librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-05-28 18:26:29	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 477 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-28 18:24:32	card stringlengths 11 1.01M
EmirhanExecute/ppo-LunarLander-try2	EmirhanExecute	2023-08-24T08:56:49Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T08:56:29Z	--- 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: 263.15 +/- 15.93 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 ... ```
bigmorning/train_from_raw_cv12__0020	bigmorning	2023-08-24T08:54:06Z	60	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-24T08:53:58Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: train_from_raw_cv12__0020 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. --> # train_from_raw_cv12__0020 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: nan - Train Accuracy: 0.0032 - Train Wermet: 8.3902 - Validation Loss: nan - Validation Accuracy: 0.0032 - Validation Wermet: 8.3902 - Epoch: 19 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:---------------:\|:-------------------:\|:-----------------:\|:-----:\| \| nan \| 0.0032 \| 8.3778 \| nan \| 0.0032 \| 8.3902 \| 0 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 1 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 2 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 3 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 4 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 5 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 6 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 7 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 8 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 9 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 10 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 11 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 12 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 13 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 14 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 15 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 16 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 17 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 18 \| \| nan \| 0.0032 \| 8.3902 \| nan \| 0.0032 \| 8.3902 \| 19 \| ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
nomsgadded/Translation	nomsgadded	2023-08-24T08:52:26Z	104	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "en", "fr", "dataset:opus_books", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-24T08:13:12Z	--- language: - en - fr license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus_books model-index: - name: Translation 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. --> # Translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books en-fr dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
922-CA/negev-gfl-rvc2-tests	922-CA	2023-08-24T08:51:21Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-08-22T08:46:16Z	--- license: openrail --- Test RVC2 models on the GFL character Negev, via various hyperparams and datasets. # negev-test-0 (~07/2023) * Trained on dataset of ~30 items, dialogue from game * Trained for ~100 epochs * First attempt # negev-test-1 - nne1_e10_s150 (08/22/2023) * Trained on dataset of ~30 items, dialogue from game * Trained for 10 epochs (150 steps) * Less artifacting but with accent # negev-test-1 - nne1_e60_s900 (08/22/2023) * Trained on dataset of ~30 items, dialogue from game * Trained for 60 epochs (900 steps) * Tends to be clearer and with less accent
ashokdavas/ppo-LunarLander-v2	ashokdavas	2023-08-24T08:44:57Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T08:44:39Z	--- 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: 252.99 +/- 16.33 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 ... ```
WhoTookMyAmogusNickname/ReasonixPajama-3B-GGML	WhoTookMyAmogusNickname	2023-08-24T08:41:41Z	0	1	null	[ "region:us" ]	null	2023-08-24T07:58:17Z	Amogus\ GGML quants of [ReasonixPajama-3b-HF](https://huggingface.co/Fredithefish/ReasonixPajama-3B-HF)
IAMNawaf/QA-History-Saudi	IAMNawaf	2023-08-24T08:30:58Z	103	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "ar", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "endpoints_compatible", "region:us" ]	question-answering	2023-08-23T14:32:32Z	--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: SA-History-NASEEJ-QA results: [] language: - ar library_name: transformers widget: - text: من كان الأكبر سنًا من آل سعود وتولى الإمارة؟ context: >- بعـد وفـاة سـعود بـن محمـد بـن مقـرن تولـى الإمـارة زيـد بـن مرخـان بـن وطبـان، وكان الأكبـر سـناً مـن آل سـعود، ولكـن حكمـه لـم يمتـد طويـ ًا لكبـر سـنه، ممـا دعـا مقـرن بـن محمـد بـن مقـرن إلـى انتـزاع الإمـارة منـه، لكـن الأمـور لـم تسـتمر طويـ ًا لمقـرن، وذلـك عندمـا حـاول الغـدر بزيـد بـن مرخـان الـذي كان يحكـم قبلـه، ممـا دعـا محمـد بـن سـعود ومقـرن بـن عبداللـه إلـى قتلـه، وكان ذلـك سـنة 1139 هــ 1727/ م. بعـد ذلـك عـاد إلـى الإمـارة زيـد بـن مرخـان، إلا أنـه عندمـا هجـم علـى إمـارة العيينـة سـعت - بعـد ذلـك - إلـى التحايـل عليـه وطلبـت التفـاوض معـه، وعندمـا ذهـب تم قتلـه، وبعـد قتـل زيـد بـن مرخـان تولـى محمـد بـن سـعود بـن مقـرن الإمـارة فـي الدرعيـة سـنة 1139 هــ 1727/ م ، وظـل حكمـه حتـى سـنة 1179 هـ 1765/ م. example_title: تاريخ المملكة العربية السعودية pipeline_tag: question-answering --- <!-- 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. --> # Naseej-SA-History-QA This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0791 ## Model description The Naseej-SA-History-QA model is a fine-tuned version of the aubmindlab/bert-base-arabertv02 pre-trained BERT model. It has been tailored and optimized for question answering tasks related to the history of Saudi Arabia. The model is designed to comprehend historical context and provide accurate answers to questions in Arabic language. ## Intended uses & limitations The Naseej-SA-History-QA model is intended to be used for answering historical questions specifically related to the history of Saudi Arabia. It can be employed in educational and research settings to assist students, scholars, and researchers in obtaining information about Saudi Arabian history. The model can also be utilized in various NLP applications where historical context is a key factor, such as building educational platforms, historical archives, and language translation tools. The model's performance is contingent upon the quality and accuracy of the training and evaluation data it has been fine-tuned on. It may struggle with questions that deviate significantly from the training data distribution. The model's understanding of historical events and context is based on the data it has been trained on. It may not perform well on questions involving more recent or less documented historical events. The model may not fully comprehend nuanced or highly specific historical inquiries that require deep contextual understanding beyond the scope of its training data. ## Training and evaluation data The Naseej-SA-History-QA model was trained using a custom dataset comprising historical questions and corresponding context passages related to the history of Saudi Arabia. The dataset covers various historical periods and events, providing the model with a wide range of historical context to learn from. The evaluation set used during training was designed to assess the model's performance on question answering tasks. The evaluation set includes a variety of questions and context passages that challenge the model's ability to accurately answer questions about Saudi Arabian history. ## Training procedure The Naseej-SA-History-QA model was fine-tuned using the aubmindlab/bert-base-arabertv02 pre-trained BERT model. The training process involved several key steps: Dataset Preparation: A custom dataset was curated for training the model. The dataset consisted of pairs of historical questions and corresponding context passages, both in Arabic language. The context passages provided the necessary historical context for answering the questions. Tokenization: The dataset was tokenized using the Tokenizers library, which converts text into a format that the model can understand. Tokenization converts words and subwords into numerical tokens that the model can process. Model Fine-Tuning: The tokenized dataset was used to fine-tune the aubmindlab/bert-base-arabertv02 base model using the Transformers library. During fine-tuning, the model was adjusted to perform well on the specific task of question answering related to Saudi Arabian history. ### 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: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 11 \| 4.9014 \| \| No log \| 2.0 \| 22 \| 4.7432 \| \| No log \| 3.0 \| 33 \| 4.6212 \| \| No log \| 4.0 \| 44 \| 4.6347 \| \| No log \| 5.0 \| 55 \| 4.6101 \| \| No log \| 6.0 \| 66 \| 4.6209 \| \| No log \| 7.0 \| 77 \| 4.6445 \| \| No log \| 8.0 \| 88 \| 4.6284 \| \| No log \| 9.0 \| 99 \| 4.6226 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
nishant-glance/path-to-save-model-2-1-priorp-lowlr	nishant-glance	2023-08-24T08:30:19Z	2	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-24T07:40:57Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - nishant-glance/path-to-save-model-2-1-priorp-lowlr This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
lordhiew/myfirsttrain	lordhiew	2023-08-24T08:25:44Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-28T07:25:03Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Stomper10/CXR_ti_nf	Stomper10	2023-08-24T08:22:49Z	13	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-24T05:22:54Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Stomper10/CXR_ti_nf These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ![img_4](./image_4.png) ![img_5](./image_5.png) ![img_6](./image_6.png) ![img_7](./image_7.png) ![img_8](./image_8.png) ![img_9](./image_9.png) ![img_10](./image_10.png) ![img_11](./image_11.png) ![img_12](./image_12.png) ![img_13](./image_13.png) ![img_14](./image_14.png) ![img_15](./image_15.png) ![img_16](./image_16.png) ![img_17](./image_17.png) ![img_18](./image_18.png) ![img_19](./image_19.png)
raygx/distilGPT-NepSA	raygx	2023-08-24T08:12:30Z	71	0	transformers	[ "transformers", "tf", "gpt2", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-13T04:59:50Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilGPT-NepSA 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. --> # distilGPT-NepSA This model is a fine-tuned version of [raygx/distilGPT-Nepali](https://huggingface.co/raygx/distilGPT-Nepali) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6068 - Validation Loss: 0.6592 - Epoch: 1 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.04} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.8415 \| 0.7254 \| 0 \| \| 0.6068 \| 0.6592 \| 1 \| ### Framework versions - Transformers 4.28.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.3
amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1	amazon	2023-08-24T08:10:03Z	27	1	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-08-24T08:09:30Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
aware-ai/wav2vec2-base-german	aware-ai	2023-08-24T08:01:53Z	104	2	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_10_0", "generated_from_trainer", "de", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-01T19:46:01Z	--- language: - de tags: - automatic-speech-recognition - mozilla-foundation/common_voice_10_0 - generated_from_trainer model-index: - name: wav2vec2-base-german 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-base-german This model is a fine-tuned version of [wav2vec2-base-german](https://huggingface.co/wav2vec2-base-german) on the MOZILLA-FOUNDATION/COMMON_VOICE_10_0 - DE dataset. It achieves the following results on the evaluation set: - Loss: 0.9302 - Wer: 0.7428 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8427 \| 1.0 \| 451 \| 1.0878 \| 0.8091 \| \| 0.722 \| 2.0 \| 902 \| 0.9732 \| 0.7593 \| \| 0.6589 \| 3.0 \| 1353 \| 0.9302 \| 0.7428 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0 - Datasets 2.4.0 - Tokenizers 0.12.1
juandalibaba/my_awesome_wnut_model	juandalibaba	2023-08-24T07:56:48Z	65	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-23T06:40:28Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: juandalibaba/my_awesome_wnut_model 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. --> # juandalibaba/my_awesome_wnut_model 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: 1.6376 - Validation Loss: 1.8223 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.7876 \| 1.9931 \| 0 \| \| 1.7614 \| 1.8223 \| 1 \| \| 1.6376 \| 1.8223 \| 2 \| ### Framework versions - Transformers 4.32.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1	amazon	2023-08-24T07:48:06Z	24	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-08-24T07:28:22Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
ArneJa/Taxi	ArneJa	2023-08-24T07:42:44Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T07:42:43Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="ArneJa/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
avasaz/avasaz-large	avasaz	2023-08-24T07:30:53Z	4	1	transformers	[ "transformers", "pytorch", "musicgen", "text-to-audio", "license:mit", "region:us" ]	text-to-audio	2023-08-23T19:46:30Z	--- inference: false tags: - musicgen license: mit --- # Avasaz Large (3.3B) - Make music directly from your ideas <p align="center"> <img src="https://huggingface.co/avasaz/avasaz-large/resolve/main/avasaz_logo.png" width=256 height=256 /> </p> ## What is Avasaz? Avasaz (which is a combinations of Persian word آوا meaning song and ساز meaning maker, literally translates to _song maker_) is a _state-of-the-art generative AI model_ which can help you turn your ideas to music in matter of a few minutes. This model has been developed by [Muhammadreza Haghiri](https://haghiri75.com/en) as an effort to make a suite of AI programs to make the world a much better place for our future generations. ## How can you use Avasaz? [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/prp-e/avasaz/blob/main/Avasaz_Inference.ipynb) Currently, Infrerence is only available on _Colab_. Codes will be here as soon as possible.
neil-code/autotrain-summarization-84573142568	neil-code	2023-08-24T07:22:08Z	111	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "summarization", "en", "dataset:neil-code/autotrain-data-summarization", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2023-08-24T07:16:47Z	--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain" datasets: - neil-code/autotrain-data-summarization co2_eq_emissions: emissions: 3.1909973371323623 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 84573142568 - CO2 Emissions (in grams): 3.1910 ## Validation Metrics - Loss: 1.445 - Rouge1: 33.737 - Rouge2: 11.210 - RougeL: 28.204 - RougeLsum: 30.262 - Gen Len: 18.836 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/neil-code/autotrain-summarization-84573142568 ```
achmaddaa/ametv2	achmaddaa	2023-08-24T07:07:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T07:04:20Z	--- license: creativeml-openrail-m ---
DineshK/dummy-model	DineshK	2023-08-24T07:05:34Z	59	0	transformers	[ "transformers", "tf", "camembert", "fill-mask", "generated_from_keras_callback", "base_model:almanach/camembert-base", "base_model:finetune:almanach/camembert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-08-24T07:03:17Z	--- license: mit base_model: camembert-base tags: - generated_from_keras_callback model-index: - name: dummy-model 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. --> # dummy-model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-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 4.32.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
LarryAIDraw/meinaalter_v3	LarryAIDraw	2023-08-24T07:01:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T06:03:44Z	--- license: creativeml-openrail-m --- https://civitai.com/models/20945?modelVersionId=112825
ardt-multipart/ardt-multipart-arrl_sgld_train_walker2d_high-2408_0701-66	ardt-multipart	2023-08-24T06:56:49Z	32	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-24T06:03:00Z	--- tags: - generated_from_trainer model-index: - name: ardt-multipart-arrl_sgld_train_walker2d_high-2408_0701-66 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. --> # ardt-multipart-arrl_sgld_train_walker2d_high-2408_0701-66 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001 - train_batch_size: 64 - 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: 1000 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
greenyslimerfahrungen/greenyslimerfahrungen	greenyslimerfahrungen	2023-08-24T06:45:50Z	0	0	espnet	[ "espnet", "Greeny Slim Erfahrungen", "en", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-08-24T06:45:09Z	--- license: cc-by-nc-sa-4.0 language: - en library_name: espnet tags: - Greeny Slim Erfahrungen --- [Greeny Slim Erfahrungen](https://supplementtycoon.com/de/greeny-slim-fruchtgummis/) Notwithstanding, it's vital to take note of that despite the fact that they are low in carbs and sugar, they ought to in any case be consumed with some restraint as a feature of a fair diet.As forever, it's prescribed to peruse the nourishment marks and fixings list cautiously prior to buying any keto gummies to guarantee they line up with your dietary objectives and inclinations. VISIT HERE FOR OFFICIAL WEBSITE:-https://supplementtycoon.com/de/greeny-slim-fruchtgummis/
k1101jh/q-Taxi-v3	k1101jh	2023-08-24T06:44:50Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T06:44:48Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.69 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="k1101jh/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
k1101jh/q-FrozenLake-v1-4x4-noSlippery	k1101jh	2023-08-24T06:38:56Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T06:38:54Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="k1101jh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
dkimds/a2c-PandaReachDense-v3	dkimds	2023-08-24T06:17:56Z	3	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T06:12:25Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.18 +/- 0.12 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
HGV1408/Data	HGV1408	2023-08-24T06:17:51Z	103	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-24T06:15:20Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum 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: 1.4834 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6997 \| 0.54 \| 500 \| 1.4834 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
IngeniousArtist/openllama-3b-finance	IngeniousArtist	2023-08-24T05:36:30Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "generated_from_trainer", "dataset:financial_phrasebank", "base_model:openlm-research/open_llama_3b_v2", "base_model:finetune:openlm-research/open_llama_3b_v2", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T20:47:22Z	--- license: apache-2.0 base_model: openlm-research/open_llama_3b_v2 tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy model-index: - name: openllama-3b-finance results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank config: sentences_50agree split: train args: sentences_50agree metrics: - name: Accuracy type: accuracy value: 0.4142561983471074 --- <!-- 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. --> # openllama-3b-finance This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 4.0296 - Accuracy: 0.4143 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 21.9655 \| 0.01 \| 20 \| 8.1663 \| 0.0816 \| \| 2.231 \| 0.01 \| 40 \| 6.3007 \| 0.4143 \| \| 2.7452 \| 0.02 \| 60 \| 4.0892 \| 0.4143 \| \| 2.4561 \| 0.02 \| 80 \| 5.0314 \| 0.4143 \| \| 2.337 \| 0.03 \| 100 \| 5.6176 \| 0.4143 \| \| 3.2226 \| 0.03 \| 120 \| 4.4963 \| 0.4143 \| \| 2.5633 \| 0.04 \| 140 \| 6.1800 \| 0.4143 \| \| 2.4764 \| 0.04 \| 160 \| 4.7059 \| 0.4143 \| \| 2.45 \| 0.05 \| 180 \| 5.0602 \| 0.4143 \| \| 1.4232 \| 0.05 \| 200 \| 5.3418 \| 0.4143 \| \| 2.7684 \| 0.06 \| 220 \| 5.1805 \| 0.4143 \| \| 1.7065 \| 0.06 \| 240 \| 4.7568 \| 0.4143 \| \| 2.3417 \| 0.07 \| 260 \| 6.1062 \| 0.4143 \| \| 1.907 \| 0.07 \| 280 \| 12.0988 \| 0.5041 \| \| 14.6043 \| 0.08 \| 300 \| 3.0283 \| 0.0816 \| \| 1.337 \| 0.08 \| 320 \| 12.7786 \| 0.4143 \| \| 4.182 \| 0.09 \| 340 \| 7.5619 \| 0.4143 \| \| 3.7365 \| 0.09 \| 360 \| 7.8581 \| 0.4143 \| \| 3.209 \| 0.1 \| 380 \| 3.2547 \| 0.4143 \| \| 3.4836 \| 0.1 \| 400 \| 89.8525 \| 0.0816 \| \| 4.5805 \| 0.11 \| 420 \| 103.0762 \| 0.4143 \| \| 4.6351 \| 0.11 \| 440 \| 91.4501 \| 0.4143 \| \| 11.0873 \| 0.12 \| 460 \| 88.0469 \| 0.4143 \| \| 1.1274 \| 0.12 \| 480 \| 86.7130 \| 0.4143 \| \| 2.0398 \| 0.13 \| 500 \| 86.4186 \| 0.4143 \| \| 18.6924 \| 0.13 \| 520 \| 80.1491 \| 0.4143 \| \| 1.2216 \| 0.14 \| 540 \| 76.8429 \| 0.4143 \| \| 1.1179 \| 0.14 \| 560 \| 78.0159 \| 0.4143 \| \| 10.0981 \| 0.15 \| 580 \| 71.1114 \| 0.4143 \| \| 9.0123 \| 0.15 \| 600 \| 66.2945 \| 0.4143 \| \| 1.9539 \| 0.16 \| 620 \| 65.6854 \| 0.4143 \| \| 8.4729 \| 0.17 \| 640 \| 62.1595 \| 0.4143 \| \| 7.816 \| 0.17 \| 660 \| 52.0763 \| 0.4143 \| \| 6.0443 \| 0.18 \| 680 \| 41.1500 \| 0.4143 \| \| 3.1804 \| 0.18 \| 700 \| 42.8007 \| 0.4143 \| \| 1.6122 \| 0.19 \| 720 \| 44.0976 \| 0.4143 \| \| 9.8927 \| 0.19 \| 740 \| 31.6381 \| 0.4143 \| \| 6.828 \| 0.2 \| 760 \| 12.7483 \| 0.4143 \| \| 3.1457 \| 0.2 \| 780 \| 13.2981 \| 0.4143 \| \| 1.9991 \| 0.21 \| 800 \| 12.4846 \| 0.4143 \| \| 2.5539 \| 0.21 \| 820 \| 13.7669 \| 0.4143 \| \| 1.3898 \| 0.22 \| 840 \| 12.8919 \| 0.0816 \| \| 2.9251 \| 0.22 \| 860 \| 15.9149 \| 0.0816 \| \| 4.0874 \| 0.23 \| 880 \| 10.5282 \| 0.4143 \| \| 2.4763 \| 0.23 \| 900 \| 3.0281 \| 0.4143 \| \| 2.2865 \| 0.24 \| 920 \| 12.2460 \| 0.4143 \| \| 4.2438 \| 0.24 \| 940 \| 10.1961 \| 0.4143 \| \| 2.547 \| 0.25 \| 960 \| 1.4099 \| 0.4143 \| \| 0.8659 \| 0.25 \| 980 \| 8.3217 \| 0.4143 \| \| 3.5331 \| 0.26 \| 1000 \| 6.3990 \| 0.4143 \| \| 2.4704 \| 0.26 \| 1020 \| 2.2337 \| 0.0816 \| \| 2.1381 \| 0.27 \| 1040 \| 10.6263 \| 0.4143 \| \| 1.5927 \| 0.27 \| 1060 \| 11.1989 \| 0.4143 \| \| 2.485 \| 0.28 \| 1080 \| 8.8174 \| 0.4143 \| \| 2.8074 \| 0.28 \| 1100 \| 5.5971 \| 0.4143 \| \| 0.8622 \| 0.29 \| 1120 \| 5.5089 \| 0.4143 \| \| 2.8085 \| 0.29 \| 1140 \| 5.4300 \| 0.4143 \| \| 1.2405 \| 0.3 \| 1160 \| 7.5657 \| 0.4143 \| \| 3.9374 \| 0.3 \| 1180 \| 2.7180 \| 0.4143 \| \| 1.7494 \| 0.31 \| 1200 \| 4.9639 \| 0.0816 \| \| 2.6094 \| 0.32 \| 1220 \| 2.1980 \| 0.4143 \| \| 2.2072 \| 0.32 \| 1240 \| 7.3392 \| 0.4143 \| \| 0.9978 \| 0.33 \| 1260 \| 7.9127 \| 0.4143 \| \| 2.3872 \| 0.33 \| 1280 \| 7.0613 \| 0.4143 \| \| 3.3129 \| 0.34 \| 1300 \| 4.4202 \| 0.4143 \| \| 1.776 \| 0.34 \| 1320 \| 6.1467 \| 0.4143 \| \| 3.1179 \| 0.35 \| 1340 \| 6.0607 \| 0.4143 \| \| 1.272 \| 0.35 \| 1360 \| 5.0484 \| 0.4143 \| \| 3.0694 \| 0.36 \| 1380 \| 3.1665 \| 0.4143 \| \| 1.9452 \| 0.36 \| 1400 \| 4.8692 \| 0.4143 \| \| 2.3689 \| 0.37 \| 1420 \| 4.9375 \| 0.4143 \| \| 2.7082 \| 0.37 \| 1440 \| 3.2108 \| 0.4143 \| \| 0.8244 \| 0.38 \| 1460 \| 7.0151 \| 0.4143 \| \| 2.6032 \| 0.38 \| 1480 \| 5.5645 \| 0.4143 \| \| 2.8745 \| 0.39 \| 1500 \| 4.2408 \| 0.4143 \| \| 2.625 \| 0.39 \| 1520 \| 6.8800 \| 0.4143 \| \| 2.5335 \| 0.4 \| 1540 \| 6.3109 \| 0.4143 \| \| 2.5495 \| 0.4 \| 1560 \| 4.4017 \| 0.4143 \| \| 1.7234 \| 0.41 \| 1580 \| 5.1739 \| 0.4143 \| \| 2.1066 \| 0.41 \| 1600 \| 6.0769 \| 0.4143 \| \| 2.5541 \| 0.42 \| 1620 \| 3.7539 \| 0.4143 \| \| 2.4598 \| 0.42 \| 1640 \| 4.2075 \| 0.4143 \| \| 1.7211 \| 0.43 \| 1660 \| 5.3975 \| 0.4143 \| \| 2.3993 \| 0.43 \| 1680 \| 4.1427 \| 0.4143 \| \| 1.6161 \| 0.44 \| 1700 \| 5.0871 \| 0.4143 \| \| 2.2361 \| 0.44 \| 1720 \| 4.3375 \| 0.4143 \| \| 2.0841 \| 0.45 \| 1740 \| 4.7357 \| 0.4143 \| \| 2.137 \| 0.45 \| 1760 \| 5.2737 \| 0.4143 \| \| 2.3819 \| 0.46 \| 1780 \| 3.1688 \| 0.4143 \| \| 2.6391 \| 0.46 \| 1800 \| 5.6169 \| 0.4143 \| \| 1.276 \| 0.47 \| 1820 \| 6.1945 \| 0.4143 \| \| 2.0694 \| 0.48 \| 1840 \| 6.3761 \| 0.4143 \| \| 2.3715 \| 0.48 \| 1860 \| 6.1666 \| 0.4143 \| \| 2.1428 \| 0.49 \| 1880 \| 6.4718 \| 0.4143 \| \| 2.0409 \| 0.49 \| 1900 \| 6.3259 \| 0.4143 \| \| 2.1924 \| 0.5 \| 1920 \| 6.0853 \| 0.4143 \| \| 2.3511 \| 0.5 \| 1940 \| 4.7199 \| 0.4143 \| \| 2.7335 \| 0.51 \| 1960 \| 4.3591 \| 0.4143 \| \| 1.6784 \| 0.51 \| 1980 \| 3.7488 \| 0.1612 \| \| 1.5525 \| 0.52 \| 2000 \| 6.0497 \| 0.4143 \| \| 2.7457 \| 0.52 \| 2020 \| 3.5952 \| 0.4143 \| \| 2.3929 \| 0.53 \| 2040 \| 4.7684 \| 0.4143 \| \| 1.9522 \| 0.53 \| 2060 \| 5.6394 \| 0.4143 \| \| 2.2257 \| 0.54 \| 2080 \| 4.5801 \| 0.4143 \| \| 1.6753 \| 0.54 \| 2100 \| 5.0521 \| 0.4143 \| \| 1.6154 \| 0.55 \| 2120 \| 5.4730 \| 0.4143 \| \| 1.7723 \| 0.55 \| 2140 \| 5.5251 \| 0.4143 \| \| 2.6963 \| 0.56 \| 2160 \| 3.5098 \| 0.4143 \| \| 1.7274 \| 0.56 \| 2180 \| 5.4262 \| 0.4143 \| \| 2.4059 \| 0.57 \| 2200 \| 4.5019 \| 0.4143 \| \| 1.6505 \| 0.57 \| 2220 \| 5.1107 \| 0.4143 \| \| 1.2469 \| 0.58 \| 2240 \| 5.3456 \| 0.4143 \| \| 1.6702 \| 0.58 \| 2260 \| 5.4103 \| 0.4143 \| \| 1.615 \| 0.59 \| 2280 \| 5.8024 \| 0.4143 \| \| 1.5622 \| 0.59 \| 2300 \| 5.6035 \| 0.4143 \| \| 2.3536 \| 0.6 \| 2320 \| 5.3779 \| 0.4143 \| \| 2.0512 \| 0.6 \| 2340 \| 5.2498 \| 0.4143 \| \| 2.1405 \| 0.61 \| 2360 \| 5.2279 \| 0.4143 \| \| 2.1926 \| 0.61 \| 2380 \| 4.3260 \| 0.4143 \| \| 2.3995 \| 0.62 \| 2400 \| 4.4445 \| 0.4143 \| \| 1.4944 \| 0.62 \| 2420 \| 4.9616 \| 0.4143 \| \| 2.6623 \| 0.63 \| 2440 \| 4.9736 \| 0.4143 \| \| 1.4095 \| 0.64 \| 2460 \| 4.6506 \| 0.4143 \| \| 2.4803 \| 0.64 \| 2480 \| 4.0971 \| 0.4143 \| \| 1.2721 \| 0.65 \| 2500 \| 4.3192 \| 0.4143 \| \| 1.8372 \| 0.65 \| 2520 \| 4.4907 \| 0.4143 \| \| 1.8942 \| 0.66 \| 2540 \| 4.7323 \| 0.4143 \| \| 2.1407 \| 0.66 \| 2560 \| 4.9554 \| 0.4143 \| \| 2.5039 \| 0.67 \| 2580 \| 5.1599 \| 0.4143 \| \| 1.7321 \| 0.67 \| 2600 \| 5.6089 \| 0.4143 \| \| 2.0621 \| 0.68 \| 2620 \| 4.8359 \| 0.4143 \| \| 2.1664 \| 0.68 \| 2640 \| 4.5581 \| 0.4143 \| \| 1.8835 \| 0.69 \| 2660 \| 5.1029 \| 0.4143 \| \| 3.0314 \| 0.69 \| 2680 \| 3.9587 \| 0.4143 \| \| 1.1781 \| 0.7 \| 2700 \| 4.4584 \| 0.4143 \| \| 3.3222 \| 0.7 \| 2720 \| 4.7628 \| 0.4143 \| \| 2.1184 \| 0.71 \| 2740 \| 4.4039 \| 0.4143 \| \| 1.9293 \| 0.71 \| 2760 \| 3.8755 \| 0.4143 \| \| 2.2448 \| 0.72 \| 2780 \| 4.4327 \| 0.4143 \| \| 2.4697 \| 0.72 \| 2800 \| 3.3026 \| 0.4143 \| \| 1.8569 \| 0.73 \| 2820 \| 3.7722 \| 0.4143 \| \| 0.8544 \| 0.73 \| 2840 \| 4.9176 \| 0.4143 \| \| 2.2445 \| 0.74 \| 2860 \| 4.3889 \| 0.4143 \| \| 1.3723 \| 0.74 \| 2880 \| 4.3280 \| 0.4143 \| \| 2.2167 \| 0.75 \| 2900 \| 4.4016 \| 0.4143 \| \| 1.98 \| 0.75 \| 2920 \| 3.8661 \| 0.4143 \| \| 1.7344 \| 0.76 \| 2940 \| 3.7919 \| 0.4143 \| \| 1.924 \| 0.76 \| 2960 \| 4.1408 \| 0.4143 \| \| 1.3811 \| 0.77 \| 2980 \| 4.3730 \| 0.4143 \| \| 1.8289 \| 0.77 \| 3000 \| 4.2872 \| 0.4143 \| \| 1.9573 \| 0.78 \| 3020 \| 4.6165 \| 0.4143 \| \| 2.4877 \| 0.78 \| 3040 \| 4.5988 \| 0.4143 \| \| 1.1749 \| 0.79 \| 3060 \| 4.7887 \| 0.4143 \| \| 2.1835 \| 0.8 \| 3080 \| 4.9018 \| 0.4143 \| \| 2.3752 \| 0.8 \| 3100 \| 4.6911 \| 0.4143 \| \| 1.9741 \| 0.81 \| 3120 \| 4.5126 \| 0.4143 \| \| 1.7513 \| 0.81 \| 3140 \| 4.6251 \| 0.4143 \| \| 3.0666 \| 0.82 \| 3160 \| 4.0260 \| 0.4143 \| \| 0.5569 \| 0.82 \| 3180 \| 4.0965 \| 0.4143 \| \| 2.1805 \| 0.83 \| 3200 \| 4.5240 \| 0.4143 \| \| 2.4319 \| 0.83 \| 3220 \| 4.3080 \| 0.4143 \| \| 2.126 \| 0.84 \| 3240 \| 3.7823 \| 0.4143 \| \| 1.6993 \| 0.84 \| 3260 \| 3.8093 \| 0.4143 \| \| 0.6861 \| 0.85 \| 3280 \| 4.1618 \| 0.4143 \| \| 0.748 \| 0.85 \| 3300 \| 4.5653 \| 0.4143 \| \| 2.5721 \| 0.86 \| 3320 \| 4.6628 \| 0.4143 \| \| 2.0137 \| 0.86 \| 3340 \| 4.2796 \| 0.4143 \| \| 2.1864 \| 0.87 \| 3360 \| 4.1173 \| 0.4143 \| \| 2.4881 \| 0.87 \| 3380 \| 3.9617 \| 0.4143 \| \| 2.6837 \| 0.88 \| 3400 \| 3.7575 \| 0.4143 \| \| 1.5951 \| 0.88 \| 3420 \| 3.6086 \| 0.4143 \| \| 2.504 \| 0.89 \| 3440 \| 3.5919 \| 0.4143 \| \| 1.4982 \| 0.89 \| 3460 \| 3.7519 \| 0.4143 \| \| 1.8994 \| 0.9 \| 3480 \| 3.7120 \| 0.4143 \| \| 1.6126 \| 0.9 \| 3500 \| 3.6854 \| 0.4143 \| \| 2.002 \| 0.91 \| 3520 \| 3.7888 \| 0.4143 \| \| 1.0264 \| 0.91 \| 3540 \| 3.7990 \| 0.4143 \| \| 1.9495 \| 0.92 \| 3560 \| 3.9635 \| 0.4143 \| \| 2.0742 \| 0.92 \| 3580 \| 3.9651 \| 0.4143 \| \| 1.7803 \| 0.93 \| 3600 \| 3.9518 \| 0.4143 \| \| 2.0843 \| 0.93 \| 3620 \| 3.9404 \| 0.4143 \| \| 1.8431 \| 0.94 \| 3640 \| 3.9334 \| 0.4143 \| \| 1.4987 \| 0.95 \| 3660 \| 3.9609 \| 0.4143 \| \| 1.8214 \| 0.95 \| 3680 \| 4.0060 \| 0.4143 \| \| 1.0964 \| 0.96 \| 3700 \| 4.0422 \| 0.4143 \| \| 0.9669 \| 0.96 \| 3720 \| 4.0549 \| 0.4143 \| \| 1.6226 \| 0.97 \| 3740 \| 4.0486 \| 0.4143 \| \| 1.8061 \| 0.97 \| 3760 \| 4.0405 \| 0.4143 \| \| 2.8738 \| 0.98 \| 3780 \| 4.0317 \| 0.4143 \| \| 1.684 \| 0.98 \| 3800 \| 4.0319 \| 0.4143 \| \| 1.1158 \| 0.99 \| 3820 \| 4.0303 \| 0.4143 \| \| 1.775 \| 0.99 \| 3840 \| 4.0294 \| 0.4143 \| \| 2.1639 \| 1.0 \| 3860 \| 4.0296 \| 0.4143 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
Afbnff/B	Afbnff	2023-08-24T05:29:13Z	0	0	null	[ "dataset:fka/awesome-chatgpt-prompts", "region:us" ]	null	2023-08-24T05:28:01Z	--- datasets: - fka/awesome-chatgpt-prompts metrics: - accuracy ---
ishvalin/mt5-small-finetuned-amazon-en-es	ishvalin	2023-08-24T05:17:08Z	9	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-08-24T04:43:56Z	--- license: apache-2.0 base_model: google/mt5-small tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-es 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-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0301 - Rouge1: 17.4531 - Rouge2: 9.0091 - Rougel: 17.0836 - Rougelsum: 17.1528 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| 3.6834 \| 1.0 \| 1209 \| 3.2483 \| 15.494 \| 7.8022 \| 15.1402 \| 15.2041 \| \| 3.6689 \| 2.0 \| 2418 \| 3.1014 \| 16.6941 \| 8.9493 \| 15.9414 \| 16.1157 \| \| 3.4493 \| 3.0 \| 3627 \| 3.0640 \| 16.5731 \| 8.2808 \| 16.0156 \| 16.1514 \| \| 3.3175 \| 4.0 \| 4836 \| 3.0375 \| 16.8245 \| 8.6021 \| 16.2052 \| 16.3956 \| \| 3.2303 \| 5.0 \| 6045 \| 3.0312 \| 17.8902 \| 9.7012 \| 17.3184 \| 17.5092 \| \| 3.1693 \| 6.0 \| 7254 \| 3.0255 \| 16.985 \| 8.7225 \| 16.6058 \| 16.7549 \| \| 3.1357 \| 7.0 \| 8463 \| 3.0235 \| 17.015 \| 9.0093 \| 16.7306 \| 16.9061 \| \| 3.1073 \| 8.0 \| 9672 \| 3.0301 \| 17.4531 \| 9.0091 \| 17.0836 \| 17.1528 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
timetoai/distilbert-base-uncased-arxiv-abstracts-10k	timetoai	2023-08-24T05:08:14Z	124	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-08-21T04:38:43Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-arxiv-abstracts-10k 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-arxiv-abstracts-10k 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: 2.1698 ## 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: 64 - 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_ratio: 0.2 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 166 \| 2.2911 \| \| No log \| 2.0 \| 332 \| 2.1673 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
JpChi/pegasus-samsum	JpChi	2023-08-24T05:07:22Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-24T04:07:05Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum 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: 1.4079 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.08 \| 0.27 \| 500 \| 1.5162 \| \| 1.6341 \| 0.54 \| 1000 \| 1.4381 \| \| 1.5749 \| 0.81 \| 1500 \| 1.4079 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
openbmb/UltraLM-65b	openbmb	2023-08-24T04:58:51Z	1,565	8	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:stingning/ultrachat", "arxiv:2305.14233", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-18T09:33:47Z	--- datasets: - stingning/ultrachat --- # UltraLM-65b <!-- Provide a quick summary of what the model is/does. --> This is UltraLM-65b delta weights, a chat language model trained upon [UltraChat](https://github.com/thunlp/UltraChat) ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> The model is fine-tuned based on LLaMA-65b with a multi-turn chat-format template as below ``` User: instruction 1 Assistant: response 1<eos_token> User: instruction 2 Assistant: response 2<eos_token> ... ``` - License: UltraLM is based on LLaMA and should be used under LLaMA's [model license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md). - Finetuned from model: LLaMA-65b - Finetuned on data: [UltraChat](https://github.com/thunlp/UltraChat) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [UltraChat](https://github.com/thunlp/UltraChat) - Paper: [arxiv](https://arxiv.org/abs/2305.14233) - Demo: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> To use this model, you need to [recover](https://github.com/thunlp/UltraChat/tree/main/UltraLM) the full model from the delta weights and perform inference following the template below: ``` [Optional]User: system prompt User: user input Assistant: ```
neil-code/autotrain-test-summarization-84415142559	neil-code	2023-08-24T04:28:12Z	109	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "summarization", "en", "dataset:neil-code/autotrain-data-test-summarization", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2023-08-24T04:23:26Z	--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain" datasets: - neil-code/autotrain-data-test-summarization co2_eq_emissions: emissions: 3.0878646296058494 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 84415142559 - CO2 Emissions (in grams): 3.0879 ## Validation Metrics - Loss: 1.534 - Rouge1: 33.336 - Rouge2: 11.361 - RougeL: 27.779 - RougeLsum: 29.966 - Gen Len: 18.773 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/neil-code/autotrain-test-summarization-84415142559 ```
larryvrh/tigerbot-13b-chat-sharegpt-lora	larryvrh	2023-08-24T04:27:43Z	0	1	null	[ "text-generation", "zh", "dataset:larryvrh/sharegpt_zh-only", "region:us" ]	text-generation	2023-08-24T02:22:02Z	--- datasets: - larryvrh/sharegpt_zh-only language: - zh pipeline_tag: text-generation --- 使用8631条中文sharegpt语料[larryvrh/sharegpt_zh-only](https://huggingface.co/datasets/larryvrh/sharegpt_zh-only)重新对齐后的[TigerResearch/tigerbot-13b-chat](https://huggingface.co/TigerResearch/tigerbot-13b-chat)。改善了模型多轮对话下的上下文关联能力。以及在部分场景下回答过于"拟人"的情况。微调前： ![](https://i.imgur.com/AvA4R4d.png) 微调后： ![](https://i.imgur.com/aei5dst.png) 可以使用配套的[webui](https://huggingface.co/larryvrh/tigerbot-13b-chat-sharegpt-lora/blob/main/chat_webui.py)来进行快速测试。 ![](https://i.imgur.com/aV3iEW5.png)
antoinerossupedu/bert-playground	antoinerossupedu	2023-08-24T04:25:14Z	108	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T04:10:04Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-playground results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.606823117358914 --- <!-- 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-playground This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8178 - Matthews Correlation: 0.6068 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.4401 \| 1.0 \| 1069 \| 0.4155 \| 0.5720 \| \| 0.3121 \| 2.0 \| 2138 \| 0.6457 \| 0.6039 \| \| 0.1764 \| 3.0 \| 3207 \| 0.8178 \| 0.6068 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
TariqJamil/llama-7b-minigunaco-0805	TariqJamil	2023-08-24T03:58:37Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-05T16:48:35Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
xszhou/ppo-LunarLander-v2	xszhou	2023-08-24T03:44:40Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T03:44:16Z	--- 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: 272.49 +/- 17.20 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 ... ```
dkqjrm/20230824104542	dkqjrm	2023-08-24T03:41:11Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T01:46:01Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824104542' 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. --> # 20230824104542 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3421 - Accuracy: 0.7256 ## 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: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 312 \| 1.0891 \| 0.5307 \| \| 0.5902 \| 2.0 \| 624 \| 0.6221 \| 0.4765 \| \| 0.5902 \| 3.0 \| 936 \| 0.4801 \| 0.5379 \| \| 0.5511 \| 4.0 \| 1248 \| 0.4461 \| 0.5054 \| \| 0.5299 \| 5.0 \| 1560 \| 0.5922 \| 0.5162 \| \| 0.5299 \| 6.0 \| 1872 \| 0.4113 \| 0.5199 \| \| 0.509 \| 7.0 \| 2184 \| 0.4885 \| 0.5451 \| \| 0.509 \| 8.0 \| 2496 \| 0.4106 \| 0.4910 \| \| 0.4976 \| 9.0 \| 2808 \| 0.5019 \| 0.4874 \| \| 0.4898 \| 10.0 \| 3120 \| 0.4132 \| 0.5307 \| \| 0.4898 \| 11.0 \| 3432 \| 0.4564 \| 0.4874 \| \| 0.4739 \| 12.0 \| 3744 \| 0.4919 \| 0.5307 \| \| 0.4594 \| 13.0 \| 4056 \| 0.4235 \| 0.4982 \| \| 0.4594 \| 14.0 \| 4368 \| 0.3937 \| 0.5812 \| \| 0.4444 \| 15.0 \| 4680 \| 0.3871 \| 0.5812 \| \| 0.4444 \| 16.0 \| 4992 \| 0.4123 \| 0.6065 \| \| 0.4334 \| 17.0 \| 5304 \| 0.3986 \| 0.6209 \| \| 0.4045 \| 18.0 \| 5616 \| 0.4088 \| 0.6029 \| \| 0.4045 \| 19.0 \| 5928 \| 0.3935 \| 0.6209 \| \| 0.3999 \| 20.0 \| 6240 \| 0.3645 \| 0.6715 \| \| 0.376 \| 21.0 \| 6552 \| 0.4230 \| 0.5740 \| \| 0.376 \| 22.0 \| 6864 \| 0.3911 \| 0.6823 \| \| 0.3683 \| 23.0 \| 7176 \| 0.5057 \| 0.6534 \| \| 0.3683 \| 24.0 \| 7488 \| 0.3273 \| 0.7040 \| \| 0.3501 \| 25.0 \| 7800 \| 0.3663 \| 0.7004 \| \| 0.344 \| 26.0 \| 8112 \| 0.3755 \| 0.6931 \| \| 0.344 \| 27.0 \| 8424 \| 0.3648 \| 0.7112 \| \| 0.3354 \| 28.0 \| 8736 \| 0.3359 \| 0.7148 \| \| 0.3288 \| 29.0 \| 9048 \| 0.3362 \| 0.7112 \| \| 0.3288 \| 30.0 \| 9360 \| 0.5539 \| 0.6787 \| \| 0.3199 \| 31.0 \| 9672 \| 0.3617 \| 0.7112 \| \| 0.3199 \| 32.0 \| 9984 \| 0.3601 \| 0.7184 \| \| 0.3166 \| 33.0 \| 10296 \| 0.3325 \| 0.7292 \| \| 0.3037 \| 34.0 \| 10608 \| 0.3274 \| 0.7256 \| \| 0.3037 \| 35.0 \| 10920 \| 0.3412 \| 0.7076 \| \| 0.2987 \| 36.0 \| 11232 \| 0.3509 \| 0.7256 \| \| 0.2842 \| 37.0 \| 11544 \| 0.3945 \| 0.7076 \| \| 0.2842 \| 38.0 \| 11856 \| 0.3224 \| 0.7365 \| \| 0.2894 \| 39.0 \| 12168 \| 0.4010 \| 0.7148 \| \| 0.2894 \| 40.0 \| 12480 \| 0.3472 \| 0.7220 \| \| 0.2764 \| 41.0 \| 12792 \| 0.3364 \| 0.7112 \| \| 0.2708 \| 42.0 \| 13104 \| 0.3379 \| 0.7040 \| \| 0.2708 \| 43.0 \| 13416 \| 0.3625 \| 0.7148 \| \| 0.2665 \| 44.0 \| 13728 \| 0.3435 \| 0.7220 \| \| 0.265 \| 45.0 \| 14040 \| 0.3762 \| 0.7292 \| \| 0.265 \| 46.0 \| 14352 \| 0.3322 \| 0.7220 \| \| 0.2618 \| 47.0 \| 14664 \| 0.3265 \| 0.7329 \| \| 0.2618 \| 48.0 \| 14976 \| 0.3752 \| 0.7292 \| \| 0.2513 \| 49.0 \| 15288 \| 0.3415 \| 0.7292 \| \| 0.2487 \| 50.0 \| 15600 \| 0.3604 \| 0.7220 \| \| 0.2487 \| 51.0 \| 15912 \| 0.3484 \| 0.7292 \| \| 0.2488 \| 52.0 \| 16224 \| 0.3598 \| 0.7329 \| \| 0.2404 \| 53.0 \| 16536 \| 0.3719 \| 0.7184 \| \| 0.2404 \| 54.0 \| 16848 \| 0.3329 \| 0.7220 \| \| 0.2359 \| 55.0 \| 17160 \| 0.3535 \| 0.7220 \| \| 0.2359 \| 56.0 \| 17472 \| 0.3606 \| 0.7256 \| \| 0.2364 \| 57.0 \| 17784 \| 0.3407 \| 0.7292 \| \| 0.2343 \| 58.0 \| 18096 \| 0.3342 \| 0.7292 \| \| 0.2343 \| 59.0 \| 18408 \| 0.3451 \| 0.7220 \| \| 0.2348 \| 60.0 \| 18720 \| 0.3421 \| 0.7256 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
chezuro/pm-fine-tuned	chezuro	2023-08-24T03:22:56Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-24T00:55:03Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
ChillyMango/results	ChillyMango	2023-08-24T03:16:01Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-hf", "base_model:finetune:NousResearch/Llama-2-7b-hf", "region:us" ]	null	2023-08-24T00:42:24Z	--- base_model: NousResearch/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on an unknown 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: 0.0002 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
ohicarip/sd-deepfashion-baseline-model	ohicarip	2023-08-24T02:45:46Z	4	1	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:ohicarip/deepfashion_bl2", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-20T19:40:51Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 datasets: - ohicarip/deepfashion_bl2 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - ohicarip/sd-deepfashion-baseline-model This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the ohicarip/deepfashion_bl2 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['This man wears a long-sleeve sweater with pure color patterns. The sweater is with cotton fabric. It has a round neckline. The pants this man wears is of long length. The pants are with denim fabric and solid color patterns. The outer clothing the gentleman wears is with cotton fabric and solid color patterns. There is an accessory on his wrist.', 'This person is wearing a short-sleeve shirt with pure color patterns. The shirt is with cotton fabric. It has a round neckline. This person wears a long trousers. The trousers are with denim fabric and lattice patterns.', 'This guy is wearing a short-sleeve shirt with solid color patterns and a long pants. The shirt is with cotton fabric and its neckline is crew. The pants are with denim fabric and solid color patterns.', 'This female is wearing a tank tank shirt with plaid patterns and a three-point shorts. The tank shirt is with cotton fabric. The neckline of the tank shirt is crew. The shorts are with cotton fabric and plaid patterns. This lady wears socks in shoes.']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("ohicarip/sd-deepfashion-baseline-model", torch_dtype=torch.float16) prompt = "This man wears a long-sleeve sweater with pure color patterns. The sweater is with cotton fabric. It has a round neckline. The pants this man wears is of long length. The pants are with denim fabric and solid color patterns. The outer clothing the gentleman wears is with cotton fabric and solid color patterns. There is an accessory on his wrist." image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 15 * Learning rate: 1e-05 * Batch size: 8 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16 More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/ohicarip/text2image-fine-tune/runs/6en1otkv).
Timucin/q-Taxi	Timucin	2023-08-24T02:44:53Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T02:44:51Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Timucin/q-Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
mcwei/rvinpaint	mcwei	2023-08-24T02:39:47Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T00:41:06Z	--- license: creativeml-openrail-m ---
Timucin/q-FrozenLake-v1-4x4-noSlippery	Timucin	2023-08-24T02:38:11Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T02:38:09Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Timucin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
AndreaHuang97/MarkupLM	AndreaHuang97	2023-08-24T02:32:52Z	49	0	transformers	[ "transformers", "pytorch", "markuplm", "text2text-generation", "en", "arxiv:2110.08518", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-24T01:40:47Z	--- language: - en pipeline_tag: text2text-generation --- # MarkupLM Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) ## Introduction MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) Junlong Li, Yiheng Xu, Lei Cui, Furu Wei, ACL 2022 ## Usage We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/markuplm) and [demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM).
LarryAIDraw/Lucy-08	LarryAIDraw	2023-08-24T02:23:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:06:40Z	--- license: creativeml-openrail-m --- https://civitai.com/models/132939/lucy-seiland-trails-of-cold-steel-4-sen-no-kiseki-4
LarryAIDraw/Aurier-10	LarryAIDraw	2023-08-24T02:23:33Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:07:09Z	--- license: creativeml-openrail-m --- https://civitai.com/models/132943/aurier-vander-trails-of-cold-steel-3-sen-no-kiseki-3
LarryAIDraw/Kuroe_Casual_wear_-V1	LarryAIDraw	2023-08-24T02:22:49Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:04:29Z	--- license: creativeml-openrail-m --- https://civitai.com/models/133037/redivekuroe-princess-connect-redive
LarryAIDraw/shizuku_yaegashi_v1	LarryAIDraw	2023-08-24T02:21:24Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:05:51Z	--- license: creativeml-openrail-m --- https://civitai.com/models/132963/shizuku-yaegashi-or-arifureta-from-commonplace-to-worlds-strongest
LarryAIDraw/MiyuCind-06	LarryAIDraw	2023-08-24T02:20:47Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:05:29Z	--- license: creativeml-openrail-m --- https://civitai.com/models/132955/miyu-mifune-idolmaster
LarryAIDraw/Fuwawa_Abyssgard-10	LarryAIDraw	2023-08-24T02:20:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:05:02Z	--- license: creativeml-openrail-m --- https://civitai.com/models/117233/fuwawa-abyssgard-hololive-en-lora
LarryAIDraw/Atago_and_Takao_20230820183759-000014	LarryAIDraw	2023-08-24T02:19:06Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:03:56Z	--- license: creativeml-openrail-m --- https://civitai.com/models/133344/atago-and-tako-lora
LarryAIDraw/shimanto	LarryAIDraw	2023-08-24T02:18:34Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:03:25Z	--- license: creativeml-openrail-m --- https://civitai.com/models/133172/ijn-shimanto-or-azur-lane
LarryAIDraw/Mary	LarryAIDraw	2023-08-24T02:17:50Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:02:59Z	--- license: creativeml-openrail-m --- https://civitai.com/models/133210/mary-the-eminence-in-shadow
LarryAIDraw/ChristinaHope	LarryAIDraw	2023-08-24T02:17:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T02:02:16Z	--- license: creativeml-openrail-m --- https://civitai.com/models/133295/christina-hope-the-eminence-in-shadow
lianlian123/Reinforce-CartPole8	lianlian123	2023-08-24T02:14:00Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-23T08:21:31Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole8 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
mmenendezg/xlm-roberta-base-finetuned-panx-de	mmenendezg	2023-08-24T02:08:36Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-22T23:21:35Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.6378279372946183 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.0742 - F1: 0.6378 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2424 \| 1.0 \| 525 \| 0.2543 \| 0.0 \| \| 0.1994 \| 2.0 \| 1050 \| 0.0977 \| 0.5081 \| \| 0.1011 \| 3.0 \| 1575 \| 0.0742 \| 0.6378 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
vicssl/test-trainer	vicssl	2023-08-24T02:04:54Z	0	0	null	[ "sentence-similarity", "region:us" ]	sentence-similarity	2023-08-23T08:47:52Z	--- pipeline_tag: sentence-similarity ---
ardt-multipart/ardt-multipart-arrl_train_walker2d_high-2408_0127-33	ardt-multipart	2023-08-24T02:03:02Z	32	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-24T00:28:40Z	--- tags: - generated_from_trainer model-index: - name: ardt-multipart-arrl_train_walker2d_high-2408_0127-33 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. --> # ardt-multipart-arrl_train_walker2d_high-2408_0127-33 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001 - train_batch_size: 64 - 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: 1000 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
JJinBBangMan/marian-finetuned-kde4-en-to-fr	JJinBBangMan	2023-08-24T02:00:41Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-08-24T00:10:39Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.853174528380514 --- <!-- 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8568 - Bleu: 52.8532 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
cooperic/distilbert-base-uncased-finetuned-emotion	cooperic	2023-08-24T01:49:06Z	108	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	2023-08-24T00:31:22Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.9283528881025964 --- <!-- 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: 0.2174 - Accuracy: 0.9285 - F1: 0.9284 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8012 \| 1.0 \| 250 \| 0.3094 \| 0.9095 \| 0.9083 \| \| 0.2454 \| 2.0 \| 500 \| 0.2174 \| 0.9285 \| 0.9284 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3
dkqjrm/20230824083011	dkqjrm	2023-08-24T01:45:30Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T23:30:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824083011' 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. --> # 20230824083011 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3090 - Accuracy: 0.7401 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.7501 \| 1.0 \| 623 \| 0.9859 \| 0.4729 \| \| 0.6252 \| 2.0 \| 1246 \| 0.4891 \| 0.4801 \| \| 0.5769 \| 3.0 \| 1869 \| 1.1271 \| 0.4729 \| \| 0.5672 \| 4.0 \| 2492 \| 0.4257 \| 0.5632 \| \| 0.5439 \| 5.0 \| 3115 \| 0.5883 \| 0.5415 \| \| 0.5426 \| 6.0 \| 3738 \| 0.3734 \| 0.6245 \| \| 0.61 \| 7.0 \| 4361 \| 0.4410 \| 0.5848 \| \| 0.4937 \| 8.0 \| 4984 \| 0.4091 \| 0.5632 \| \| 0.4293 \| 9.0 \| 5607 \| 0.3712 \| 0.6282 \| \| 0.3897 \| 10.0 \| 6230 \| 0.3441 \| 0.6931 \| \| 0.3759 \| 11.0 \| 6853 \| 0.3400 \| 0.7004 \| \| 0.379 \| 12.0 \| 7476 \| 0.3802 \| 0.6787 \| \| 0.3661 \| 13.0 \| 8099 \| 0.3456 \| 0.7184 \| \| 0.374 \| 14.0 \| 8722 \| 0.3545 \| 0.6859 \| \| 0.3441 \| 15.0 \| 9345 \| 0.3219 \| 0.7112 \| \| 0.3339 \| 16.0 \| 9968 \| 0.3192 \| 0.7184 \| \| 0.3324 \| 17.0 \| 10591 \| 0.3290 \| 0.7184 \| \| 0.324 \| 18.0 \| 11214 \| 0.3284 \| 0.7112 \| \| 0.3641 \| 19.0 \| 11837 \| 0.3100 \| 0.7292 \| \| 0.3138 \| 20.0 \| 12460 \| 0.3102 \| 0.7365 \| \| 0.3099 \| 21.0 \| 13083 \| 0.3887 \| 0.7076 \| \| 0.3095 \| 22.0 \| 13706 \| 0.3443 \| 0.7004 \| \| 0.3039 \| 23.0 \| 14329 \| 0.3937 \| 0.6895 \| \| 0.287 \| 24.0 \| 14952 \| 0.3071 \| 0.7473 \| \| 0.2718 \| 25.0 \| 15575 \| 0.3097 \| 0.7184 \| \| 0.2711 \| 26.0 \| 16198 \| 0.2888 \| 0.7329 \| \| 0.2738 \| 27.0 \| 16821 \| 0.2920 \| 0.7220 \| \| 0.2697 \| 28.0 \| 17444 \| 0.2986 \| 0.7329 \| \| 0.2589 \| 29.0 \| 18067 \| 0.3092 \| 0.7437 \| \| 0.2536 \| 30.0 \| 18690 \| 0.3141 \| 0.7292 \| \| 0.2564 \| 31.0 \| 19313 \| 0.3134 \| 0.7401 \| \| 0.2493 \| 32.0 \| 19936 \| 0.2962 \| 0.7365 \| \| 0.2428 \| 33.0 \| 20559 \| 0.3358 \| 0.7256 \| \| 0.2425 \| 34.0 \| 21182 \| 0.3155 \| 0.7148 \| \| 0.2342 \| 35.0 \| 21805 \| 0.3000 \| 0.7220 \| \| 0.2394 \| 36.0 \| 22428 \| 0.2955 \| 0.7329 \| \| 0.2257 \| 37.0 \| 23051 \| 0.3070 \| 0.7509 \| \| 0.2272 \| 38.0 \| 23674 \| 0.2959 \| 0.7365 \| \| 0.2197 \| 39.0 \| 24297 \| 0.3100 \| 0.7401 \| \| 0.2144 \| 40.0 \| 24920 \| 0.3009 \| 0.7365 \| \| 0.2164 \| 41.0 \| 25543 \| 0.2957 \| 0.7256 \| \| 0.2129 \| 42.0 \| 26166 \| 0.3133 \| 0.7292 \| \| 0.2106 \| 43.0 \| 26789 \| 0.3110 \| 0.7329 \| \| 0.2069 \| 44.0 \| 27412 \| 0.3072 \| 0.7329 \| \| 0.2051 \| 45.0 \| 28035 \| 0.3300 \| 0.7292 \| \| 0.2064 \| 46.0 \| 28658 \| 0.3106 \| 0.7256 \| \| 0.2039 \| 47.0 \| 29281 \| 0.3114 \| 0.7292 \| \| 0.2106 \| 48.0 \| 29904 \| 0.3180 \| 0.7365 \| \| 0.2008 \| 49.0 \| 30527 \| 0.3099 \| 0.7329 \| \| 0.1945 \| 50.0 \| 31150 \| 0.3066 \| 0.7329 \| \| 0.1958 \| 51.0 \| 31773 \| 0.3124 \| 0.7401 \| \| 0.1939 \| 52.0 \| 32396 \| 0.3230 \| 0.7401 \| \| 0.1942 \| 53.0 \| 33019 \| 0.3105 \| 0.7365 \| \| 0.1887 \| 54.0 \| 33642 \| 0.3014 \| 0.7256 \| \| 0.185 \| 55.0 \| 34265 \| 0.3052 \| 0.7365 \| \| 0.1868 \| 56.0 \| 34888 \| 0.3155 \| 0.7365 \| \| 0.1888 \| 57.0 \| 35511 \| 0.3056 \| 0.7256 \| \| 0.1885 \| 58.0 \| 36134 \| 0.3069 \| 0.7329 \| \| 0.192 \| 59.0 \| 36757 \| 0.3076 \| 0.7329 \| \| 0.1807 \| 60.0 \| 37380 \| 0.3090 \| 0.7401 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Ramoslee/whisper-small-th_10000	Ramoslee	2023-08-24T01:41:08Z	3	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "th", "dataset:mozilla-foundation/common_voice_11_0", "base_model:Ramoslee/Whishper-small-th", "base_model:finetune:Ramoslee/Whishper-small-th", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-23T13:02:11Z	--- language: - th license: apache-2.0 base_model: Ramoslee/Whishper-small-th tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Thai results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 th type: mozilla-foundation/common_voice_11_0 config: th split: test args: th metrics: - name: Wer type: wer value: 18.87614018843608 --- <!-- 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 Thai This model is a fine-tuned version of [Ramoslee/Whishper-small-th](https://huggingface.co/Ramoslee/Whishper-small-th) on the mozilla-foundation/common_voice_11_0 th dataset. It achieves the following results on the evaluation set: - Loss: 0.1836 - Wer: 18.8761 ## 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: 6 - eval_batch_size: 10 - 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: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.1262 \| 0.27 \| 1000 \| 0.2267 \| 25.1536 \| \| 0.1174 \| 0.55 \| 2000 \| 0.2190 \| 24.6093 \| \| 0.1363 \| 0.82 \| 3000 \| 0.2059 \| 24.5492 \| \| 0.0618 \| 1.1 \| 4000 \| 0.1970 \| 22.1944 \| \| 0.0686 \| 1.37 \| 5000 \| 0.1916 \| 21.2372 \| \| 0.0722 \| 1.65 \| 6000 \| 0.1854 \| 20.3488 \| \| 0.0771 \| 1.92 \| 7000 \| 0.1801 \| 19.8033 \| \| 0.0191 \| 2.2 \| 8000 \| 0.1859 \| 19.5656 \| \| 0.0237 \| 2.47 \| 9000 \| 0.1862 \| 19.1376 \| \| 0.0205 \| 2.74 \| 10000 \| 0.1836 \| 18.8761 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 1.12.1+cu116 - Datasets 2.14.4.dev0 - Tokenizers 0.12.1
dkqjrm/20230824083855	dkqjrm	2023-08-24T01:40:49Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T23:39:15Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824083855' 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. --> # 20230824083855 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.0821 - Accuracy: 0.7473 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.5366 \| 1.0 \| 623 \| 0.8415 \| 0.4729 \| \| 0.3757 \| 2.0 \| 1246 \| 0.3098 \| 0.4693 \| \| 0.3001 \| 3.0 \| 1869 \| 0.5999 \| 0.4729 \| \| 0.3227 \| 4.0 \| 2492 \| 0.2808 \| 0.4729 \| \| 0.3109 \| 5.0 \| 3115 \| 0.2772 \| 0.5487 \| \| 0.3034 \| 6.0 \| 3738 \| 0.1529 \| 0.6029 \| \| 0.2648 \| 7.0 \| 4361 \| 0.1565 \| 0.6029 \| \| 0.2104 \| 8.0 \| 4984 \| 0.1394 \| 0.6245 \| \| 0.1926 \| 9.0 \| 5607 \| 0.1404 \| 0.6390 \| \| 0.175 \| 10.0 \| 6230 \| 0.1292 \| 0.6859 \| \| 0.1634 \| 11.0 \| 6853 \| 0.1174 \| 0.7004 \| \| 0.1618 \| 12.0 \| 7476 \| 0.1228 \| 0.6787 \| \| 0.1555 \| 13.0 \| 8099 \| 0.1287 \| 0.6534 \| \| 0.1534 \| 14.0 \| 8722 \| 0.1461 \| 0.6570 \| \| 0.1523 \| 15.0 \| 9345 \| 0.1356 \| 0.6426 \| \| 0.1448 \| 16.0 \| 9968 \| 0.1065 \| 0.6968 \| \| 0.1402 \| 17.0 \| 10591 \| 0.1011 \| 0.7292 \| \| 0.1342 \| 18.0 \| 11214 \| 0.1112 \| 0.6643 \| \| 0.1388 \| 19.0 \| 11837 \| 0.1255 \| 0.6823 \| \| 0.1281 \| 20.0 \| 12460 \| 0.0965 \| 0.7220 \| \| 0.128 \| 21.0 \| 13083 \| 0.0985 \| 0.7040 \| \| 0.1236 \| 22.0 \| 13706 \| 0.1339 \| 0.7040 \| \| 0.1267 \| 23.0 \| 14329 \| 0.1238 \| 0.7365 \| \| 0.1186 \| 24.0 \| 14952 \| 0.0942 \| 0.7292 \| \| 0.1101 \| 25.0 \| 15575 \| 0.0923 \| 0.7220 \| \| 0.1122 \| 26.0 \| 16198 \| 0.0919 \| 0.7401 \| \| 0.1088 \| 27.0 \| 16821 \| 0.0893 \| 0.7292 \| \| 0.1059 \| 28.0 \| 17444 \| 0.0897 \| 0.7401 \| \| 0.106 \| 29.0 \| 18067 \| 0.0878 \| 0.7509 \| \| 0.1019 \| 30.0 \| 18690 \| 0.0945 \| 0.7365 \| \| 0.1047 \| 31.0 \| 19313 \| 0.0900 \| 0.7256 \| \| 0.1011 \| 32.0 \| 19936 \| 0.0884 \| 0.7437 \| \| 0.0962 \| 33.0 \| 20559 \| 0.0874 \| 0.7329 \| \| 0.0971 \| 34.0 \| 21182 \| 0.0933 \| 0.7329 \| \| 0.0914 \| 35.0 \| 21805 \| 0.0845 \| 0.7473 \| \| 0.0965 \| 36.0 \| 22428 \| 0.0914 \| 0.7365 \| \| 0.0914 \| 37.0 \| 23051 \| 0.0855 \| 0.7292 \| \| 0.0894 \| 38.0 \| 23674 \| 0.0867 \| 0.7256 \| \| 0.087 \| 39.0 \| 24297 \| 0.0861 \| 0.7329 \| \| 0.0865 \| 40.0 \| 24920 \| 0.0830 \| 0.7329 \| \| 0.0851 \| 41.0 \| 25543 \| 0.0827 \| 0.7473 \| \| 0.0837 \| 42.0 \| 26166 \| 0.0818 \| 0.7365 \| \| 0.0865 \| 43.0 \| 26789 \| 0.0840 \| 0.7401 \| \| 0.0807 \| 44.0 \| 27412 \| 0.0815 \| 0.7292 \| \| 0.0829 \| 45.0 \| 28035 \| 0.0840 \| 0.7365 \| \| 0.0814 \| 46.0 \| 28658 \| 0.0851 \| 0.7401 \| \| 0.0798 \| 47.0 \| 29281 \| 0.0841 \| 0.7401 \| \| 0.0806 \| 48.0 \| 29904 \| 0.0838 \| 0.7473 \| \| 0.0773 \| 49.0 \| 30527 \| 0.0823 \| 0.7401 \| \| 0.0769 \| 50.0 \| 31150 \| 0.0813 \| 0.7329 \| \| 0.0763 \| 51.0 \| 31773 \| 0.0822 \| 0.7509 \| \| 0.0792 \| 52.0 \| 32396 \| 0.0833 \| 0.7365 \| \| 0.0772 \| 53.0 \| 33019 \| 0.0819 \| 0.7365 \| \| 0.0732 \| 54.0 \| 33642 \| 0.0810 \| 0.7365 \| \| 0.0708 \| 55.0 \| 34265 \| 0.0808 \| 0.7365 \| \| 0.0741 \| 56.0 \| 34888 \| 0.0824 \| 0.7509 \| \| 0.0725 \| 57.0 \| 35511 \| 0.0816 \| 0.7437 \| \| 0.072 \| 58.0 \| 36134 \| 0.0812 \| 0.7437 \| \| 0.0712 \| 59.0 \| 36757 \| 0.0827 \| 0.7401 \| \| 0.0707 \| 60.0 \| 37380 \| 0.0821 \| 0.7473 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230824084116	dkqjrm	2023-08-24T01:39:38Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T23:41:35Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824084116' 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. --> # 20230824084116 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6747 - Accuracy: 0.7329 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.0144 \| 1.0 \| 623 \| 1.2485 \| 0.4729 \| \| 0.8551 \| 2.0 \| 1246 \| 0.7296 \| 0.5415 \| \| 0.9621 \| 3.0 \| 1869 \| 1.3927 \| 0.4729 \| \| 0.8648 \| 4.0 \| 2492 \| 0.6253 \| 0.6173 \| \| 0.8311 \| 5.0 \| 3115 \| 0.6509 \| 0.6606 \| \| 0.8365 \| 6.0 \| 3738 \| 0.6018 \| 0.6895 \| \| 0.772 \| 7.0 \| 4361 \| 0.7314 \| 0.6751 \| \| 0.7306 \| 8.0 \| 4984 \| 1.0930 \| 0.5957 \| \| 0.763 \| 9.0 \| 5607 \| 0.7093 \| 0.7076 \| \| 0.6931 \| 10.0 \| 6230 \| 0.6302 \| 0.6968 \| \| 0.6465 \| 11.0 \| 6853 \| 1.1188 \| 0.5776 \| \| 0.6503 \| 12.0 \| 7476 \| 0.6957 \| 0.7112 \| \| 0.6657 \| 13.0 \| 8099 \| 0.6470 \| 0.7112 \| \| 0.6315 \| 14.0 \| 8722 \| 0.7099 \| 0.7112 \| \| 0.5491 \| 15.0 \| 9345 \| 0.5178 \| 0.7184 \| \| 0.4908 \| 16.0 \| 9968 \| 0.6282 \| 0.7365 \| \| 0.4742 \| 17.0 \| 10591 \| 0.6553 \| 0.7256 \| \| 0.4653 \| 18.0 \| 11214 \| 0.5637 \| 0.7112 \| \| 0.492 \| 19.0 \| 11837 \| 0.5870 \| 0.7184 \| \| 0.4519 \| 20.0 \| 12460 \| 0.8201 \| 0.7292 \| \| 0.4198 \| 21.0 \| 13083 \| 0.6294 \| 0.7365 \| \| 0.403 \| 22.0 \| 13706 \| 0.6998 \| 0.7220 \| \| 0.4017 \| 23.0 \| 14329 \| 0.8424 \| 0.7220 \| \| 0.368 \| 24.0 \| 14952 \| 0.6179 \| 0.7401 \| \| 0.3514 \| 25.0 \| 15575 \| 0.6303 \| 0.7256 \| \| 0.3458 \| 26.0 \| 16198 \| 0.6241 \| 0.7292 \| \| 0.3488 \| 27.0 \| 16821 \| 0.6348 \| 0.7365 \| \| 0.33 \| 28.0 \| 17444 \| 0.6663 \| 0.7292 \| \| 0.3133 \| 29.0 \| 18067 \| 0.6231 \| 0.7437 \| \| 0.3108 \| 30.0 \| 18690 \| 0.6940 \| 0.7220 \| \| 0.3156 \| 31.0 \| 19313 \| 0.7685 \| 0.7256 \| \| 0.2887 \| 32.0 \| 19936 \| 0.5912 \| 0.7365 \| \| 0.2871 \| 33.0 \| 20559 \| 0.6539 \| 0.7401 \| \| 0.2835 \| 34.0 \| 21182 \| 0.7319 \| 0.7292 \| \| 0.2587 \| 35.0 \| 21805 \| 0.6106 \| 0.7365 \| \| 0.2767 \| 36.0 \| 22428 \| 0.6255 \| 0.7329 \| \| 0.2621 \| 37.0 \| 23051 \| 0.7181 \| 0.7329 \| \| 0.2733 \| 38.0 \| 23674 \| 0.6841 \| 0.7365 \| \| 0.2473 \| 39.0 \| 24297 \| 0.7042 \| 0.7329 \| \| 0.2467 \| 40.0 \| 24920 \| 0.6123 \| 0.7329 \| \| 0.2357 \| 41.0 \| 25543 \| 0.6681 \| 0.7365 \| \| 0.2333 \| 42.0 \| 26166 \| 0.7094 \| 0.7292 \| \| 0.2387 \| 43.0 \| 26789 \| 0.6546 \| 0.7365 \| \| 0.2248 \| 44.0 \| 27412 \| 0.7021 \| 0.7329 \| \| 0.2271 \| 45.0 \| 28035 \| 0.6913 \| 0.7545 \| \| 0.2288 \| 46.0 \| 28658 \| 0.6855 \| 0.7365 \| \| 0.2159 \| 47.0 \| 29281 \| 0.6495 \| 0.7401 \| \| 0.2107 \| 48.0 \| 29904 \| 0.6568 \| 0.7292 \| \| 0.2204 \| 49.0 \| 30527 \| 0.7337 \| 0.7329 \| \| 0.2038 \| 50.0 \| 31150 \| 0.6391 \| 0.7365 \| \| 0.2183 \| 51.0 \| 31773 \| 0.6593 \| 0.7437 \| \| 0.2041 \| 52.0 \| 32396 \| 0.6518 \| 0.7220 \| \| 0.2107 \| 53.0 \| 33019 \| 0.6677 \| 0.7256 \| \| 0.2076 \| 54.0 \| 33642 \| 0.6716 \| 0.7292 \| \| 0.1946 \| 55.0 \| 34265 \| 0.6957 \| 0.7256 \| \| 0.1974 \| 56.0 \| 34888 \| 0.6858 \| 0.7256 \| \| 0.2047 \| 57.0 \| 35511 \| 0.6721 \| 0.7329 \| \| 0.2001 \| 58.0 \| 36134 \| 0.6747 \| 0.7365 \| \| 0.1899 \| 59.0 \| 36757 \| 0.6842 \| 0.7329 \| \| 0.1872 \| 60.0 \| 37380 \| 0.6747 \| 0.7329 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230824082958	dkqjrm	2023-08-24T01:33:05Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T23:30:18Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824082958' 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. --> # 20230824082958 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5547 - Accuracy: 0.7581 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.1252 \| 1.0 \| 623 \| 0.6915 \| 0.5415 \| \| 0.9382 \| 2.0 \| 1246 \| 0.7221 \| 0.5307 \| \| 1.0555 \| 3.0 \| 1869 \| 0.7387 \| 0.5199 \| \| 0.9336 \| 4.0 \| 2492 \| 0.9751 \| 0.6390 \| \| 0.8894 \| 5.0 \| 3115 \| 0.9277 \| 0.6643 \| \| 0.9066 \| 6.0 \| 3738 \| 1.1836 \| 0.6931 \| \| 0.8496 \| 7.0 \| 4361 \| 0.8242 \| 0.7184 \| \| 0.7761 \| 8.0 \| 4984 \| 0.9061 \| 0.6859 \| \| 0.8175 \| 9.0 \| 5607 \| 0.7474 \| 0.7220 \| \| 0.7575 \| 10.0 \| 6230 \| 0.8582 \| 0.7292 \| \| 0.747 \| 11.0 \| 6853 \| 0.8351 \| 0.7256 \| \| 0.728 \| 12.0 \| 7476 \| 0.8912 \| 0.7148 \| \| 0.8296 \| 13.0 \| 8099 \| 0.9471 \| 0.7220 \| \| 0.7327 \| 14.0 \| 8722 \| 1.1407 \| 0.7148 \| \| 0.7284 \| 15.0 \| 9345 \| 0.7681 \| 0.7256 \| \| 0.6642 \| 16.0 \| 9968 \| 1.4084 \| 0.6679 \| \| 0.5888 \| 17.0 \| 10591 \| 0.8413 \| 0.7329 \| \| 0.6074 \| 18.0 \| 11214 \| 0.7461 \| 0.7401 \| \| 0.625 \| 19.0 \| 11837 \| 0.9516 \| 0.7545 \| \| 0.5911 \| 20.0 \| 12460 \| 1.3395 \| 0.7292 \| \| 0.5322 \| 21.0 \| 13083 \| 1.3924 \| 0.7509 \| \| 0.5247 \| 22.0 \| 13706 \| 1.1553 \| 0.7256 \| \| 0.5146 \| 23.0 \| 14329 \| 1.6692 \| 0.7040 \| \| 0.4493 \| 24.0 \| 14952 \| 1.2315 \| 0.7437 \| \| 0.399 \| 25.0 \| 15575 \| 1.2710 \| 0.7545 \| \| 0.3644 \| 26.0 \| 16198 \| 1.5049 \| 0.7473 \| \| 0.4031 \| 27.0 \| 16821 \| 1.5735 \| 0.7401 \| \| 0.386 \| 28.0 \| 17444 \| 1.4749 \| 0.7220 \| \| 0.3735 \| 29.0 \| 18067 \| 0.9541 \| 0.7365 \| \| 0.356 \| 30.0 \| 18690 \| 1.3936 \| 0.7473 \| \| 0.3496 \| 31.0 \| 19313 \| 0.9982 \| 0.7437 \| \| 0.3149 \| 32.0 \| 19936 \| 0.9572 \| 0.7581 \| \| 0.3094 \| 33.0 \| 20559 \| 1.5663 \| 0.7256 \| \| 0.2886 \| 34.0 \| 21182 \| 1.5993 \| 0.7365 \| \| 0.2545 \| 35.0 \| 21805 \| 1.1515 \| 0.7545 \| \| 0.276 \| 36.0 \| 22428 \| 1.2768 \| 0.7473 \| \| 0.2645 \| 37.0 \| 23051 \| 1.4290 \| 0.7509 \| \| 0.262 \| 38.0 \| 23674 \| 1.2363 \| 0.7617 \| \| 0.2261 \| 39.0 \| 24297 \| 1.3446 \| 0.7617 \| \| 0.2291 \| 40.0 \| 24920 \| 1.0532 \| 0.7509 \| \| 0.2178 \| 41.0 \| 25543 \| 1.4745 \| 0.7509 \| \| 0.2104 \| 42.0 \| 26166 \| 1.3830 \| 0.7545 \| \| 0.217 \| 43.0 \| 26789 \| 1.7099 \| 0.7473 \| \| 0.214 \| 44.0 \| 27412 \| 1.7054 \| 0.7401 \| \| 0.1856 \| 45.0 \| 28035 \| 1.4350 \| 0.7545 \| \| 0.2014 \| 46.0 \| 28658 \| 1.7266 \| 0.7473 \| \| 0.1759 \| 47.0 \| 29281 \| 1.2659 \| 0.7581 \| \| 0.2027 \| 48.0 \| 29904 \| 1.8336 \| 0.7401 \| \| 0.1871 \| 49.0 \| 30527 \| 1.3398 \| 0.7509 \| \| 0.1586 \| 50.0 \| 31150 \| 1.4948 \| 0.7509 \| \| 0.1619 \| 51.0 \| 31773 \| 1.3787 \| 0.7545 \| \| 0.1665 \| 52.0 \| 32396 \| 1.6532 \| 0.7545 \| \| 0.1786 \| 53.0 \| 33019 \| 1.4697 \| 0.7581 \| \| 0.1609 \| 54.0 \| 33642 \| 1.5462 \| 0.7653 \| \| 0.1304 \| 55.0 \| 34265 \| 1.3577 \| 0.7581 \| \| 0.1576 \| 56.0 \| 34888 \| 1.7004 \| 0.7617 \| \| 0.1522 \| 57.0 \| 35511 \| 1.4629 \| 0.7581 \| \| 0.1496 \| 58.0 \| 36134 \| 1.6336 \| 0.7581 \| \| 0.1406 \| 59.0 \| 36757 \| 1.5699 \| 0.7545 \| \| 0.1268 \| 60.0 \| 37380 \| 1.5547 \| 0.7581 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
nxnhjrjtbjfzhrovwl/limarp-llongma2-8k-ggml-f16	nxnhjrjtbjfzhrovwl	2023-08-24T01:12:05Z	0	0	null	[ "arxiv:2305.11206", "license:agpl-3.0", "region:us" ]	null	2023-08-23T18:04:50Z	--- '[object Object]': null license: agpl-3.0 --- This repository contains the unquantized merge of [limarp-llongma2-8k lora](https://huggingface.co/lemonilia/limarp-llongma2-8k) in ggml format. You can quantize the f16 ggml to the quantization of your choice by following the below steps: 1. Download and extract the [llama.cpp binaries](https://github.com/ggerganov/llama.cpp/releases/download/master-41c6741/llama-master-41c6741-bin-win-avx2-x64.zip) ([or compile it yourself if you're on Linux](https://github.com/ggerganov/llama.cpp#build)) 2. Move the "quantize" executable to the same folder where you downloaded the f16 ggml model. 3. Open a command prompt window in that same folder and write the following command, making the changes that you see fit. ```bash quantize.exe limarp-llongma2-13b.ggmlv3.f16.bin limarp-llongma2-13b.ggmlv3.q4_0.bin q4_0 ``` 4. Press enter to run the command and the quantized model will be generated in the folder. The below are the contents of the original model card: # Model Card for LimaRP-LLongMA2-8k-v2 LimaRP-LLongMA2-8k is an experimental [Llama2](https://huggingface.co/meta-llama) finetune narrowly focused on novel-style roleplay chatting, and a continuation of the previously released [LimaRP-llama2](https://huggingface.co/lemonilia/limarp-llama2) with a larger number of training tokens (+95%). To considerably facilitate uploading, distribution and merging with other models, LoRA adapters are provided. LimaRP-LLongMA2 LoRA adapters, as their name suggests, are intended to be applied on LLongMA-2 models with 8k context ([7B](https://huggingface.co/conceptofmind/LLongMA-2-7b) and [13B](https://huggingface.co/conceptofmind/LLongMA-2-13b)) and their derivatives. Data updates may be posted in the future. The current version is v3. ## Model Details ### Model Description This is an experimental attempt at creating an RP-oriented fine-tune using a manually-curated, high-quality dataset of human-generated conversations. The main rationale for this are the observations from [Zhou et al.](https://arxiv.org/abs/2305.11206). The authors suggested that just 1000-2000 carefully curated training examples may yield high quality output for assistant-type chatbots. This is in contrast with the commonly employed strategy where a very large number of training examples (tens of thousands to even millions) of widely varying quality are used. For LimaRP a similar approach was used, with the difference that the conversational data is almost entirely human-generated. Every training example is manually compiled and selected to comply with subjective quality parameters, with virtually no chance for OpenAI-style alignment responses to come up. ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> The model is intended to approximate the experience of 1-on-1 roleplay as observed on many Internet forums dedicated on roleplaying. It _must_ be used with a specific format similar to that of this template: ``` <<SYSTEM>> Character's Persona: {bot character description} User's Persona: {user character description} Scenario: {what happens in the story} Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length. <<AIBOT>> Character: {utterance} <<HUMAN>> User: {utterance} [etc.] ``` With `<<SYSTEM>>`, `<<AIBOT>>` and `<<HUMAN>>` being special instruct-mode sequences. The text under curly braces must be replaced with appropriate text in _natural language_. Replace `User` and `Character` with actual character names. This more graphical breakdown of the prompt format with a practical example might make it clearer: ![graphical explanation](https://files.catbox.moe/fq8ner.png) ### More detailed notes on prompt format, usage and other settings - The model has been tested mainly using Oobabooga's `text-generation-webui` as a backend - Preferably respect spacing and newlines shown above. This might not be possible yet with some frontends. - Replace `Character` and `User` in the above template with your desired names. - The scenario description has a large influence on what the character will do. Try to keep it more open-ended to lessen its impact. - The model expects users and characters to use third-person narration in simple past and enclose dialogues with standard quotation marks `" "`. Other formats are not supported (= not in the training data). - Do not use newlines in Persona and Scenario. Use natural language. - The last line in `<<SYSTEM>>` does not need to be written exactly as depicted, but should mention that `Character` and `User` will engage in roleplay and specify the length of `Character`'s messages - The message lengths used during training are: `tiny`, `short`, `average`, `long`, `huge`, `humongous`. However, there might not have been enough training examples for each length for this instruction to have a significant impact. The preferred lengths for this type of role-playing are `average` or `long`. - Suggested text generation settings: - Temperature ~0.70 - Tail-Free Sampling 0.85 - Repetition penalty ~1.10 (Compared to LLaMAv1, Llama2 appears to require a somewhat higher rep.pen.) - Not used: Top-P (disabled/set to 1.0), Top-K (disabled/set to 0), Typical P (disabled/set to 1.0) ### Sample character cards Here are a few example SillyTavern character cards following the required format; download and import into SillyTavern. Feel free to modify and adapt them to your purposes. - [Carina, a 'big sister' android maid](https://files.catbox.moe/1qcqqj.png) - [Charlotte, a cute android maid](https://files.catbox.moe/k1x9a7.png) - [Etma, an 'aligned' AI assistant](https://files.catbox.moe/dj8ggi.png) - [Mila, an anthro pet catgirl](https://files.catbox.moe/amnsew.png) - [Samuel, a handsome vampire](https://files.catbox.moe/f9uiw1.png) And here is a sample of how the model is intended to behave with proper chat and prompt formatting: https://files.catbox.moe/egfd90.png ### Other tips It's possible to make the model automatically generate random character information and scenario by adding just `<<SYSTEM>>` and the character name in text completion mode in `text-generation-webui`, as done here (click to enlarge). The format generally closely matches that of the training data: ![example](https://files.catbox.moe/5ntmcj.png) ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> The model has not been tested for: - IRC-style chat - Markdown-style roleplay (asterisks for actions, dialogue lines without quotation marks) - Storywriting - Usage without the suggested prompt format Furthermore, the model is not intended nor expected to provide factual and accurate information on any subject. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> The model may easily output disturbing and socially inappropriate content and therefore should not be used by minors or within environments where a general audience is expected. Its outputs will have in general a strong NSFW bias unless the character card/description de-emphasizes it. ## How to Get Started with the Model Download and load with `text-generation-webui` as a back-end application. It's suggested to start the `webui` via command line. Assuming you have copied the LoRA files under a subdirectory called `lora/limarp-llongma2-7b`, you would use something like this for the 7B model: ``` python server.py --api --verbose --model LLongMA-7B --lora limarp-llongma2-7b ``` When using 4-bit `bitsnbytes` it is suggested to use double quantization to increase accuracy. The starting command may be something like this: ``` python server.py --verbose --api --model LLongMA-2-13B --lora limarp13-llongma2-13b --load-in-4bit --use_double_quant ``` Then, preferably use [SillyTavern](https://github.com/SillyTavern/SillyTavern) as a front-end using the following settings: ![SillyTavern settings](https://files.catbox.moe/nd8v12.png) In addition of enabling the instruct mode with the correct sequences, it's particularly important to enable "Include names", as the model was trained with them at the start of each utterance. If it's disabled, the model can start getting confused and often write for the user in its responses. To take advantage of this model's larger context length, unlock the context size and set it up to any length up to 8192 tokens, depending on your VRAM constraints. On most consumer GPUs this will likely need to be set to a lower value. ![Unlock context size](https://files.catbox.moe/wfj8vv.png) It is recommended to ban/disable the EOS token as it can for instance apparently give [artifacts or tokenization issues](https://files.catbox.moe/cxfrzu.png) when it ends up getting generated close to punctuation or quotation marks, at least in SillyTavern. These would typically happen with AI responses. ![Ban EOS](https://files.catbox.moe/xslnhb.png) ## Training Details ### Training Data The training data comprises about 1500 manually edited roleplaying conversation threads from various Internet RP forums, for about 24 megabytes of data. Character and Scenario information was initially filled in for every thread with the help of mainly `gpt-4`. Later on this has been accomplished with a custom summarizer. Conversations in the dataset are almost entirely human-generated except for a handful of messages. Character names in the RP stories have been isolated and replaced with standard placeholder strings. Usernames, out-of-context (OOC) messages and personal information have not been intentionally included. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> The version of LimaRP uploaded on this repository was trained using a small NVidia A40 cluster in 8-bit with regular LoRA adapters and 8-bit AdamW optimizer. #### Training Hyperparameters The most important settings were as follows: - --learning_rate 0.000065 - --lr_scheduler_type cosine - --lora_r 8 - --lora_alpha 16 - --lora_dropout 0.01 - --num_train_epochs 2 - --bf16 True - --tf32 True - --bits 8 - --per_device_train_batch_size 1 - --gradient_accumulation_steps 1 - --optim adamw_bnb_8bit All linear LoRA layers were targeted. An effective batch size of 1 was found to yield the lowest loss curves during fine-tuning. It was also found that using `--train_on_source False` with the entire training example at the output yields similar results. These LoRAs have been trained in this way (similar to what was done with [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) or as with unsupervised finetuning). <!-- ## Evaluation --> <!-- This section describes the evaluation protocols and provides the results. --> ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Finetuning this model on 8 NVidia A40 48GB in parallel takes about 25 minutes (7B) or 45 minutes (13B).
davidggphy/whisper-tiny-finetuned-minds14-enUS	davidggphy	2023-08-24T01:07:40Z	74	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-23T23:30:00Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-finetuned-minds14-enUS_2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 metrics: - name: Wer type: wer value: 0.33943329397874855 --- <!-- 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-tiny-finetuned-minds14-enUS_2 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.7508 - Wer Ortho: 0.3356 - Wer: 0.3394 - Cer: 0.2613 - Cer Ortho: 0.2623 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| Cer \| Cer Ortho \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:---------:\| \| 0.0136 \| 7.14 \| 100 \| 0.6142 \| 0.3362 \| 0.3388 \| 0.2587 \| 0.2614 \| \| 0.0009 \| 14.29 \| 200 \| 0.6704 \| 0.3288 \| 0.3300 \| 0.2515 \| 0.2534 \| \| 0.0011 \| 21.43 \| 300 \| 0.6858 \| 0.3054 \| 0.3093 \| 0.2363 \| 0.2374 \| \| 0.0005 \| 28.57 \| 400 \| 0.7081 \| 0.3455 \| 0.3477 \| 0.2699 \| 0.2711 \| \| 0.0004 \| 35.71 \| 500 \| 0.7191 \| 0.3467 \| 0.3501 \| 0.2727 \| 0.2736 \| \| 0.0001 \| 42.86 \| 600 \| 0.7337 \| 0.3405 \| 0.3447 \| 0.2652 \| 0.2662 \| \| 0.0001 \| 50.0 \| 700 \| 0.7418 \| 0.3393 \| 0.3430 \| 0.2636 \| 0.2645 \| \| 0.0001 \| 57.14 \| 800 \| 0.7466 \| 0.3387 \| 0.3424 \| 0.2634 \| 0.2644 \| \| 0.0001 \| 64.29 \| 900 \| 0.7496 \| 0.3350 \| 0.3388 \| 0.2604 \| 0.2614 \| \| 0.0001 \| 71.43 \| 1000 \| 0.7508 \| 0.3356 \| 0.3394 \| 0.2613 \| 0.2623 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
michaelriedl/MonsterForgeFusion-sd-2-base	michaelriedl	2023-08-24T01:06:20Z	5	0	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-base", "base_model:adapter:stabilityai/stable-diffusion-2-base", "license:openrail++", "region:us" ]	text-to-image	2023-08-24T00:46:11Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-2-base tags: - stable-diffusion - text-to-image - diffusers - lora inference: true ---
LBR47/wav2vec2-base-finetuned-gtzan	LBR47	2023-08-24T01:05:57Z	159	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:bookbot/distil-ast-audioset", "base_model:finetune:bookbot/distil-ast-audioset", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-08-14T04:15:04Z	--- license: apache-2.0 base_model: bookbot/distil-ast-audioset tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: train split: train args: train metrics: - name: Accuracy type: accuracy value: 0.89 --- <!-- 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. --> # distil-ast-audioset-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7907 - Accuracy: 0.89 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
aiknight87/falcon-40b-instruct-test-system	aiknight87	2023-08-24T00:53:18Z	1	0	peft	[ "peft", "RefinedWeb", "custom_code", "4-bit", "bitsandbytes", "region:us" ]	null	2023-08-23T06:41:11Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
jimmyofdoom/a2c-PandaReachDense-v3	jimmyofdoom	2023-08-24T00:48:10Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T00:42:51Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.25 +/- 0.14 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
DuyTa/vi_whisper-small	DuyTa	2023-08-24T00:33:35Z	80	1	transformers	[ "transformers", "pytorch", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-13T14:16:43Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: vi_whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Vivos + Commonvoice type: vivos config: None split: None metrics: - name: Wer type: wer value: 21.8855 --- <!-- 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. --> # vi_whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mixing of Vivos and CommonVoice dataset. It achieves the following results on the evaluation set: - Loss: 0.2894 - Wer: 21.8855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data In training phase i used VIVOS dataset and cleaned CommonVoice The VIVOS evaluation dataset was used ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 1000 - training_steps: 8000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.249 \| 1.1 \| 1000 \| 0.3766 \| 32.1678 \| \| 0.1416 \| 2.2 \| 2000 \| 0.2881 \| 46.4646 \| \| 0.0839 \| 3.3 \| 3000 \| 0.2799 \| 22.7791 \| \| 0.0546 \| 4.41 \| 4000 \| 0.2894 \| 21.8855 \| \| 0.0256 \| 5.51 \| 5000 \| 0.3023 \| 32.2973 \| \| 0.0111 \| 6.61 \| 6000 \| 0.3061 \| 31.0153 \| \| 0.0028 \| 7.71 \| 7000 \| 0.3143 \| 27.1691 \| \| 0.0014 \| 8.81 \| 8000 \| 0.3187 \| 27.3634 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
pabloyesteb/a2c-PandaReachDense-v3	pabloyesteb	2023-08-24T00:21:05Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T00:15:07Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.10 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
ardt-multipart/ardt-multipart-combo_train_walker2d_v2-2308_2328-99	ardt-multipart	2023-08-24T00:20:53Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-23T22:30:17Z	--- tags: - generated_from_trainer model-index: - name: ardt-multipart-combo_train_walker2d_v2-2308_2328-99 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. --> # ardt-multipart-combo_train_walker2d_v2-2308_2328-99 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001 - train_batch_size: 64 - 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: 1000 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
nlpnlp/xlm-roberta-base-finetuned-panx-de	nlpnlp	2023-08-24T00:04:07Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-23T17:08:22Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8600170502983802 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1391 - F1: 0.8600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2598 \| 1.0 \| 525 \| 0.1697 \| 0.8177 \| \| 0.1253 \| 2.0 \| 1050 \| 0.1343 \| 0.8509 \| \| 0.0812 \| 3.0 \| 1575 \| 0.1391 \| 0.8600 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3
azhang1212/angela_punc_untranslated_eval	azhang1212	2023-08-23T23:44:42Z	8	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-23T20:30:13Z	--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: angela_punc_untranslated_eval 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. --> # angela_punc_untranslated_eval This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1902 - Precision: 0.3889 - Recall: 0.2568 - F1: 0.3093 - Accuracy: 0.9517 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1524 \| 1.0 \| 1283 \| 0.1547 \| 0.4163 \| 0.1471 \| 0.2174 \| 0.9546 \| \| 0.1295 \| 2.0 \| 2566 \| 0.1518 \| 0.4489 \| 0.1943 \| 0.2712 \| 0.9556 \| \| 0.1113 \| 3.0 \| 3849 \| 0.1614 \| 0.4152 \| 0.2323 \| 0.2979 \| 0.9538 \| \| 0.0896 \| 4.0 \| 5132 \| 0.1784 \| 0.4248 \| 0.2346 \| 0.3023 \| 0.9542 \| \| 0.073 \| 5.0 \| 6415 \| 0.1902 \| 0.3889 \| 0.2568 \| 0.3093 \| 0.9517 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
dkqjrm/20230824064444	dkqjrm	2023-08-23T23:38:44Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T21:45:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824064444' 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. --> # 20230824064444 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.0709 - Accuracy: 0.7329 ## 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: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 312 \| 0.4733 \| 0.5307 \| \| 0.3538 \| 2.0 \| 624 \| 0.1917 \| 0.5126 \| \| 0.3538 \| 3.0 \| 936 \| 0.1696 \| 0.5560 \| \| 0.2775 \| 4.0 \| 1248 \| 0.1700 \| 0.5271 \| \| 0.2538 \| 5.0 \| 1560 \| 0.3497 \| 0.5343 \| \| 0.2538 \| 6.0 \| 1872 \| 0.2183 \| 0.5632 \| \| 0.259 \| 7.0 \| 2184 \| 0.1783 \| 0.5018 \| \| 0.259 \| 8.0 \| 2496 \| 0.2321 \| 0.5848 \| \| 0.2587 \| 9.0 \| 2808 \| 0.2081 \| 0.6101 \| \| 0.2211 \| 10.0 \| 3120 \| 0.1194 \| 0.6715 \| \| 0.2211 \| 11.0 \| 3432 \| 0.1505 \| 0.6390 \| \| 0.198 \| 12.0 \| 3744 \| 0.1130 \| 0.7004 \| \| 0.1939 \| 13.0 \| 4056 \| 0.1187 \| 0.6679 \| \| 0.1939 \| 14.0 \| 4368 \| 0.1175 \| 0.6787 \| \| 0.1687 \| 15.0 \| 4680 \| 0.1092 \| 0.7040 \| \| 0.1687 \| 16.0 \| 4992 \| 0.0984 \| 0.7076 \| \| 0.1511 \| 17.0 \| 5304 \| 0.1032 \| 0.7076 \| \| 0.1448 \| 18.0 \| 5616 \| 0.1024 \| 0.7401 \| \| 0.1448 \| 19.0 \| 5928 \| 0.0902 \| 0.7112 \| \| 0.1392 \| 20.0 \| 6240 \| 0.0972 \| 0.7112 \| \| 0.1283 \| 21.0 \| 6552 \| 0.0880 \| 0.7184 \| \| 0.1283 \| 22.0 \| 6864 \| 0.0892 \| 0.7329 \| \| 0.1257 \| 23.0 \| 7176 \| 0.1156 \| 0.7401 \| \| 0.1257 \| 24.0 \| 7488 \| 0.0940 \| 0.7329 \| \| 0.1215 \| 25.0 \| 7800 \| 0.0876 \| 0.7401 \| \| 0.1184 \| 26.0 \| 8112 \| 0.1289 \| 0.7437 \| \| 0.1184 \| 27.0 \| 8424 \| 0.0808 \| 0.7256 \| \| 0.1112 \| 28.0 \| 8736 \| 0.0823 \| 0.7401 \| \| 0.1139 \| 29.0 \| 9048 \| 0.0838 \| 0.7256 \| \| 0.1139 \| 30.0 \| 9360 \| 0.0855 \| 0.7220 \| \| 0.1095 \| 31.0 \| 9672 \| 0.0813 \| 0.7256 \| \| 0.1095 \| 32.0 \| 9984 \| 0.0765 \| 0.7256 \| \| 0.106 \| 33.0 \| 10296 \| 0.0847 \| 0.7365 \| \| 0.1034 \| 34.0 \| 10608 \| 0.0844 \| 0.7509 \| \| 0.1034 \| 35.0 \| 10920 \| 0.0811 \| 0.7184 \| \| 0.0991 \| 36.0 \| 11232 \| 0.0811 \| 0.7292 \| \| 0.0938 \| 37.0 \| 11544 \| 0.0847 \| 0.7365 \| \| 0.0938 \| 38.0 \| 11856 \| 0.0824 \| 0.7256 \| \| 0.0973 \| 39.0 \| 12168 \| 0.0760 \| 0.7292 \| \| 0.0973 \| 40.0 \| 12480 \| 0.0786 \| 0.7220 \| \| 0.0908 \| 41.0 \| 12792 \| 0.0732 \| 0.7473 \| \| 0.0894 \| 42.0 \| 13104 \| 0.0763 \| 0.7401 \| \| 0.0894 \| 43.0 \| 13416 \| 0.0811 \| 0.7365 \| \| 0.0896 \| 44.0 \| 13728 \| 0.0734 \| 0.7473 \| \| 0.0882 \| 45.0 \| 14040 \| 0.0747 \| 0.7329 \| \| 0.0882 \| 46.0 \| 14352 \| 0.0729 \| 0.7401 \| \| 0.0847 \| 47.0 \| 14664 \| 0.0723 \| 0.7329 \| \| 0.0847 \| 48.0 \| 14976 \| 0.0748 \| 0.7401 \| \| 0.0854 \| 49.0 \| 15288 \| 0.0755 \| 0.7292 \| \| 0.0813 \| 50.0 \| 15600 \| 0.0715 \| 0.7329 \| \| 0.0813 \| 51.0 \| 15912 \| 0.0719 \| 0.7292 \| \| 0.0845 \| 52.0 \| 16224 \| 0.0721 \| 0.7401 \| \| 0.0821 \| 53.0 \| 16536 \| 0.0711 \| 0.7292 \| \| 0.0821 \| 54.0 \| 16848 \| 0.0714 \| 0.7437 \| \| 0.0802 \| 55.0 \| 17160 \| 0.0711 \| 0.7401 \| \| 0.0802 \| 56.0 \| 17472 \| 0.0718 \| 0.7329 \| \| 0.0798 \| 57.0 \| 17784 \| 0.0708 \| 0.7220 \| \| 0.0796 \| 58.0 \| 18096 \| 0.0715 \| 0.7365 \| \| 0.0796 \| 59.0 \| 18408 \| 0.0712 \| 0.7329 \| \| 0.0806 \| 60.0 \| 18720 \| 0.0709 \| 0.7329 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230824062849	dkqjrm	2023-08-23T23:29:46Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T21:29:08Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824062849' 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. --> # 20230824062849 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 1.2256 - Accuracy: 0.7473 ## 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: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 312 \| 1.2170 \| 0.5307 \| \| 0.9844 \| 2.0 \| 624 \| 0.7365 \| 0.5090 \| \| 0.9844 \| 3.0 \| 936 \| 0.6978 \| 0.5632 \| \| 0.8956 \| 4.0 \| 1248 \| 0.8855 \| 0.4765 \| \| 0.8957 \| 5.0 \| 1560 \| 1.0223 \| 0.5379 \| \| 0.8957 \| 6.0 \| 1872 \| 0.6873 \| 0.6137 \| \| 0.7665 \| 7.0 \| 2184 \| 0.8629 \| 0.6173 \| \| 0.7665 \| 8.0 \| 2496 \| 0.6861 \| 0.6570 \| \| 0.734 \| 9.0 \| 2808 \| 0.6714 \| 0.7076 \| \| 0.7238 \| 10.0 \| 3120 \| 0.6298 \| 0.7184 \| \| 0.7238 \| 11.0 \| 3432 \| 0.5975 \| 0.7184 \| \| 0.6786 \| 12.0 \| 3744 \| 0.8311 \| 0.6968 \| \| 0.6396 \| 13.0 \| 4056 \| 0.7136 \| 0.6751 \| \| 0.6396 \| 14.0 \| 4368 \| 0.7183 \| 0.6859 \| \| 0.6481 \| 15.0 \| 4680 \| 0.6652 \| 0.7076 \| \| 0.6481 \| 16.0 \| 4992 \| 1.0367 \| 0.6823 \| \| 0.6106 \| 17.0 \| 5304 \| 0.7197 \| 0.6895 \| \| 0.6011 \| 18.0 \| 5616 \| 0.6058 \| 0.7292 \| \| 0.6011 \| 19.0 \| 5928 \| 0.7227 \| 0.7112 \| \| 0.5978 \| 20.0 \| 6240 \| 1.1472 \| 0.6570 \| \| 0.5309 \| 21.0 \| 6552 \| 0.6741 \| 0.7256 \| \| 0.5309 \| 22.0 \| 6864 \| 0.9335 \| 0.6787 \| \| 0.5392 \| 23.0 \| 7176 \| 0.8296 \| 0.7365 \| \| 0.5392 \| 24.0 \| 7488 \| 0.9097 \| 0.7040 \| \| 0.5058 \| 25.0 \| 7800 \| 0.8278 \| 0.7292 \| \| 0.4669 \| 26.0 \| 8112 \| 1.0859 \| 0.6498 \| \| 0.4669 \| 27.0 \| 8424 \| 0.9387 \| 0.7184 \| \| 0.462 \| 28.0 \| 8736 \| 1.0893 \| 0.7365 \| \| 0.4757 \| 29.0 \| 9048 \| 1.3568 \| 0.6859 \| \| 0.4757 \| 30.0 \| 9360 \| 1.0252 \| 0.7040 \| \| 0.4237 \| 31.0 \| 9672 \| 1.0489 \| 0.7329 \| \| 0.4237 \| 32.0 \| 9984 \| 0.8661 \| 0.7292 \| \| 0.4275 \| 33.0 \| 10296 \| 0.9781 \| 0.7437 \| \| 0.3722 \| 34.0 \| 10608 \| 0.8879 \| 0.7329 \| \| 0.3722 \| 35.0 \| 10920 \| 0.9932 \| 0.7292 \| \| 0.3741 \| 36.0 \| 11232 \| 1.0509 \| 0.7365 \| \| 0.3358 \| 37.0 \| 11544 \| 1.3875 \| 0.7329 \| \| 0.3358 \| 38.0 \| 11856 \| 1.2366 \| 0.7220 \| \| 0.3415 \| 39.0 \| 12168 \| 1.0563 \| 0.7329 \| \| 0.3415 \| 40.0 \| 12480 \| 0.9688 \| 0.7401 \| \| 0.3357 \| 41.0 \| 12792 \| 0.8598 \| 0.7329 \| \| 0.3094 \| 42.0 \| 13104 \| 1.0506 \| 0.7329 \| \| 0.3094 \| 43.0 \| 13416 \| 1.3257 \| 0.7365 \| \| 0.2947 \| 44.0 \| 13728 \| 1.1759 \| 0.7365 \| \| 0.2832 \| 45.0 \| 14040 \| 1.1699 \| 0.7329 \| \| 0.2832 \| 46.0 \| 14352 \| 1.1070 \| 0.7401 \| \| 0.2808 \| 47.0 \| 14664 \| 1.1519 \| 0.7473 \| \| 0.2808 \| 48.0 \| 14976 \| 1.0674 \| 0.7401 \| \| 0.2715 \| 49.0 \| 15288 \| 1.1491 \| 0.7401 \| \| 0.252 \| 50.0 \| 15600 \| 1.0819 \| 0.7473 \| \| 0.252 \| 51.0 \| 15912 \| 0.9650 \| 0.7473 \| \| 0.2577 \| 52.0 \| 16224 \| 1.0753 \| 0.7437 \| \| 0.2579 \| 53.0 \| 16536 \| 1.0896 \| 0.7473 \| \| 0.2579 \| 54.0 \| 16848 \| 1.0579 \| 0.7401 \| \| 0.2395 \| 55.0 \| 17160 \| 1.1172 \| 0.7509 \| \| 0.2395 \| 56.0 \| 17472 \| 1.1540 \| 0.7509 \| \| 0.2392 \| 57.0 \| 17784 \| 1.2162 \| 0.7509 \| \| 0.22 \| 58.0 \| 18096 \| 1.1978 \| 0.7509 \| \| 0.22 \| 59.0 \| 18408 \| 1.2381 \| 0.7473 \| \| 0.2242 \| 60.0 \| 18720 \| 1.2256 \| 0.7473 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
DrishtiSharma/codet5-small-Generate-docstrings-for-Python-bs-32	DrishtiSharma	2023-08-23T23:28:11Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:Salesforce/codet5-small", "base_model:finetune:Salesforce/codet5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-23T16:05:23Z	--- license: apache-2.0 base_model: Salesforce/codet5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: codet5-small-Generate-docstrings-for-Python-bs-32 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. --> # codet5-small-Generate-docstrings-for-Python-bs-32 This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1105 - Rouge1: 0.3307 - Rouge2: 0.16 - Rougel: 0.297 - Rougelsum: 0.3149 - Gen Len: 16.7441 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 2.7701 \| 1.0 \| 4472 \| 2.3322 \| 0.3225 \| 0.1525 \| 0.2894 \| 0.3067 \| 16.3153 \| \| 2.4907 \| 2.0 \| 8944 \| 2.2464 \| 0.328 \| 0.1555 \| 0.293 \| 0.3119 \| 17.0097 \| \| 2.405 \| 3.0 \| 13416 \| 2.2004 \| 0.3267 \| 0.1562 \| 0.2934 \| 0.311 \| 16.4531 \| \| 2.3512 \| 4.0 \| 17888 \| 2.1696 \| 0.3292 \| 0.1571 \| 0.2944 \| 0.3134 \| 17.3872 \| \| 2.3144 \| 5.0 \| 22360 \| 2.1503 \| 0.3293 \| 0.1586 \| 0.2954 \| 0.3137 \| 16.932 \| \| 2.2862 \| 6.0 \| 26832 \| 2.1355 \| 0.3307 \| 0.1588 \| 0.2962 \| 0.3149 \| 17.0269 \| \| 2.2666 \| 7.0 \| 31304 \| 2.1246 \| 0.33 \| 0.1594 \| 0.2962 \| 0.3144 \| 16.7064 \| \| 2.2514 \| 8.0 \| 35776 \| 2.1163 \| 0.3305 \| 0.1595 \| 0.2968 \| 0.3145 \| 16.4765 \| \| 2.2401 \| 9.0 \| 40248 \| 2.1120 \| 0.3305 \| 0.1595 \| 0.2967 \| 0.3147 \| 16.763 \| \| 2.2333 \| 10.0 \| 44720 \| 2.1105 \| 0.3307 \| 0.16 \| 0.297 \| 0.3149 \| 16.7441 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4.dev0 - Tokenizers 0.13.3
NobodyExistsOnTheInternet/convenience2epochs	NobodyExistsOnTheInternet	2023-08-23T23:22:33Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-23T23:21:27Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
tenkomati/dqn-SpaceInvaderstest	tenkomati	2023-08-23T23:07:59Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-23T23:07:18Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 652.00 +/- 219.36 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tenkomati -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tenkomati -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tenkomati ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ghze/dqn-SpaceInvadersNoFrameskip-v4	ghze	2023-08-23T22:53:37Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-23T22:52:57Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 573.50 +/- 132.53 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ghze -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ghze -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ghze ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
sabre-code/distilbert-base-uncased-finetuned-emotion	sabre-code	2023-08-23T22:19:49Z	121	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:dair-ai/emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T20:23:59Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - dair-ai/emotion model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] language: - en metrics: - accuracy --- <!-- 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. ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
redstonehero/anythingqingmix25d_v30	redstonehero	2023-08-23T22:07:47Z	29	1	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-23T21:10:44Z	--- license: creativeml-openrail-m library_name: diffusers ---
redstonehero/comimicry_v10fp16	redstonehero	2023-08-23T22:07:44Z	29	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-23T21:10:35Z	--- license: creativeml-openrail-m library_name: diffusers ---
redstonehero/airfuckswildmix_v10	redstonehero	2023-08-23T22:07:39Z	41	2	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-23T21:10:56Z	--- license: creativeml-openrail-m library_name: diffusers ---
redstonehero/furryvixens_v20bakedvae	redstonehero	2023-08-23T21:42:47Z	29	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-23T20:44:31Z	--- license: creativeml-openrail-m library_name: diffusers ---
felipebandeira/donutlicenses3v3	felipebandeira	2023-08-23T21:40:06Z	114	4	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "image-to-text", "en", "dataset:felipebandeira/driverlicenses2k", "license:mit", "endpoints_compatible", "region:us" ]	image-to-text	2023-08-16T12:35:01Z	--- license: mit datasets: - felipebandeira/driverlicenses2k language: - en metrics: - accuracy pipeline_tag: image-to-text --- This model extracts information from EU driver's licenses and returns it as JSON. For optimal performance, we recommend that input images: - have a size of 1192x772 - have high resolution and do not contain light reflection effects Accuracy - on validation set: 98% - on set of real licenses: 63.93% Article describing model: https://medium.com/@ofelipebandeira/transformers-vs-ocr-who-can-read-better-192e6b044dd3 Article describing synthetic dataset used in training: https://python.plainenglish.io/how-to-create-synthetic-datasets-of-document-images-5f140dee5e40
redstonehero/frozenanimation_v10	redstonehero	2023-08-23T21:36:07Z	30	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-23T20:44:15Z	--- license: creativeml-openrail-m library_name: diffusers ---
him1411/EDGAR-BART-Base	him1411	2023-08-23T21:35:55Z	111	0	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "en", "dataset:him1411/EDGAR10-Q", "arxiv:2109.08079", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-03T18:32:38Z	--- license: mit datasets: - him1411/EDGAR10-Q language: - en metrics: - rouge --- license: mit language: - en tags: - finance - ContextNER - language models datasets: - him1411/EDGAR10-Q metrics: - rouge --- EDGAR-BART-Base ============= BART base model finetuned on [EDGAR10-Q dataset](https://huggingface.co/datasets/him1411/EDGAR10-Q) You may want to check out * Our paper: [CONTEXT-NER: Contextual Phrase Generation at Scale](https://arxiv.org/abs/2109.08079/) * GitHub: [Click Here](https://github.com/him1411/edgar10q-dataset) Direct Use ============= It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. It should not be directly used for production or work that may directly impact people. How to Use ============= You can very easily load the models with Transformers, instead of downloading them manually. The [bart-base model](https://huggingface.co/facebook/bart-base) is the backbone of our model. Here is how to use the model in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("him1411/EDGAR-BART-Base") model = AutoModelForSeq2SeqLM.from_pretrained("him1411/EDGAR-BART-Base") ``` Or just clone the model repo ``` git lfs install git clone https://huggingface.co/him1411/EDGAR-BART-Base ``` Inference Example ============= Here, we provide an example for the "ContextNER" task. Below is an example of one instance. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("him1411/EDGAR-BART-Base") model = AutoModelForSeq2SeqLM.from_pretrained("him1411/EDGAR-BART-Base") # Input shows how we have appended instruction from our file for HoC dataset with instance. input = "14.5 years . The definite lived intangible assets related to the contracts and trade names had estimated weighted average useful lives of 5.9 years and 14.5 years, respectively, at acquisition." tokenized_input= tokenizer(input) # Ideal output for this input is 'Definite lived intangible assets weighted average remaining useful life' output = model(tokenized_input) ``` BibTeX Entry and Citation Info =============== If you are using our model, please cite our paper: ```bibtex @article{gupta2021context, title={Context-NER: Contextual Phrase Generation at Scale}, author={Gupta, Himanshu and Verma, Shreyas and Kumar, Tarun and Mishra, Swaroop and Agrawal, Tamanna and Badugu, Amogh and Bhatt, Himanshu Sharad}, journal={arXiv preprint arXiv:2109.08079}, year={2021} } ```
ofri-r/ppo-Huggy	ofri-r	2023-08-23T21:32:26Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-08-23T21:32:20Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ofri-r/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
daochf/Lora-HuggyLlama7b-PuceDS-v03x50	daochf	2023-08-23T21:32:22Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-23T21:32:16Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0
dkqjrm/20230824042730	dkqjrm	2023-08-23T21:28:35Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T19:27:51Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824042730' 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. --> # 20230824042730 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5547 - Accuracy: 0.7581 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.1252 \| 1.0 \| 623 \| 0.6915 \| 0.5415 \| \| 0.9382 \| 2.0 \| 1246 \| 0.7221 \| 0.5307 \| \| 1.0555 \| 3.0 \| 1869 \| 0.7387 \| 0.5199 \| \| 0.9336 \| 4.0 \| 2492 \| 0.9751 \| 0.6390 \| \| 0.8894 \| 5.0 \| 3115 \| 0.9277 \| 0.6643 \| \| 0.9066 \| 6.0 \| 3738 \| 1.1836 \| 0.6931 \| \| 0.8496 \| 7.0 \| 4361 \| 0.8242 \| 0.7184 \| \| 0.7761 \| 8.0 \| 4984 \| 0.9061 \| 0.6859 \| \| 0.8175 \| 9.0 \| 5607 \| 0.7474 \| 0.7220 \| \| 0.7575 \| 10.0 \| 6230 \| 0.8582 \| 0.7292 \| \| 0.747 \| 11.0 \| 6853 \| 0.8351 \| 0.7256 \| \| 0.728 \| 12.0 \| 7476 \| 0.8912 \| 0.7148 \| \| 0.8296 \| 13.0 \| 8099 \| 0.9471 \| 0.7220 \| \| 0.7327 \| 14.0 \| 8722 \| 1.1407 \| 0.7148 \| \| 0.7284 \| 15.0 \| 9345 \| 0.7681 \| 0.7256 \| \| 0.6642 \| 16.0 \| 9968 \| 1.4084 \| 0.6679 \| \| 0.5888 \| 17.0 \| 10591 \| 0.8413 \| 0.7329 \| \| 0.6074 \| 18.0 \| 11214 \| 0.7461 \| 0.7401 \| \| 0.625 \| 19.0 \| 11837 \| 0.9516 \| 0.7545 \| \| 0.5911 \| 20.0 \| 12460 \| 1.3395 \| 0.7292 \| \| 0.5322 \| 21.0 \| 13083 \| 1.3924 \| 0.7509 \| \| 0.5247 \| 22.0 \| 13706 \| 1.1553 \| 0.7256 \| \| 0.5146 \| 23.0 \| 14329 \| 1.6692 \| 0.7040 \| \| 0.4493 \| 24.0 \| 14952 \| 1.2315 \| 0.7437 \| \| 0.399 \| 25.0 \| 15575 \| 1.2710 \| 0.7545 \| \| 0.3644 \| 26.0 \| 16198 \| 1.5049 \| 0.7473 \| \| 0.4031 \| 27.0 \| 16821 \| 1.5735 \| 0.7401 \| \| 0.386 \| 28.0 \| 17444 \| 1.4749 \| 0.7220 \| \| 0.3735 \| 29.0 \| 18067 \| 0.9541 \| 0.7365 \| \| 0.356 \| 30.0 \| 18690 \| 1.3936 \| 0.7473 \| \| 0.3496 \| 31.0 \| 19313 \| 0.9982 \| 0.7437 \| \| 0.3149 \| 32.0 \| 19936 \| 0.9572 \| 0.7581 \| \| 0.3094 \| 33.0 \| 20559 \| 1.5663 \| 0.7256 \| \| 0.2886 \| 34.0 \| 21182 \| 1.5993 \| 0.7365 \| \| 0.2545 \| 35.0 \| 21805 \| 1.1515 \| 0.7545 \| \| 0.276 \| 36.0 \| 22428 \| 1.2768 \| 0.7473 \| \| 0.2645 \| 37.0 \| 23051 \| 1.4290 \| 0.7509 \| \| 0.262 \| 38.0 \| 23674 \| 1.2363 \| 0.7617 \| \| 0.2261 \| 39.0 \| 24297 \| 1.3446 \| 0.7617 \| \| 0.2291 \| 40.0 \| 24920 \| 1.0532 \| 0.7509 \| \| 0.2178 \| 41.0 \| 25543 \| 1.4745 \| 0.7509 \| \| 0.2104 \| 42.0 \| 26166 \| 1.3830 \| 0.7545 \| \| 0.217 \| 43.0 \| 26789 \| 1.7099 \| 0.7473 \| \| 0.214 \| 44.0 \| 27412 \| 1.7054 \| 0.7401 \| \| 0.1856 \| 45.0 \| 28035 \| 1.4350 \| 0.7545 \| \| 0.2014 \| 46.0 \| 28658 \| 1.7266 \| 0.7473 \| \| 0.1759 \| 47.0 \| 29281 \| 1.2659 \| 0.7581 \| \| 0.2027 \| 48.0 \| 29904 \| 1.8336 \| 0.7401 \| \| 0.1871 \| 49.0 \| 30527 \| 1.3398 \| 0.7509 \| \| 0.1586 \| 50.0 \| 31150 \| 1.4948 \| 0.7509 \| \| 0.1619 \| 51.0 \| 31773 \| 1.3787 \| 0.7545 \| \| 0.1665 \| 52.0 \| 32396 \| 1.6532 \| 0.7545 \| \| 0.1786 \| 53.0 \| 33019 \| 1.4697 \| 0.7581 \| \| 0.1609 \| 54.0 \| 33642 \| 1.5462 \| 0.7653 \| \| 0.1304 \| 55.0 \| 34265 \| 1.3577 \| 0.7581 \| \| 0.1576 \| 56.0 \| 34888 \| 1.7004 \| 0.7617 \| \| 0.1522 \| 57.0 \| 35511 \| 1.4629 \| 0.7581 \| \| 0.1496 \| 58.0 \| 36134 \| 1.6336 \| 0.7581 \| \| 0.1406 \| 59.0 \| 36757 \| 1.5699 \| 0.7545 \| \| 0.1268 \| 60.0 \| 37380 \| 1.5547 \| 0.7581 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
casonshep/spam_message_classification	casonshep	2023-08-23T21:19:27Z	103	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-23T21:14:40Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: spam_message_classification 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. --> # spam_message_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 297 \| 0.0719 \| 0.9757 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Kajtson/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan	Kajtson	2023-08-23T21:10:18Z	159	0	transformers	[ "transformers", "pytorch", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:MIT/ast-finetuned-audioset-10-10-0.4593", "base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593", "license:bsd-3-clause", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-08-23T20:22:24Z	--- license: bsd-3-clause base_model: MIT/ast-finetuned-audioset-10-10-0.4593 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.89 --- <!-- 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. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6857 - Accuracy: 0.89 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8586 \| 1.0 \| 450 \| 1.3795 \| 0.55 \| \| 0.7835 \| 2.0 \| 900 \| 1.0814 \| 0.76 \| \| 0.1489 \| 3.0 \| 1350 \| 1.0447 \| 0.81 \| \| 0.2136 \| 4.0 \| 1800 \| 0.9784 \| 0.82 \| \| 0.0001 \| 5.0 \| 2250 \| 0.7678 \| 0.86 \| \| 0.0 \| 6.0 \| 2700 \| 0.5670 \| 0.92 \| \| 1.2125 \| 7.0 \| 3150 \| 0.8058 \| 0.85 \| \| 0.0 \| 8.0 \| 3600 \| 0.7256 \| 0.87 \| \| 0.0 \| 9.0 \| 4050 \| 0.6878 \| 0.89 \| \| 0.0 \| 10.0 \| 4500 \| 0.6857 \| 0.89 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
yellowsproket/path-to-save-model	yellowsproket	2023-08-23T21:02:29Z	30	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-23T20:54:37Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - yellowsproket/path-to-save-model This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
vuminhtue/bert-finetuned-squad	vuminhtue	2023-08-23T21:02:09Z	70	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-23T18:52:58Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: vuminhtue/bert-finetuned-squad 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. --> # vuminhtue/bert-finetuned-squad 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: - Train Loss: 0.5714 - 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, '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 \| Epoch \| \|:----------:\|:-----:\| \| 1.2738 \| 0 \| \| 0.7819 \| 1 \| \| 0.5714 \| 2 \| ### Framework versions - Transformers 4.32.0 - TensorFlow 2.9.1 - Datasets 2.14.4 - Tokenizers 0.13.3
marhatha/ppo-LunarLander-v2	marhatha	2023-08-23T21:01:14Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-23T21:00:46Z	--- 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: 261.81 +/- 20.15 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 ... ```

EmirhanExecute/ppo-LunarLander-try2

EmirhanExecute

2023-08-24T08:56:49Z

0

stable-baselines3

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

reinforcement-learning

2023-08-24T08:56:29Z

--- 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: 263.15 +/- 15.93 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 ... ```

bigmorning/train_from_raw_cv12__0020

bigmorning

2023-08-24T08:54:06Z

60

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-24T08:53:58Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: train_from_raw_cv12__0020 results: [] ---  # train_from_raw_cv12__0020 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: nan - Train Accuracy: 0.0032 - Train Wermet: 8.3902 - Validation Loss: nan - Validation Accuracy: 0.0032 - Validation Wermet: 8.3902 - Epoch: 19 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | nan | 0.0032 | 8.3778 | nan | 0.0032 | 8.3902 | 0 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 1 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 2 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 3 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 4 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 5 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 6 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 7 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 8 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 9 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 10 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 11 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 12 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 13 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 14 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 15 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 16 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 17 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 18 | | nan | 0.0032 | 8.3902 | nan | 0.0032 | 8.3902 | 19 | ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

nomsgadded/Translation

nomsgadded

2023-08-24T08:52:26Z

104

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "en", "fr", "dataset:opus_books", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-24T08:13:12Z

--- language: - en - fr license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus_books model-index: - name: Translation results: [] ---  # Translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books en-fr dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

922-CA/negev-gfl-rvc2-tests

922-CA

2023-08-24T08:51:21Z

0

null

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

null

2023-08-22T08:46:16Z

--- license: openrail --- Test RVC2 models on the GFL character Negev, via various hyperparams and datasets. # negev-test-0 (~07/2023) * Trained on dataset of ~30 items, dialogue from game * Trained for ~100 epochs * First attempt # negev-test-1 - nne1_e10_s150 (08/22/2023) * Trained on dataset of ~30 items, dialogue from game * Trained for 10 epochs (150 steps) * Less artifacting but with accent # negev-test-1 - nne1_e60_s900 (08/22/2023) * Trained on dataset of ~30 items, dialogue from game * Trained for 60 epochs (900 steps) * Tends to be clearer and with less accent

ashokdavas/ppo-LunarLander-v2

ashokdavas

2023-08-24T08:44:57Z

0

stable-baselines3

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

reinforcement-learning

2023-08-24T08:44:39Z

--- 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: 252.99 +/- 16.33 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 ... ```

WhoTookMyAmogusNickname/ReasonixPajama-3B-GGML

WhoTookMyAmogusNickname

2023-08-24T08:41:41Z

0

1

null

[ "region:us" ]

null

2023-08-24T07:58:17Z

Amogus\ GGML quants of [ReasonixPajama-3b-HF](https://huggingface.co/Fredithefish/ReasonixPajama-3B-HF)

IAMNawaf/QA-History-Saudi

IAMNawaf

2023-08-24T08:30:58Z

103

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "ar", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "endpoints_compatible", "region:us" ]

question-answering

2023-08-23T14:32:32Z

--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: SA-History-NASEEJ-QA results: [] language: - ar library_name: transformers widget: - text: من كان الأكبر سنًا من آل سعود وتولى الإمارة؟ context: >- بعـد وفـاة سـعود بـن محمـد بـن مقـرن تولـى الإمـارة زيـد بـن مرخـان بـن وطبـان، وكان الأكبـر سـناً مـن آل سـعود، ولكـن حكمـه لـم يمتـد طويـ ًا لكبـر سـنه، ممـا دعـا مقـرن بـن محمـد بـن مقـرن إلـى انتـزاع الإمـارة منـه، لكـن الأمـور لـم تسـتمر طويـ ًا لمقـرن، وذلـك عندمـا حـاول الغـدر بزيـد بـن مرخـان الـذي كان يحكـم قبلـه، ممـا دعـا محمـد بـن سـعود ومقـرن بـن عبداللـه إلـى قتلـه، وكان ذلـك سـنة 1139 هــ 1727/ م. بعـد ذلـك عـاد إلـى الإمـارة زيـد بـن مرخـان، إلا أنـه عندمـا هجـم علـى إمـارة العيينـة سـعت - بعـد ذلـك - إلـى التحايـل عليـه وطلبـت التفـاوض معـه، وعندمـا ذهـب تم قتلـه، وبعـد قتـل زيـد بـن مرخـان تولـى محمـد بـن سـعود بـن مقـرن الإمـارة فـي الدرعيـة سـنة 1139 هــ 1727/ م ، وظـل حكمـه حتـى سـنة 1179 هـ 1765/ م. example_title: تاريخ المملكة العربية السعودية pipeline_tag: question-answering ---  # Naseej-SA-History-QA This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0791 ## Model description The Naseej-SA-History-QA model is a fine-tuned version of the aubmindlab/bert-base-arabertv02 pre-trained BERT model. It has been tailored and optimized for question answering tasks related to the history of Saudi Arabia. The model is designed to comprehend historical context and provide accurate answers to questions in Arabic language. ## Intended uses & limitations The Naseej-SA-History-QA model is intended to be used for answering historical questions specifically related to the history of Saudi Arabia. It can be employed in educational and research settings to assist students, scholars, and researchers in obtaining information about Saudi Arabian history. The model can also be utilized in various NLP applications where historical context is a key factor, such as building educational platforms, historical archives, and language translation tools. The model's performance is contingent upon the quality and accuracy of the training and evaluation data it has been fine-tuned on. It may struggle with questions that deviate significantly from the training data distribution. The model's understanding of historical events and context is based on the data it has been trained on. It may not perform well on questions involving more recent or less documented historical events. The model may not fully comprehend nuanced or highly specific historical inquiries that require deep contextual understanding beyond the scope of its training data. ## Training and evaluation data The Naseej-SA-History-QA model was trained using a custom dataset comprising historical questions and corresponding context passages related to the history of Saudi Arabia. The dataset covers various historical periods and events, providing the model with a wide range of historical context to learn from. The evaluation set used during training was designed to assess the model's performance on question answering tasks. The evaluation set includes a variety of questions and context passages that challenge the model's ability to accurately answer questions about Saudi Arabian history. ## Training procedure The Naseej-SA-History-QA model was fine-tuned using the aubmindlab/bert-base-arabertv02 pre-trained BERT model. The training process involved several key steps: Dataset Preparation: A custom dataset was curated for training the model. The dataset consisted of pairs of historical questions and corresponding context passages, both in Arabic language. The context passages provided the necessary historical context for answering the questions. Tokenization: The dataset was tokenized using the Tokenizers library, which converts text into a format that the model can understand. Tokenization converts words and subwords into numerical tokens that the model can process. Model Fine-Tuning: The tokenized dataset was used to fine-tune the aubmindlab/bert-base-arabertv02 base model using the Transformers library. During fine-tuning, the model was adjusted to perform well on the specific task of question answering related to Saudi Arabian history. ### 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: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 11 | 4.9014 | | No log | 2.0 | 22 | 4.7432 | | No log | 3.0 | 33 | 4.6212 | | No log | 4.0 | 44 | 4.6347 | | No log | 5.0 | 55 | 4.6101 | | No log | 6.0 | 66 | 4.6209 | | No log | 7.0 | 77 | 4.6445 | | No log | 8.0 | 88 | 4.6284 | | No log | 9.0 | 99 | 4.6226 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3

nishant-glance/path-to-save-model-2-1-priorp-lowlr

nishant-glance

2023-08-24T08:30:19Z

2

1

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-24T07:40:57Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - nishant-glance/path-to-save-model-2-1-priorp-lowlr This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

lordhiew/myfirsttrain

lordhiew

2023-08-24T08:25:44Z

0

peft

[ "peft", "region:us" ]

null

2023-07-28T07:25:03Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

Stomper10/CXR_ti_nf

Stomper10

2023-08-24T08:22:49Z

13

0

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-24T05:22:54Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Stomper10/CXR_ti_nf These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ![img_4](./image_4.png) ![img_5](./image_5.png) ![img_6](./image_6.png) ![img_7](./image_7.png) ![img_8](./image_8.png) ![img_9](./image_9.png) ![img_10](./image_10.png) ![img_11](./image_11.png) ![img_12](./image_12.png) ![img_13](./image_13.png) ![img_14](./image_14.png) ![img_15](./image_15.png) ![img_16](./image_16.png) ![img_17](./image_17.png) ![img_18](./image_18.png) ![img_19](./image_19.png)

raygx/distilGPT-NepSA

raygx

2023-08-24T08:12:30Z

71

0

transformers

[ "transformers", "tf", "gpt2", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-13T04:59:50Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilGPT-NepSA results: [] ---  # distilGPT-NepSA This model is a fine-tuned version of [raygx/distilGPT-Nepali](https://huggingface.co/raygx/distilGPT-Nepali) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6068 - Validation Loss: 0.6592 - Epoch: 1 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.04} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.8415 | 0.7254 | 0 | | 0.6068 | 0.6592 | 1 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.3

amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1

amazon

2023-08-24T08:10:03Z

27

1

sentence-transformers

[ "sentence-transformers", "pytorch", "roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-08-24T08:09:30Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-all-roberta-large-model-v0.1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

aware-ai/wav2vec2-base-german

aware-ai

2023-08-24T08:01:53Z

104

2

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_10_0", "generated_from_trainer", "de", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-09-01T19:46:01Z

--- language: - de tags: - automatic-speech-recognition - mozilla-foundation/common_voice_10_0 - generated_from_trainer model-index: - name: wav2vec2-base-german results: [] ---  # wav2vec2-base-german This model is a fine-tuned version of [wav2vec2-base-german](https://huggingface.co/wav2vec2-base-german) on the MOZILLA-FOUNDATION/COMMON_VOICE_10_0 - DE dataset. It achieves the following results on the evaluation set: - Loss: 0.9302 - Wer: 0.7428 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8427 | 1.0 | 451 | 1.0878 | 0.8091 | | 0.722 | 2.0 | 902 | 0.9732 | 0.7593 | | 0.6589 | 3.0 | 1353 | 0.9302 | 0.7428 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0 - Datasets 2.4.0 - Tokenizers 0.12.1

juandalibaba/my_awesome_wnut_model

juandalibaba

2023-08-24T07:56:48Z

65

0

transformers

[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-23T06:40:28Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: juandalibaba/my_awesome_wnut_model results: [] ---  # juandalibaba/my_awesome_wnut_model 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: 1.6376 - Validation Loss: 1.8223 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.7876 | 1.9931 | 0 | | 1.7614 | 1.8223 | 1 | | 1.6376 | 1.8223 | 2 | ### Framework versions - Transformers 4.32.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1

amazon

2023-08-24T07:48:06Z

24

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-08-24T07:28:22Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amazon/sm-hackathon-actionability-9-multi-outputs-setfit-model-v0.1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

ArneJa/Taxi

ArneJa

2023-08-24T07:42:44Z

0

null

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

reinforcement-learning

2023-08-24T07:42:43Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="ArneJa/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

avasaz/avasaz-large

avasaz

2023-08-24T07:30:53Z

4

1

transformers

[ "transformers", "pytorch", "musicgen", "text-to-audio", "license:mit", "region:us" ]

text-to-audio

2023-08-23T19:46:30Z

--- inference: false tags: - musicgen license: mit --- # Avasaz Large (3.3B) - Make music directly from your ideas <img src="https://huggingface.co/avasaz/avasaz-large/resolve/main/avasaz_logo.png" width=256 height=256 /> ## What is Avasaz? Avasaz (which is a combinations of Persian word آوا meaning song and ساز meaning maker, literally translates to _song maker_) is a _state-of-the-art generative AI model_ which can help you turn your ideas to music in matter of a few minutes. This model has been developed by [Muhammadreza Haghiri](https://haghiri75.com/en) as an effort to make a suite of AI programs to make the world a much better place for our future generations. ## How can you use Avasaz? [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/prp-e/avasaz/blob/main/Avasaz_Inference.ipynb) Currently, Infrerence is only available on _Colab_. Codes will be here as soon as possible.

neil-code/autotrain-summarization-84573142568

neil-code

2023-08-24T07:22:08Z

111

0

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "summarization", "en", "dataset:neil-code/autotrain-data-summarization", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2023-08-24T07:16:47Z

--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain" datasets: - neil-code/autotrain-data-summarization co2_eq_emissions: emissions: 3.1909973371323623 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 84573142568 - CO2 Emissions (in grams): 3.1910 ## Validation Metrics - Loss: 1.445 - Rouge1: 33.737 - Rouge2: 11.210 - RougeL: 28.204 - RougeLsum: 30.262 - Gen Len: 18.836 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/neil-code/autotrain-summarization-84573142568 ```

achmaddaa/ametv2

achmaddaa

2023-08-24T07:07:14Z

0

null

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

null

2023-08-24T07:04:20Z

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

DineshK/dummy-model

DineshK

2023-08-24T07:05:34Z

59

0

transformers

[ "transformers", "tf", "camembert", "fill-mask", "generated_from_keras_callback", "base_model:almanach/camembert-base", "base_model:finetune:almanach/camembert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-08-24T07:03:17Z

--- license: mit base_model: camembert-base tags: - generated_from_keras_callback model-index: - name: dummy-model results: [] ---  # dummy-model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-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 4.32.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

LarryAIDraw/meinaalter_v3

LarryAIDraw

2023-08-24T07:01:23Z

0

null

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

null

2023-08-24T06:03:44Z

--- license: creativeml-openrail-m --- https://civitai.com/models/20945?modelVersionId=112825

ardt-multipart/ardt-multipart-arrl_sgld_train_walker2d_high-2408_0701-66

ardt-multipart

2023-08-24T06:56:49Z

32

0

transformers

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

null

2023-08-24T06:03:00Z

--- tags: - generated_from_trainer model-index: - name: ardt-multipart-arrl_sgld_train_walker2d_high-2408_0701-66 results: [] ---  # ardt-multipart-arrl_sgld_train_walker2d_high-2408_0701-66 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001 - train_batch_size: 64 - 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: 1000 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

greenyslimerfahrungen/greenyslimerfahrungen

greenyslimerfahrungen

2023-08-24T06:45:50Z

0

espnet

[ "espnet", "Greeny Slim Erfahrungen", "en", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2023-08-24T06:45:09Z

--- license: cc-by-nc-sa-4.0 language: - en library_name: espnet tags: - Greeny Slim Erfahrungen --- [Greeny Slim Erfahrungen](https://supplementtycoon.com/de/greeny-slim-fruchtgummis/) Notwithstanding, it's vital to take note of that despite the fact that they are low in carbs and sugar, they ought to in any case be consumed with some restraint as a feature of a fair diet.As forever, it's prescribed to peruse the nourishment marks and fixings list cautiously prior to buying any keto gummies to guarantee they line up with your dietary objectives and inclinations. VISIT HERE FOR OFFICIAL WEBSITE:-https://supplementtycoon.com/de/greeny-slim-fruchtgummis/

k1101jh/q-Taxi-v3

k1101jh

2023-08-24T06:44:50Z

0

null

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

reinforcement-learning

2023-08-24T06:44:48Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.69 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="k1101jh/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

k1101jh/q-FrozenLake-v1-4x4-noSlippery

k1101jh

2023-08-24T06:38:56Z

0

null

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

reinforcement-learning

2023-08-24T06:38:54Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="k1101jh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

dkimds/a2c-PandaReachDense-v3

dkimds

2023-08-24T06:17:56Z

3

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-24T06:12:25Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.18 +/- 0.12 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```

HGV1408/Data

HGV1408

2023-08-24T06:17:51Z

103

0

transformers

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

text2text-generation

2023-08-24T06:15:20Z

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum 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: 1.4834 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6997 | 0.54 | 500 | 1.4834 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

IngeniousArtist/openllama-3b-finance

IngeniousArtist

2023-08-24T05:36:30Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-classification", "generated_from_trainer", "dataset:financial_phrasebank", "base_model:openlm-research/open_llama_3b_v2", "base_model:finetune:openlm-research/open_llama_3b_v2", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2023-08-23T20:47:22Z

--- license: apache-2.0 base_model: openlm-research/open_llama_3b_v2 tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy model-index: - name: openllama-3b-finance results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank config: sentences_50agree split: train args: sentences_50agree metrics: - name: Accuracy type: accuracy value: 0.4142561983471074 ---  # openllama-3b-finance This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 4.0296 - Accuracy: 0.4143 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 21.9655 | 0.01 | 20 | 8.1663 | 0.0816 | | 2.231 | 0.01 | 40 | 6.3007 | 0.4143 | | 2.7452 | 0.02 | 60 | 4.0892 | 0.4143 | | 2.4561 | 0.02 | 80 | 5.0314 | 0.4143 | | 2.337 | 0.03 | 100 | 5.6176 | 0.4143 | | 3.2226 | 0.03 | 120 | 4.4963 | 0.4143 | | 2.5633 | 0.04 | 140 | 6.1800 | 0.4143 | | 2.4764 | 0.04 | 160 | 4.7059 | 0.4143 | | 2.45 | 0.05 | 180 | 5.0602 | 0.4143 | | 1.4232 | 0.05 | 200 | 5.3418 | 0.4143 | | 2.7684 | 0.06 | 220 | 5.1805 | 0.4143 | | 1.7065 | 0.06 | 240 | 4.7568 | 0.4143 | | 2.3417 | 0.07 | 260 | 6.1062 | 0.4143 | | 1.907 | 0.07 | 280 | 12.0988 | 0.5041 | | 14.6043 | 0.08 | 300 | 3.0283 | 0.0816 | | 1.337 | 0.08 | 320 | 12.7786 | 0.4143 | | 4.182 | 0.09 | 340 | 7.5619 | 0.4143 | | 3.7365 | 0.09 | 360 | 7.8581 | 0.4143 | | 3.209 | 0.1 | 380 | 3.2547 | 0.4143 | | 3.4836 | 0.1 | 400 | 89.8525 | 0.0816 | | 4.5805 | 0.11 | 420 | 103.0762 | 0.4143 | | 4.6351 | 0.11 | 440 | 91.4501 | 0.4143 | | 11.0873 | 0.12 | 460 | 88.0469 | 0.4143 | | 1.1274 | 0.12 | 480 | 86.7130 | 0.4143 | | 2.0398 | 0.13 | 500 | 86.4186 | 0.4143 | | 18.6924 | 0.13 | 520 | 80.1491 | 0.4143 | | 1.2216 | 0.14 | 540 | 76.8429 | 0.4143 | | 1.1179 | 0.14 | 560 | 78.0159 | 0.4143 | | 10.0981 | 0.15 | 580 | 71.1114 | 0.4143 | | 9.0123 | 0.15 | 600 | 66.2945 | 0.4143 | | 1.9539 | 0.16 | 620 | 65.6854 | 0.4143 | | 8.4729 | 0.17 | 640 | 62.1595 | 0.4143 | | 7.816 | 0.17 | 660 | 52.0763 | 0.4143 | | 6.0443 | 0.18 | 680 | 41.1500 | 0.4143 | | 3.1804 | 0.18 | 700 | 42.8007 | 0.4143 | | 1.6122 | 0.19 | 720 | 44.0976 | 0.4143 | | 9.8927 | 0.19 | 740 | 31.6381 | 0.4143 | | 6.828 | 0.2 | 760 | 12.7483 | 0.4143 | | 3.1457 | 0.2 | 780 | 13.2981 | 0.4143 | | 1.9991 | 0.21 | 800 | 12.4846 | 0.4143 | | 2.5539 | 0.21 | 820 | 13.7669 | 0.4143 | | 1.3898 | 0.22 | 840 | 12.8919 | 0.0816 | | 2.9251 | 0.22 | 860 | 15.9149 | 0.0816 | | 4.0874 | 0.23 | 880 | 10.5282 | 0.4143 | | 2.4763 | 0.23 | 900 | 3.0281 | 0.4143 | | 2.2865 | 0.24 | 920 | 12.2460 | 0.4143 | | 4.2438 | 0.24 | 940 | 10.1961 | 0.4143 | | 2.547 | 0.25 | 960 | 1.4099 | 0.4143 | | 0.8659 | 0.25 | 980 | 8.3217 | 0.4143 | | 3.5331 | 0.26 | 1000 | 6.3990 | 0.4143 | | 2.4704 | 0.26 | 1020 | 2.2337 | 0.0816 | | 2.1381 | 0.27 | 1040 | 10.6263 | 0.4143 | | 1.5927 | 0.27 | 1060 | 11.1989 | 0.4143 | | 2.485 | 0.28 | 1080 | 8.8174 | 0.4143 | | 2.8074 | 0.28 | 1100 | 5.5971 | 0.4143 | | 0.8622 | 0.29 | 1120 | 5.5089 | 0.4143 | | 2.8085 | 0.29 | 1140 | 5.4300 | 0.4143 | | 1.2405 | 0.3 | 1160 | 7.5657 | 0.4143 | | 3.9374 | 0.3 | 1180 | 2.7180 | 0.4143 | | 1.7494 | 0.31 | 1200 | 4.9639 | 0.0816 | | 2.6094 | 0.32 | 1220 | 2.1980 | 0.4143 | | 2.2072 | 0.32 | 1240 | 7.3392 | 0.4143 | | 0.9978 | 0.33 | 1260 | 7.9127 | 0.4143 | | 2.3872 | 0.33 | 1280 | 7.0613 | 0.4143 | | 3.3129 | 0.34 | 1300 | 4.4202 | 0.4143 | | 1.776 | 0.34 | 1320 | 6.1467 | 0.4143 | | 3.1179 | 0.35 | 1340 | 6.0607 | 0.4143 | | 1.272 | 0.35 | 1360 | 5.0484 | 0.4143 | | 3.0694 | 0.36 | 1380 | 3.1665 | 0.4143 | | 1.9452 | 0.36 | 1400 | 4.8692 | 0.4143 | | 2.3689 | 0.37 | 1420 | 4.9375 | 0.4143 | | 2.7082 | 0.37 | 1440 | 3.2108 | 0.4143 | | 0.8244 | 0.38 | 1460 | 7.0151 | 0.4143 | | 2.6032 | 0.38 | 1480 | 5.5645 | 0.4143 | | 2.8745 | 0.39 | 1500 | 4.2408 | 0.4143 | | 2.625 | 0.39 | 1520 | 6.8800 | 0.4143 | | 2.5335 | 0.4 | 1540 | 6.3109 | 0.4143 | | 2.5495 | 0.4 | 1560 | 4.4017 | 0.4143 | | 1.7234 | 0.41 | 1580 | 5.1739 | 0.4143 | | 2.1066 | 0.41 | 1600 | 6.0769 | 0.4143 | | 2.5541 | 0.42 | 1620 | 3.7539 | 0.4143 | | 2.4598 | 0.42 | 1640 | 4.2075 | 0.4143 | | 1.7211 | 0.43 | 1660 | 5.3975 | 0.4143 | | 2.3993 | 0.43 | 1680 | 4.1427 | 0.4143 | | 1.6161 | 0.44 | 1700 | 5.0871 | 0.4143 | | 2.2361 | 0.44 | 1720 | 4.3375 | 0.4143 | | 2.0841 | 0.45 | 1740 | 4.7357 | 0.4143 | | 2.137 | 0.45 | 1760 | 5.2737 | 0.4143 | | 2.3819 | 0.46 | 1780 | 3.1688 | 0.4143 | | 2.6391 | 0.46 | 1800 | 5.6169 | 0.4143 | | 1.276 | 0.47 | 1820 | 6.1945 | 0.4143 | | 2.0694 | 0.48 | 1840 | 6.3761 | 0.4143 | | 2.3715 | 0.48 | 1860 | 6.1666 | 0.4143 | | 2.1428 | 0.49 | 1880 | 6.4718 | 0.4143 | | 2.0409 | 0.49 | 1900 | 6.3259 | 0.4143 | | 2.1924 | 0.5 | 1920 | 6.0853 | 0.4143 | | 2.3511 | 0.5 | 1940 | 4.7199 | 0.4143 | | 2.7335 | 0.51 | 1960 | 4.3591 | 0.4143 | | 1.6784 | 0.51 | 1980 | 3.7488 | 0.1612 | | 1.5525 | 0.52 | 2000 | 6.0497 | 0.4143 | | 2.7457 | 0.52 | 2020 | 3.5952 | 0.4143 | | 2.3929 | 0.53 | 2040 | 4.7684 | 0.4143 | | 1.9522 | 0.53 | 2060 | 5.6394 | 0.4143 | | 2.2257 | 0.54 | 2080 | 4.5801 | 0.4143 | | 1.6753 | 0.54 | 2100 | 5.0521 | 0.4143 | | 1.6154 | 0.55 | 2120 | 5.4730 | 0.4143 | | 1.7723 | 0.55 | 2140 | 5.5251 | 0.4143 | | 2.6963 | 0.56 | 2160 | 3.5098 | 0.4143 | | 1.7274 | 0.56 | 2180 | 5.4262 | 0.4143 | | 2.4059 | 0.57 | 2200 | 4.5019 | 0.4143 | | 1.6505 | 0.57 | 2220 | 5.1107 | 0.4143 | | 1.2469 | 0.58 | 2240 | 5.3456 | 0.4143 | | 1.6702 | 0.58 | 2260 | 5.4103 | 0.4143 | | 1.615 | 0.59 | 2280 | 5.8024 | 0.4143 | | 1.5622 | 0.59 | 2300 | 5.6035 | 0.4143 | | 2.3536 | 0.6 | 2320 | 5.3779 | 0.4143 | | 2.0512 | 0.6 | 2340 | 5.2498 | 0.4143 | | 2.1405 | 0.61 | 2360 | 5.2279 | 0.4143 | | 2.1926 | 0.61 | 2380 | 4.3260 | 0.4143 | | 2.3995 | 0.62 | 2400 | 4.4445 | 0.4143 | | 1.4944 | 0.62 | 2420 | 4.9616 | 0.4143 | | 2.6623 | 0.63 | 2440 | 4.9736 | 0.4143 | | 1.4095 | 0.64 | 2460 | 4.6506 | 0.4143 | | 2.4803 | 0.64 | 2480 | 4.0971 | 0.4143 | | 1.2721 | 0.65 | 2500 | 4.3192 | 0.4143 | | 1.8372 | 0.65 | 2520 | 4.4907 | 0.4143 | | 1.8942 | 0.66 | 2540 | 4.7323 | 0.4143 | | 2.1407 | 0.66 | 2560 | 4.9554 | 0.4143 | | 2.5039 | 0.67 | 2580 | 5.1599 | 0.4143 | | 1.7321 | 0.67 | 2600 | 5.6089 | 0.4143 | | 2.0621 | 0.68 | 2620 | 4.8359 | 0.4143 | | 2.1664 | 0.68 | 2640 | 4.5581 | 0.4143 | | 1.8835 | 0.69 | 2660 | 5.1029 | 0.4143 | | 3.0314 | 0.69 | 2680 | 3.9587 | 0.4143 | | 1.1781 | 0.7 | 2700 | 4.4584 | 0.4143 | | 3.3222 | 0.7 | 2720 | 4.7628 | 0.4143 | | 2.1184 | 0.71 | 2740 | 4.4039 | 0.4143 | | 1.9293 | 0.71 | 2760 | 3.8755 | 0.4143 | | 2.2448 | 0.72 | 2780 | 4.4327 | 0.4143 | | 2.4697 | 0.72 | 2800 | 3.3026 | 0.4143 | | 1.8569 | 0.73 | 2820 | 3.7722 | 0.4143 | | 0.8544 | 0.73 | 2840 | 4.9176 | 0.4143 | | 2.2445 | 0.74 | 2860 | 4.3889 | 0.4143 | | 1.3723 | 0.74 | 2880 | 4.3280 | 0.4143 | | 2.2167 | 0.75 | 2900 | 4.4016 | 0.4143 | | 1.98 | 0.75 | 2920 | 3.8661 | 0.4143 | | 1.7344 | 0.76 | 2940 | 3.7919 | 0.4143 | | 1.924 | 0.76 | 2960 | 4.1408 | 0.4143 | | 1.3811 | 0.77 | 2980 | 4.3730 | 0.4143 | | 1.8289 | 0.77 | 3000 | 4.2872 | 0.4143 | | 1.9573 | 0.78 | 3020 | 4.6165 | 0.4143 | | 2.4877 | 0.78 | 3040 | 4.5988 | 0.4143 | | 1.1749 | 0.79 | 3060 | 4.7887 | 0.4143 | | 2.1835 | 0.8 | 3080 | 4.9018 | 0.4143 | | 2.3752 | 0.8 | 3100 | 4.6911 | 0.4143 | | 1.9741 | 0.81 | 3120 | 4.5126 | 0.4143 | | 1.7513 | 0.81 | 3140 | 4.6251 | 0.4143 | | 3.0666 | 0.82 | 3160 | 4.0260 | 0.4143 | | 0.5569 | 0.82 | 3180 | 4.0965 | 0.4143 | | 2.1805 | 0.83 | 3200 | 4.5240 | 0.4143 | | 2.4319 | 0.83 | 3220 | 4.3080 | 0.4143 | | 2.126 | 0.84 | 3240 | 3.7823 | 0.4143 | | 1.6993 | 0.84 | 3260 | 3.8093 | 0.4143 | | 0.6861 | 0.85 | 3280 | 4.1618 | 0.4143 | | 0.748 | 0.85 | 3300 | 4.5653 | 0.4143 | | 2.5721 | 0.86 | 3320 | 4.6628 | 0.4143 | | 2.0137 | 0.86 | 3340 | 4.2796 | 0.4143 | | 2.1864 | 0.87 | 3360 | 4.1173 | 0.4143 | | 2.4881 | 0.87 | 3380 | 3.9617 | 0.4143 | | 2.6837 | 0.88 | 3400 | 3.7575 | 0.4143 | | 1.5951 | 0.88 | 3420 | 3.6086 | 0.4143 | | 2.504 | 0.89 | 3440 | 3.5919 | 0.4143 | | 1.4982 | 0.89 | 3460 | 3.7519 | 0.4143 | | 1.8994 | 0.9 | 3480 | 3.7120 | 0.4143 | | 1.6126 | 0.9 | 3500 | 3.6854 | 0.4143 | | 2.002 | 0.91 | 3520 | 3.7888 | 0.4143 | | 1.0264 | 0.91 | 3540 | 3.7990 | 0.4143 | | 1.9495 | 0.92 | 3560 | 3.9635 | 0.4143 | | 2.0742 | 0.92 | 3580 | 3.9651 | 0.4143 | | 1.7803 | 0.93 | 3600 | 3.9518 | 0.4143 | | 2.0843 | 0.93 | 3620 | 3.9404 | 0.4143 | | 1.8431 | 0.94 | 3640 | 3.9334 | 0.4143 | | 1.4987 | 0.95 | 3660 | 3.9609 | 0.4143 | | 1.8214 | 0.95 | 3680 | 4.0060 | 0.4143 | | 1.0964 | 0.96 | 3700 | 4.0422 | 0.4143 | | 0.9669 | 0.96 | 3720 | 4.0549 | 0.4143 | | 1.6226 | 0.97 | 3740 | 4.0486 | 0.4143 | | 1.8061 | 0.97 | 3760 | 4.0405 | 0.4143 | | 2.8738 | 0.98 | 3780 | 4.0317 | 0.4143 | | 1.684 | 0.98 | 3800 | 4.0319 | 0.4143 | | 1.1158 | 0.99 | 3820 | 4.0303 | 0.4143 | | 1.775 | 0.99 | 3840 | 4.0294 | 0.4143 | | 2.1639 | 1.0 | 3860 | 4.0296 | 0.4143 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

Afbnff/B

Afbnff

2023-08-24T05:29:13Z

0

null

[ "dataset:fka/awesome-chatgpt-prompts", "region:us" ]

null

2023-08-24T05:28:01Z

--- datasets: - fka/awesome-chatgpt-prompts metrics: - accuracy ---

ishvalin/mt5-small-finetuned-amazon-en-es

ishvalin

2023-08-24T05:17:08Z

9

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-08-24T04:43:56Z

--- license: apache-2.0 base_model: google/mt5-small tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-es results: [] ---  # mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0301 - Rouge1: 17.4531 - Rouge2: 9.0091 - Rougel: 17.0836 - Rougelsum: 17.1528 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.6834 | 1.0 | 1209 | 3.2483 | 15.494 | 7.8022 | 15.1402 | 15.2041 | | 3.6689 | 2.0 | 2418 | 3.1014 | 16.6941 | 8.9493 | 15.9414 | 16.1157 | | 3.4493 | 3.0 | 3627 | 3.0640 | 16.5731 | 8.2808 | 16.0156 | 16.1514 | | 3.3175 | 4.0 | 4836 | 3.0375 | 16.8245 | 8.6021 | 16.2052 | 16.3956 | | 3.2303 | 5.0 | 6045 | 3.0312 | 17.8902 | 9.7012 | 17.3184 | 17.5092 | | 3.1693 | 6.0 | 7254 | 3.0255 | 16.985 | 8.7225 | 16.6058 | 16.7549 | | 3.1357 | 7.0 | 8463 | 3.0235 | 17.015 | 9.0093 | 16.7306 | 16.9061 | | 3.1073 | 8.0 | 9672 | 3.0301 | 17.4531 | 9.0091 | 17.0836 | 17.1528 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

timetoai/distilbert-base-uncased-arxiv-abstracts-10k

timetoai

2023-08-24T05:08:14Z

124

0

transformers

[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-08-21T04:38:43Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-arxiv-abstracts-10k results: [] ---  # distilbert-base-uncased-arxiv-abstracts-10k 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: 2.1698 ## 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: 64 - 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_ratio: 0.2 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 166 | 2.2911 | | No log | 2.0 | 332 | 2.1673 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

JpChi/pegasus-samsum

JpChi

2023-08-24T05:07:22Z

103

0

transformers

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

text2text-generation

2023-08-24T04:07:05Z

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum 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: 1.4079 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.08 | 0.27 | 500 | 1.5162 | | 1.6341 | 0.54 | 1000 | 1.4381 | | 1.5749 | 0.81 | 1500 | 1.4079 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3

openbmb/UltraLM-65b

openbmb

2023-08-24T04:58:51Z

1,565

8

transformers

[ "transformers", "pytorch", "llama", "text-generation", "dataset:stingning/ultrachat", "arxiv:2305.14233", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-18T09:33:47Z

--- datasets: - stingning/ultrachat --- # UltraLM-65b  This is UltraLM-65b delta weights, a chat language model trained upon [UltraChat](https://github.com/thunlp/UltraChat) ## Model Details ### Model Description  The model is fine-tuned based on LLaMA-65b with a multi-turn chat-format template as below ``` User: instruction 1 Assistant: response 1<eos_token> User: instruction 2 Assistant: response 2<eos_token> ... ``` - **License:** UltraLM is based on LLaMA and should be used under LLaMA's [model license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md). - **Finetuned from model:** LLaMA-65b - **Finetuned on data:** [UltraChat](https://github.com/thunlp/UltraChat) ### Model Sources  - **Repository:** [UltraChat](https://github.com/thunlp/UltraChat) - **Paper:** [arxiv](https://arxiv.org/abs/2305.14233) - **Demo:** [More Information Needed] ## Uses  To use this model, you need to [recover](https://github.com/thunlp/UltraChat/tree/main/UltraLM) the full model from the delta weights and perform inference following the template below: ``` [Optional]User: system prompt User: user input Assistant: ```

neil-code/autotrain-test-summarization-84415142559

neil-code

2023-08-24T04:28:12Z

109

0

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "summarization", "en", "dataset:neil-code/autotrain-data-test-summarization", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2023-08-24T04:23:26Z

--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain" datasets: - neil-code/autotrain-data-test-summarization co2_eq_emissions: emissions: 3.0878646296058494 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 84415142559 - CO2 Emissions (in grams): 3.0879 ## Validation Metrics - Loss: 1.534 - Rouge1: 33.336 - Rouge2: 11.361 - RougeL: 27.779 - RougeLsum: 29.966 - Gen Len: 18.773 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/neil-code/autotrain-test-summarization-84415142559 ```

larryvrh/tigerbot-13b-chat-sharegpt-lora

larryvrh

2023-08-24T04:27:43Z

0

1

null

[ "text-generation", "zh", "dataset:larryvrh/sharegpt_zh-only", "region:us" ]

text-generation

2023-08-24T02:22:02Z

--- datasets: - larryvrh/sharegpt_zh-only language: - zh pipeline_tag: text-generation --- 使用8631条中文sharegpt语料[larryvrh/sharegpt_zh-only](https://huggingface.co/datasets/larryvrh/sharegpt_zh-only)重新对齐后的[TigerResearch/tigerbot-13b-chat](https://huggingface.co/TigerResearch/tigerbot-13b-chat)。改善了模型多轮对话下的上下文关联能力。以及在部分场景下回答过于"拟人"的情况。微调前： ![](https://i.imgur.com/AvA4R4d.png) 微调后： ![](https://i.imgur.com/aei5dst.png) 可以使用配套的[webui](https://huggingface.co/larryvrh/tigerbot-13b-chat-sharegpt-lora/blob/main/chat_webui.py)来进行快速测试。 ![](https://i.imgur.com/aV3iEW5.png)

antoinerossupedu/bert-playground

antoinerossupedu

2023-08-24T04:25:14Z

108

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-24T04:10:04Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-playground results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.606823117358914 ---  # bert-playground This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8178 - Matthews Correlation: 0.6068 ## 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 | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4401 | 1.0 | 1069 | 0.4155 | 0.5720 | | 0.3121 | 2.0 | 2138 | 0.6457 | 0.6039 | | 0.1764 | 3.0 | 3207 | 0.8178 | 0.6068 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

TariqJamil/llama-7b-minigunaco-0805

TariqJamil

2023-08-24T03:58:37Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-05T16:48:35Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0

xszhou/ppo-LunarLander-v2

xszhou

2023-08-24T03:44:40Z

0

stable-baselines3

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

reinforcement-learning

2023-08-24T03:44:16Z

--- 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: 272.49 +/- 17.20 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 ... ```

dkqjrm/20230824104542

dkqjrm

2023-08-24T03:41:11Z

105

0

transformers

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

text-classification

2023-08-24T01:46:01Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824104542' results: [] ---  # 20230824104542 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3421 - Accuracy: 0.7256 ## 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: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 312 | 1.0891 | 0.5307 | | 0.5902 | 2.0 | 624 | 0.6221 | 0.4765 | | 0.5902 | 3.0 | 936 | 0.4801 | 0.5379 | | 0.5511 | 4.0 | 1248 | 0.4461 | 0.5054 | | 0.5299 | 5.0 | 1560 | 0.5922 | 0.5162 | | 0.5299 | 6.0 | 1872 | 0.4113 | 0.5199 | | 0.509 | 7.0 | 2184 | 0.4885 | 0.5451 | | 0.509 | 8.0 | 2496 | 0.4106 | 0.4910 | | 0.4976 | 9.0 | 2808 | 0.5019 | 0.4874 | | 0.4898 | 10.0 | 3120 | 0.4132 | 0.5307 | | 0.4898 | 11.0 | 3432 | 0.4564 | 0.4874 | | 0.4739 | 12.0 | 3744 | 0.4919 | 0.5307 | | 0.4594 | 13.0 | 4056 | 0.4235 | 0.4982 | | 0.4594 | 14.0 | 4368 | 0.3937 | 0.5812 | | 0.4444 | 15.0 | 4680 | 0.3871 | 0.5812 | | 0.4444 | 16.0 | 4992 | 0.4123 | 0.6065 | | 0.4334 | 17.0 | 5304 | 0.3986 | 0.6209 | | 0.4045 | 18.0 | 5616 | 0.4088 | 0.6029 | | 0.4045 | 19.0 | 5928 | 0.3935 | 0.6209 | | 0.3999 | 20.0 | 6240 | 0.3645 | 0.6715 | | 0.376 | 21.0 | 6552 | 0.4230 | 0.5740 | | 0.376 | 22.0 | 6864 | 0.3911 | 0.6823 | | 0.3683 | 23.0 | 7176 | 0.5057 | 0.6534 | | 0.3683 | 24.0 | 7488 | 0.3273 | 0.7040 | | 0.3501 | 25.0 | 7800 | 0.3663 | 0.7004 | | 0.344 | 26.0 | 8112 | 0.3755 | 0.6931 | | 0.344 | 27.0 | 8424 | 0.3648 | 0.7112 | | 0.3354 | 28.0 | 8736 | 0.3359 | 0.7148 | | 0.3288 | 29.0 | 9048 | 0.3362 | 0.7112 | | 0.3288 | 30.0 | 9360 | 0.5539 | 0.6787 | | 0.3199 | 31.0 | 9672 | 0.3617 | 0.7112 | | 0.3199 | 32.0 | 9984 | 0.3601 | 0.7184 | | 0.3166 | 33.0 | 10296 | 0.3325 | 0.7292 | | 0.3037 | 34.0 | 10608 | 0.3274 | 0.7256 | | 0.3037 | 35.0 | 10920 | 0.3412 | 0.7076 | | 0.2987 | 36.0 | 11232 | 0.3509 | 0.7256 | | 0.2842 | 37.0 | 11544 | 0.3945 | 0.7076 | | 0.2842 | 38.0 | 11856 | 0.3224 | 0.7365 | | 0.2894 | 39.0 | 12168 | 0.4010 | 0.7148 | | 0.2894 | 40.0 | 12480 | 0.3472 | 0.7220 | | 0.2764 | 41.0 | 12792 | 0.3364 | 0.7112 | | 0.2708 | 42.0 | 13104 | 0.3379 | 0.7040 | | 0.2708 | 43.0 | 13416 | 0.3625 | 0.7148 | | 0.2665 | 44.0 | 13728 | 0.3435 | 0.7220 | | 0.265 | 45.0 | 14040 | 0.3762 | 0.7292 | | 0.265 | 46.0 | 14352 | 0.3322 | 0.7220 | | 0.2618 | 47.0 | 14664 | 0.3265 | 0.7329 | | 0.2618 | 48.0 | 14976 | 0.3752 | 0.7292 | | 0.2513 | 49.0 | 15288 | 0.3415 | 0.7292 | | 0.2487 | 50.0 | 15600 | 0.3604 | 0.7220 | | 0.2487 | 51.0 | 15912 | 0.3484 | 0.7292 | | 0.2488 | 52.0 | 16224 | 0.3598 | 0.7329 | | 0.2404 | 53.0 | 16536 | 0.3719 | 0.7184 | | 0.2404 | 54.0 | 16848 | 0.3329 | 0.7220 | | 0.2359 | 55.0 | 17160 | 0.3535 | 0.7220 | | 0.2359 | 56.0 | 17472 | 0.3606 | 0.7256 | | 0.2364 | 57.0 | 17784 | 0.3407 | 0.7292 | | 0.2343 | 58.0 | 18096 | 0.3342 | 0.7292 | | 0.2343 | 59.0 | 18408 | 0.3451 | 0.7220 | | 0.2348 | 60.0 | 18720 | 0.3421 | 0.7256 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

chezuro/pm-fine-tuned

chezuro

2023-08-24T03:22:56Z

0

peft

[ "peft", "region:us" ]

null

2023-08-24T00:55:03Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0

ChillyMango/results

ChillyMango

2023-08-24T03:16:01Z

0

null

[ "tensorboard", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-hf", "base_model:finetune:NousResearch/Llama-2-7b-hf", "region:us" ]

null

2023-08-24T00:42:24Z

--- base_model: NousResearch/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on an unknown 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: 0.0002 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

ohicarip/sd-deepfashion-baseline-model

ohicarip

2023-08-24T02:45:46Z

4

1

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:ohicarip/deepfashion_bl2", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-20T19:40:51Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 datasets: - ohicarip/deepfashion_bl2 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - ohicarip/sd-deepfashion-baseline-model This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **ohicarip/deepfashion_bl2** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['This man wears a long-sleeve sweater with pure color patterns. The sweater is with cotton fabric. It has a round neckline. The pants this man wears is of long length. The pants are with denim fabric and solid color patterns. The outer clothing the gentleman wears is with cotton fabric and solid color patterns. There is an accessory on his wrist.', 'This person is wearing a short-sleeve shirt with pure color patterns. The shirt is with cotton fabric. It has a round neckline. This person wears a long trousers. The trousers are with denim fabric and lattice patterns.', 'This guy is wearing a short-sleeve shirt with solid color patterns and a long pants. The shirt is with cotton fabric and its neckline is crew. The pants are with denim fabric and solid color patterns.', 'This female is wearing a tank tank shirt with plaid patterns and a three-point shorts. The tank shirt is with cotton fabric. The neckline of the tank shirt is crew. The shorts are with cotton fabric and plaid patterns. This lady wears socks in shoes.']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("ohicarip/sd-deepfashion-baseline-model", torch_dtype=torch.float16) prompt = "This man wears a long-sleeve sweater with pure color patterns. The sweater is with cotton fabric. It has a round neckline. The pants this man wears is of long length. The pants are with denim fabric and solid color patterns. The outer clothing the gentleman wears is with cotton fabric and solid color patterns. There is an accessory on his wrist." image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 15 * Learning rate: 1e-05 * Batch size: 8 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16 More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/ohicarip/text2image-fine-tune/runs/6en1otkv).

Timucin/q-Taxi

Timucin

2023-08-24T02:44:53Z

0

null

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

reinforcement-learning

2023-08-24T02:44:51Z

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

mcwei/rvinpaint

mcwei

2023-08-24T02:39:47Z

0

null

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

null

2023-08-24T00:41:06Z

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

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

Timucin

2023-08-24T02:38:11Z

0

null

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

reinforcement-learning

2023-08-24T02:38:09Z

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

AndreaHuang97/MarkupLM

AndreaHuang97

2023-08-24T02:32:52Z

49

0

transformers

[ "transformers", "pytorch", "markuplm", "text2text-generation", "en", "arxiv:2110.08518", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-24T01:40:47Z

--- language: - en pipeline_tag: text2text-generation --- # MarkupLM **Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)** ## Introduction MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) Junlong Li, Yiheng Xu, Lei Cui, Furu Wei, ACL 2022 ## Usage We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/markuplm) and [demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM).

LarryAIDraw/Lucy-08

LarryAIDraw

2023-08-24T02:23:59Z

0

null

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

null

2023-08-24T02:06:40Z

--- license: creativeml-openrail-m --- https://civitai.com/models/132939/lucy-seiland-trails-of-cold-steel-4-sen-no-kiseki-4

LarryAIDraw/Aurier-10

LarryAIDraw

2023-08-24T02:23:33Z

0

null

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

null

2023-08-24T02:07:09Z

--- license: creativeml-openrail-m --- https://civitai.com/models/132943/aurier-vander-trails-of-cold-steel-3-sen-no-kiseki-3

LarryAIDraw/Kuroe_Casual_wear_-V1

LarryAIDraw

2023-08-24T02:22:49Z

0

null

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

null

2023-08-24T02:04:29Z

--- license: creativeml-openrail-m --- https://civitai.com/models/133037/redivekuroe-princess-connect-redive

LarryAIDraw/shizuku_yaegashi_v1

LarryAIDraw

2023-08-24T02:21:24Z

0

null

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

null

2023-08-24T02:05:51Z

--- license: creativeml-openrail-m --- https://civitai.com/models/132963/shizuku-yaegashi-or-arifureta-from-commonplace-to-worlds-strongest

LarryAIDraw/MiyuCind-06

LarryAIDraw

2023-08-24T02:20:47Z

0

null

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

null

2023-08-24T02:05:29Z

--- license: creativeml-openrail-m --- https://civitai.com/models/132955/miyu-mifune-idolmaster

LarryAIDraw/Fuwawa_Abyssgard-10

LarryAIDraw

2023-08-24T02:20:14Z

0

null

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

null

2023-08-24T02:05:02Z

--- license: creativeml-openrail-m --- https://civitai.com/models/117233/fuwawa-abyssgard-hololive-en-lora

LarryAIDraw/Atago_and_Takao_20230820183759-000014

LarryAIDraw

2023-08-24T02:19:06Z

0

null

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

null

2023-08-24T02:03:56Z

--- license: creativeml-openrail-m --- https://civitai.com/models/133344/atago-and-tako-lora

LarryAIDraw/shimanto

LarryAIDraw

2023-08-24T02:18:34Z

0

null

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

null

2023-08-24T02:03:25Z

--- license: creativeml-openrail-m --- https://civitai.com/models/133172/ijn-shimanto-or-azur-lane

LarryAIDraw/Mary

LarryAIDraw

2023-08-24T02:17:50Z

0

null

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

null

2023-08-24T02:02:59Z

--- license: creativeml-openrail-m --- https://civitai.com/models/133210/mary-the-eminence-in-shadow

LarryAIDraw/ChristinaHope

LarryAIDraw

2023-08-24T02:17:07Z

0

null

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

null

2023-08-24T02:02:16Z

--- license: creativeml-openrail-m --- https://civitai.com/models/133295/christina-hope-the-eminence-in-shadow

lianlian123/Reinforce-CartPole8

lianlian123

2023-08-24T02:14:00Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-08-23T08:21:31Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole8 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

mmenendezg/xlm-roberta-base-finetuned-panx-de

mmenendezg

2023-08-24T02:08:36Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-22T23:21:35Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.6378279372946183 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.0742 - F1: 0.6378 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2424 | 1.0 | 525 | 0.2543 | 0.0 | | 0.1994 | 2.0 | 1050 | 0.0977 | 0.5081 | | 0.1011 | 3.0 | 1575 | 0.0742 | 0.6378 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3

vicssl/test-trainer

vicssl

2023-08-24T02:04:54Z

0

null

[ "sentence-similarity", "region:us" ]

sentence-similarity

2023-08-23T08:47:52Z

--- pipeline_tag: sentence-similarity ---

ardt-multipart/ardt-multipart-arrl_train_walker2d_high-2408_0127-33

ardt-multipart

2023-08-24T02:03:02Z

32

0

transformers

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

null

2023-08-24T00:28:40Z

--- tags: - generated_from_trainer model-index: - name: ardt-multipart-arrl_train_walker2d_high-2408_0127-33 results: [] ---  # ardt-multipart-arrl_train_walker2d_high-2408_0127-33 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001 - train_batch_size: 64 - 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: 1000 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

JJinBBangMan/marian-finetuned-kde4-en-to-fr

JJinBBangMan

2023-08-24T02:00:41Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-08-24T00:10:39Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.853174528380514 ---  # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8568 - Bleu: 52.8532 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

cooperic/distilbert-base-uncased-finetuned-emotion

cooperic

2023-08-24T01:49:06Z

108

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

2023-08-24T00:31:22Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.9283528881025964 ---  # 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: 0.2174 - Accuracy: 0.9285 - F1: 0.9284 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8012 | 1.0 | 250 | 0.3094 | 0.9095 | 0.9083 | | 0.2454 | 2.0 | 500 | 0.2174 | 0.9285 | 0.9284 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3

dkqjrm/20230824083011

dkqjrm

2023-08-24T01:45:30Z

105

0

transformers

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

text-classification

2023-08-23T23:30:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824083011' results: [] ---  # 20230824083011 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3090 - Accuracy: 0.7401 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7501 | 1.0 | 623 | 0.9859 | 0.4729 | | 0.6252 | 2.0 | 1246 | 0.4891 | 0.4801 | | 0.5769 | 3.0 | 1869 | 1.1271 | 0.4729 | | 0.5672 | 4.0 | 2492 | 0.4257 | 0.5632 | | 0.5439 | 5.0 | 3115 | 0.5883 | 0.5415 | | 0.5426 | 6.0 | 3738 | 0.3734 | 0.6245 | | 0.61 | 7.0 | 4361 | 0.4410 | 0.5848 | | 0.4937 | 8.0 | 4984 | 0.4091 | 0.5632 | | 0.4293 | 9.0 | 5607 | 0.3712 | 0.6282 | | 0.3897 | 10.0 | 6230 | 0.3441 | 0.6931 | | 0.3759 | 11.0 | 6853 | 0.3400 | 0.7004 | | 0.379 | 12.0 | 7476 | 0.3802 | 0.6787 | | 0.3661 | 13.0 | 8099 | 0.3456 | 0.7184 | | 0.374 | 14.0 | 8722 | 0.3545 | 0.6859 | | 0.3441 | 15.0 | 9345 | 0.3219 | 0.7112 | | 0.3339 | 16.0 | 9968 | 0.3192 | 0.7184 | | 0.3324 | 17.0 | 10591 | 0.3290 | 0.7184 | | 0.324 | 18.0 | 11214 | 0.3284 | 0.7112 | | 0.3641 | 19.0 | 11837 | 0.3100 | 0.7292 | | 0.3138 | 20.0 | 12460 | 0.3102 | 0.7365 | | 0.3099 | 21.0 | 13083 | 0.3887 | 0.7076 | | 0.3095 | 22.0 | 13706 | 0.3443 | 0.7004 | | 0.3039 | 23.0 | 14329 | 0.3937 | 0.6895 | | 0.287 | 24.0 | 14952 | 0.3071 | 0.7473 | | 0.2718 | 25.0 | 15575 | 0.3097 | 0.7184 | | 0.2711 | 26.0 | 16198 | 0.2888 | 0.7329 | | 0.2738 | 27.0 | 16821 | 0.2920 | 0.7220 | | 0.2697 | 28.0 | 17444 | 0.2986 | 0.7329 | | 0.2589 | 29.0 | 18067 | 0.3092 | 0.7437 | | 0.2536 | 30.0 | 18690 | 0.3141 | 0.7292 | | 0.2564 | 31.0 | 19313 | 0.3134 | 0.7401 | | 0.2493 | 32.0 | 19936 | 0.2962 | 0.7365 | | 0.2428 | 33.0 | 20559 | 0.3358 | 0.7256 | | 0.2425 | 34.0 | 21182 | 0.3155 | 0.7148 | | 0.2342 | 35.0 | 21805 | 0.3000 | 0.7220 | | 0.2394 | 36.0 | 22428 | 0.2955 | 0.7329 | | 0.2257 | 37.0 | 23051 | 0.3070 | 0.7509 | | 0.2272 | 38.0 | 23674 | 0.2959 | 0.7365 | | 0.2197 | 39.0 | 24297 | 0.3100 | 0.7401 | | 0.2144 | 40.0 | 24920 | 0.3009 | 0.7365 | | 0.2164 | 41.0 | 25543 | 0.2957 | 0.7256 | | 0.2129 | 42.0 | 26166 | 0.3133 | 0.7292 | | 0.2106 | 43.0 | 26789 | 0.3110 | 0.7329 | | 0.2069 | 44.0 | 27412 | 0.3072 | 0.7329 | | 0.2051 | 45.0 | 28035 | 0.3300 | 0.7292 | | 0.2064 | 46.0 | 28658 | 0.3106 | 0.7256 | | 0.2039 | 47.0 | 29281 | 0.3114 | 0.7292 | | 0.2106 | 48.0 | 29904 | 0.3180 | 0.7365 | | 0.2008 | 49.0 | 30527 | 0.3099 | 0.7329 | | 0.1945 | 50.0 | 31150 | 0.3066 | 0.7329 | | 0.1958 | 51.0 | 31773 | 0.3124 | 0.7401 | | 0.1939 | 52.0 | 32396 | 0.3230 | 0.7401 | | 0.1942 | 53.0 | 33019 | 0.3105 | 0.7365 | | 0.1887 | 54.0 | 33642 | 0.3014 | 0.7256 | | 0.185 | 55.0 | 34265 | 0.3052 | 0.7365 | | 0.1868 | 56.0 | 34888 | 0.3155 | 0.7365 | | 0.1888 | 57.0 | 35511 | 0.3056 | 0.7256 | | 0.1885 | 58.0 | 36134 | 0.3069 | 0.7329 | | 0.192 | 59.0 | 36757 | 0.3076 | 0.7329 | | 0.1807 | 60.0 | 37380 | 0.3090 | 0.7401 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Ramoslee/whisper-small-th_10000

Ramoslee

2023-08-24T01:41:08Z

3

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "th", "dataset:mozilla-foundation/common_voice_11_0", "base_model:Ramoslee/Whishper-small-th", "base_model:finetune:Ramoslee/Whishper-small-th", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-23T13:02:11Z

--- language: - th license: apache-2.0 base_model: Ramoslee/Whishper-small-th tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Thai results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 th type: mozilla-foundation/common_voice_11_0 config: th split: test args: th metrics: - name: Wer type: wer value: 18.87614018843608 ---  # Whisper Small Thai This model is a fine-tuned version of [Ramoslee/Whishper-small-th](https://huggingface.co/Ramoslee/Whishper-small-th) on the mozilla-foundation/common_voice_11_0 th dataset. It achieves the following results on the evaluation set: - Loss: 0.1836 - Wer: 18.8761 ## 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: 6 - eval_batch_size: 10 - 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: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1262 | 0.27 | 1000 | 0.2267 | 25.1536 | | 0.1174 | 0.55 | 2000 | 0.2190 | 24.6093 | | 0.1363 | 0.82 | 3000 | 0.2059 | 24.5492 | | 0.0618 | 1.1 | 4000 | 0.1970 | 22.1944 | | 0.0686 | 1.37 | 5000 | 0.1916 | 21.2372 | | 0.0722 | 1.65 | 6000 | 0.1854 | 20.3488 | | 0.0771 | 1.92 | 7000 | 0.1801 | 19.8033 | | 0.0191 | 2.2 | 8000 | 0.1859 | 19.5656 | | 0.0237 | 2.47 | 9000 | 0.1862 | 19.1376 | | 0.0205 | 2.74 | 10000 | 0.1836 | 18.8761 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 1.12.1+cu116 - Datasets 2.14.4.dev0 - Tokenizers 0.12.1

dkqjrm/20230824083855

dkqjrm

2023-08-24T01:40:49Z

105

0

transformers

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

text-classification

2023-08-23T23:39:15Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824083855' results: [] ---  # 20230824083855 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.0821 - Accuracy: 0.7473 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5366 | 1.0 | 623 | 0.8415 | 0.4729 | | 0.3757 | 2.0 | 1246 | 0.3098 | 0.4693 | | 0.3001 | 3.0 | 1869 | 0.5999 | 0.4729 | | 0.3227 | 4.0 | 2492 | 0.2808 | 0.4729 | | 0.3109 | 5.0 | 3115 | 0.2772 | 0.5487 | | 0.3034 | 6.0 | 3738 | 0.1529 | 0.6029 | | 0.2648 | 7.0 | 4361 | 0.1565 | 0.6029 | | 0.2104 | 8.0 | 4984 | 0.1394 | 0.6245 | | 0.1926 | 9.0 | 5607 | 0.1404 | 0.6390 | | 0.175 | 10.0 | 6230 | 0.1292 | 0.6859 | | 0.1634 | 11.0 | 6853 | 0.1174 | 0.7004 | | 0.1618 | 12.0 | 7476 | 0.1228 | 0.6787 | | 0.1555 | 13.0 | 8099 | 0.1287 | 0.6534 | | 0.1534 | 14.0 | 8722 | 0.1461 | 0.6570 | | 0.1523 | 15.0 | 9345 | 0.1356 | 0.6426 | | 0.1448 | 16.0 | 9968 | 0.1065 | 0.6968 | | 0.1402 | 17.0 | 10591 | 0.1011 | 0.7292 | | 0.1342 | 18.0 | 11214 | 0.1112 | 0.6643 | | 0.1388 | 19.0 | 11837 | 0.1255 | 0.6823 | | 0.1281 | 20.0 | 12460 | 0.0965 | 0.7220 | | 0.128 | 21.0 | 13083 | 0.0985 | 0.7040 | | 0.1236 | 22.0 | 13706 | 0.1339 | 0.7040 | | 0.1267 | 23.0 | 14329 | 0.1238 | 0.7365 | | 0.1186 | 24.0 | 14952 | 0.0942 | 0.7292 | | 0.1101 | 25.0 | 15575 | 0.0923 | 0.7220 | | 0.1122 | 26.0 | 16198 | 0.0919 | 0.7401 | | 0.1088 | 27.0 | 16821 | 0.0893 | 0.7292 | | 0.1059 | 28.0 | 17444 | 0.0897 | 0.7401 | | 0.106 | 29.0 | 18067 | 0.0878 | 0.7509 | | 0.1019 | 30.0 | 18690 | 0.0945 | 0.7365 | | 0.1047 | 31.0 | 19313 | 0.0900 | 0.7256 | | 0.1011 | 32.0 | 19936 | 0.0884 | 0.7437 | | 0.0962 | 33.0 | 20559 | 0.0874 | 0.7329 | | 0.0971 | 34.0 | 21182 | 0.0933 | 0.7329 | | 0.0914 | 35.0 | 21805 | 0.0845 | 0.7473 | | 0.0965 | 36.0 | 22428 | 0.0914 | 0.7365 | | 0.0914 | 37.0 | 23051 | 0.0855 | 0.7292 | | 0.0894 | 38.0 | 23674 | 0.0867 | 0.7256 | | 0.087 | 39.0 | 24297 | 0.0861 | 0.7329 | | 0.0865 | 40.0 | 24920 | 0.0830 | 0.7329 | | 0.0851 | 41.0 | 25543 | 0.0827 | 0.7473 | | 0.0837 | 42.0 | 26166 | 0.0818 | 0.7365 | | 0.0865 | 43.0 | 26789 | 0.0840 | 0.7401 | | 0.0807 | 44.0 | 27412 | 0.0815 | 0.7292 | | 0.0829 | 45.0 | 28035 | 0.0840 | 0.7365 | | 0.0814 | 46.0 | 28658 | 0.0851 | 0.7401 | | 0.0798 | 47.0 | 29281 | 0.0841 | 0.7401 | | 0.0806 | 48.0 | 29904 | 0.0838 | 0.7473 | | 0.0773 | 49.0 | 30527 | 0.0823 | 0.7401 | | 0.0769 | 50.0 | 31150 | 0.0813 | 0.7329 | | 0.0763 | 51.0 | 31773 | 0.0822 | 0.7509 | | 0.0792 | 52.0 | 32396 | 0.0833 | 0.7365 | | 0.0772 | 53.0 | 33019 | 0.0819 | 0.7365 | | 0.0732 | 54.0 | 33642 | 0.0810 | 0.7365 | | 0.0708 | 55.0 | 34265 | 0.0808 | 0.7365 | | 0.0741 | 56.0 | 34888 | 0.0824 | 0.7509 | | 0.0725 | 57.0 | 35511 | 0.0816 | 0.7437 | | 0.072 | 58.0 | 36134 | 0.0812 | 0.7437 | | 0.0712 | 59.0 | 36757 | 0.0827 | 0.7401 | | 0.0707 | 60.0 | 37380 | 0.0821 | 0.7473 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230824084116

dkqjrm

2023-08-24T01:39:38Z

105

0

transformers

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

text-classification

2023-08-23T23:41:35Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824084116' results: [] ---  # 20230824084116 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6747 - Accuracy: 0.7329 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0144 | 1.0 | 623 | 1.2485 | 0.4729 | | 0.8551 | 2.0 | 1246 | 0.7296 | 0.5415 | | 0.9621 | 3.0 | 1869 | 1.3927 | 0.4729 | | 0.8648 | 4.0 | 2492 | 0.6253 | 0.6173 | | 0.8311 | 5.0 | 3115 | 0.6509 | 0.6606 | | 0.8365 | 6.0 | 3738 | 0.6018 | 0.6895 | | 0.772 | 7.0 | 4361 | 0.7314 | 0.6751 | | 0.7306 | 8.0 | 4984 | 1.0930 | 0.5957 | | 0.763 | 9.0 | 5607 | 0.7093 | 0.7076 | | 0.6931 | 10.0 | 6230 | 0.6302 | 0.6968 | | 0.6465 | 11.0 | 6853 | 1.1188 | 0.5776 | | 0.6503 | 12.0 | 7476 | 0.6957 | 0.7112 | | 0.6657 | 13.0 | 8099 | 0.6470 | 0.7112 | | 0.6315 | 14.0 | 8722 | 0.7099 | 0.7112 | | 0.5491 | 15.0 | 9345 | 0.5178 | 0.7184 | | 0.4908 | 16.0 | 9968 | 0.6282 | 0.7365 | | 0.4742 | 17.0 | 10591 | 0.6553 | 0.7256 | | 0.4653 | 18.0 | 11214 | 0.5637 | 0.7112 | | 0.492 | 19.0 | 11837 | 0.5870 | 0.7184 | | 0.4519 | 20.0 | 12460 | 0.8201 | 0.7292 | | 0.4198 | 21.0 | 13083 | 0.6294 | 0.7365 | | 0.403 | 22.0 | 13706 | 0.6998 | 0.7220 | | 0.4017 | 23.0 | 14329 | 0.8424 | 0.7220 | | 0.368 | 24.0 | 14952 | 0.6179 | 0.7401 | | 0.3514 | 25.0 | 15575 | 0.6303 | 0.7256 | | 0.3458 | 26.0 | 16198 | 0.6241 | 0.7292 | | 0.3488 | 27.0 | 16821 | 0.6348 | 0.7365 | | 0.33 | 28.0 | 17444 | 0.6663 | 0.7292 | | 0.3133 | 29.0 | 18067 | 0.6231 | 0.7437 | | 0.3108 | 30.0 | 18690 | 0.6940 | 0.7220 | | 0.3156 | 31.0 | 19313 | 0.7685 | 0.7256 | | 0.2887 | 32.0 | 19936 | 0.5912 | 0.7365 | | 0.2871 | 33.0 | 20559 | 0.6539 | 0.7401 | | 0.2835 | 34.0 | 21182 | 0.7319 | 0.7292 | | 0.2587 | 35.0 | 21805 | 0.6106 | 0.7365 | | 0.2767 | 36.0 | 22428 | 0.6255 | 0.7329 | | 0.2621 | 37.0 | 23051 | 0.7181 | 0.7329 | | 0.2733 | 38.0 | 23674 | 0.6841 | 0.7365 | | 0.2473 | 39.0 | 24297 | 0.7042 | 0.7329 | | 0.2467 | 40.0 | 24920 | 0.6123 | 0.7329 | | 0.2357 | 41.0 | 25543 | 0.6681 | 0.7365 | | 0.2333 | 42.0 | 26166 | 0.7094 | 0.7292 | | 0.2387 | 43.0 | 26789 | 0.6546 | 0.7365 | | 0.2248 | 44.0 | 27412 | 0.7021 | 0.7329 | | 0.2271 | 45.0 | 28035 | 0.6913 | 0.7545 | | 0.2288 | 46.0 | 28658 | 0.6855 | 0.7365 | | 0.2159 | 47.0 | 29281 | 0.6495 | 0.7401 | | 0.2107 | 48.0 | 29904 | 0.6568 | 0.7292 | | 0.2204 | 49.0 | 30527 | 0.7337 | 0.7329 | | 0.2038 | 50.0 | 31150 | 0.6391 | 0.7365 | | 0.2183 | 51.0 | 31773 | 0.6593 | 0.7437 | | 0.2041 | 52.0 | 32396 | 0.6518 | 0.7220 | | 0.2107 | 53.0 | 33019 | 0.6677 | 0.7256 | | 0.2076 | 54.0 | 33642 | 0.6716 | 0.7292 | | 0.1946 | 55.0 | 34265 | 0.6957 | 0.7256 | | 0.1974 | 56.0 | 34888 | 0.6858 | 0.7256 | | 0.2047 | 57.0 | 35511 | 0.6721 | 0.7329 | | 0.2001 | 58.0 | 36134 | 0.6747 | 0.7365 | | 0.1899 | 59.0 | 36757 | 0.6842 | 0.7329 | | 0.1872 | 60.0 | 37380 | 0.6747 | 0.7329 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230824082958

dkqjrm

2023-08-24T01:33:05Z

107

0

transformers

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

text-classification

2023-08-23T23:30:18Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824082958' results: [] ---  # 20230824082958 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5547 - Accuracy: 0.7581 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1252 | 1.0 | 623 | 0.6915 | 0.5415 | | 0.9382 | 2.0 | 1246 | 0.7221 | 0.5307 | | 1.0555 | 3.0 | 1869 | 0.7387 | 0.5199 | | 0.9336 | 4.0 | 2492 | 0.9751 | 0.6390 | | 0.8894 | 5.0 | 3115 | 0.9277 | 0.6643 | | 0.9066 | 6.0 | 3738 | 1.1836 | 0.6931 | | 0.8496 | 7.0 | 4361 | 0.8242 | 0.7184 | | 0.7761 | 8.0 | 4984 | 0.9061 | 0.6859 | | 0.8175 | 9.0 | 5607 | 0.7474 | 0.7220 | | 0.7575 | 10.0 | 6230 | 0.8582 | 0.7292 | | 0.747 | 11.0 | 6853 | 0.8351 | 0.7256 | | 0.728 | 12.0 | 7476 | 0.8912 | 0.7148 | | 0.8296 | 13.0 | 8099 | 0.9471 | 0.7220 | | 0.7327 | 14.0 | 8722 | 1.1407 | 0.7148 | | 0.7284 | 15.0 | 9345 | 0.7681 | 0.7256 | | 0.6642 | 16.0 | 9968 | 1.4084 | 0.6679 | | 0.5888 | 17.0 | 10591 | 0.8413 | 0.7329 | | 0.6074 | 18.0 | 11214 | 0.7461 | 0.7401 | | 0.625 | 19.0 | 11837 | 0.9516 | 0.7545 | | 0.5911 | 20.0 | 12460 | 1.3395 | 0.7292 | | 0.5322 | 21.0 | 13083 | 1.3924 | 0.7509 | | 0.5247 | 22.0 | 13706 | 1.1553 | 0.7256 | | 0.5146 | 23.0 | 14329 | 1.6692 | 0.7040 | | 0.4493 | 24.0 | 14952 | 1.2315 | 0.7437 | | 0.399 | 25.0 | 15575 | 1.2710 | 0.7545 | | 0.3644 | 26.0 | 16198 | 1.5049 | 0.7473 | | 0.4031 | 27.0 | 16821 | 1.5735 | 0.7401 | | 0.386 | 28.0 | 17444 | 1.4749 | 0.7220 | | 0.3735 | 29.0 | 18067 | 0.9541 | 0.7365 | | 0.356 | 30.0 | 18690 | 1.3936 | 0.7473 | | 0.3496 | 31.0 | 19313 | 0.9982 | 0.7437 | | 0.3149 | 32.0 | 19936 | 0.9572 | 0.7581 | | 0.3094 | 33.0 | 20559 | 1.5663 | 0.7256 | | 0.2886 | 34.0 | 21182 | 1.5993 | 0.7365 | | 0.2545 | 35.0 | 21805 | 1.1515 | 0.7545 | | 0.276 | 36.0 | 22428 | 1.2768 | 0.7473 | | 0.2645 | 37.0 | 23051 | 1.4290 | 0.7509 | | 0.262 | 38.0 | 23674 | 1.2363 | 0.7617 | | 0.2261 | 39.0 | 24297 | 1.3446 | 0.7617 | | 0.2291 | 40.0 | 24920 | 1.0532 | 0.7509 | | 0.2178 | 41.0 | 25543 | 1.4745 | 0.7509 | | 0.2104 | 42.0 | 26166 | 1.3830 | 0.7545 | | 0.217 | 43.0 | 26789 | 1.7099 | 0.7473 | | 0.214 | 44.0 | 27412 | 1.7054 | 0.7401 | | 0.1856 | 45.0 | 28035 | 1.4350 | 0.7545 | | 0.2014 | 46.0 | 28658 | 1.7266 | 0.7473 | | 0.1759 | 47.0 | 29281 | 1.2659 | 0.7581 | | 0.2027 | 48.0 | 29904 | 1.8336 | 0.7401 | | 0.1871 | 49.0 | 30527 | 1.3398 | 0.7509 | | 0.1586 | 50.0 | 31150 | 1.4948 | 0.7509 | | 0.1619 | 51.0 | 31773 | 1.3787 | 0.7545 | | 0.1665 | 52.0 | 32396 | 1.6532 | 0.7545 | | 0.1786 | 53.0 | 33019 | 1.4697 | 0.7581 | | 0.1609 | 54.0 | 33642 | 1.5462 | 0.7653 | | 0.1304 | 55.0 | 34265 | 1.3577 | 0.7581 | | 0.1576 | 56.0 | 34888 | 1.7004 | 0.7617 | | 0.1522 | 57.0 | 35511 | 1.4629 | 0.7581 | | 0.1496 | 58.0 | 36134 | 1.6336 | 0.7581 | | 0.1406 | 59.0 | 36757 | 1.5699 | 0.7545 | | 0.1268 | 60.0 | 37380 | 1.5547 | 0.7581 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

nxnhjrjtbjfzhrovwl/limarp-llongma2-8k-ggml-f16

nxnhjrjtbjfzhrovwl

2023-08-24T01:12:05Z

0

null

[ "arxiv:2305.11206", "license:agpl-3.0", "region:us" ]

null

2023-08-23T18:04:50Z

--- '[object Object]': null license: agpl-3.0 --- This repository contains the unquantized merge of [limarp-llongma2-8k lora](https://huggingface.co/lemonilia/limarp-llongma2-8k) in ggml format. You can quantize the f16 ggml to the quantization of your choice by following the below steps: 1. Download and extract the [llama.cpp binaries](https://github.com/ggerganov/llama.cpp/releases/download/master-41c6741/llama-master-41c6741-bin-win-avx2-x64.zip) ([or compile it yourself if you're on Linux](https://github.com/ggerganov/llama.cpp#build)) 2. Move the "quantize" executable to the same folder where you downloaded the f16 ggml model. 3. Open a command prompt window in that same folder and write the following command, making the changes that you see fit. ```bash quantize.exe limarp-llongma2-13b.ggmlv3.f16.bin limarp-llongma2-13b.ggmlv3.q4_0.bin q4_0 ``` 4. Press enter to run the command and the quantized model will be generated in the folder. The below are the contents of the original model card: # Model Card for LimaRP-LLongMA2-8k-v2 LimaRP-LLongMA2-8k is an experimental [Llama2](https://huggingface.co/meta-llama) finetune narrowly focused on novel-style roleplay chatting, and a continuation of the previously released [LimaRP-llama2](https://huggingface.co/lemonilia/limarp-llama2) with a larger number of training tokens (+95%). To considerably facilitate uploading, distribution and merging with other models, LoRA adapters are provided. LimaRP-LLongMA2 LoRA adapters, as their name suggests, are intended to be applied on LLongMA-2 models with 8k context ([7B](https://huggingface.co/conceptofmind/LLongMA-2-7b) and [13B](https://huggingface.co/conceptofmind/LLongMA-2-13b)) and their derivatives. Data updates may be posted in the future. The current version is **v3**. ## Model Details ### Model Description This is an experimental attempt at creating an RP-oriented fine-tune using a manually-curated, high-quality dataset of human-generated conversations. The main rationale for this are the observations from [Zhou et al.](https://arxiv.org/abs/2305.11206). The authors suggested that just 1000-2000 carefully curated training examples may yield high quality output for assistant-type chatbots. This is in contrast with the commonly employed strategy where a very large number of training examples (tens of thousands to even millions) of widely varying quality are used. For LimaRP a similar approach was used, with the difference that the conversational data is almost entirely human-generated. Every training example is manually compiled and selected to comply with subjective quality parameters, with virtually no chance for OpenAI-style alignment responses to come up. ## Uses  The model is intended to approximate the experience of 1-on-1 roleplay as observed on many Internet forums dedicated on roleplaying. It _must_ be used with a specific format similar to that of this template: ``` <<SYSTEM>> Character's Persona: {bot character description} User's Persona: {user character description} Scenario: {what happens in the story} Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length. <<AIBOT>> Character: {utterance} <<HUMAN>> User: {utterance} [etc.] ``` With `<<SYSTEM>>`, `<<AIBOT>>` and `<<HUMAN>>` being special instruct-mode sequences. The text under curly braces must be replaced with appropriate text in _natural language_. Replace `User` and `Character` with actual character names. This more graphical breakdown of the prompt format with a practical example might make it clearer: ![graphical explanation](https://files.catbox.moe/fq8ner.png) ### More detailed notes on prompt format, usage and other settings - **The model has been tested mainly using Oobabooga's `text-generation-webui` as a backend** - Preferably respect spacing and newlines shown above. This might not be possible yet with some frontends. - Replace `Character` and `User` in the above template with your desired names. - The scenario description has a large influence on what the character will do. Try to keep it more open-ended to lessen its impact. - **The model expects users and characters to use third-person narration in simple past and enclose dialogues with standard quotation marks `" "`.** Other formats are not supported (= not in the training data). - Do not use newlines in Persona and Scenario. Use natural language. - The last line in `<<SYSTEM>>` does not need to be written exactly as depicted, but should mention that `Character` and `User` will engage in roleplay and specify the length of `Character`'s messages - The message lengths used during training are: `tiny`, `short`, `average`, `long`, `huge`, `humongous`. However, there might not have been enough training examples for each length for this instruction to have a significant impact. The preferred lengths for this type of role-playing are `average` or `long`. - Suggested text generation settings: - Temperature ~0.70 - Tail-Free Sampling 0.85 - Repetition penalty ~1.10 (Compared to LLaMAv1, Llama2 appears to require a somewhat higher rep.pen.) - Not used: Top-P (disabled/set to 1.0), Top-K (disabled/set to 0), Typical P (disabled/set to 1.0) ### Sample character cards Here are a few example **SillyTavern character cards** following the required format; download and import into SillyTavern. Feel free to modify and adapt them to your purposes. - [Carina, a 'big sister' android maid](https://files.catbox.moe/1qcqqj.png) - [Charlotte, a cute android maid](https://files.catbox.moe/k1x9a7.png) - [Etma, an 'aligned' AI assistant](https://files.catbox.moe/dj8ggi.png) - [Mila, an anthro pet catgirl](https://files.catbox.moe/amnsew.png) - [Samuel, a handsome vampire](https://files.catbox.moe/f9uiw1.png) And here is a sample of how the model is intended to behave with proper chat and prompt formatting: https://files.catbox.moe/egfd90.png ### Other tips It's possible to make the model automatically generate random character information and scenario by adding just `<<SYSTEM>>` and the character name in text completion mode in `text-generation-webui`, as done here (click to enlarge). The format generally closely matches that of the training data: ![example](https://files.catbox.moe/5ntmcj.png) ### Out-of-Scope Use  The model has not been tested for: - IRC-style chat - Markdown-style roleplay (asterisks for actions, dialogue lines without quotation marks) - Storywriting - Usage without the suggested prompt format Furthermore, the model is not intended nor expected to provide factual and accurate information on any subject. ## Bias, Risks, and Limitations  The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. ### Recommendations  The model may easily output disturbing and socially inappropriate content and therefore should not be used by minors or within environments where a general audience is expected. Its outputs will have in general a strong NSFW bias unless the character card/description de-emphasizes it. ## How to Get Started with the Model Download and load with `text-generation-webui` as a back-end application. It's suggested to start the `webui` via command line. Assuming you have copied the LoRA files under a subdirectory called `lora/limarp-llongma2-7b`, you would use something like this for the 7B model: ``` python server.py --api --verbose --model LLongMA-7B --lora limarp-llongma2-7b ``` When using 4-bit `bitsnbytes` it is suggested to use double quantization to increase accuracy. The starting command may be something like this: ``` python server.py --verbose --api --model LLongMA-2-13B --lora limarp13-llongma2-13b --load-in-4bit --use_double_quant ``` Then, preferably use [SillyTavern](https://github.com/SillyTavern/SillyTavern) as a front-end using the following settings: ![SillyTavern settings](https://files.catbox.moe/nd8v12.png) In addition of enabling the instruct mode with the correct sequences, it's particularly important to **enable "Include names"**, as the model was trained with them at the start of each utterance. If it's disabled, the model can start getting confused and often write for the user in its responses. To take advantage of this model's larger context length, unlock the context size and set it up to any length up to 8192 tokens, depending on your VRAM constraints. On most consumer GPUs this will likely need to be set to a lower value. ![Unlock context size](https://files.catbox.moe/wfj8vv.png) It is **recommended to ban/disable the EOS token** as it can for instance apparently give [artifacts or tokenization issues](https://files.catbox.moe/cxfrzu.png) when it ends up getting generated close to punctuation or quotation marks, at least in SillyTavern. These would typically happen with AI responses. ![Ban EOS](https://files.catbox.moe/xslnhb.png) ## Training Details ### Training Data The training data comprises about **1500** manually edited roleplaying conversation threads from various Internet RP forums, for about **24 megabytes** of data. Character and Scenario information was initially filled in for every thread with the help of mainly `gpt-4`. Later on this has been accomplished with a custom summarizer. Conversations in the dataset are almost entirely human-generated except for a handful of messages. Character names in the RP stories have been isolated and replaced with standard placeholder strings. Usernames, out-of-context (OOC) messages and personal information have not been intentionally included. ### Training Procedure  The version of LimaRP uploaded on this repository was trained using a small NVidia A40 cluster in 8-bit with regular LoRA adapters and 8-bit AdamW optimizer. #### Training Hyperparameters The most important settings were as follows: - --learning_rate 0.000065 - --lr_scheduler_type cosine - --lora_r 8 - --lora_alpha 16 - --lora_dropout 0.01 - --num_train_epochs 2 - --bf16 True - --tf32 True - --bits 8 - --per_device_train_batch_size 1 - --gradient_accumulation_steps 1 - --optim adamw_bnb_8bit **All linear LoRA layers** were targeted. An effective batch size of 1 was found to yield the lowest loss curves during fine-tuning. It was also found that using `--train_on_source False` with the entire training example at the output yields similar results. These LoRAs have been trained in this way (similar to what was done with [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) or as with unsupervised finetuning).   ## Environmental Impact  Finetuning this model on 8 NVidia A40 48GB in parallel takes about 25 minutes (7B) or 45 minutes (13B).

davidggphy/whisper-tiny-finetuned-minds14-enUS

davidggphy

2023-08-24T01:07:40Z

74

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-23T23:30:00Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-finetuned-minds14-enUS_2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 metrics: - name: Wer type: wer value: 0.33943329397874855 ---  # whisper-tiny-finetuned-minds14-enUS_2 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.7508 - Wer Ortho: 0.3356 - Wer: 0.3394 - Cer: 0.2613 - Cer Ortho: 0.2623 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer | Cer Ortho | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:---------:| | 0.0136 | 7.14 | 100 | 0.6142 | 0.3362 | 0.3388 | 0.2587 | 0.2614 | | 0.0009 | 14.29 | 200 | 0.6704 | 0.3288 | 0.3300 | 0.2515 | 0.2534 | | 0.0011 | 21.43 | 300 | 0.6858 | 0.3054 | 0.3093 | 0.2363 | 0.2374 | | 0.0005 | 28.57 | 400 | 0.7081 | 0.3455 | 0.3477 | 0.2699 | 0.2711 | | 0.0004 | 35.71 | 500 | 0.7191 | 0.3467 | 0.3501 | 0.2727 | 0.2736 | | 0.0001 | 42.86 | 600 | 0.7337 | 0.3405 | 0.3447 | 0.2652 | 0.2662 | | 0.0001 | 50.0 | 700 | 0.7418 | 0.3393 | 0.3430 | 0.2636 | 0.2645 | | 0.0001 | 57.14 | 800 | 0.7466 | 0.3387 | 0.3424 | 0.2634 | 0.2644 | | 0.0001 | 64.29 | 900 | 0.7496 | 0.3350 | 0.3388 | 0.2604 | 0.2614 | | 0.0001 | 71.43 | 1000 | 0.7508 | 0.3356 | 0.3394 | 0.2613 | 0.2623 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

michaelriedl/MonsterForgeFusion-sd-2-base

michaelriedl

2023-08-24T01:06:20Z

5

0

diffusers

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

text-to-image

2023-08-24T00:46:11Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-2-base tags: - stable-diffusion - text-to-image - diffusers - lora inference: true ---

LBR47/wav2vec2-base-finetuned-gtzan

LBR47

2023-08-24T01:05:57Z

159

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:bookbot/distil-ast-audioset", "base_model:finetune:bookbot/distil-ast-audioset", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-08-14T04:15:04Z

--- license: apache-2.0 base_model: bookbot/distil-ast-audioset tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: train split: train args: train metrics: - name: Accuracy type: accuracy value: 0.89 ---  # distil-ast-audioset-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7907 - Accuracy: 0.89 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

aiknight87/falcon-40b-instruct-test-system

aiknight87

2023-08-24T00:53:18Z

1

0

peft

[ "peft", "RefinedWeb", "custom_code", "4-bit", "bitsandbytes", "region:us" ]

null

2023-08-23T06:41:11Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0

jimmyofdoom/a2c-PandaReachDense-v3

jimmyofdoom

2023-08-24T00:48:10Z

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-24T00:42:51Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.25 +/- 0.14 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```

DuyTa/vi_whisper-small

DuyTa

2023-08-24T00:33:35Z

80

1

transformers

[ "transformers", "pytorch", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-13T14:16:43Z

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: vi_whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Vivos + Commonvoice type: vivos config: None split: None metrics: - name: Wer type: wer value: 21.8855 ---  # vi_whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mixing of Vivos and CommonVoice dataset. It achieves the following results on the evaluation set: - Loss: 0.2894 - Wer: 21.8855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data In training phase i used VIVOS dataset and cleaned CommonVoice The VIVOS evaluation dataset was used ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 1000 - training_steps: 8000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.249 | 1.1 | 1000 | 0.3766 | 32.1678 | | 0.1416 | 2.2 | 2000 | 0.2881 | 46.4646 | | 0.0839 | 3.3 | 3000 | 0.2799 | 22.7791 | | 0.0546 | 4.41 | 4000 | 0.2894 | 21.8855 | | 0.0256 | 5.51 | 5000 | 0.3023 | 32.2973 | | 0.0111 | 6.61 | 6000 | 0.3061 | 31.0153 | | 0.0028 | 7.71 | 7000 | 0.3143 | 27.1691 | | 0.0014 | 8.81 | 8000 | 0.3187 | 27.3634 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

pabloyesteb/a2c-PandaReachDense-v3

pabloyesteb

2023-08-24T00:21:05Z

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-24T00:15:07Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.10 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```

ardt-multipart/ardt-multipart-combo_train_walker2d_v2-2308_2328-99

ardt-multipart

2023-08-24T00:20:53Z

31

0

transformers

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

null

2023-08-23T22:30:17Z

--- tags: - generated_from_trainer model-index: - name: ardt-multipart-combo_train_walker2d_v2-2308_2328-99 results: [] ---  # ardt-multipart-combo_train_walker2d_v2-2308_2328-99 This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001 - train_batch_size: 64 - 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: 1000 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

nlpnlp/xlm-roberta-base-finetuned-panx-de

nlpnlp

2023-08-24T00:04:07Z

105

0

transformers

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

token-classification

2023-08-23T17:08:22Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8600170502983802 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1391 - F1: 0.8600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2598 | 1.0 | 525 | 0.1697 | 0.8177 | | 0.1253 | 2.0 | 1050 | 0.1343 | 0.8509 | | 0.0812 | 3.0 | 1575 | 0.1391 | 0.8600 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3

azhang1212/angela_punc_untranslated_eval

azhang1212

2023-08-23T23:44:42Z

8

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-23T20:30:13Z

--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: angela_punc_untranslated_eval results: [] ---  # angela_punc_untranslated_eval This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1902 - Precision: 0.3889 - Recall: 0.2568 - F1: 0.3093 - Accuracy: 0.9517 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1524 | 1.0 | 1283 | 0.1547 | 0.4163 | 0.1471 | 0.2174 | 0.9546 | | 0.1295 | 2.0 | 2566 | 0.1518 | 0.4489 | 0.1943 | 0.2712 | 0.9556 | | 0.1113 | 3.0 | 3849 | 0.1614 | 0.4152 | 0.2323 | 0.2979 | 0.9538 | | 0.0896 | 4.0 | 5132 | 0.1784 | 0.4248 | 0.2346 | 0.3023 | 0.9542 | | 0.073 | 5.0 | 6415 | 0.1902 | 0.3889 | 0.2568 | 0.3093 | 0.9517 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

dkqjrm/20230824064444

dkqjrm

2023-08-23T23:38:44Z

105

0

transformers

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

text-classification

2023-08-23T21:45:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824064444' results: [] ---  # 20230824064444 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.0709 - Accuracy: 0.7329 ## 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: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 312 | 0.4733 | 0.5307 | | 0.3538 | 2.0 | 624 | 0.1917 | 0.5126 | | 0.3538 | 3.0 | 936 | 0.1696 | 0.5560 | | 0.2775 | 4.0 | 1248 | 0.1700 | 0.5271 | | 0.2538 | 5.0 | 1560 | 0.3497 | 0.5343 | | 0.2538 | 6.0 | 1872 | 0.2183 | 0.5632 | | 0.259 | 7.0 | 2184 | 0.1783 | 0.5018 | | 0.259 | 8.0 | 2496 | 0.2321 | 0.5848 | | 0.2587 | 9.0 | 2808 | 0.2081 | 0.6101 | | 0.2211 | 10.0 | 3120 | 0.1194 | 0.6715 | | 0.2211 | 11.0 | 3432 | 0.1505 | 0.6390 | | 0.198 | 12.0 | 3744 | 0.1130 | 0.7004 | | 0.1939 | 13.0 | 4056 | 0.1187 | 0.6679 | | 0.1939 | 14.0 | 4368 | 0.1175 | 0.6787 | | 0.1687 | 15.0 | 4680 | 0.1092 | 0.7040 | | 0.1687 | 16.0 | 4992 | 0.0984 | 0.7076 | | 0.1511 | 17.0 | 5304 | 0.1032 | 0.7076 | | 0.1448 | 18.0 | 5616 | 0.1024 | 0.7401 | | 0.1448 | 19.0 | 5928 | 0.0902 | 0.7112 | | 0.1392 | 20.0 | 6240 | 0.0972 | 0.7112 | | 0.1283 | 21.0 | 6552 | 0.0880 | 0.7184 | | 0.1283 | 22.0 | 6864 | 0.0892 | 0.7329 | | 0.1257 | 23.0 | 7176 | 0.1156 | 0.7401 | | 0.1257 | 24.0 | 7488 | 0.0940 | 0.7329 | | 0.1215 | 25.0 | 7800 | 0.0876 | 0.7401 | | 0.1184 | 26.0 | 8112 | 0.1289 | 0.7437 | | 0.1184 | 27.0 | 8424 | 0.0808 | 0.7256 | | 0.1112 | 28.0 | 8736 | 0.0823 | 0.7401 | | 0.1139 | 29.0 | 9048 | 0.0838 | 0.7256 | | 0.1139 | 30.0 | 9360 | 0.0855 | 0.7220 | | 0.1095 | 31.0 | 9672 | 0.0813 | 0.7256 | | 0.1095 | 32.0 | 9984 | 0.0765 | 0.7256 | | 0.106 | 33.0 | 10296 | 0.0847 | 0.7365 | | 0.1034 | 34.0 | 10608 | 0.0844 | 0.7509 | | 0.1034 | 35.0 | 10920 | 0.0811 | 0.7184 | | 0.0991 | 36.0 | 11232 | 0.0811 | 0.7292 | | 0.0938 | 37.0 | 11544 | 0.0847 | 0.7365 | | 0.0938 | 38.0 | 11856 | 0.0824 | 0.7256 | | 0.0973 | 39.0 | 12168 | 0.0760 | 0.7292 | | 0.0973 | 40.0 | 12480 | 0.0786 | 0.7220 | | 0.0908 | 41.0 | 12792 | 0.0732 | 0.7473 | | 0.0894 | 42.0 | 13104 | 0.0763 | 0.7401 | | 0.0894 | 43.0 | 13416 | 0.0811 | 0.7365 | | 0.0896 | 44.0 | 13728 | 0.0734 | 0.7473 | | 0.0882 | 45.0 | 14040 | 0.0747 | 0.7329 | | 0.0882 | 46.0 | 14352 | 0.0729 | 0.7401 | | 0.0847 | 47.0 | 14664 | 0.0723 | 0.7329 | | 0.0847 | 48.0 | 14976 | 0.0748 | 0.7401 | | 0.0854 | 49.0 | 15288 | 0.0755 | 0.7292 | | 0.0813 | 50.0 | 15600 | 0.0715 | 0.7329 | | 0.0813 | 51.0 | 15912 | 0.0719 | 0.7292 | | 0.0845 | 52.0 | 16224 | 0.0721 | 0.7401 | | 0.0821 | 53.0 | 16536 | 0.0711 | 0.7292 | | 0.0821 | 54.0 | 16848 | 0.0714 | 0.7437 | | 0.0802 | 55.0 | 17160 | 0.0711 | 0.7401 | | 0.0802 | 56.0 | 17472 | 0.0718 | 0.7329 | | 0.0798 | 57.0 | 17784 | 0.0708 | 0.7220 | | 0.0796 | 58.0 | 18096 | 0.0715 | 0.7365 | | 0.0796 | 59.0 | 18408 | 0.0712 | 0.7329 | | 0.0806 | 60.0 | 18720 | 0.0709 | 0.7329 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230824062849

dkqjrm

2023-08-23T23:29:46Z

105

0

transformers

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

text-classification

2023-08-23T21:29:08Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824062849' results: [] ---  # 20230824062849 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 1.2256 - Accuracy: 0.7473 ## 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: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 312 | 1.2170 | 0.5307 | | 0.9844 | 2.0 | 624 | 0.7365 | 0.5090 | | 0.9844 | 3.0 | 936 | 0.6978 | 0.5632 | | 0.8956 | 4.0 | 1248 | 0.8855 | 0.4765 | | 0.8957 | 5.0 | 1560 | 1.0223 | 0.5379 | | 0.8957 | 6.0 | 1872 | 0.6873 | 0.6137 | | 0.7665 | 7.0 | 2184 | 0.8629 | 0.6173 | | 0.7665 | 8.0 | 2496 | 0.6861 | 0.6570 | | 0.734 | 9.0 | 2808 | 0.6714 | 0.7076 | | 0.7238 | 10.0 | 3120 | 0.6298 | 0.7184 | | 0.7238 | 11.0 | 3432 | 0.5975 | 0.7184 | | 0.6786 | 12.0 | 3744 | 0.8311 | 0.6968 | | 0.6396 | 13.0 | 4056 | 0.7136 | 0.6751 | | 0.6396 | 14.0 | 4368 | 0.7183 | 0.6859 | | 0.6481 | 15.0 | 4680 | 0.6652 | 0.7076 | | 0.6481 | 16.0 | 4992 | 1.0367 | 0.6823 | | 0.6106 | 17.0 | 5304 | 0.7197 | 0.6895 | | 0.6011 | 18.0 | 5616 | 0.6058 | 0.7292 | | 0.6011 | 19.0 | 5928 | 0.7227 | 0.7112 | | 0.5978 | 20.0 | 6240 | 1.1472 | 0.6570 | | 0.5309 | 21.0 | 6552 | 0.6741 | 0.7256 | | 0.5309 | 22.0 | 6864 | 0.9335 | 0.6787 | | 0.5392 | 23.0 | 7176 | 0.8296 | 0.7365 | | 0.5392 | 24.0 | 7488 | 0.9097 | 0.7040 | | 0.5058 | 25.0 | 7800 | 0.8278 | 0.7292 | | 0.4669 | 26.0 | 8112 | 1.0859 | 0.6498 | | 0.4669 | 27.0 | 8424 | 0.9387 | 0.7184 | | 0.462 | 28.0 | 8736 | 1.0893 | 0.7365 | | 0.4757 | 29.0 | 9048 | 1.3568 | 0.6859 | | 0.4757 | 30.0 | 9360 | 1.0252 | 0.7040 | | 0.4237 | 31.0 | 9672 | 1.0489 | 0.7329 | | 0.4237 | 32.0 | 9984 | 0.8661 | 0.7292 | | 0.4275 | 33.0 | 10296 | 0.9781 | 0.7437 | | 0.3722 | 34.0 | 10608 | 0.8879 | 0.7329 | | 0.3722 | 35.0 | 10920 | 0.9932 | 0.7292 | | 0.3741 | 36.0 | 11232 | 1.0509 | 0.7365 | | 0.3358 | 37.0 | 11544 | 1.3875 | 0.7329 | | 0.3358 | 38.0 | 11856 | 1.2366 | 0.7220 | | 0.3415 | 39.0 | 12168 | 1.0563 | 0.7329 | | 0.3415 | 40.0 | 12480 | 0.9688 | 0.7401 | | 0.3357 | 41.0 | 12792 | 0.8598 | 0.7329 | | 0.3094 | 42.0 | 13104 | 1.0506 | 0.7329 | | 0.3094 | 43.0 | 13416 | 1.3257 | 0.7365 | | 0.2947 | 44.0 | 13728 | 1.1759 | 0.7365 | | 0.2832 | 45.0 | 14040 | 1.1699 | 0.7329 | | 0.2832 | 46.0 | 14352 | 1.1070 | 0.7401 | | 0.2808 | 47.0 | 14664 | 1.1519 | 0.7473 | | 0.2808 | 48.0 | 14976 | 1.0674 | 0.7401 | | 0.2715 | 49.0 | 15288 | 1.1491 | 0.7401 | | 0.252 | 50.0 | 15600 | 1.0819 | 0.7473 | | 0.252 | 51.0 | 15912 | 0.9650 | 0.7473 | | 0.2577 | 52.0 | 16224 | 1.0753 | 0.7437 | | 0.2579 | 53.0 | 16536 | 1.0896 | 0.7473 | | 0.2579 | 54.0 | 16848 | 1.0579 | 0.7401 | | 0.2395 | 55.0 | 17160 | 1.1172 | 0.7509 | | 0.2395 | 56.0 | 17472 | 1.1540 | 0.7509 | | 0.2392 | 57.0 | 17784 | 1.2162 | 0.7509 | | 0.22 | 58.0 | 18096 | 1.1978 | 0.7509 | | 0.22 | 59.0 | 18408 | 1.2381 | 0.7473 | | 0.2242 | 60.0 | 18720 | 1.2256 | 0.7473 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

DrishtiSharma/codet5-small-Generate-docstrings-for-Python-bs-32

DrishtiSharma

2023-08-23T23:28:11Z

104

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:Salesforce/codet5-small", "base_model:finetune:Salesforce/codet5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-23T16:05:23Z

--- license: apache-2.0 base_model: Salesforce/codet5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: codet5-small-Generate-docstrings-for-Python-bs-32 results: [] ---  # codet5-small-Generate-docstrings-for-Python-bs-32 This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1105 - Rouge1: 0.3307 - Rouge2: 0.16 - Rougel: 0.297 - Rougelsum: 0.3149 - Gen Len: 16.7441 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 2.7701 | 1.0 | 4472 | 2.3322 | 0.3225 | 0.1525 | 0.2894 | 0.3067 | 16.3153 | | 2.4907 | 2.0 | 8944 | 2.2464 | 0.328 | 0.1555 | 0.293 | 0.3119 | 17.0097 | | 2.405 | 3.0 | 13416 | 2.2004 | 0.3267 | 0.1562 | 0.2934 | 0.311 | 16.4531 | | 2.3512 | 4.0 | 17888 | 2.1696 | 0.3292 | 0.1571 | 0.2944 | 0.3134 | 17.3872 | | 2.3144 | 5.0 | 22360 | 2.1503 | 0.3293 | 0.1586 | 0.2954 | 0.3137 | 16.932 | | 2.2862 | 6.0 | 26832 | 2.1355 | 0.3307 | 0.1588 | 0.2962 | 0.3149 | 17.0269 | | 2.2666 | 7.0 | 31304 | 2.1246 | 0.33 | 0.1594 | 0.2962 | 0.3144 | 16.7064 | | 2.2514 | 8.0 | 35776 | 2.1163 | 0.3305 | 0.1595 | 0.2968 | 0.3145 | 16.4765 | | 2.2401 | 9.0 | 40248 | 2.1120 | 0.3305 | 0.1595 | 0.2967 | 0.3147 | 16.763 | | 2.2333 | 10.0 | 44720 | 2.1105 | 0.3307 | 0.16 | 0.297 | 0.3149 | 16.7441 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4.dev0 - Tokenizers 0.13.3

NobodyExistsOnTheInternet/convenience2epochs

NobodyExistsOnTheInternet

2023-08-23T23:22:33Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-23T23:21:27Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0

tenkomati/dqn-SpaceInvaderstest

tenkomati

2023-08-23T23:07:59Z

0

stable-baselines3

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

reinforcement-learning

2023-08-23T23:07:18Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 652.00 +/- 219.36 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tenkomati -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tenkomati -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tenkomati ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

ghze/dqn-SpaceInvadersNoFrameskip-v4

ghze

2023-08-23T22:53:37Z

0

stable-baselines3

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

reinforcement-learning

2023-08-23T22:52:57Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 573.50 +/- 132.53 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ghze -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ghze -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ghze ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

sabre-code/distilbert-base-uncased-finetuned-emotion

sabre-code

2023-08-23T22:19:49Z

121

1

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:dair-ai/emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-23T20:23:59Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - dair-ai/emotion model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] language: - en metrics: - accuracy ---  # 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. ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

redstonehero/anythingqingmix25d_v30

redstonehero

2023-08-23T22:07:47Z

29

1

diffusers

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

text-to-image

2023-08-23T21:10:44Z

--- license: creativeml-openrail-m library_name: diffusers ---

redstonehero/comimicry_v10fp16

redstonehero

2023-08-23T22:07:44Z

29

0

diffusers

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

text-to-image

2023-08-23T21:10:35Z

--- license: creativeml-openrail-m library_name: diffusers ---

redstonehero/airfuckswildmix_v10

redstonehero

2023-08-23T22:07:39Z

41

2

diffusers

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

text-to-image

2023-08-23T21:10:56Z

--- license: creativeml-openrail-m library_name: diffusers ---

redstonehero/furryvixens_v20bakedvae

redstonehero

2023-08-23T21:42:47Z

29

0

diffusers

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

text-to-image

2023-08-23T20:44:31Z

--- license: creativeml-openrail-m library_name: diffusers ---

felipebandeira/donutlicenses3v3

felipebandeira

2023-08-23T21:40:06Z

114

4

transformers

[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "image-to-text", "en", "dataset:felipebandeira/driverlicenses2k", "license:mit", "endpoints_compatible", "region:us" ]

image-to-text

2023-08-16T12:35:01Z

--- license: mit datasets: - felipebandeira/driverlicenses2k language: - en metrics: - accuracy pipeline_tag: image-to-text --- This model extracts information from EU driver's licenses and returns it as JSON. For optimal performance, we recommend that input images: - have a size of 1192x772 - have high resolution and do not contain light reflection effects Accuracy - on validation set: 98% - on set of real licenses: 63.93% Article describing model: https://medium.com/@ofelipebandeira/transformers-vs-ocr-who-can-read-better-192e6b044dd3 Article describing synthetic dataset used in training: https://python.plainenglish.io/how-to-create-synthetic-datasets-of-document-images-5f140dee5e40

redstonehero/frozenanimation_v10

redstonehero

2023-08-23T21:36:07Z

30

0

diffusers

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

text-to-image

2023-08-23T20:44:15Z

--- license: creativeml-openrail-m library_name: diffusers ---

him1411/EDGAR-BART-Base

him1411

2023-08-23T21:35:55Z

111

0

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "en", "dataset:him1411/EDGAR10-Q", "arxiv:2109.08079", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-04-03T18:32:38Z

--- license: mit datasets: - him1411/EDGAR10-Q language: - en metrics: - rouge --- license: mit language: - en tags: - finance - ContextNER - language models datasets: - him1411/EDGAR10-Q metrics: - rouge --- EDGAR-BART-Base ============= BART base model finetuned on [EDGAR10-Q dataset](https://huggingface.co/datasets/him1411/EDGAR10-Q) You may want to check out * Our paper: [CONTEXT-NER: Contextual Phrase Generation at Scale](https://arxiv.org/abs/2109.08079/) * GitHub: [Click Here](https://github.com/him1411/edgar10q-dataset) Direct Use ============= It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. **It should not be directly used for production or work that may directly impact people.** How to Use ============= You can very easily load the models with Transformers, instead of downloading them manually. The [bart-base model](https://huggingface.co/facebook/bart-base) is the backbone of our model. Here is how to use the model in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("him1411/EDGAR-BART-Base") model = AutoModelForSeq2SeqLM.from_pretrained("him1411/EDGAR-BART-Base") ``` Or just clone the model repo ``` git lfs install git clone https://huggingface.co/him1411/EDGAR-BART-Base ``` Inference Example ============= Here, we provide an example for the "ContextNER" task. Below is an example of one instance. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("him1411/EDGAR-BART-Base") model = AutoModelForSeq2SeqLM.from_pretrained("him1411/EDGAR-BART-Base") # Input shows how we have appended instruction from our file for HoC dataset with instance. input = "14.5 years . The definite lived intangible assets related to the contracts and trade names had estimated weighted average useful lives of 5.9 years and 14.5 years, respectively, at acquisition." tokenized_input= tokenizer(input) # Ideal output for this input is 'Definite lived intangible assets weighted average remaining useful life' output = model(tokenized_input) ``` BibTeX Entry and Citation Info =============== If you are using our model, please cite our paper: ```bibtex @article{gupta2021context, title={Context-NER: Contextual Phrase Generation at Scale}, author={Gupta, Himanshu and Verma, Shreyas and Kumar, Tarun and Mishra, Swaroop and Agrawal, Tamanna and Badugu, Amogh and Bhatt, Himanshu Sharad}, journal={arXiv preprint arXiv:2109.08079}, year={2021} } ```

ofri-r/ppo-Huggy

ofri-r

2023-08-23T21:32:26Z

0

ml-agents

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

reinforcement-learning

2023-08-23T21:32:20Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ofri-r/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

daochf/Lora-HuggyLlama7b-PuceDS-v03x50

daochf

2023-08-23T21:32:22Z

0

peft

[ "peft", "region:us" ]

null

2023-08-23T21:32:16Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0

dkqjrm/20230824042730

dkqjrm

2023-08-23T21:28:35Z

105

0

transformers

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

text-classification

2023-08-23T19:27:51Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230824042730' results: [] ---  # 20230824042730 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5547 - Accuracy: 0.7581 ## 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: 0.003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1252 | 1.0 | 623 | 0.6915 | 0.5415 | | 0.9382 | 2.0 | 1246 | 0.7221 | 0.5307 | | 1.0555 | 3.0 | 1869 | 0.7387 | 0.5199 | | 0.9336 | 4.0 | 2492 | 0.9751 | 0.6390 | | 0.8894 | 5.0 | 3115 | 0.9277 | 0.6643 | | 0.9066 | 6.0 | 3738 | 1.1836 | 0.6931 | | 0.8496 | 7.0 | 4361 | 0.8242 | 0.7184 | | 0.7761 | 8.0 | 4984 | 0.9061 | 0.6859 | | 0.8175 | 9.0 | 5607 | 0.7474 | 0.7220 | | 0.7575 | 10.0 | 6230 | 0.8582 | 0.7292 | | 0.747 | 11.0 | 6853 | 0.8351 | 0.7256 | | 0.728 | 12.0 | 7476 | 0.8912 | 0.7148 | | 0.8296 | 13.0 | 8099 | 0.9471 | 0.7220 | | 0.7327 | 14.0 | 8722 | 1.1407 | 0.7148 | | 0.7284 | 15.0 | 9345 | 0.7681 | 0.7256 | | 0.6642 | 16.0 | 9968 | 1.4084 | 0.6679 | | 0.5888 | 17.0 | 10591 | 0.8413 | 0.7329 | | 0.6074 | 18.0 | 11214 | 0.7461 | 0.7401 | | 0.625 | 19.0 | 11837 | 0.9516 | 0.7545 | | 0.5911 | 20.0 | 12460 | 1.3395 | 0.7292 | | 0.5322 | 21.0 | 13083 | 1.3924 | 0.7509 | | 0.5247 | 22.0 | 13706 | 1.1553 | 0.7256 | | 0.5146 | 23.0 | 14329 | 1.6692 | 0.7040 | | 0.4493 | 24.0 | 14952 | 1.2315 | 0.7437 | | 0.399 | 25.0 | 15575 | 1.2710 | 0.7545 | | 0.3644 | 26.0 | 16198 | 1.5049 | 0.7473 | | 0.4031 | 27.0 | 16821 | 1.5735 | 0.7401 | | 0.386 | 28.0 | 17444 | 1.4749 | 0.7220 | | 0.3735 | 29.0 | 18067 | 0.9541 | 0.7365 | | 0.356 | 30.0 | 18690 | 1.3936 | 0.7473 | | 0.3496 | 31.0 | 19313 | 0.9982 | 0.7437 | | 0.3149 | 32.0 | 19936 | 0.9572 | 0.7581 | | 0.3094 | 33.0 | 20559 | 1.5663 | 0.7256 | | 0.2886 | 34.0 | 21182 | 1.5993 | 0.7365 | | 0.2545 | 35.0 | 21805 | 1.1515 | 0.7545 | | 0.276 | 36.0 | 22428 | 1.2768 | 0.7473 | | 0.2645 | 37.0 | 23051 | 1.4290 | 0.7509 | | 0.262 | 38.0 | 23674 | 1.2363 | 0.7617 | | 0.2261 | 39.0 | 24297 | 1.3446 | 0.7617 | | 0.2291 | 40.0 | 24920 | 1.0532 | 0.7509 | | 0.2178 | 41.0 | 25543 | 1.4745 | 0.7509 | | 0.2104 | 42.0 | 26166 | 1.3830 | 0.7545 | | 0.217 | 43.0 | 26789 | 1.7099 | 0.7473 | | 0.214 | 44.0 | 27412 | 1.7054 | 0.7401 | | 0.1856 | 45.0 | 28035 | 1.4350 | 0.7545 | | 0.2014 | 46.0 | 28658 | 1.7266 | 0.7473 | | 0.1759 | 47.0 | 29281 | 1.2659 | 0.7581 | | 0.2027 | 48.0 | 29904 | 1.8336 | 0.7401 | | 0.1871 | 49.0 | 30527 | 1.3398 | 0.7509 | | 0.1586 | 50.0 | 31150 | 1.4948 | 0.7509 | | 0.1619 | 51.0 | 31773 | 1.3787 | 0.7545 | | 0.1665 | 52.0 | 32396 | 1.6532 | 0.7545 | | 0.1786 | 53.0 | 33019 | 1.4697 | 0.7581 | | 0.1609 | 54.0 | 33642 | 1.5462 | 0.7653 | | 0.1304 | 55.0 | 34265 | 1.3577 | 0.7581 | | 0.1576 | 56.0 | 34888 | 1.7004 | 0.7617 | | 0.1522 | 57.0 | 35511 | 1.4629 | 0.7581 | | 0.1496 | 58.0 | 36134 | 1.6336 | 0.7581 | | 0.1406 | 59.0 | 36757 | 1.5699 | 0.7545 | | 0.1268 | 60.0 | 37380 | 1.5547 | 0.7581 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

casonshep/spam_message_classification

casonshep

2023-08-23T21:19:27Z

103

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-23T21:14:40Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: spam_message_classification results: [] ---  # spam_message_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 297 | 0.0719 | 0.9757 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Kajtson/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

Kajtson

2023-08-23T21:10:18Z

159

0

transformers

[ "transformers", "pytorch", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:MIT/ast-finetuned-audioset-10-10-0.4593", "base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593", "license:bsd-3-clause", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-08-23T20:22:24Z

--- license: bsd-3-clause base_model: MIT/ast-finetuned-audioset-10-10-0.4593 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.89 ---  # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6857 - Accuracy: 0.89 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8586 | 1.0 | 450 | 1.3795 | 0.55 | | 0.7835 | 2.0 | 900 | 1.0814 | 0.76 | | 0.1489 | 3.0 | 1350 | 1.0447 | 0.81 | | 0.2136 | 4.0 | 1800 | 0.9784 | 0.82 | | 0.0001 | 5.0 | 2250 | 0.7678 | 0.86 | | 0.0 | 6.0 | 2700 | 0.5670 | 0.92 | | 1.2125 | 7.0 | 3150 | 0.8058 | 0.85 | | 0.0 | 8.0 | 3600 | 0.7256 | 0.87 | | 0.0 | 9.0 | 4050 | 0.6878 | 0.89 | | 0.0 | 10.0 | 4500 | 0.6857 | 0.89 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

yellowsproket/path-to-save-model

yellowsproket

2023-08-23T21:02:29Z

30

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-23T20:54:37Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - yellowsproket/path-to-save-model This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

vuminhtue/bert-finetuned-squad

vuminhtue

2023-08-23T21:02:09Z

70

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-23T18:52:58Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: vuminhtue/bert-finetuned-squad results: [] ---  # vuminhtue/bert-finetuned-squad 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: - Train Loss: 0.5714 - 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, '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 | Epoch | |:----------:|:-----:| | 1.2738 | 0 | | 0.7819 | 1 | | 0.5714 | 2 | ### Framework versions - Transformers 4.32.0 - TensorFlow 2.9.1 - Datasets 2.14.4 - Tokenizers 0.13.3

marhatha/ppo-LunarLander-v2

marhatha

2023-08-23T21:01:14Z

0

stable-baselines3

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

reinforcement-learning

2023-08-23T21:00:46Z

--- 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: 261.81 +/- 20.15 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 ... ```