 | 3.37 | 1700 | 0.2356 | 0.9467 | | 0.0518 | 3.47 | 1750 | 0.1910 | 0.96 | | 0.0741 | 3.56 | 1800 | 0.1328 | 0.9733 | | 0.072 | 3.66 | 1850 | 0.2769 | 0.9467 | | 0.0534 | 3.76 | 1900 | 0.3501 | 0.94 | | 0.0776 | 3.86 | 1950 | 0.3171 | 0.94 | | 0.0537 | 3.96 | 2000 | 0.2138 | 0.9533 | | 0.0683 | 4.06 | 2050 | 0.2934 | 0.94 | | 0.015 | 4.16 | 2100 | 0.2233 | 0.9533 | | 0.0236 | 4.26 | 2150 | 0.2673 | 0.9533 | | 0.0357 | 4.36 | 2200 | 0.2279 | 0.96 | | 0.0298 | 4.46 | 2250 | 0.3017 | 0.9467 | | 0.0357 | 4.55 | 2300 | 0.2910 | 0.9467 | | 0.0208 | 4.65 | 2350 | 0.2498 | 0.9533 | | 0.0345 | 4.75 | 2400 | 0.2259 | 0.9667 | | 0.0174 | 4.85 | 2450 | 0.2274 | 0.9667 | | 0.0393 | 4.95 | 2500 | 0.2389 | 0.9533 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

anikethjr/PromoGen_K562_2080Ti_restart

anikethjr

2022-11-28T21:24:36Z

91

0

transformers

[ "transformers", "pytorch", "tensorboard", "prophetnet", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-11-27T05:27:24Z

--- tags: - generated_from_trainer model-index: - name: PromoGen_K562_2080Ti_restart results: [] ---  # PromoGen_K562_2080Ti_restart This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 ## 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: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.7676 | 0.49 | 2500 | 0.7383 | | 0.7121 | 0.97 | 5000 | 0.6867 | | 0.6914 | 1.46 | 7500 | 0.6705 | | 0.6837 | 1.95 | 10000 | 0.6622 | | 0.6778 | 2.44 | 12500 | 0.6558 | | 0.6748 | 2.92 | 15000 | 0.6517 | | 0.6676 | 3.41 | 17500 | 0.6433 | | 0.6593 | 3.9 | 20000 | 0.6358 | | 0.6584 | 4.38 | 22500 | 0.6320 | | 0.6557 | 4.87 | 25000 | 0.6301 | | 0.6523 | 5.36 | 27500 | 0.6257 | | 0.6478 | 5.84 | 30000 | 0.6236 | | 0.6393 | 6.33 | 32500 | 0.6145 | | 0.6039 | 6.82 | 35000 | 0.5658 | | 0.5616 | 7.31 | 37500 | 0.5376 | | 0.5518 | 7.79 | 40000 | 0.5310 | | 0.5509 | 8.28 | 42500 | 0.5273 | | 0.5487 | 8.77 | 45000 | 0.5261 | | 0.5479 | 9.25 | 47500 | 0.5249 | | 0.546 | 9.74 | 50000 | 0.5242 | | 0.5447 | 10.23 | 52500 | 0.5229 | | 0.5439 | 10.71 | 55000 | 0.5220 | | 0.5433 | 11.2 | 57500 | 0.5209 | | 0.5394 | 11.69 | 60000 | 0.5162 | | 0.5153 | 12.18 | 62500 | 0.4944 | | 0.5137 | 12.66 | 65000 | 0.4932 | | 0.514 | 13.15 | 67500 | 0.4924 | | 0.5131 | 13.64 | 70000 | 0.4919 | | 0.5104 | 14.12 | 72500 | 0.4914 | | 0.5122 | 14.61 | 75000 | 0.4906 | | 0.5089 | 15.1 | 77500 | 0.4901 | | 0.5076 | 15.59 | 80000 | 0.4891 | | 0.4986 | 16.07 | 82500 | 0.4721 | | 0.4875 | 16.56 | 85000 | 0.4672 | | 0.4887 | 17.05 | 87500 | 0.4669 | | 0.4839 | 17.53 | 90000 | 0.4661 | | 0.4849 | 18.02 | 92500 | 0.4654 | | 0.4848 | 18.51 | 95000 | 0.4649 | | 0.4831 | 18.99 | 97500 | 0.4646 | | 0.4816 | 19.48 | 100000 | 0.4644 | | 0.4808 | 19.97 | 102500 | 0.4637 | | 0.4812 | 20.46 | 105000 | 0.4634 | | 0.4813 | 20.94 | 107500 | 0.4633 | | 0.4818 | 21.43 | 110000 | 0.4631 | | 0.4813 | 21.92 | 112500 | 0.4629 | | 0.4782 | 22.4 | 115000 | 0.4628 | | 0.4804 | 22.89 | 117500 | 0.4626 | | 0.4815 | 23.38 | 120000 | 0.4625 | | 0.4812 | 23.87 | 122500 | 0.4625 | | 0.4785 | 24.35 | 125000 | 0.4624 | | 0.4795 | 24.84 | 127500 | 0.4624 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.0 - Tokenizers 0.13.0.dev0

Inayat/Fine_tune_whisper_small

Inayat

2022-11-28T21:14:32Z

79

0

transformers

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

automatic-speech-recognition

2022-11-14T19:18:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Fine_tune_whisper_small results: [] ---  # Fine_tune_whisper_small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8238 - Wer: 42.9362 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 900 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2994 | 3.92 | 200 | 0.6607 | 44.0797 | | 0.0201 | 7.84 | 400 | 0.7371 | 42.6042 | | 0.002 | 11.76 | 600 | 0.8027 | 42.5304 | | 0.0011 | 15.69 | 800 | 0.8238 | 42.9362 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

pig4431/TweetEval_DistilBERT_5E

pig4431

2022-11-28T21:09:36Z

103

0

transformers

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

text-classification

2022-11-28T21:03:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_DistilBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9133333333333333 ---  # TweetEval_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.4043 - Accuracy: 0.9133 ## 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: 8 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5747 | 0.04 | 50 | 0.4843 | 0.7333 | | 0.4336 | 0.08 | 100 | 0.2888 | 0.8667 | | 0.3437 | 0.12 | 150 | 0.2895 | 0.8667 | | 0.3375 | 0.16 | 200 | 0.2864 | 0.8733 | | 0.3072 | 0.2 | 250 | 0.2577 | 0.8867 | | 0.3019 | 0.24 | 300 | 0.2574 | 0.8933 | | 0.2662 | 0.28 | 350 | 0.2621 | 0.8867 | | 0.283 | 0.32 | 400 | 0.2340 | 0.92 | | 0.2949 | 0.37 | 450 | 0.2482 | 0.8933 | | 0.3066 | 0.41 | 500 | 0.2537 | 0.9 | | 0.2457 | 0.45 | 550 | 0.2473 | 0.9 | | 0.295 | 0.49 | 600 | 0.2177 | 0.9133 | | 0.2862 | 0.53 | 650 | 0.2215 | 0.9133 | | 0.2603 | 0.57 | 700 | 0.2272 | 0.9133 | | 0.2976 | 0.61 | 750 | 0.2298 | 0.9067 | | 0.2823 | 0.65 | 800 | 0.2451 | 0.8933 | | 0.2583 | 0.69 | 850 | 0.2645 | 0.8933 | | 0.2694 | 0.73 | 900 | 0.2352 | 0.9 | | 0.2433 | 0.77 | 950 | 0.2322 | 0.9133 | | 0.2598 | 0.81 | 1000 | 0.2300 | 0.9 | | 0.2701 | 0.85 | 1050 | 0.2162 | 0.9 | | 0.2227 | 0.89 | 1100 | 0.2135 | 0.8933 | | 0.2045 | 0.93 | 1150 | 0.2233 | 0.9133 | | 0.2821 | 0.97 | 1200 | 0.2194 | 0.9 | | 0.2342 | 1.01 | 1250 | 0.2488 | 0.88 | | 0.2028 | 1.06 | 1300 | 0.2451 | 0.8867 | | 0.1509 | 1.1 | 1350 | 0.3174 | 0.88 | | 0.1888 | 1.14 | 1400 | 0.2537 | 0.9133 | | 0.1825 | 1.18 | 1450 | 0.2559 | 0.9067 | | 0.1721 | 1.22 | 1500 | 0.2511 | 0.92 | | 0.2137 | 1.26 | 1550 | 0.2963 | 0.9133 | | 0.2153 | 1.3 | 1600 | 0.2210 | 0.92 | | 0.1989 | 1.34 | 1650 | 0.2231 | 0.9133 | | 0.2155 | 1.38 | 1700 | 0.1991 | 0.9133 | | 0.1912 | 1.42 | 1750 | 0.2146 | 0.92 | | 0.1623 | 1.46 | 1800 | 0.2721 | 0.9 | | 0.2236 | 1.5 | 1850 | 0.2301 | 0.9267 | | 0.1907 | 1.54 | 1900 | 0.1988 | 0.92 | | 0.1286 | 1.58 | 1950 | 0.2326 | 0.9 | | 0.2147 | 1.62 | 2000 | 0.2432 | 0.9267 | | 0.2018 | 1.66 | 2050 | 0.2162 | 0.9067 | | 0.2073 | 1.7 | 2100 | 0.2153 | 0.9133 | | 0.1498 | 1.75 | 2150 | 0.2335 | 0.92 | | 0.1812 | 1.79 | 2200 | 0.2275 | 0.9267 | | 0.1482 | 1.83 | 2250 | 0.2734 | 0.9 | | 0.2233 | 1.87 | 2300 | 0.2454 | 0.9 | | 0.1673 | 1.91 | 2350 | 0.2394 | 0.92 | | 0.1555 | 1.95 | 2400 | 0.2725 | 0.92 | | 0.2082 | 1.99 | 2450 | 0.2684 | 0.9133 | | 0.1545 | 2.03 | 2500 | 0.3049 | 0.9067 | | 0.1384 | 2.07 | 2550 | 0.2960 | 0.9133 | | 0.1201 | 2.11 | 2600 | 0.3259 | 0.9 | | 0.1348 | 2.15 | 2650 | 0.3091 | 0.9133 | | 0.1046 | 2.19 | 2700 | 0.2916 | 0.9267 | | 0.1506 | 2.23 | 2750 | 0.2910 | 0.9133 | | 0.1481 | 2.27 | 2800 | 0.2855 | 0.9067 | | 0.1318 | 2.31 | 2850 | 0.3075 | 0.9 | | 0.1204 | 2.35 | 2900 | 0.3169 | 0.8933 | | 0.1669 | 2.39 | 2950 | 0.3050 | 0.9067 | | 0.1725 | 2.44 | 3000 | 0.2970 | 0.9133 | | 0.1305 | 2.48 | 3050 | 0.3065 | 0.9 | | 0.1508 | 2.52 | 3100 | 0.3079 | 0.9133 | | 0.184 | 2.56 | 3150 | 0.3482 | 0.9067 | | 0.1263 | 2.6 | 3200 | 0.3310 | 0.9 | | 0.1282 | 2.64 | 3250 | 0.3520 | 0.8933 | | 0.1217 | 2.68 | 3300 | 0.3158 | 0.9067 | | 0.1203 | 2.72 | 3350 | 0.3351 | 0.92 | | 0.1068 | 2.76 | 3400 | 0.3239 | 0.92 | | 0.1517 | 2.8 | 3450 | 0.3247 | 0.92 | | 0.113 | 2.84 | 3500 | 0.3269 | 0.9133 | | 0.1276 | 2.88 | 3550 | 0.3162 | 0.92 | | 0.1548 | 2.92 | 3600 | 0.3196 | 0.9133 | | 0.1305 | 2.96 | 3650 | 0.3163 | 0.92 | | 0.149 | 3.0 | 3700 | 0.3013 | 0.92 | | 0.0816 | 3.04 | 3750 | 0.3097 | 0.9267 | | 0.0884 | 3.08 | 3800 | 0.3028 | 0.92 | | 0.0727 | 3.12 | 3850 | 0.3487 | 0.9133 | | 0.1018 | 3.17 | 3900 | 0.3447 | 0.92 | | 0.1266 | 3.21 | 3950 | 0.3589 | 0.9133 | | 0.1216 | 3.25 | 4000 | 0.3464 | 0.92 | | 0.091 | 3.29 | 4050 | 0.3454 | 0.92 | | 0.0829 | 3.33 | 4100 | 0.3450 | 0.92 | | 0.1084 | 3.37 | 4150 | 0.3670 | 0.92 | | 0.0754 | 3.41 | 4200 | 0.3661 | 0.92 | | 0.094 | 3.45 | 4250 | 0.3588 | 0.9067 | | 0.0641 | 3.49 | 4300 | 0.3936 | 0.92 | | 0.1138 | 3.53 | 4350 | 0.3616 | 0.92 | | 0.0744 | 3.57 | 4400 | 0.3562 | 0.92 | | 0.0697 | 3.61 | 4450 | 0.3532 | 0.9267 | | 0.1083 | 3.65 | 4500 | 0.3451 | 0.9267 | | 0.0701 | 3.69 | 4550 | 0.3307 | 0.92 | | 0.0849 | 3.73 | 4600 | 0.3797 | 0.92 | | 0.09 | 3.77 | 4650 | 0.3746 | 0.9267 | | 0.0799 | 3.81 | 4700 | 0.3799 | 0.92 | | 0.0589 | 3.86 | 4750 | 0.3805 | 0.92 | | 0.0578 | 3.9 | 4800 | 0.3910 | 0.9133 | | 0.0816 | 3.94 | 4850 | 0.3856 | 0.9133 | | 0.1366 | 3.98 | 4900 | 0.3707 | 0.92 | | 0.0846 | 4.02 | 4950 | 0.3802 | 0.92 | | 0.0401 | 4.06 | 5000 | 0.3842 | 0.92 | | 0.0851 | 4.1 | 5050 | 0.3773 | 0.9267 | | 0.0514 | 4.14 | 5100 | 0.3922 | 0.9133 | | 0.0909 | 4.18 | 5150 | 0.3893 | 0.92 | | 0.0764 | 4.22 | 5200 | 0.3818 | 0.9133 | | 0.1208 | 4.26 | 5250 | 0.4096 | 0.92 | | 0.0689 | 4.3 | 5300 | 0.3940 | 0.9133 | | 0.0524 | 4.34 | 5350 | 0.4020 | 0.9133 | | 0.0733 | 4.38 | 5400 | 0.4002 | 0.9133 | | 0.0699 | 4.42 | 5450 | 0.4013 | 0.9133 | | 0.0712 | 4.46 | 5500 | 0.4037 | 0.9067 | | 0.0557 | 4.5 | 5550 | 0.4121 | 0.92 | | 0.0679 | 4.55 | 5600 | 0.4067 | 0.9133 | | 0.0651 | 4.59 | 5650 | 0.4194 | 0.9133 | | 0.0607 | 4.63 | 5700 | 0.4007 | 0.9133 | | 0.0676 | 4.67 | 5750 | 0.4013 | 0.9133 | | 0.0303 | 4.71 | 5800 | 0.3984 | 0.9133 | | 0.0674 | 4.75 | 5850 | 0.4037 | 0.9133 | | 0.0842 | 4.79 | 5900 | 0.4072 | 0.9133 | | 0.0516 | 4.83 | 5950 | 0.4096 | 0.9133 | | 0.0556 | 4.87 | 6000 | 0.4111 | 0.92 | | 0.0277 | 4.91 | 6050 | 0.4079 | 0.9133 | | 0.0629 | 4.95 | 6100 | 0.4053 | 0.9133 | | 0.0426 | 4.99 | 6150 | 0.4043 | 0.9133 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2

michaelmayo704/sd-class-butterflies-32

michaelmayo704

2022-11-28T21:00:24Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T20:59:37Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(michaelmayo704/sd-class-butterflies-32) image = pipeline().images[0] image ```

pig4431/YELP_roBERTa_5E

pig4431

2022-11-28T20:50:36Z

108

0

transformers

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

text-classification

2022-11-28T20:34:22Z

--- license: mit tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_roBERTa_5E results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: train args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.9866666666666667 ---  # YELP_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.0995 - Accuracy: 0.9867 ## 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: 8 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5721 | 0.03 | 50 | 0.3248 | 0.88 | | 0.2836 | 0.06 | 100 | 0.1190 | 0.9733 | | 0.1793 | 0.1 | 150 | 0.1707 | 0.96 | | 0.2196 | 0.13 | 200 | 0.0841 | 0.9733 | | 0.2102 | 0.16 | 250 | 0.0634 | 0.9867 | | 0.2197 | 0.19 | 300 | 0.0763 | 0.98 | | 0.1866 | 0.22 | 350 | 0.0640 | 0.9867 | | 0.1717 | 0.26 | 400 | 0.0612 | 0.9867 | | 0.1443 | 0.29 | 450 | 0.0844 | 0.9733 | | 0.1669 | 0.32 | 500 | 0.1297 | 0.9667 | | 0.2005 | 0.35 | 550 | 0.0644 | 0.9867 | | 0.1543 | 0.38 | 600 | 0.0874 | 0.9867 | | 0.1345 | 0.42 | 650 | 0.1853 | 0.96 | | 0.1664 | 0.45 | 700 | 0.1157 | 0.9667 | | 0.1876 | 0.48 | 750 | 0.0474 | 0.9733 | | 0.111 | 0.51 | 800 | 0.0645 | 0.98 | | 0.1511 | 0.54 | 850 | 0.0432 | 0.9933 | | 0.1846 | 0.58 | 900 | 0.0505 | 0.9867 | | 0.151 | 0.61 | 950 | 0.0452 | 0.98 | | 0.1338 | 0.64 | 1000 | 0.1007 | 0.98 | | 0.1175 | 0.67 | 1050 | 0.0747 | 0.9867 | | 0.1818 | 0.7 | 1100 | 0.0852 | 0.98 | | 0.1557 | 0.74 | 1150 | 0.0255 | 0.9933 | | 0.1487 | 0.77 | 1200 | 0.1266 | 0.9733 | | 0.1315 | 0.8 | 1250 | 0.0593 | 0.9867 | | 0.1059 | 0.83 | 1300 | 0.0697 | 0.9867 | | 0.108 | 0.86 | 1350 | 0.0459 | 0.9933 | | 0.1525 | 0.9 | 1400 | 0.0446 | 0.9933 | | 0.1185 | 0.93 | 1450 | 0.0528 | 0.9867 | | 0.1611 | 0.96 | 1500 | 0.0582 | 0.9867 | | 0.1556 | 0.99 | 1550 | 0.0726 | 0.98 | | 0.0902 | 1.02 | 1600 | 0.0466 | 0.9867 | | 0.1535 | 1.06 | 1650 | 0.0850 | 0.9733 | | 0.0787 | 1.09 | 1700 | 0.0869 | 0.9867 | | 0.1019 | 1.12 | 1750 | 0.0984 | 0.98 | | 0.1234 | 1.15 | 1800 | 0.0358 | 0.9933 | | 0.0884 | 1.18 | 1850 | 0.0621 | 0.9867 | | 0.0785 | 1.22 | 1900 | 0.0507 | 0.9867 | | 0.1454 | 1.25 | 1950 | 0.0793 | 0.98 | | 0.1035 | 1.28 | 2000 | 0.0501 | 0.9867 | | 0.0579 | 1.31 | 2050 | 0.0935 | 0.9867 | | 0.1215 | 1.34 | 2100 | 0.0079 | 1.0 | | 0.0958 | 1.38 | 2150 | 0.0673 | 0.9867 | | 0.106 | 1.41 | 2200 | 0.0875 | 0.9867 | | 0.095 | 1.44 | 2250 | 0.0745 | 0.9867 | | 0.0958 | 1.47 | 2300 | 0.0715 | 0.9867 | | 0.085 | 1.5 | 2350 | 0.0742 | 0.9867 | | 0.082 | 1.54 | 2400 | 0.1053 | 0.9733 | | 0.1202 | 1.57 | 2450 | 0.0711 | 0.9867 | | 0.1041 | 1.6 | 2500 | 0.0723 | 0.9867 | | 0.1145 | 1.63 | 2550 | 0.0361 | 0.9867 | | 0.0909 | 1.66 | 2600 | 0.0868 | 0.9867 | | 0.1029 | 1.7 | 2650 | 0.0680 | 0.9867 | | 0.1083 | 1.73 | 2700 | 0.0599 | 0.9867 | | 0.0871 | 1.76 | 2750 | 0.0452 | 0.9867 | | 0.1506 | 1.79 | 2800 | 0.0344 | 0.9933 | | 0.0778 | 1.82 | 2850 | 0.0380 | 0.9933 | | 0.0982 | 1.86 | 2900 | 0.0349 | 0.9933 | | 0.1296 | 1.89 | 2950 | 0.0713 | 0.9867 | | 0.0836 | 1.92 | 3000 | 0.0693 | 0.9867 | | 0.0699 | 1.95 | 3050 | 0.1023 | 0.98 | | 0.0631 | 1.98 | 3100 | 0.0852 | 0.98 | | 0.0724 | 2.02 | 3150 | 0.0835 | 0.9867 | | 0.0898 | 2.05 | 3200 | 0.0872 | 0.9867 | | 0.0642 | 2.08 | 3250 | 0.0427 | 0.9933 | | 0.0524 | 2.11 | 3300 | 0.0731 | 0.9867 | | 0.0415 | 2.14 | 3350 | 0.0632 | 0.9867 | | 0.0604 | 2.18 | 3400 | 0.0428 | 0.9867 | | 0.0701 | 2.21 | 3450 | 0.0671 | 0.9867 | | 0.0668 | 2.24 | 3500 | 0.0360 | 0.9933 | | 0.0442 | 2.27 | 3550 | 0.0454 | 0.9933 | | 0.0677 | 2.3 | 3600 | 0.0517 | 0.9867 | | 0.0965 | 2.34 | 3650 | 0.0659 | 0.98 | | 0.0781 | 2.37 | 3700 | 0.0732 | 0.9867 | | 0.0421 | 2.4 | 3750 | 0.0855 | 0.9867 | | 0.0674 | 2.43 | 3800 | 0.0813 | 0.9867 | | 0.0613 | 2.46 | 3850 | 0.0859 | 0.98 | | 0.0679 | 2.5 | 3900 | 0.0721 | 0.9867 | | 0.0417 | 2.53 | 3950 | 0.0977 | 0.9867 | | 0.0616 | 2.56 | 4000 | 0.0789 | 0.9867 | | 0.0678 | 2.59 | 4050 | 0.0804 | 0.9867 | | 0.0651 | 2.62 | 4100 | 0.0994 | 0.98 | | 0.0714 | 2.66 | 4150 | 0.0744 | 0.98 | | 0.034 | 2.69 | 4200 | 0.0679 | 0.9867 | | 0.0356 | 2.72 | 4250 | 0.0432 | 0.9933 | | 0.0813 | 2.75 | 4300 | 0.0483 | 0.9933 | | 0.052 | 2.78 | 4350 | 0.0689 | 0.9867 | | 0.0611 | 2.82 | 4400 | 0.0474 | 0.9867 | | 0.0615 | 2.85 | 4450 | 0.0557 | 0.9867 | | 0.0569 | 2.88 | 4500 | 0.1056 | 0.98 | | 0.0352 | 2.91 | 4550 | 0.0443 | 0.9933 | | 0.0312 | 2.94 | 4600 | 0.1026 | 0.98 | | 0.0662 | 2.98 | 4650 | 0.0677 | 0.9867 | | 0.0694 | 3.01 | 4700 | 0.0368 | 0.9933 | | 0.0144 | 3.04 | 4750 | 0.0647 | 0.9867 | | 0.0378 | 3.07 | 4800 | 0.0893 | 0.9867 | | 0.0393 | 3.1 | 4850 | 0.0841 | 0.9867 | | 0.0598 | 3.13 | 4900 | 0.0594 | 0.9867 | | 0.0329 | 3.17 | 4950 | 0.0933 | 0.9867 | | 0.036 | 3.2 | 5000 | 0.0974 | 0.9867 | | 0.0166 | 3.23 | 5050 | 0.0962 | 0.9867 | | 0.0189 | 3.26 | 5100 | 0.0827 | 0.9867 | | 0.0482 | 3.29 | 5150 | 0.0955 | 0.9867 | | 0.0105 | 3.33 | 5200 | 0.0745 | 0.9867 | | 0.0447 | 3.36 | 5250 | 0.1038 | 0.9867 | | 0.0495 | 3.39 | 5300 | 0.0684 | 0.9867 | | 0.0445 | 3.42 | 5350 | 0.0815 | 0.9867 | | 0.0006 | 3.45 | 5400 | 0.1012 | 0.9867 | | 0.0214 | 3.49 | 5450 | 0.0707 | 0.9867 | | 0.0289 | 3.52 | 5500 | 0.1000 | 0.9867 | | 0.0304 | 3.55 | 5550 | 0.1069 | 0.9867 | | 0.0339 | 3.58 | 5600 | 0.1079 | 0.9867 | | 0.0227 | 3.61 | 5650 | 0.1032 | 0.9867 | | 0.0626 | 3.65 | 5700 | 0.0978 | 0.9867 | | 0.04 | 3.68 | 5750 | 0.0965 | 0.9867 | | 0.0358 | 3.71 | 5800 | 0.1048 | 0.9867 | | 0.0287 | 3.74 | 5850 | 0.0921 | 0.9867 | | 0.049 | 3.77 | 5900 | 0.1108 | 0.98 | | 0.0497 | 3.81 | 5950 | 0.0795 | 0.9867 | | 0.0047 | 3.84 | 6000 | 0.0979 | 0.9867 | | 0.0252 | 3.87 | 6050 | 0.1071 | 0.9867 | | 0.0691 | 3.9 | 6100 | 0.0821 | 0.9867 | | 0.0419 | 3.93 | 6150 | 0.0896 | 0.9867 | | 0.0197 | 3.97 | 6200 | 0.0943 | 0.9867 | | 0.0281 | 4.0 | 6250 | 0.0901 | 0.9867 | | 0.0118 | 4.03 | 6300 | 0.0950 | 0.9867 | | 0.0057 | 4.06 | 6350 | 0.1031 | 0.9867 | | 0.0335 | 4.09 | 6400 | 0.0896 | 0.9867 | | 0.0095 | 4.13 | 6450 | 0.0966 | 0.9867 | | 0.05 | 4.16 | 6500 | 0.0977 | 0.9867 | | 0.0142 | 4.19 | 6550 | 0.1174 | 0.98 | | 0.018 | 4.22 | 6600 | 0.0963 | 0.9867 | | 0.0274 | 4.25 | 6650 | 0.0953 | 0.9867 | | 0.0199 | 4.29 | 6700 | 0.0968 | 0.9867 | | 0.0171 | 4.32 | 6750 | 0.0963 | 0.9867 | | 0.0195 | 4.35 | 6800 | 0.0916 | 0.9867 | | 0.0091 | 4.38 | 6850 | 0.0954 | 0.9867 | | 0.0115 | 4.41 | 6900 | 0.0974 | 0.9867 | | 0.0299 | 4.45 | 6950 | 0.0971 | 0.9867 | | 0.0338 | 4.48 | 7000 | 0.0922 | 0.9867 | | 0.0107 | 4.51 | 7050 | 0.0964 | 0.9867 | | 0.0063 | 4.54 | 7100 | 0.0921 | 0.9867 | | 0.0099 | 4.57 | 7150 | 0.0923 | 0.9867 | | 0.0101 | 4.61 | 7200 | 0.0971 | 0.9867 | | 0.0262 | 4.64 | 7250 | 0.1008 | 0.9867 | | 0.0097 | 4.67 | 7300 | 0.0999 | 0.9867 | | 0.0302 | 4.7 | 7350 | 0.0980 | 0.9867 | | 0.0225 | 4.73 | 7400 | 0.0976 | 0.9867 | | 0.0235 | 4.77 | 7450 | 0.1016 | 0.9867 | | 0.0106 | 4.8 | 7500 | 0.1034 | 0.9867 | | 0.0495 | 4.83 | 7550 | 0.1135 | 0.98 | | 0.0228 | 4.86 | 7600 | 0.1034 | 0.9867 | | 0.0229 | 4.89 | 7650 | 0.0990 | 0.9867 | | 0.0206 | 4.93 | 7700 | 0.0993 | 0.9867 | | 0.0188 | 4.96 | 7750 | 0.0993 | 0.9867 | | 0.0189 | 4.99 | 7800 | 0.0995 | 0.9867 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2

SwePalm/sd-class-butterflies-64

SwePalm

2022-11-28T20:42:14Z

34

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T20:41:56Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(SwePalm/sd-class-butterflies-64) image = pipeline().images[0] image ```

pig4431/TUF_XLNET_5E

pig4431

2022-11-28T20:40:06Z

89

0

transformers

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

text-classification

2022-11-28T20:20:56Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_XLNET_5E results: [] ---  # TUF_XLNET_5E This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2725 - Accuracy: 0.9533 ## 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: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4817 | 0.1 | 50 | 0.2602 | 0.8733 | | 0.2405 | 0.2 | 100 | 0.5818 | 0.88 | | 0.2172 | 0.3 | 150 | 0.1851 | 0.9533 | | 0.2697 | 0.4 | 200 | 0.1692 | 0.9267 | | 0.2313 | 0.5 | 250 | 0.1086 | 0.9467 | | 0.2245 | 0.59 | 300 | 0.2031 | 0.9267 | | 0.1805 | 0.69 | 350 | 0.1414 | 0.9467 | | 0.1896 | 0.79 | 400 | 0.0824 | 0.9733 | | 0.1969 | 0.89 | 450 | 0.1499 | 0.9533 | | 0.1745 | 0.99 | 500 | 0.1827 | 0.9267 | | 0.1143 | 1.09 | 550 | 0.1923 | 0.9533 | | 0.1478 | 1.19 | 600 | 0.1718 | 0.94 | | 0.1368 | 1.29 | 650 | 0.1170 | 0.9733 | | 0.1288 | 1.39 | 700 | 0.1418 | 0.9667 | | 0.1689 | 1.49 | 750 | 0.1173 | 0.9733 | | 0.1078 | 1.58 | 800 | 0.2784 | 0.9333 | | 0.1343 | 1.68 | 850 | 0.1555 | 0.9533 | | 0.1104 | 1.78 | 900 | 0.1361 | 0.9533 | | 0.1267 | 1.88 | 950 | 0.1936 | 0.9267 | | 0.0928 | 1.98 | 1000 | 0.3070 | 0.94 | | 0.0949 | 2.08 | 1050 | 0.1905 | 0.94 | | 0.0329 | 2.18 | 1100 | 0.2296 | 0.9533 | | 0.0406 | 2.28 | 1150 | 0.3202 | 0.94 | | 0.0983 | 2.38 | 1200 | 0.4515 | 0.9267 | | 0.0533 | 2.48 | 1250 | 0.2152 | 0.9533 | | 0.0878 | 2.57 | 1300 | 0.1573 | 0.9533 | | 0.0595 | 2.67 | 1350 | 0.1699 | 0.96 | | 0.0937 | 2.77 | 1400 | 0.2825 | 0.9333 | | 0.0817 | 2.87 | 1450 | 0.2325 | 0.9467 | | 0.0845 | 2.97 | 1500 | 0.1918 | 0.9533 | | 0.0711 | 3.07 | 1550 | 0.3186 | 0.94 | | 0.033 | 3.17 | 1600 | 0.2571 | 0.94 | | 0.0134 | 3.27 | 1650 | 0.2733 | 0.94 | | 0.0546 | 3.37 | 1700 | 0.1934 | 0.9533 | | 0.0277 | 3.47 | 1750 | 0.2731 | 0.94 | | 0.0081 | 3.56 | 1800 | 0.2531 | 0.9467 | | 0.0387 | 3.66 | 1850 | 0.2121 | 0.96 | | 0.0014 | 3.76 | 1900 | 0.2601 | 0.96 | | 0.0379 | 3.86 | 1950 | 0.2501 | 0.9467 | | 0.0271 | 3.96 | 2000 | 0.2899 | 0.94 | | 0.0182 | 4.06 | 2050 | 0.2197 | 0.9533 | | 0.0263 | 4.16 | 2100 | 0.2374 | 0.9533 | | 0.0079 | 4.26 | 2150 | 0.3192 | 0.94 | | 0.0239 | 4.36 | 2200 | 0.3755 | 0.9333 | | 0.02 | 4.46 | 2250 | 0.2702 | 0.9467 | | 0.0072 | 4.55 | 2300 | 0.2055 | 0.9533 | | 0.0124 | 4.65 | 2350 | 0.2299 | 0.9533 | | 0.0072 | 4.75 | 2400 | 0.2813 | 0.9533 | | 0.0125 | 4.85 | 2450 | 0.2696 | 0.9533 | | 0.0205 | 4.95 | 2500 | 0.2725 | 0.9533 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

rmartinshort/sd-class-butterflies-64

rmartinshort

2022-11-28T20:32:13Z

36

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T20:31:54Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(rmartinshort/sd-class-butterflies-64) image = pipeline().images[0] image ```

CyantifiCQ/noisy_butterflied_diffusion

CyantifiCQ

2022-11-28T20:23:45Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T20:22:34Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(CyantifiCQ/noisy_butterflied_diffusion) image = pipeline().images[0] image ```

futuredatascience/from-classifier-v1

futuredatascience

2022-11-28T20:07:27Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-28T20:07:15Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 53 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 530, "warmup_steps": 53, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

SwePalm/sd-class-butterflies-32

SwePalm

2022-11-28T20:01:43Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T20:00:51Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of (not so?) cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(SwePalm/sd-class-butterflies-32) image = pipeline().images[0] image ```

reubenjohn/stack-overflow-open-status-classifier-pt

reubenjohn

2022-11-28T20:01:21Z

4

0

transformers

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

text-classification

2022-11-16T03:44:14Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: stack-overflow-open-status-classifier-pt results: [] ---  # stack-overflow-open-status-classifier-pt This model is a fine-tuned version of [reubenjohn/stack-overflow-open-status-classifier-pt](https://huggingface.co/reubenjohn/stack-overflow-open-status-classifier-pt) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9448 - eval_runtime: 3.554 - eval_samples_per_second: 28.137 - eval_steps_per_second: 0.563 - epoch: 0.01 - step: 60 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 1 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

UKP-SQuARE/tweac_16

UKP-SQuARE

2022-11-28T19:43:48Z

102

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "QA", "en", "dataset:BoolQ", "dataset:CommonSenseQA", "dataset:DROP", "dataset:DuoRC", "dataset:HellaSWAG", "dataset:HotpotQA", "dataset:HybridQA", "dataset:NarrativeQA", "dataset:NaturalQuestionsShort", "dataset:NewsQA", "dataset:QAMR", "dataset:RACE", "dataset:SearchQA", "dataset:SIQA", "dataset:SQuAD", "dataset:TriviaQA-web", "arxiv:2104.07081", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-09T18:34:07Z

--- language: - en tags: - QA license: cc-by-4.0 datasets: - BoolQ - CommonSenseQA - DROP - DuoRC - HellaSWAG - HotpotQA - HybridQA - NarrativeQA - NaturalQuestionsShort - NewsQA - QAMR - RACE - SearchQA - SIQA - SQuAD - TriviaQA-web metrics: - Accuracy - Precision - Recall - F1 - MRR - R@3 - R@5 --- BERT for Sequence Classification trained on QA Dataset prediction task. - Input: question. - Output: dataset from where that question comes from. Original paper: TWEAC: Transformer with Extendable QA Agent Classifiers https://arxiv.org/abs/2104.07081 Datasets used for training: ``` list_datasets = ['BoolQ','CommonSenseQA','DROP','DuoRC','HellaSWAG','HotpotQA','HybridQA','NarrativeQA','NaturalQuestionsShort','NewsQA','QAMR','RACE','SearchQA','SIQA','SQuAD','TriviaQA-web'] ``` Results for all datasets: - Accuracy: 0.7919096825783123 - Precision: 0.731586272892176 - Recall: 0.7919096825783123 - F1: 0.7494425609552463 - MRR: 0.8720871733637521 - R@3: 0.9438690810655046 - R@5: 0.9745318608004427 - Queries/second: 6052.33538824659 Results per dataset: ``` "BoolQ": { "accuracy": 0.998776758409786, "mrr": 0.999388379204893, "r@3": 1.0, "r@5": 1.0, "query_per_second": 6978.947907596168, "precision": 0.8649364406779662, "recall": 0.998776758409786, "f1": 0.9270508089696281 }, "CommonSenseQA": { "accuracy": 0.9247135842880524, "mrr": 0.9476358338878795, "r@3": 0.9705400981996727, "r@5": 0.9705400981996727, "query_per_second": 5823.984138936813, "precision": 0.442443226311668, "recall": 0.9247135842880524, "f1": 0.5985169491525425 }, "DROP": { "accuracy": 0.9075083892617449, "mrr": 0.9378200367399193, "r@3": 0.9609899328859061, "r@5": 0.9786073825503355, "query_per_second": 6440.988897129248, "precision": 0.8636726546906187, "recall": 0.9075083892617449, "f1": 0.8850480670893842 }, "DuoRC": { "accuracy": 0.5555803405457654, "mrr": 0.7368963429107307, "r@3": 0.9092125808610305, "r@5": 0.9596996059186557, "query_per_second": 6853.643198794893, "precision": 0.646814404432133, "recall": 0.5555803405457654, "f1": 0.5977360905563778 }, "HellaSWAG": { "accuracy": 0.998406691894045, "mrr": 0.9990705702715262, "r@3": 1.0, "r@5": 1.0, "query_per_second": 3091.5012960785157, "precision": 0.9974134500596896, "recall": 0.998406691894045, "f1": 0.9979098238280083 }, "HotpotQA": { "accuracy": 0.7414435784479837, "mrr": 0.8435804344945315, "r@3": 0.9325652321247034, "r@5": 0.973568281938326, "query_per_second": 4972.668019223381, "precision": 0.7352150537634409, "recall": 0.7414435784479837, "f1": 0.7383161801923401 }, "HybridQA": { "accuracy": 0.7934218118869013, "mrr": 0.8806947764680021, "r@3": 0.964800923254472, "r@5": 0.9930755914598961, "query_per_second": 4886.494046259562, "precision": 0.7198952879581152, "recall": 0.7934218118869013, "f1": 0.7548723579467472 }, "NarrativeQA": { "accuracy": 0.5623756749076442, "mrr": 0.7416681781060867, "r@3": 0.9011082693947144, "r@5": 0.9580373212086767, "query_per_second": 7081.067049796865, "precision": 0.5623224095472628, "recall": 0.5623756749076442, "f1": 0.5623490409661377 }, "NaturalQuestionsShort": { "accuracy": 0.7985353692739171, "mrr": 0.8743599435345307, "r@3": 0.9439077594266126, "r@5": 0.9774072919912745, "query_per_second": 7136.590426649795, "precision": 0.7963020509633313, "recall": 0.7985353692739171, "f1": 0.7974171464135678 }, "NewsQA": { "accuracy": 0.5375118708452041, "mrr": 0.71192075967717, "r@3": 0.855650522317189, "r@5": 0.939696106362773, "query_per_second": 7193.851409052092, "precision": 0.18757249378624688, "recall": 0.5375118708452041, "f1": 0.2780985136961061 }, "QAMR": { "accuracy": 0.6658497602557272, "mrr": 0.7969741223377345, "r@3": 0.9207778369738945, "r@5": 0.973361747469366, "query_per_second": 7321.775044800525, "precision": 0.8654525309881587, "recall": 0.6658497602557272, "f1": 0.7526421968624852 }, "RACE": { "accuracy": 0.8771538617474154, "mrr": 0.917901778042666, "r@3": 0.9489154672613015, "r@5": 0.9693898236367322, "query_per_second": 6952.225120744351, "precision": 0.8767983789260385, "recall": 0.8771538617474154, "f1": 0.8769760843129306 }, "SearchQA": { "accuracy": 0.9762073027090695, "mrr": 0.9865069592101393, "r@3": 0.9972909305064782, "r@5": 0.9984687868080094, "query_per_second": 4031.0193826035634, "precision": 0.9870191735143503, "recall": 0.9762073027090695, "f1": 0.9815834665719192 }, "SIQA": { "accuracy": 0.9969293756397134, "mrr": 0.9977823268509042, "r@3": 0.9979529170931423, "r@5": 1.0, "query_per_second": 6711.547709005977, "precision": 0.9329501915708812, "recall": 0.9969293756397134, "f1": 0.9638792676892627 }, "SQuAD": { "accuracy": 0.550628092881614, "mrr": 0.7164538452390565, "r@3": 0.8660068519223448, "r@5": 0.9366197183098591, "query_per_second": 7033.420124363291, "precision": 0.48613678373382624, "recall": 0.550628092881614, "f1": 0.5163766175814368 }, "TriviaQA-web": { "accuracy": 0.7855124582584125, "mrr": 0.8647404868442627, "r@3": 0.9321859748266119, "r@5": 0.9640380169535063, "query_per_second": 4327.642440910395, "precision": 0.7404358353510896, "recall": 0.7855124582584125, "f1": 0.7623083634550667 }, ```

essayproj/syntax

essayproj

2022-11-28T19:15:52Z

103

0

transformers

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

text-classification

2022-11-28T18:59:34Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: syntax results: [] ---  # syntax This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1395 - Accuracy: 0.6111 - F1: 0.4596 ## 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: 8 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

huggingtweets/ttunguz

huggingtweets

2022-11-28T19:09:57Z

115

0

transformers

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

text-generation

2022-11-24T01:44:13Z

--- language: en thumbnail: http://www.huggingtweets.com/ttunguz/1669662593098/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/901542400559992832/yDp0b2Al_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tomasz Tunguz</div> <div style="text-align: center; font-size: 14px;">@ttunguz</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tomasz Tunguz. | Data | Tomasz Tunguz | | --- | --- | | Tweets downloaded | 3239 | | Retweets | 590 | | Short tweets | 50 | | Tweets kept | 2599 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vxbo3iui/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ttunguz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/190cyogq) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/190cyogq/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ttunguz') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

essayproj/roberta-base-essay

essayproj

2022-11-28T19:08:54Z

59

0

transformers

[ "transformers", "tf", "roberta", "feature-extraction", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-28T19:08:03Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: roberta-base-essay results: [] ---  # roberta-base-essay This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Tokenizers 0.13.2

leonrafael29/bert2bert_uncased_english_to_spanish

leonrafael29

2022-11-28T18:52:56Z

13

0

transformers

[ "transformers", "encoder-decoder", "text2text-generation", "translation", "en", "es", "dataset:news_commentary", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-11-28T17:32:46Z

--- language: - en - es tags: - translation datasets: - news_commentary metrics: - bleurt ---

FrancoisDongier/sd-class-butterflies-32

FrancoisDongier

2022-11-28T18:19:31Z

34

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T18:16:21Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(FrancoisDongier/sd-class-butterflies-32) image = pipeline().images[0] image ```

ashu1318/lilt-en-funsd

ashu1318

2022-11-28T18:17:59Z

80

0

transformers

[ "transformers", "pytorch", "tensorboard", "lilt", "token-classification", "generated_from_trainer", "dataset:funsd-layoutlmv3", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-28T17:49:59Z

--- license: mit tags: - generated_from_trainer datasets: - funsd-layoutlmv3 model-index: - name: lilt-en-funsd results: [] ---  # lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.8731 - Answer: {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817} - Header: {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119} - Question: {'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077} - Overall Precision: 0.8792 - Overall Recall: 0.8857 - Overall F1: 0.8825 - Overall Accuracy: 0.7976 ## 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 - training_steps: 2500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4323 | 10.53 | 200 | 1.0423 | {'precision': 0.8369195922989807, 'recall': 0.9045287637698899, 'f1': 0.8694117647058823, 'number': 817} | {'precision': 0.5405405405405406, 'recall': 0.5042016806722689, 'f1': 0.5217391304347826, 'number': 119} | {'precision': 0.8869323447636701, 'recall': 0.8885793871866295, 'f1': 0.8877551020408162, 'number': 1077} | 0.8471 | 0.8723 | 0.8595 | 0.7981 | | 0.045 | 21.05 | 400 | 1.2757 | {'precision': 0.8435374149659864, 'recall': 0.9106487148102815, 'f1': 0.8758092995879929, 'number': 817} | {'precision': 0.5795454545454546, 'recall': 0.42857142857142855, 'f1': 0.49275362318840576, 'number': 119} | {'precision': 0.8626943005181347, 'recall': 0.9275766016713092, 'f1': 0.8939597315436242, 'number': 1077} | 0.8430 | 0.8912 | 0.8665 | 0.8026 | | 0.0133 | 31.58 | 600 | 1.4887 | {'precision': 0.8632075471698113, 'recall': 0.8959608323133414, 'f1': 0.8792792792792793, 'number': 817} | {'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119} | {'precision': 0.8791887125220459, 'recall': 0.9257195914577531, 'f1': 0.9018543645409318, 'number': 1077} | 0.8596 | 0.8882 | 0.8737 | 0.7983 | | 0.0051 | 42.11 | 800 | 1.7382 | {'precision': 0.8601645123384254, 'recall': 0.8959608323133414, 'f1': 0.8776978417266187, 'number': 817} | {'precision': 0.5636363636363636, 'recall': 0.5210084033613446, 'f1': 0.5414847161572053, 'number': 119} | {'precision': 0.9032558139534884, 'recall': 0.9015784586815228, 'f1': 0.9024163568773235, 'number': 1077} | 0.8669 | 0.8768 | 0.8718 | 0.7925 | | 0.004 | 52.63 | 1000 | 1.7599 | {'precision': 0.8307349665924276, 'recall': 0.9130966952264382, 'f1': 0.8699708454810495, 'number': 817} | {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119} | {'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077} | 0.8530 | 0.8907 | 0.8714 | 0.7941 | | 0.002 | 63.16 | 1200 | 1.8409 | {'precision': 0.8312985571587126, 'recall': 0.9167686658506732, 'f1': 0.8719441210710128, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} | {'precision': 0.8814949863263446, 'recall': 0.8978644382544104, 'f1': 0.8896044158233671, 'number': 1077} | 0.8461 | 0.8847 | 0.8650 | 0.7876 | | 0.0013 | 73.68 | 1400 | 1.7795 | {'precision': 0.81445523193096, 'recall': 0.9241126070991432, 'f1': 0.8658256880733943, 'number': 817} | {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119} | {'precision': 0.888785046728972, 'recall': 0.883008356545961, 'f1': 0.8858872845831393, 'number': 1077} | 0.8432 | 0.8788 | 0.8606 | 0.7934 | | 0.0011 | 84.21 | 1600 | 1.8386 | {'precision': 0.8338833883388339, 'recall': 0.9277845777233782, 'f1': 0.8783314020857474, 'number': 817} | {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119} | {'precision': 0.8943985307621671, 'recall': 0.904363974001857, 'f1': 0.8993536472760849, 'number': 1077} | 0.8573 | 0.8922 | 0.8744 | 0.7945 | | 0.0048 | 94.74 | 1800 | 1.8664 | {'precision': 0.8589595375722543, 'recall': 0.9094247246022031, 'f1': 0.8834720570749108, 'number': 817} | {'precision': 0.6504854368932039, 'recall': 0.5630252100840336, 'f1': 0.6036036036036037, 'number': 119} | {'precision': 0.9003656307129799, 'recall': 0.914577530176416, 'f1': 0.9074159373560571, 'number': 1077} | 0.8705 | 0.8917 | 0.8810 | 0.7927 | | 0.0004 | 105.26 | 2000 | 1.8672 | {'precision': 0.8634772462077013, 'recall': 0.9057527539779682, 'f1': 0.8841099163679809, 'number': 817} | {'precision': 0.7093023255813954, 'recall': 0.5126050420168067, 'f1': 0.5951219512195123, 'number': 119} | {'precision': 0.8923076923076924, 'recall': 0.9155060352831941, 'f1': 0.9037580201649862, 'number': 1077} | 0.8726 | 0.8877 | 0.8801 | 0.7953 | | 0.0005 | 115.79 | 2200 | 1.8731 | {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817} | {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119} | {'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077} | 0.8792 | 0.8857 | 0.8825 | 0.7976 | | 0.0002 | 126.32 | 2400 | 1.9408 | {'precision': 0.8408071748878924, 'recall': 0.9179926560587516, 'f1': 0.8777062609713283, 'number': 817} | {'precision': 0.6310679611650486, 'recall': 0.5462184873949579, 'f1': 0.5855855855855856, 'number': 119} | {'precision': 0.9091760299625468, 'recall': 0.9015784586815228, 'f1': 0.9053613053613054, 'number': 1077} | 0.8657 | 0.8872 | 0.8763 | 0.7935 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

kejian/final-filter-again

kejian

2022-11-28T17:39:16Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-28T01:33:32Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-filter-again results: [] ---  # kejian/final-filter-again This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 64 - 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_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-filter-again', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/25z4zfy3

akmmsr/mt5-small-finetuned-amazon-en-es_akmmsr

akmmsr

2022-11-28T17:15:10Z

61

0

transformers

[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-28T16:23:28Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: akmmsr/mt5-small-finetuned-amazon-en-es_akmmsr results: [] ---  # akmmsr/mt5-small-finetuned-amazon-en-es_akmmsr This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0336 - Validation Loss: 3.3393 - Epoch: 7 ## 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, '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} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.6397 | 4.2364 | 0 | | 5.8621 | 3.7162 | 1 | | 5.0948 | 3.5552 | 2 | | 4.6724 | 3.4873 | 3 | | 4.4007 | 3.4245 | 4 | | 4.2162 | 3.3792 | 5 | | 4.0985 | 3.3499 | 6 | | 4.0336 | 3.3393 | 7 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

wa3dbk/whisper-small-ar

wa3dbk

2022-11-28T17:11:32Z

113

0

transformers

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

automatic-speech-recognition

2022-11-25T18:33:06Z

## whisper-small-ar This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset (language=Arabic).

antgrutta/sd-class-butterflies-32

antgrutta

2022-11-28T16:59:10Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T16:58:32Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(antgrutta/sd-class-butterflies-32) image = pipeline().images[0] image ```

EmnaBou/bert-finetuned-DT

EmnaBou

2022-11-28T16:49:12Z

123

0

transformers

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

token-classification

2022-11-28T15:20:02Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-DT results: [] ---  # bert-finetuned-DT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6697 - Precision: 0.2381 - Recall: 0.0321 - F1: 0.0565 - Accuracy: 0.8179 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 99 | 0.7505 | 0.0 | 0.0 | 0.0 | 0.8196 | | No log | 2.0 | 198 | 0.7033 | 0.0 | 0.0 | 0.0 | 0.8196 | | No log | 3.0 | 297 | 0.6697 | 0.2381 | 0.0321 | 0.0565 | 0.8179 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

luisgasco/distilbert-base-uncased-finetuned-emotion

luisgasco

2022-11-28T16:17:49Z

110

0

transformers

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

text-classification

2022-11-28T16:03:30Z

--- 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 args: default metrics: - name: Accuracy type: accuracy value: 0.892 - name: F1 type: f1 value: 0.8873822002431591 ---  # 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: 0.3693 - Accuracy: 0.892 - F1: 0.8874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5715 | 0.8275 | 0.8047 | | 0.7552 | 2.0 | 250 | 0.3693 | 0.892 | 0.8874 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

tomekkorbak/awesome_ride

tomekkorbak

2022-11-28T16:12:40Z

0

null

[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]

null

2022-11-28T16:12:19Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: awesome_ride results: [] ---  # awesome_ride This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'awesome_ride', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3m98rnwq

alexziweiwang/pure-start-epoch2

alexziweiwang

2022-11-28T16:08:48Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-28T15:52:06Z

--- tags: - generated_from_trainer model-index: - name: pure-start-epoch2 results: [] ---  # pure-start-epoch2 This model is a fine-tuned version of [alexziweiwang/pure-start-epoch1](https://huggingface.co/alexziweiwang/pure-start-epoch1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.7447 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Wer | Correct | Total | Strlen | |:-------------:|:-----:|:----:|:---------------:|:-----:|:---:|:-------:|:-----:|:------:| | No log | 0.01 | 2 | 20.4002 | 0.095 | 1.0 | 19 | 200 | 200 | | No log | 0.02 | 4 | 19.9080 | 0.095 | 1.0 | 19 | 200 | 200 | | No log | 0.03 | 6 | 19.4711 | 0.095 | 1.0 | 19 | 200 | 200 | | No log | 0.03 | 8 | 19.1535 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.04 | 10 | 18.6684 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.05 | 12 | 18.1640 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.06 | 14 | 17.6937 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.07 | 16 | 17.2710 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.08 | 18 | 16.8469 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.08 | 20 | 16.4418 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.09 | 22 | 16.0409 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.1 | 24 | 15.6677 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.11 | 26 | 15.3291 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.12 | 28 | 15.0097 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.13 | 30 | 14.6776 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.13 | 32 | 14.3788 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.14 | 34 | 14.0924 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.15 | 36 | 13.8133 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.16 | 38 | 13.5539 | 0.095 | 1.0 | 19 | 200 | 200 | | 34.4057 | 0.17 | 40 | 13.3095 | 0.095 | 1.0 | 19 | 200 | 200 | | 34.4057 | 0.18 | 42 | 13.0804 | 0.095 | 1.0 | 19 | 200 | 200 | | 34.4057 | 0.19 | 44 | 12.8580 | 0.105 | 1.0 | 21 | 200 | 200 | | 34.4057 | 0.19 | 46 | 12.6532 | 0.115 | 1.0 | 23 | 200 | 200 | | 34.4057 | 0.2 | 48 | 12.4532 | 0.13 | 1.0 | 26 | 200 | 200 | | 33.2759 | 0.21 | 50 | 12.2452 | 0.14 | 1.0 | 28 | 200 | 200 | | 33.2759 | 0.22 | 52 | 12.0666 | 0.13 | 1.0 | 26 | 200 | 200 | | 33.2759 | 0.23 | 54 | 11.8976 | 0.165 | 1.0 | 33 | 200 | 200 | | 33.2759 | 0.24 | 56 | 11.7373 | 0.175 | 1.0 | 35 | 200 | 200 | | 33.2759 | 0.24 | 58 | 11.5933 | 0.17 | 1.0 | 34 | 200 | 200 | | 29.8129 | 0.25 | 60 | 11.4281 | 0.15 | 1.0 | 30 | 200 | 200 | | 29.8129 | 0.26 | 62 | 11.2665 | 0.14 | 1.0 | 28 | 200 | 200 | | 29.8129 | 0.27 | 64 | 11.1158 | 0.145 | 1.0 | 29 | 200 | 200 | | 29.8129 | 0.28 | 66 | 10.9840 | 0.135 | 1.0 | 27 | 200 | 200 | | 29.8129 | 0.29 | 68 | 10.8502 | 0.15 | 1.0 | 30 | 200 | 200 | | 38.792 | 0.3 | 70 | 10.7341 | 0.15 | 1.0 | 30 | 200 | 200 | | 38.792 | 0.3 | 72 | 10.6082 | 0.165 | 1.0 | 33 | 200 | 200 | | 38.792 | 0.31 | 74 | 10.4944 | 0.18 | 1.0 | 36 | 200 | 200 | | 38.792 | 0.32 | 76 | 10.3818 | 0.21 | 1.0 | 42 | 200 | 200 | | 38.792 | 0.33 | 78 | 10.2719 | 0.235 | 1.0 | 47 | 200 | 200 | | 28.0092 | 0.34 | 80 | 10.1636 | 0.235 | 1.0 | 47 | 200 | 200 | | 28.0092 | 0.35 | 82 | 10.0709 | 0.24 | 1.0 | 48 | 200 | 200 | | 28.0092 | 0.35 | 84 | 9.9797 | 0.24 | 1.0 | 48 | 200 | 200 | | 28.0092 | 0.36 | 86 | 9.8958 | 0.24 | 1.0 | 48 | 200 | 200 | | 28.0092 | 0.37 | 88 | 9.7977 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.38 | 90 | 9.7015 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.39 | 92 | 9.6150 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.4 | 94 | 9.5304 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.4 | 96 | 9.4521 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.41 | 98 | 9.3832 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.42 | 100 | 9.3148 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.43 | 102 | 9.2563 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.44 | 104 | 9.1944 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.45 | 106 | 9.1323 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.46 | 108 | 9.0717 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.46 | 110 | 9.0245 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.47 | 112 | 8.9772 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.48 | 114 | 8.9390 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.49 | 116 | 8.9013 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.5 | 118 | 8.8605 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.51 | 120 | 8.8126 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.51 | 122 | 8.7503 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.52 | 124 | 8.6921 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.53 | 126 | 8.6378 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.54 | 128 | 8.5927 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.55 | 130 | 8.5520 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.56 | 132 | 8.5126 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.56 | 134 | 8.4743 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.57 | 136 | 8.4369 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.58 | 138 | 8.3993 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.59 | 140 | 8.3636 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.6 | 142 | 8.3311 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.61 | 144 | 8.2983 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.62 | 146 | 8.2652 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.62 | 148 | 8.2345 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.63 | 150 | 8.2064 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.64 | 152 | 8.1818 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.65 | 154 | 8.1603 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.66 | 156 | 8.1403 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.67 | 158 | 8.1180 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.67 | 160 | 8.0997 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.68 | 162 | 8.0791 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.69 | 164 | 8.0563 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.7 | 166 | 8.0342 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.71 | 168 | 8.0130 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.72 | 170 | 7.9936 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.72 | 172 | 7.9756 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.73 | 174 | 7.9594 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.74 | 176 | 7.9439 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.75 | 178 | 7.9298 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.76 | 180 | 7.9157 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.77 | 182 | 7.9021 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.78 | 184 | 7.8899 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.78 | 186 | 7.8796 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.79 | 188 | 7.8697 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.8 | 190 | 7.8598 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.81 | 192 | 7.8490 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.82 | 194 | 7.8390 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.83 | 196 | 7.8293 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.83 | 198 | 7.8211 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.84 | 200 | 7.8135 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.85 | 202 | 7.8064 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.86 | 204 | 7.7991 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.87 | 206 | 7.7924 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.88 | 208 | 7.7862 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.89 | 210 | 7.7803 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.89 | 212 | 7.7749 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.9 | 214 | 7.7701 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.91 | 216 | 7.7657 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.92 | 218 | 7.7628 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.93 | 220 | 7.7595 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.94 | 222 | 7.7567 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.94 | 224 | 7.7541 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.95 | 226 | 7.7518 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.96 | 228 | 7.7497 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.97 | 230 | 7.7479 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.98 | 232 | 7.7463 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.99 | 234 | 7.7453 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.99 | 236 | 7.7447 | 0.24 | 1.0 | 48 | 200 | 200 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

SYH99999/autotrain-translator-2261971987

SYH99999

2022-11-28T15:30:31Z

104

0

transformers

[ "transformers", "pytorch", "autotrain", "translation", "ja", "en", "dataset:SYH99999/autotrain-data-translator-3c03831c-5fcf2e86-839aa322-a7658498-cb30b55a-eefc0458", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

translation

2022-11-28T11:53:31Z

--- tags: - autotrain - translation language: - ja - en datasets: - SYH99999/autotrain-data-translator-3c03831c-5fcf2e86-839aa322-a7658498-cb30b55a-eefc0458 co2_eq_emissions: emissions: 234.5986254372695 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 2261971987 - CO2 Emissions (in grams): 234.5986 ## Validation Metrics - Loss: 4.237 - SacreBLEU: 0.697 - Gen len: 256.387

fathyshalab/all-roberta-large-v1-banking-2-2-1

fathyshalab

2022-11-28T15:28:40Z

105

0

transformers

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

text-classification

2022-11-28T15:27:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-2-2-1 results: [] ---  # all-roberta-large-v1-banking-2-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6817 - Accuracy: 0.1022 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.653 | 1.0 | 5 | 2.6817 | 0.1022 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

ConvLab/ddpt-policy-sgd_0.01multiwoz21

ConvLab

2022-11-28T15:24:29Z

0

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]

null

2022-11-28T15:21:11Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd_0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) and afterwards on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

ConvLab/ddpt-policy-0.01multiwoz21

ConvLab

2022-11-28T15:20:35Z

0

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]

null

2022-11-28T15:18:28Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

fathyshalab/all-roberta-large-v1-banking-1-2-1

fathyshalab

2022-11-28T15:12:05Z

105

0

transformers

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

text-classification

2022-11-28T15:10:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1-2-1 results: [] ---  # all-roberta-large-v1-banking-1-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6235 - Accuracy: 0.2578 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6542 | 1.0 | 3 | 2.6235 | 0.2578 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

ConvLab/mle-policy-multiwoz21

ConvLab

2022-11-28T15:11:19Z

0

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/multiwoz21", "license:apache-2.0", "region:us" ]

null

2022-11-28T15:07:50Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/multiwoz21 --- # mle-policy-multiwoz21 This is a MLE model trained on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - seed: 0 - optimizer: Adam - num_epochs: 24 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

ConvLab/ddpt-policy-sgd

ConvLab

2022-11-28T15:01:15Z

0

1

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]

null

2022-11-28T13:21:09Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 1 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

regel-corpus/hunflair-tfbs

regel-corpus

2022-11-28T14:37:52Z

3

0

flair

[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]

token-classification

2022-03-29T11:26:41Z

--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "It contains a functional GCGGCGGCG Egr-1-binding site" --- ## HunFlair model for Transcription Factor Binding Site (TFBS) [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for TFBS entity. Predicts 1 tag: | **tag** | **meaning** | |---------------------------------|-----------| | Tfbs | DNA region bound by transcription factor | --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-tfbs") text = "We found that Egr-1 specifically binds to the PTEN 5' untranslated region, which contains a functional GCGGCGGCG Egr-1-binding site." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [19,20,21]: "GCGGCGGCG Egr-1-binding site" [− Labels: Tfbs (0.9631)] ``` So, the entity "*GCGGCGGCG Egr-1-binding site*" is found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ``` ---

regel-corpus/hunflair-enhancer

regel-corpus

2022-11-28T14:37:03Z

4

0

flair

[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]

token-classification

2022-03-29T09:09:18Z

--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "Isolate an enhancer element located between -89 and -50 bp in PAI-1" --- ## HunFlair model for ENHANCER [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for enhancer entity. Predicts 1 tag: | **tag** | **meaning** | |---------------------------------|-----------| | Enhancer | DNA enhancer region | --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-promoter") text = "An upstream activator of the mitogen-activated protein (MAP) kinase pathways was used to isolate an enhancer element located between -89 and -50 bp in PAI-1 promoter that was activated by MEKK-1." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [18,19,20,21,22,23,24,25,26,27,28,29,30]: "enhancer element located between - 89 and - 50 bp in PAI-1 promoter" [− Labels: Enhancer (0.992)] ``` So, the entity "*enhancer element located between - 89 and - 50 bp in PAI-1*" (labeled as a **enhancer**) is found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ``` ---

regel-corpus/hunflair-promoter

regel-corpus

2022-11-28T14:36:20Z

7

0

flair

[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]

token-classification

2022-03-29T11:22:27Z

--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "Two putative extended promoters consensus sequences (p1 and p2)." --- ## HunFlair model for PROMOTER [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for promoter entity. Predicts 1 tag: | **tag** | **meaning** | |---------------------------------|-----------| | Promoter | DNA promoter region | --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-promoter") text = "The upstream region of the glnA gene contained two putative extended promoter consensus sequences (p1 and p2)." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [16]: "p1" [− Labels: Promoter (0.9878)] Span [18]: "p2" [− Labels: Promoter (0.9216)] ``` So, the entities "*p1*" and "*p2*" (labeled as a **promoter**) are found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ```

fathyshalab/all-roberta-large-v1-banking-1

fathyshalab

2022-11-28T14:25:57Z

104

0

transformers

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

text-classification

2022-11-28T14:24:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1 results: [] ---  # all-roberta-large-v1-banking-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6515 - Accuracy: 0.1644 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.5795 | 1.0 | 3 | 2.6515 | 0.1644 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

fathyshalab/bert-uncased-massive-intent-classification-banking-1

fathyshalab

2022-11-28T14:15:57Z

103

0

transformers

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

text-classification

2022-11-28T14:11:34Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification-banking-1 results: [] ---  # bert-uncased-massive-intent-classification-banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7010 - Accuracy: 0.1289 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6675 | 1.0 | 3 | 2.7010 | 0.1289 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

Fabiuas/test

Fabiuas

2022-11-28T14:01:56Z

187

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-28T13:42:00Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: test results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # test Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### cat ![cat](images/cat.jpg) #### dog ![dog](images/dog.jpg)

fathyshalab/bert-uncased-massive-intent-classification_banking-1

fathyshalab

2022-11-28T13:48:29Z

103

0

transformers

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

text-classification

2022-11-28T13:40:09Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification_banking-1 results: [] ---  # bert-uncased-massive-intent-classification_banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6770 - Accuracy: 0.1378 ## 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: 6 - eval_batch_size: 6 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8977 | 1.0 | 3 | 2.7353 | 0.0622 | | 2.5889 | 2.0 | 6 | 2.7109 | 0.0933 | | 2.4362 | 3.0 | 9 | 2.6940 | 0.1111 | | 2.3175 | 4.0 | 12 | 2.6817 | 0.1333 | | 2.2524 | 5.0 | 15 | 2.6770 | 0.1378 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

fathyshalab/bert-uncased-massive-intent-classification-finetuned-banking

fathyshalab

2022-11-28T12:54:50Z

104

0

transformers

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

text-classification

2022-11-28T11:50:48Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification-finetuned-banking results: [] ---  # bert-uncased-massive-intent-classification-finetuned-banking This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5965 - Accuracy: 0.12 ## 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: 6 - eval_batch_size: 6 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.731 | 1.0 | 3 | 2.6423 | 0.1067 | | 2.4424 | 2.0 | 6 | 2.6178 | 0.1067 | | 2.2005 | 3.0 | 9 | 2.6028 | 0.1111 | | 2.1954 | 4.0 | 12 | 2.5965 | 0.12 | | 2.0599 | 5.0 | 15 | 2.5935 | 0.12 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

minhtoan/t5-small-vietnamese-news

minhtoan

2022-11-28T12:52:14Z

122

4

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "summarization", "vi", "dataset:Wikilingua", "dataset:Vietnews", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-11-24T08:01:28Z

--- language: vi datasets: - Wikilingua - Vietnews tags: - summarization license: mit widget: - text: 'VKS cáo buộc ông Nguyễn Thế Hiệp có sai phạm trong vụ cháy gần Bệnh viện Nhi trung ương khiến 2 người chết, thiệt hại 1,9 tỷ đồng song bị cáo khẳng định vô tội. Mức án đề nghị 9-10 năm tù với bị cáo 73 tuổi được đại diện VKSND quận Ba Đình đưa ra chiều 28/11, quy buộc phạm tội Vi phạm quy định về phòng cháy chữa cháy, theo Điều 313 Bộ luật Hình sự. VKS nhận định ông Hiệp có lỗi trong việc vận hành nhà trọ không phép, không đủ điều kiện an toàn phòng cháy chữa cháy, gây thiệt hại về tài sản và khiến hai người chết. Tuy nhiên, bị cáo chưa bồi thường. Bản luận tội nêu, tại phiên tòa hôm nay ông Hiệp "chưa tỏ thái độ ăn năn hối hận, có nhân thân đặc biệt xấu". Từ hàng chục năm trước, ông từng 11 lần bị lập danh chỉ bản về hành vi trộm cắp, năm 1985 lại nhận 18 năm tù về các tội cướp tài sản, hiếp dâm, đưa hối lộ...' inference: parameters: max_length: 150 --- # Text summarization for Vietnamese Language State-of-the-art lightweights pretrained Transformer-based encoder-decoder model for Vietnamese. Model trained on dataset Vietnamese News with input length = 512, output length = 150 ## How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Example test data on VNExpress: https://vnexpress.net/ong-hiep-khung-khong-nhan-toi-trong-vu-chay-gan-benh-vien-nhi-4541483.html tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-small-vietnamese-news") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-small-vietnamese-news") model.cuda() src = 'VKS cáo buộc ông Nguyễn Thế Hiệp có sai phạm trong vụ cháy gần Bệnh viện Nhi trung ương khiến 2 người chết, thiệt hại 1,9 tỷ đồng song bị cáo khẳng định vô tội. Mức án đề nghị 9-10 năm tù với bị cáo 73 tuổi được đại diện VKSND quận Ba Đình đưa ra chiều 28/11, quy buộc phạm tội Vi phạm quy định về phòng cháy chữa cháy, theo Điều 313 Bộ luật Hình sự. VKS nhận định ông Hiệp có lỗi trong việc vận hành nhà trọ không phép, không đủ điều kiện an toàn phòng cháy chữa cháy, gây thiệt hại về tài sản và khiến hai người chết. Tuy nhiên, bị cáo chưa bồi thường. Bản luận tội nêu, tại phiên tòa hôm nay ông Hiệp "chưa tỏ thái độ ăn năn hối hận, có nhân thân đặc biệt xấu". Từ hàng chục năm trước, ông từng 11 lần bị lập danh chỉ bản về hành vi trộm cắp, năm 1985 lại nhận 18 năm tù về các tội cướp tài sản, hiếp dâm, đưa hối lộ...' tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda() model.eval() summary_ids = model.generate(tokenized_text, max_length=150) output = tokenizer.decode(summary_ids[0], skip_special_tokens=True) output ``` ## Author ` Phan Minh Toan `

team-nave/distilbert-base-uncased-distilled-clinc

team-nave

2022-11-28T12:14:29Z

108

0

transformers

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

text-classification

2022-11-28T12:06:14Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9367741935483871 ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.4175 - Accuracy: 0.9368 ## 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: 96 - eval_batch_size: 96 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 159 | 3.3516 | 0.6652 | | 3.4274 | 2.0 | 318 | 2.2866 | 0.7848 | | 3.4274 | 3.0 | 477 | 1.5064 | 0.8545 | | 1.6307 | 4.0 | 636 | 1.0204 | 0.8971 | | 1.6307 | 5.0 | 795 | 0.7421 | 0.9177 | | 0.7641 | 6.0 | 954 | 0.5838 | 0.9258 | | 0.7641 | 7.0 | 1113 | 0.4986 | 0.9306 | | 0.4482 | 8.0 | 1272 | 0.4489 | 0.9365 | | 0.4482 | 9.0 | 1431 | 0.4258 | 0.9368 | | 0.3442 | 10.0 | 1590 | 0.4175 | 0.9368 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3

tomekkorbak/zealous_almeida

tomekkorbak

2022-11-28T12:04:20Z

0

null

[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]

null

2022-11-28T12:04:13Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: zealous_almeida results: [] ---  # zealous_almeida This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'zealous_almeida', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/llhbsik2

cardiffnlp/twitter-roberta-base-offensive

cardiffnlp

2022-11-28T11:36:23Z

35,866

27

transformers

[ "transformers", "pytorch", "tf", "jax", "roberta", "text-classification", "arxiv:2010.12421", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

# Twitter-roBERTa-base for Offensive Language Identification This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark. - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='offensive' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) not-offensive 0.9073 2) offensive 0.0927 ```

biu-nlp/f-coref

biu-nlp

2022-11-28T11:35:52Z

88,201

18

transformers

[ "transformers", "pytorch", "roberta", "fast", "coreference-resolution", "en", "dataset:multi_news", "dataset:ontonotes", "arxiv:2209.04280", "arxiv:2205.12644", "arxiv:1907.10529", "arxiv:2101.00434", "arxiv:2109.04127", "license:mit", "model-index", "endpoints_compatible", "region:us" ]

null

2022-08-19T12:01:10Z

--- language: - en tags: - fast - coreference-resolution license: mit datasets: - multi_news - ontonotes metrics: - CoNLL task_categories: - coreference-resolution model-index: - name: biu-nlp/f-coref results: - task: type: coreference-resolution name: coreference-resolution dataset: name: ontonotes type: coreference metrics: - name: Avg. F1 type: CoNLL value: 78.5 --- ## F-Coref: Fast, Accurate and Easy to Use Coreference Resolution [F-Coref](https://arxiv.org/abs/2209.04280) allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the [LingMess](https://arxiv.org/abs/2205.12644) model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy. The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover Please check the [official repository](https://github.com/shon-otmazgin/fastcoref) for more details and updates. #### Experiments | Model | Runtime | Memory | |-----------------------|---------|---------| | [Joshi et al. (2020)](https://arxiv.org/abs/1907.10529) | 12:06 | 27.4 | | [Otmazgin et al. (2022)](https://arxiv.org/abs/2205.12644) | 06:43 | 4.6 | | + Batching | 06:00 | 6.6 | | [Kirstain et al. (2021)](https://arxiv.org/abs/2101.00434) | 04:37 | 4.4 | | [Dobrovolskii (2021)](https://arxiv.org/abs/2109.04127) | 03:49 | 3.5 | | [F-Coref](https://arxiv.org/abs/2209.04280) | 00:45 | 3.3 | | + Batching | 00:35 | 4.5 | | + Leftovers batching | 00:25 | 4.0 | The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2. ### Citation ``` @inproceedings{Otmazgin2022FcorefFA, title={F-coref: Fast, Accurate and Easy to Use Coreference Resolution}, author={Shon Otmazgin and Arie Cattan and Yoav Goldberg}, booktitle={AACL}, year={2022} } ``` [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://aclanthology.org/2022.aacl-demo.6) (Otmazgin et al., AACL-IJCNLP 2022)

projecte-aina/roberta-base-ca-v2-cased-wikicat-ca

projecte-aina

2022-11-28T11:03:27Z

106

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "catalan", "text classification", "WikiCAT_ca", "CaText", "Catalan Textual Corpus", "ca", "dataset:projecte-aina/WikiCAT_ca", "arxiv:1907.11692", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-11T12:00:58Z

--- language: - ca license: apache-2.0 tags: - "catalan" - "text classification" - "WikiCAT_ca" - "CaText" - "Catalan Textual Corpus" datasets: - "projecte-aina/WikiCAT_ca" metrics: - f1 model-index: - name: roberta-base-ca-v2-cased-wikicat-ca results: - task: type: text-classification dataset: type: projecte-aina/WikiCAT_ca name: WikiCAT_ca metrics: - name: F1 type: f1 value: 77.823 widget: - text: "La ressonància magnètica és una prova diagnòstica clau per a moltes malalties." - text: "Les tres idees bàsiques del noümen són l'ànima, el món i Déu, i és una continuació de les tres substàncies de Descartes (tot i que el francès anomenava jo o ment l'ànima)." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Viquipedia-based Text Classification. ## Table of Contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-uses-and-limitations) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Disclaimer](#disclaimer) </details> ## Model description The **roberta-base-ca-v2-cased-wikicat-ca** is a Text Classification model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). Dataset used is https://huggingface.co/datasets/projecte-aina/WikiCAT_ca, automatically created from Wikipedia and Wikidata sources ## Intended uses and limitations **roberta-base-ca-v2-cased-wikicat-ca** model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="roberta-base-ca-v2-cased-wikicat-ca") example = "La ressonància magnètica és una prova diagnòstica clau per a moltes malalties." tc_results = nlp(example) pprint(tc_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data We used the TC dataset in Catalan called [WikiCAT_ca](https://huggingface.co/datasets/projecte-aina/WikiCAT_ca) for training and evaluation. ### Training procedure The model was trained with a batch size of 16 and three learning rates (1e-5, 3e-5, 5e-5) for 10 epochs. We then selected the best learning rate (3e-5) and checkpoint (epoch 3, step 1857) using the downstream task metric in the corresponding development set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 (weighted) score. ### Evaluation results We evaluated the _roberta-base-ca-v2-cased-wikicat-ca_ on the WikiCAT_ca dev set: | Model | WikiCAT_ca (F1)| | ------------|:-------------| | roberta-base-ca-v2-cased-wikicat-ca | 77.823 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

projecte-aina/roberta-base-ca-v2-cased-tc

projecte-aina

2022-11-28T11:02:09Z

110

1

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "catalan", "text classification", "tecla", "CaText", "Catalan Textual Corpus", "ca", "dataset:projecte-aina/tecla", "arxiv:1907.11692", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-30T07:55:23Z

--- language: - ca tags: - "catalan" - "text classification" - "tecla" - "CaText" - "Catalan Textual Corpus" datasets: - "projecte-aina/tecla" metrics: - accuracy model-index: - name: roberta-base-ca-v2-cased-tc results: - task: type: text-classification dataset: name: TeCla type: projecte-aina/tecla metrics: - name: Accuracy type: accuracy value: 0.8034 widget: - text: "Els Pets presenten el seu nou treball al Palau Sant Jordi." - text: "Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la pandèmia." - text: "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." - text: "Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la vacuna covid els propers dies." - text: "Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification. ## Table of Contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Tokenization](#tokenization) - [Hyperparameters](#hyperparameters) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description The **roberta-base-ca-v2-cased-tc** is a Text Classification (TC) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). The previous version of this model, which was trained on the old TeCla dataset (v1), can still be accessed through the "v1" tag. ## Intended uses and limitations **roberta-base-ca-v2-cased-tc** model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-tc") example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." tc_results = nlp(example) pprint(tc_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data We used the TC dataset in Catalan called [TeCla](https://huggingface.co/datasets/projecte-aina/tecla) for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training. ### Training procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 (weighted). ## Evaluation results We evaluated the _roberta-base-ca-v2-cased-tc_ on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1'). | Model | TeCla - label1 (Accuracy) | TeCla - label2 (Accuracy) | | ------------|:-------------|:-------------| | roberta-base-ca-v2 | 96.31 | 80.34 | | roberta-large-ca-v2 | **96.51** | **80.68** | | mBERT | 95.72 | 78.47 | | XLM-RoBERTa | 95.66 | 78.01 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Citation Information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` ### Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

JapaNLP/t5-efficient-xl-nl6-japanese

JapaNLP

2022-11-28T10:09:07Z

104

0

transformers

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

text2text-generation

2022-11-28T09:58:01Z

--- license: afl-3.0 --- # Overview `t5-efficient-xl-nl6-ja` is a Japanese version of [`google/t5-efficient-xl-nl6`](https://huggingface.co/google/t5-efficient-xl-nl6). # Results - Under construction - If you get some experimental results of this model on downstream tasks, please feel free to make Pull Requests. ## Question Answering ## Others # Acknowledgement Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)

mn367/radio-mlm

mn367

2022-11-28T09:52:57Z

61

0

transformers

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

fill-mask

2022-11-28T09:42:20Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mn367/radio-mlm results: [] ---  # mn367/radio-mlm 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: 4.6630 - Validation Loss: 4.6014 - 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': 39000, '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 | |:----------:|:---------------:|:-----:| | 4.6630 | 4.6014 | 0 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

pkachhad/t5-small-finetuned-parth

pkachhad

2022-11-28T09:19:48Z

105

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

2022-11-28T07:51:00Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-parth results: [] ---  # t5-small-finetuned-parth This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9468 - Rouge1: 26.5826 - Rouge2: 21.7867 - Rougel: 25.1629 - Rougelsum: 26.2364 - Gen Len: 16.9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 4 | 3.3692 | 25.2983 | 20.639 | 24.0087 | 25.0732 | 16.2 | | No log | 2.0 | 8 | 3.1818 | 25.4926 | 20.9783 | 24.0651 | 25.2635 | 16.3 | | No log | 3.0 | 12 | 3.0498 | 26.2652 | 21.5076 | 24.8077 | 25.9478 | 16.65 | | No log | 4.0 | 16 | 2.9742 | 26.5826 | 21.7867 | 25.1629 | 26.2364 | 16.9 | | No log | 5.0 | 20 | 2.9468 | 26.5826 | 21.7867 | 25.1629 | 26.2364 | 16.9 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

huggingtweets/bobkerns

huggingtweets

2022-11-28T08:14:20Z

115

0

transformers

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

text-generation

2022-11-28T08:14:12Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/3653376550/f40f9602f2e8e185eb7ddce332157ffe_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bob (Moderna #5) Kerns</div> <div style="text-align: center; font-size: 14px;">@bobkerns</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Bob (Moderna #5) Kerns. | Data | Bob (Moderna #5) Kerns | | --- | --- | | Tweets downloaded | 3234 | | Retweets | 315 | | Short tweets | 42 | | Tweets kept | 2877 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/390ksfue/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bobkerns's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/bobkerns') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

pere/whisper-NST2-unfreeze-constanti-low-lr

pere

2022-11-28T07:41:42Z

9

0

transformers

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

automatic-speech-recognition

2022-11-23T10:34:53Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-NST2-unfreeze-constanti-low-lr results: [] ---  # whisper-NST2-unfreeze-constanti-low-lr This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3562 - Wer: 8.5519 ## 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: 96 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1901 | 0.05 | 1000 | 0.3069 | 14.8233 | | 0.1323 | 0.1 | 2000 | 0.2687 | 11.2885 | | 0.1137 | 0.15 | 3000 | 0.2620 | 10.8324 | | 0.1022 | 0.2 | 4000 | 0.2976 | 9.0080 | | 0.0937 | 0.25 | 5000 | 0.2584 | 9.5781 | | 0.0875 | 0.3 | 6000 | 0.2704 | 20.2965 | | 0.0592 | 1.05 | 7000 | 0.2751 | 9.0080 | | 0.0488 | 1.1 | 8000 | 0.2778 | 8.6659 | | 0.0475 | 1.15 | 9000 | 0.2792 | 9.4641 | | 0.0439 | 1.2 | 10000 | 0.2880 | 8.3238 | | 0.0425 | 1.25 | 11000 | 0.2954 | 8.5519 | | 0.0416 | 1.3 | 12000 | 0.2896 | 20.2965 | | 0.0289 | 2.05 | 13000 | 0.2990 | 7.9818 | | 0.0229 | 2.1 | 14000 | 0.3027 | 7.4116 | | 0.0248 | 2.15 | 15000 | 0.2968 | 8.6659 | | 0.0225 | 2.2 | 16000 | 0.3100 | 8.5519 | | 0.0222 | 2.25 | 17000 | 0.3132 | 9.3501 | | 0.0219 | 2.3 | 18000 | 0.3230 | 7.6397 | | 0.0162 | 3.04 | 19000 | 0.3380 | 9.8062 | | 0.0132 | 3.09 | 20000 | 0.3562 | 8.5519 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1

linfuyou/bert-squad-training

linfuyou

2022-11-28T07:41:14Z

117

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "endpoints_compatible", "region:us" ]

question-answering

2022-11-15T09:15:55Z

bert-base-cased-squadv1.1-training

Shubham09/whispertestlocal

Shubham09

2022-11-28T06:40:40Z

77

0

transformers

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

automatic-speech-recognition

2022-11-25T09:13:41Z

--- tags: - generated_from_trainer metrics: - wer model-index: - name: whispertestlocal results: [] ---  # whispertestlocal This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4481 - Wer: 46.1754 ## 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: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1886 | 1.12 | 100 | 0.4481 | 46.1754 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2

amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2

amagzari

2022-11-28T05:20:40Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-28T03:55:08Z

--- tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: pegasus-cnn_dailymail-finetuned-samsum-v2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 45.3045 ---  # pegasus-cnn_dailymail-finetuned-samsum-v2 This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.5218 - Rouge1: 45.3045 - Rouge2: 21.7601 - Rougel: 35.8643 - Rougelsum: 41.6595 - Gen Len: 35.4425 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6997 | 1.0 | 1841 | 1.5218 | 45.3045 | 21.7601 | 35.8643 | 41.6595 | 35.4425 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

inkoziev/sbert_pq

inkoziev

2022-11-28T04:45:46Z

309

16

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "ru", "license:unlicense", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-17T13:27:40Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: ru license: unlicense widget: - source_sentence: "Кошка ловит мышку." sentences: ["Кто ловит мышку?", "Где живет кошка?", "Как мышку зовут?"] --- # SBERT_PQ Это [sentence-transformers](https://www.SBERT.net) модель, предназначенная для определения релевантности короткого текста (преимущественно одно предложение длиной до 10-15 слов) и вопроса. Модель вычисляет для текста и вопроса векторы размерностью 312. Косинус угла между этими векторами дает оценку того, содержит ли текст ответ на заданный вопрос. В [проекте диалоговой системы](https://github.com/Koziev/chatbot) она используется для семантического поиска записей в базе фактов по заданному собеседником вопросу. # Скорость и точность Модель основана на [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2). Она имеет очень небольшой размер и быстро выполняет инференс даже на CPU. Максимальное значение метрики cossim_f1 на тестовой выборке (10% датасета) равно **0.986**. При использовании модели sberbank-ai/ruBert-base в качестве базовой, максимум cossim_f1 составляет **0.992**. ## Использование с библиотекой (Sentence-Transformers) Необходимо установить [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` Чтобы определить релевантность в одной паре "текст-вопрос", можно использовать такой код: ``` import sentence_transformers sentences = ["Кошка ловит мышку.", "Чем занята кошка?"] model = sentence_transformers.SentenceTransformer('inkoziev/sbert_pq') embeddings = model.encode(sentences) s = sentence_transformers.util.cos_sim(a=embeddings[0], b=embeddings[1]) print('text={} question={} cossim={}'.format(sentences[0], sentences[1], s)) ``` ## Контакты и цитирование ``` @MISC{rugpt_chitchat, author = {Ilya Koziev}, title = {Texts & Questions Relevancy Model}, url = {https://huggingface.co/inkoziev/sbert_pq}, year = 2022 } ```

cavitcakir/swin-tiny-patch4-window7-224-finetuned-eurosat

cavitcakir

2022-11-28T04:30:00Z

206

0

transformers

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

image-classification

2022-11-28T04:24:46Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: [] ---  # swin-tiny-patch4-window7-224-finetuned-eurosat 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.5373 - Accuracy: 0.7639 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6855 | 0.98 | 10 | 0.6436 | 0.625 | | 0.6499 | 1.98 | 20 | 0.5745 | 0.7083 | | 0.6021 | 2.98 | 30 | 0.5373 | 0.7639 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

speedrunner/atitanstrawberry

speedrunner

2022-11-28T03:30:00Z

0

4

null

[ "region:us" ]

null

2022-11-28T00:55:15Z

not my work - all credit to original author!

thisisHJLee/wav2vec2-large-xls-r-1b-korean-sample2

thisisHJLee

2022-11-28T02:25:48Z

18

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-25T04:56:38Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-1b-korean-sample2 results: [] ---  # wav2vec2-large-xls-r-1b-korean-sample2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1283 - Cer: 0.0294 ## 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: 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_steps: 1000 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3415 | 1.0 | 11471 | 0.2666 | 0.0750 | | 0.1997 | 2.0 | 22942 | 0.1617 | 0.0415 | | 0.1153 | 3.0 | 34413 | 0.1283 | 0.0294 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.11.0

minimaxir/midjourney_sd_2_0

minimaxir

2022-11-28T02:13:38Z

0

12

null

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

null

2022-11-28T02:04:00Z

--- license: mit --- ### Midjourney Style for Stable Diffusion 2.0 A textual inversion embedding for the `<midjourney>` token, adapted for Stable Diffusion 2.0 from [sd-concepts-library/midjourney-style](https://huggingface.co/sd-concepts-library/midjourney-style). It's recommended to use the following as an addition to a prompt: ```txt in the style of <midjourney> ```

huggingtweets/tarunchitra

huggingtweets

2022-11-28T02:11:02Z

115

0

transformers

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

text-generation

2022-11-28T02:09:42Z

--- language: en thumbnail: http://www.huggingtweets.com/tarunchitra/1669601459083/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1587539091444432897/Z6_nmrCB_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tarun Chitra</div> <div style="text-align: center; font-size: 14px;">@tarunchitra</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tarun Chitra. | Data | Tarun Chitra | | --- | --- | | Tweets downloaded | 3234 | | Retweets | 439 | | Short tweets | 362 | | Tweets kept | 2433 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ex37piz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tarunchitra's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12p1kbwc) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12p1kbwc/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/tarunchitra') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

lewispons/large-email-classifier

lewispons

2022-11-28T01:56:52Z

2

1

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-26T22:47:23Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{lewispons/large-email-classifier}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 752 with parameters: ``` {'batch_size': 50, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 2256, "warmup_steps": 226, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors

fanpu/final_model_output_subreddit-wallstreetbets_3

fanpu

2022-11-28T01:42:49Z

117

0

transformers

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

text-generation

2022-11-27T19:02:43Z

--- tags: - generated_from_trainer model-index: - name: final_model_output_subreddit-wallstreetbets_3 results: [] ---  # final_model_output_subreddit-wallstreetbets_3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6824 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2588 | 1.25 | 5000 | 3.6824 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

erkanxyzalaca/turkishReviews-ds-mini

erkanxyzalaca

2022-11-28T01:38:07Z

61

0

transformers

[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-11-27T22:00:36Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: turkishReviews-ds-mini results: [] ---  # turkishReviews-ds-mini This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.3867 - Validation Loss: 8.3741 - Epoch: 2 ## 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': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -765, '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 | |:----------:|:---------------:|:-----:| | 10.2149 | 9.6891 | 0 | | 9.0695 | 8.7610 | 1 | | 8.3867 | 8.3741 | 2 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.10.1 - Datasets 2.7.1 - Tokenizers 0.13.2

ohrenn/lorepass

ohrenn

2022-11-28T00:28:39Z

0

null

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

null

2022-11-28T00:28:39Z

--- license: bigscience-bloom-rail-1.0 ---

Tara2301/PPO-LunarLander-v22

Tara2301

2022-11-27T23:31:17Z

0

stable-baselines3

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

reinforcement-learning

2022-11-27T22:02:04Z

--- 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: 253.24 +/- 19.64 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 ... ```

wnordmann/klaus_weights

wnordmann

2022-11-27T23:23:06Z

0

null

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

null

2022-11-27T18:39:46Z

--- license: openrail --- # Klaus the Cat This is trained on 30+ picture of my Cat Klaus. Klaus has Manx Syndrom which means he has no tail and limited feeling in his legs. He's a super cute yellow kitten my family loves ## Prompt `nord klaus`

jasoneden/BLOOM-560-QuestionAnswering-CDC-Covid19-Tuned

jasoneden

2022-11-27T23:16:23Z

48

1

transformers

[ "transformers", "pytorch", "bloom", "question-answering", "generated_from_trainer", "dataset:dataset", "license:bigscience-bloom-rail-1.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2022-11-24T04:56:03Z

--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer datasets: - dataset model-index: - name: cdcmodel_train02 results: [] ---  # cdcmodel_train02 This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the dataset dataset. Currently will not load. ## 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: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

Ueumol/Utapri_Style

Ueumol

2022-11-27T22:09:28Z

0

null

[ "region:us" ]

null

2022-11-27T21:19:16Z

Need to use prompt - Utapristyle

cmudrc/wave-energy-analysis

cmudrc

2022-11-27T22:08:42Z

12

1

tf-keras

[ "tf-keras", "mechanical-engineering", "simulation", "hydrodynamics", "en", "dataset:cmudrc/wave-energy", "license:mit", "region:us" ]

null

2022-11-27T04:33:25Z

--- license: mit language: en datasets: - cmudrc/wave-energy tags: - mechanical-engineering - simulation - hydrodynamics ---

cmudrc/wave-energy-synthesis

cmudrc

2022-11-27T21:30:39Z

3

1

tf-keras

[ "tf-keras", "en", "dataset:cmudrc/wave-energy", "license:mit", "region:us" ]

null

2022-11-27T04:33:15Z

--- license: mit language: en datasets: - cmudrc/wave-energy ---

ProGamerGov/Object-Taped-To-Wall-Diffusion-V1

ProGamerGov

2022-11-27T21:17:56Z

0

15

null

[ "stable-diffusion", "text-to-image", "dataset:ProGamerGov/StableDiffusion-v1-5-Regularization-Images", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2022-11-24T01:24:34Z

--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image datasets: - ProGamerGov/StableDiffusion-v1-5-Regularization-Images --- **Object-Taped-To-Wall-Diffusion** This fine-tuned Stable Diffusion v1.5 model was trained for 2000 iterations with a batch size of 4, on a selection of photos of things taped to a wall. Training was performed using [ShivamShrirao/diffusers](https://github.com/ShivamShrirao/diffusers) with full precision, prior-preservation loss, the train-text-encoder feature, and the new [1.5 MSE VAE from Stability AI](https://huggingface.co/stabilityai/sd-vae-ft-mse). A total of 2100 regularization / class images were used from [here](https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images). Regularization images were generated using the prompt "artwork style" with 50 DPM++ 2S a Karras steps and a CFG of 7, using the MSE VAE. A negative prompt of "text" was also used for this dataset. Use the tokens **ttw style** in your prompts for the effect. Note that the effect also appears to occur at a much weaker strength on prompts that steer the output towards specific artistic styles. This model will likely not perform well on taping objects that are not traditionally able to be taped to walls. <div align="center"> <img src="https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png) Example images were generated with the v1 2000 iteration model using DPM++ 2S a Karras: ``` ttw style, <object> taped to wall ``` This model was inspired by the 2019 art piece [*Comedian* by Italian artist Maurizio Cattelan](https://en.wikipedia.org/wiki/Comedian_(artwork\)), where a banana was duct taped to a wall.

Davimartins/Farias123

Davimartins

2022-11-27T20:50:51Z

0

null

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

null

2022-11-27T20:50:50Z

--- license: bigscience-openrail-m ---