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 2025-09-03 00:36:49	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 535 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-03 00:36:49	card stringlengths 11 1.01M
NethamTSG/ppo-LunarLander-v5	NethamTSG	2023-08-22T15:09:22Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T15:08:58Z	--- 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: 287.98 +/- 17.19 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 ... ```
pbeyens/mydataset-repo	pbeyens	2023-08-22T15:08:45Z	78	0	transformers	[ "transformers", "pytorch", "tensorboard", "lilt", "token-classification", "generated_from_trainer", "dataset:mydataset", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-21T14:03:22Z	--- license: mit tags: - generated_from_trainer datasets: - mydataset model-index: - name: mydataset-repo 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. --> # mydataset-repo This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the mydataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Total-str: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} - Total-val: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} - Overall Precision: 1.0 - Overall Recall: 1.0 - Overall F1: 1.0 - Overall Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Total-str \| Total-val \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:----------------------------------------------------------:\|:----------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.0674 \| 28.57 \| 200 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0001 \| 57.14 \| 400 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 85.71 \| 600 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 114.29 \| 800 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 142.86 \| 1000 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 171.43 \| 1200 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 200.0 \| 1400 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 228.57 \| 1600 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 257.14 \| 1800 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 285.71 \| 2000 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 314.29 \| 2200 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 342.86 \| 2400 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 371.43 \| 2600 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 400.0 \| 2800 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 428.57 \| 3000 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 457.14 \| 3200 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 485.71 \| 3400 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 514.29 \| 3600 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 542.86 \| 3800 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 571.43 \| 4000 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 600.0 \| 4200 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 628.57 \| 4400 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 657.14 \| 4600 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 685.71 \| 4800 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0 \| 714.29 \| 5000 \| 0.0000 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.13.2
Tarti/xlm-roberta-base-finetuned-panx-de	Tarti	2023-08-22T14:58:26Z	134	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-22T10:41:31Z	--- 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 args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8602627537962806 --- <!-- 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: 0.1355 - F1: 0.8603 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2574 \| 1.0 \| 525 \| 0.1627 \| 0.8221 \| \| 0.1295 \| 2.0 \| 1050 \| 0.1435 \| 0.8467 \| \| 0.0815 \| 3.0 \| 1575 \| 0.1355 \| 0.8603 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu117 - Datasets 1.16.1 - Tokenizers 0.13.3
agarc15/gpt2-finetuned-PRC	agarc15	2023-08-22T14:52:00Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-classification", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-08-21T07:48:25Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: gpt2-finetuned-PRC 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. --> # gpt2-finetuned-PRC This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4564 - Accuracy: 0.8711 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 391 \| 0.5038 \| 0.8420 \| \| 0.7758 \| 2.0 \| 782 \| 0.4486 \| 0.8629 \| \| 0.4031 \| 3.0 \| 1173 \| 0.4664 \| 0.8678 \| \| 0.3225 \| 4.0 \| 1564 \| 0.4564 \| 0.8711 \| \| 0.3225 \| 5.0 \| 1955 \| 0.4637 \| 0.8693 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
camenduru/seamless-m4t-medium	camenduru	2023-08-22T14:50:58Z	0	1	null	[ "SeamlessM4T", "license:cc-by-nc-4.0", "region:us" ]	null	2023-08-22T14:45:53Z	--- inference: false tags: - SeamlessM4T license: cc-by-nc-4.0 --- # SeamlessM4T Medium SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. SeamlessM4T covers: - 📥 101 languages for speech input - ⌨️ 96 Languages for text input/output - 🗣️ 35 languages for speech output. This is the "medium" variant of the unified model, which enables multiple tasks without relying on multiple separate models: - Speech-to-speech translation (S2ST) - Speech-to-text translation (S2TT) - Text-to-speech translation (T2ST) - Text-to-text translation (T2TT) - Automatic speech recognition (ASR) ## SeamlessM4T models The SeamlessM4T models come in two checkpoints of different size: \| Model Name \| #params \| checkpoint \| metrics \| \| - \| - \| - \| - \| \| [SeamlessM4T-Medium]((https://huggingface.co/facebook/seamless-m4t-medium) \| 1.2B \| [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) \| [metrics]() \| \| [SeamlessM4T-Large](https://huggingface.co/facebook/seamless-m4t-large) \| 2.3B \| [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt) \| [metrics]() \| We provide extensive evaluation results of SeamlessM4T-Medium and SeamlessM4T-Large in the SeamlessM4T paper (as averages) in the `metrics` files above. ## Instructions to run inference with SeamlessM4T models The SeamlessM4T models are currently available through the `seamless_communication` package. The `seamless_communication` package can be installed by following the instructions outlined here: [Installation](https://github.com/fairinternal/seamless_communication/tree/main#installation). Once installed, a [`Translator`](https://github.com/fairinternal/seamless_communication/blob/590547965b343b590d15847a0aa25a6779fc3753/src/seamless_communication/models/inference/translator.py#L47) object can be instantiated to perform all five of the spoken langauge tasks. The `Translator` is instantiated with three arguments: 1. model_name_or_card: SeamlessM4T checkpoint. Can be either `seamlessM4T_medium` for the medium model, or `seamlessM4T_large` for the large model 2. vocoder_name_or_card: vocoder checkpoint (`vocoder_36langs`) 3. device: Torch device ```python import torch from seamless_communication.models.inference import Translator # Initialize a Translator object with a multitask model, vocoder on the GPU. translator = Translator("seamlessM4T_medium", vocoder_name_or_card="vocoder_36langs", device=torch.device("cuda:0")) ``` Once instantiated, the `predict()` method can be used to run inference as many times on any of the supported tasks. Given an input audio with `<path_to_input_audio>` or an input text `<input_text>` in `<src_lang>`, we can translate into `<tgt_lang>` as follows. ### S2ST and T2ST: ```python # S2ST translated_text, wav, sr = translator.predict(<path_to_input_audio>, "s2st", <tgt_lang>) # T2ST translated_text, wav, sr = translator.predict(<input_text>, "t2st", <tgt_lang>, src_lang=<src_lang>) ``` Note that `<src_lang>` must be specified for T2ST. The generated units are synthesized and the output audio file is saved with: ```python wav, sr = translator.synthesize_speech(<speech_units>, <tgt_lang>) # Save the translated audio generation. torchaudio.save( <path_to_save_audio>, wav[0].cpu(), sample_rate=sr, ) ``` ### S2TT, T2TT and ASR: ```python # S2TT translated_text, _, _ = translator.predict(<path_to_input_audio>, "s2tt", <tgt_lang>) # ASR # This is equivalent to S2TT with `<tgt_lang>=<src_lang>`. transcribed_text, _, _ = translator.predict(<path_to_input_audio>, "asr", <src_lang>) # T2TT translated_text, _, _ = translator.predict(<input_text>, "t2tt", <tgt_lang>, src_lang=<src_lang>) ``` Note that `<src_lang>` must be specified for T2TT. ## Citation If you plan to use SeamlessM4T in your work or any models/datasets/artifacts published in SeamlessM4T, please cite: ```bibtex @article{seamlessm4t2023, title={"SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation"}, author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang}, journal={ArXiv}, year={2023} } ``` ## License The Seamless Communication code and weights are CC-BY-NC 4.0 licensed.
sarankup-newgen/llama2-13b-email-trained	sarankup-newgen	2023-08-22T14:49:42Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-22T14:49:16Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
pknayak/speecht5_finetuned_voxpopuli_es	pknayak	2023-08-22T14:46:12Z	82	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-08-22T14:06:01Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_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. --> # speecht5_finetuned_voxpopuli_es This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4620 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4902 \| 153.85 \| 500 \| 0.4620 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Amirhnrn/Reinforce-Cartpole	Amirhnrn	2023-08-22T14:40:23Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T07:30:33Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole 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
Luciferio/MiniLLM-finetuned	Luciferio	2023-08-22T14:39:58Z	105	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:microsoft/MiniLM-L12-H384-uncased", "base_model:finetune:microsoft/MiniLM-L12-H384-uncased", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-22T12:57:56Z	--- license: mit base_model: microsoft/MiniLM-L12-H384-uncased tags: - generated_from_trainer datasets: - emotion metrics: - f1 model-index: - name: MiniLLM-finetuned results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: F1 type: f1 value: 0.922353805579638 --- <!-- 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. --> # MiniLLM-finetuned This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2932 - F1: 0.9224 ## Model description More information needed ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 2000 \| 0.4408 \| 0.8888 \| \| No log \| 2.0 \| 4000 \| 0.2932 \| 0.9224 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
ALM-AHME/beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled-3rd	ALM-AHME	2023-08-22T14:31:20Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "base_model:microsoft/beit-large-patch16-224", "base_model:finetune:microsoft/beit-large-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-08-22T11:54:04Z	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled-3rd 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. --> # beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled-3rd This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0488 - Accuracy: 0.9901 ## Model description More information needed ## 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-06 - 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 - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9835 \| 1.0 \| 114 \| 1.9296 \| 0.2315 \| \| 1.6045 \| 2.0 \| 229 \| 1.4334 \| 0.5172 \| \| 1.0525 \| 3.0 \| 343 \| 0.9298 \| 0.6962 \| \| 0.795 \| 4.0 \| 458 \| 0.6580 \| 0.7709 \| \| 0.5739 \| 5.0 \| 572 \| 0.4717 \| 0.8366 \| \| 0.5821 \| 6.0 \| 687 \| 0.3511 \| 0.8851 \| \| 0.4566 \| 7.0 \| 801 \| 0.2705 \| 0.9204 \| \| 0.2751 \| 8.0 \| 916 \| 0.2114 \| 0.9384 \| \| 0.2352 \| 9.0 \| 1030 \| 0.1303 \| 0.9688 \| \| 0.1831 \| 10.0 \| 1145 \| 0.1194 \| 0.9688 \| \| 0.1515 \| 11.0 \| 1259 \| 0.0673 \| 0.9869 \| \| 0.204 \| 11.95 \| 1368 \| 0.0488 \| 0.9901 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Toobese/lilt-invoices	Toobese	2023-08-22T14:24:48Z	113	0	transformers	[ "transformers", "pytorch", "tensorboard", "lilt", "token-classification", "generated_from_trainer", "base_model:SCUT-DLVCLab/lilt-roberta-en-base", "base_model:finetune:SCUT-DLVCLab/lilt-roberta-en-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-21T13:32:34Z	--- license: mit base_model: SCUT-DLVCLab/lilt-roberta-en-base tags: - generated_from_trainer model-index: - name: lilt-invoices results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lilt-invoices This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Endorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} - Escription: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} - Illingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} - Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} - Nitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} - Nvoicedate: {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} - Nvoicetotal: {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} - Otaltax: {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} - Uantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} - Ubtotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} - Overall Precision: 0.9988 - Overall Recall: 0.9982 - Overall F1: 0.9985 - Overall Accuracy: 0.9994 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Endorname \| Escription \| Illingaddress \| Mount \| Nitprice \| Nvoicedate \| Nvoicetotal \| Otaltax \| Uantity \| Ubtotal \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------------------------------------------------------:\|:-----------------------------------------------------------:\|:-----------------------------------------------------------------------------------------:\|:-----------------------------------------------------------:\|:-----------------------------------------------------------:\|:-----------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:-----------------------------------------------------------------------------------------:\|:-----------------------------------------------------------:\|:-----------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.1736 \| 21.74 \| 500 \| 0.0031 \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} \| {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} \| {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} \| {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} \| {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} \| {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} \| 0.9988 \| 0.9982 \| 0.9985 \| 0.9994 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
wjbmattingly/distilbert-base-uncased-finetuned-imdb	wjbmattingly	2023-08-22T14:18:48Z	115	0	transformers	[ "transformers", "pytorch", "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	2023-08-22T14:00:15Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb 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: 2.9420 ## Model description More information needed ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1689 \| 1.0 \| 8 \| 3.0928 \| \| 2.9706 \| 2.0 \| 16 \| 2.7093 \| \| 2.9273 \| 3.0 \| 24 \| 2.7136 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3
Tarti/xlm-roberta-base-finetuned-panx-de-fr	Tarti	2023-08-22T14:14:38Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-22T14:02:58Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1600 - F1: 0.8591 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.288 \| 1.0 \| 715 \| 0.1823 \| 0.8218 \| \| 0.1458 \| 2.0 \| 1430 \| 0.1533 \| 0.8503 \| \| 0.0934 \| 3.0 \| 2145 \| 0.1600 \| 0.8591 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu117 - Datasets 1.16.1 - Tokenizers 0.13.3
loicspigeleer/q-Taxi-v3	loicspigeleer	2023-08-22T14:11:38Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T14:11:35Z	--- 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: 7.50 +/- 2.78 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="loicspigeleer/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"]) ```
facebook/blaser-2.0-ref	facebook	2023-08-22T14:07:37Z	0	5	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2023-08-21T13:25:31Z	--- license: cc-by-nc-4.0 --- # BLASER 2.0 [[Blog]](https://ai.meta.com/resources/models-and-libraries/seamless-communication/) [[Code](https://github.com/facebookresearch/SONAR)] BLASER 2.0 is the new version of BLASER ([Chen et al., 2023](https://aclanthology.org/2023.acl-long.504/)), a family of models for automatic evaluation of machine translation quality. BLASER 2.0 is based on [SONAR](https://huggingface.co/facebook/SONAR) sentence embeddings and works with both speech and text modalities. The actual model predicts a similarity score for the translated sentence based on the the translation, the source sentence, and the reference translation. Its sibling model, [BLASER 2.0 QE](https://huggingface.co/facebook/blaser-2.0-qe), does not use references. Supervised BLASER models are trained to predict cross-lingual semantic similarity scores, XSTS ([Licht et al., 2022](https://aclanthology.org/2022.amta-research.24/)), on a scale where 1 corresponds to completely unrelated sentences and 5 corresponds to fully semantically equivalent sentences. The models predictions, though, are unbounded and can occasionally surpass these limits. ## Installation See the SONAR github [repo](https://github.com/facebookresearch/SONAR) for the installation instructions. ## Usage BLASER 2.0 models accept 1024-dimensional SONAR sentence embeddings as inputs, and produce a single score as an output. The code below illustrates their usage with text embeddings: ```Python from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline from sonar.models.blaser.loader import load_blaser_model blaser = load_blaser_model("blaser_2_0_ref").eval() text_embedder = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder", tokenizer="text_sonar_basic_encoder") src_embs = text_embedder.predict(["Le chat s'assit sur le tapis."], source_lang="fra_Latn") ref_embs = text_embedder.predict(["The cat sat on the mat."], source_lang="eng_Latn") mt_embs = text_embedder.predict(["The cat sat down on the carpet."], source_lang="eng_Latn") print(blaser(src=src_embs, ref=ref_embs, mt=mt_embs).item()) # 4.688 ``` With BLASER 2.0 models, SONAR text and speech embeddings can be used interchangeably. ## Model details - Developed by: Seamless Communication et al. - License: CC-BY-NC 4.0 license - Citation: If you use BLASER 2.0 in your work, please cite [the paper](https://ai.meta.com/resources/models-and-libraries/seamless-communication/): ```bibtex @article{seamlessm4t2023, title={SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation}, author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang}, journal={ArXiv}, year={2023} } ```
facebook/blaser-2.0-qe	facebook	2023-08-22T14:07:31Z	0	7	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2023-08-21T15:01:40Z	--- license: cc-by-nc-4.0 --- # BLASER 2.0 [[Blog]](https://ai.meta.com/resources/models-and-libraries/seamless-communication/) [[Code](https://github.com/facebookresearch/SONAR)] BLASER 2.0 is the new version of BLASER ([Chen et al., 2023](https://aclanthology.org/2023.acl-long.504/)), a family of models for automatic evaluation of machine translation quality. BLASER 2.0 is based on [SONAR](https://huggingface.co/facebook/SONAR) sentence embeddings and works with both speech and text modalities. The actual model predicts a similarity score for the translated sentence based on the translation and the source sentence. This, it can be applied in settings where reference translations are missing or if their quality is questionable. In contrast, its sibling model, [BLASER 2.0-referenced](https://huggingface.co/facebook/blaser-2.0-ref), requires also a reference translation. Supervised BLASER models are trained to predict cross-lingual semantic similarity scores, XSTS ([Licht et al., 2022](https://aclanthology.org/2022.amta-research.24/)), on a scale where 1 corresponds to completely unrelated sentences and 5 corresponds to fully semantically equivalent sentences. The models predictions, though, are unbounded and can occasionally surpass these limits. ## Installation See the SONAR github [repo](https://github.com/facebookresearch/SONAR) for the installation instructions. ## Usage BLASER 2.0 models accept 1024-dimensional SONAR sentence embeddings as inputs, and produce a single score as an output. The code below illustrates their usage with text embeddings: ```Python from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline from sonar.models.blaser.loader import load_blaser_model blaser = load_blaser_model("blaser_2_0_qe").eval() text_embedder = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder", tokenizer="text_sonar_basic_encoder") src_embs = text_embedder.predict(["Le chat s'assit sur le tapis."], source_lang="fra_Latn") mt_embs = text_embedder.predict(["The cat sat down on the carpet."], source_lang="eng_Latn") print(blaser(src=src_embs, mt=mt_embs).item()) # 4.708 ``` With BLASER 2.0 models, SONAR text and speech embeddings can be used interchangeably. ## Model details - Developed by: Seamless Communication et al. - License: CC-BY-NC 4.0 license - Citation: If you use BLASER 2.0 in your work, please cite [the paper](https://ai.meta.com/resources/models-and-libraries/seamless-communication/): ```bibtex @article{seamlessm4t2023, title={SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation}, author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang}, journal={ArXiv}, year={2023} } ```
ColdChair/hw_1	ColdChair	2023-08-22T13:53:18Z	0	0	null	[ "dataset:roneneldan/TinyStories", "license:openrail", "region:us" ]	null	2023-08-22T13:51:46Z	--- license: openrail datasets: - roneneldan/TinyStories ---
natfil/pmserver_bloom-3b	natfil	2023-08-22T13:45:44Z	3	0	peft	[ "peft", "region:us" ]	null	2023-08-22T13:45:33Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Jbdddsai/lora-trained-xl-colab_gieskanne_500it_lr_1e-4	Jbdddsai	2023-08-22T13:20:10Z	4	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-08-22T10:04:27Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of datadrivers watering can tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Jbddai/lora-trained-xl-colab_gieskanne_500it_lr_1e-4 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of datadrivers watering can using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ![harbour1.png](harbour1.png) ![plant.png](plant.png) ![beach1](beach1.png) ![beach2](beach2.png) ![detail1.png](detail1.png) ![garden1](garden1.png) ![garden2](garden2.png) ![advertising_campaign](advertising_campaign.png) ![detail1.png](detail1.png) ![office1.png](office1.png) ![office2.png](office2.png) ![gieskanne_office.png](gieskanne_office.png) ![ny_black_white.png](ny_black_white.png) ![times_square.png](times_square.png) ![tv_add.png](tv_add.png)
dkqjrm/20230822202040	dkqjrm	2023-08-22T13:20:05Z	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	2023-08-22T11:20:58Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202040' 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. --> # 20230822202040 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: 0.5208 - Accuracy: 0.7365 ## Model description More information needed ## 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 \| 0.7722 \| 0.5271 \| \| 0.7133 \| 2.0 \| 624 \| 0.5588 \| 0.4982 \| \| 0.7133 \| 3.0 \| 936 \| 0.6273 \| 0.4729 \| \| 0.6364 \| 4.0 \| 1248 \| 0.5976 \| 0.4946 \| \| 0.6219 \| 5.0 \| 1560 \| 0.7382 \| 0.5415 \| \| 0.6219 \| 6.0 \| 1872 \| 0.5328 \| 0.6282 \| \| 0.5974 \| 7.0 \| 2184 \| 0.5253 \| 0.6282 \| \| 0.5974 \| 8.0 \| 2496 \| 0.8677 \| 0.5668 \| \| 0.5614 \| 9.0 \| 2808 \| 0.5249 \| 0.5884 \| \| 0.5732 \| 10.0 \| 3120 \| 0.5113 \| 0.6895 \| \| 0.5732 \| 11.0 \| 3432 \| 0.5092 \| 0.6931 \| \| 0.5559 \| 12.0 \| 3744 \| 0.4693 \| 0.7148 \| \| 0.5301 \| 13.0 \| 4056 \| 0.4781 \| 0.7256 \| \| 0.5301 \| 14.0 \| 4368 \| 0.5693 \| 0.6823 \| \| 0.4999 \| 15.0 \| 4680 \| 0.4649 \| 0.7256 \| \| 0.4999 \| 16.0 \| 4992 \| 0.5702 \| 0.6859 \| \| 0.4712 \| 17.0 \| 5304 \| 0.4598 \| 0.7401 \| \| 0.4431 \| 18.0 \| 5616 \| 0.4750 \| 0.7076 \| \| 0.4431 \| 19.0 \| 5928 \| 0.4782 \| 0.7184 \| \| 0.4348 \| 20.0 \| 6240 \| 0.6236 \| 0.6570 \| \| 0.4113 \| 21.0 \| 6552 \| 0.5125 \| 0.7473 \| \| 0.4113 \| 22.0 \| 6864 \| 0.5703 \| 0.6787 \| \| 0.4035 \| 23.0 \| 7176 \| 0.5080 \| 0.7112 \| \| 0.4035 \| 24.0 \| 7488 \| 0.4619 \| 0.7365 \| \| 0.3898 \| 25.0 \| 7800 \| 0.5639 \| 0.7076 \| \| 0.3736 \| 26.0 \| 8112 \| 0.4968 \| 0.7292 \| \| 0.3736 \| 27.0 \| 8424 \| 0.4483 \| 0.7509 \| \| 0.3708 \| 28.0 \| 8736 \| 0.4929 \| 0.7220 \| \| 0.3656 \| 29.0 \| 9048 \| 0.5168 \| 0.7401 \| \| 0.3656 \| 30.0 \| 9360 \| 0.5618 \| 0.7256 \| \| 0.3545 \| 31.0 \| 9672 \| 0.4900 \| 0.7365 \| \| 0.3545 \| 32.0 \| 9984 \| 0.4676 \| 0.7256 \| \| 0.3474 \| 33.0 \| 10296 \| 0.5222 \| 0.7220 \| \| 0.3326 \| 34.0 \| 10608 \| 0.4861 \| 0.7437 \| \| 0.3326 \| 35.0 \| 10920 \| 0.4560 \| 0.7401 \| \| 0.3313 \| 36.0 \| 11232 \| 0.5375 \| 0.7256 \| \| 0.3209 \| 37.0 \| 11544 \| 0.5606 \| 0.7329 \| \| 0.3209 \| 38.0 \| 11856 \| 0.5173 \| 0.7401 \| \| 0.3169 \| 39.0 \| 12168 \| 0.5060 \| 0.7329 \| \| 0.3169 \| 40.0 \| 12480 \| 0.5250 \| 0.7365 \| \| 0.3096 \| 41.0 \| 12792 \| 0.5133 \| 0.7256 \| \| 0.3097 \| 42.0 \| 13104 \| 0.5012 \| 0.7437 \| \| 0.3097 \| 43.0 \| 13416 \| 0.5274 \| 0.7401 \| \| 0.3049 \| 44.0 \| 13728 \| 0.5086 \| 0.7329 \| \| 0.2929 \| 45.0 \| 14040 \| 0.4934 \| 0.7329 \| \| 0.2929 \| 46.0 \| 14352 \| 0.5667 \| 0.7401 \| \| 0.293 \| 47.0 \| 14664 \| 0.5047 \| 0.7437 \| \| 0.293 \| 48.0 \| 14976 \| 0.5353 \| 0.7292 \| \| 0.291 \| 49.0 \| 15288 \| 0.5280 \| 0.7401 \| \| 0.2817 \| 50.0 \| 15600 \| 0.5142 \| 0.7365 \| \| 0.2817 \| 51.0 \| 15912 \| 0.5141 \| 0.7329 \| \| 0.2822 \| 52.0 \| 16224 \| 0.4990 \| 0.7329 \| \| 0.2758 \| 53.0 \| 16536 \| 0.5074 \| 0.7292 \| \| 0.2758 \| 54.0 \| 16848 \| 0.5147 \| 0.7329 \| \| 0.2763 \| 55.0 \| 17160 \| 0.5138 \| 0.7365 \| \| 0.2763 \| 56.0 \| 17472 \| 0.5291 \| 0.7365 \| \| 0.2782 \| 57.0 \| 17784 \| 0.5204 \| 0.7329 \| \| 0.272 \| 58.0 \| 18096 \| 0.5093 \| 0.7365 \| \| 0.272 \| 59.0 \| 18408 \| 0.5217 \| 0.7365 \| \| 0.2758 \| 60.0 \| 18720 \| 0.5208 \| 0.7365 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Polo123/llama2-qlora-finetunined-task	Polo123	2023-08-22T13:19:45Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-22T13:19:12Z	--- 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 ### Framework versions - PEFT 0.6.0.dev0
dkqjrm/20230822202124	dkqjrm	2023-08-22T13:12:14Z	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	2023-08-22T11:21:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202124' 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. --> # 20230822202124 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: 0.4836 - Accuracy: 0.7437 ## Model description More information needed ## 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: 16 - 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 \| 156 \| 0.5548 \| 0.4693 \| \| No log \| 2.0 \| 312 \| 0.5565 \| 0.4838 \| \| No log \| 3.0 \| 468 \| 0.5531 \| 0.4729 \| \| 0.6259 \| 4.0 \| 624 \| 0.5810 \| 0.4729 \| \| 0.6259 \| 5.0 \| 780 \| 0.6010 \| 0.5596 \| \| 0.6259 \| 6.0 \| 936 \| 0.4969 \| 0.6462 \| \| 0.5907 \| 7.0 \| 1092 \| 0.7982 \| 0.5487 \| \| 0.5907 \| 8.0 \| 1248 \| 0.4883 \| 0.6318 \| \| 0.5907 \| 9.0 \| 1404 \| 0.4714 \| 0.6931 \| \| 0.5602 \| 10.0 \| 1560 \| 0.9236 \| 0.5560 \| \| 0.5602 \| 11.0 \| 1716 \| 0.4972 \| 0.6968 \| \| 0.5602 \| 12.0 \| 1872 \| 0.5116 \| 0.6895 \| \| 0.5015 \| 13.0 \| 2028 \| 0.4913 \| 0.7076 \| \| 0.5015 \| 14.0 \| 2184 \| 0.4683 \| 0.7112 \| \| 0.5015 \| 15.0 \| 2340 \| 0.5265 \| 0.6895 \| \| 0.5015 \| 16.0 \| 2496 \| 0.4616 \| 0.7040 \| \| 0.4782 \| 17.0 \| 2652 \| 0.5788 \| 0.6679 \| \| 0.4782 \| 18.0 \| 2808 \| 0.4471 \| 0.7292 \| \| 0.4782 \| 19.0 \| 2964 \| 0.4588 \| 0.7545 \| \| 0.4628 \| 20.0 \| 3120 \| 0.6477 \| 0.6426 \| \| 0.4628 \| 21.0 \| 3276 \| 0.5305 \| 0.6968 \| \| 0.4628 \| 22.0 \| 3432 \| 0.4549 \| 0.7292 \| \| 0.4248 \| 23.0 \| 3588 \| 0.5101 \| 0.7256 \| \| 0.4248 \| 24.0 \| 3744 \| 0.4763 \| 0.7184 \| \| 0.4248 \| 25.0 \| 3900 \| 0.5809 \| 0.6895 \| \| 0.4067 \| 26.0 \| 4056 \| 0.4461 \| 0.7473 \| \| 0.4067 \| 27.0 \| 4212 \| 0.4460 \| 0.7473 \| \| 0.4067 \| 28.0 \| 4368 \| 0.4454 \| 0.7509 \| \| 0.3941 \| 29.0 \| 4524 \| 0.4664 \| 0.7365 \| \| 0.3941 \| 30.0 \| 4680 \| 0.5039 \| 0.7292 \| \| 0.3941 \| 31.0 \| 4836 \| 0.4548 \| 0.7473 \| \| 0.3941 \| 32.0 \| 4992 \| 0.4484 \| 0.7437 \| \| 0.3749 \| 33.0 \| 5148 \| 0.4924 \| 0.7473 \| \| 0.3749 \| 34.0 \| 5304 \| 0.4569 \| 0.7473 \| \| 0.3749 \| 35.0 \| 5460 \| 0.4604 \| 0.7617 \| \| 0.3586 \| 36.0 \| 5616 \| 0.4448 \| 0.7653 \| \| 0.3586 \| 37.0 \| 5772 \| 0.4768 \| 0.7365 \| \| 0.3586 \| 38.0 \| 5928 \| 0.5052 \| 0.7473 \| \| 0.3521 \| 39.0 \| 6084 \| 0.5167 \| 0.7329 \| \| 0.3521 \| 40.0 \| 6240 \| 0.4425 \| 0.7509 \| \| 0.3521 \| 41.0 \| 6396 \| 0.4730 \| 0.7545 \| \| 0.3407 \| 42.0 \| 6552 \| 0.4624 \| 0.7509 \| \| 0.3407 \| 43.0 \| 6708 \| 0.4847 \| 0.7509 \| \| 0.3407 \| 44.0 \| 6864 \| 0.5371 \| 0.7329 \| \| 0.3329 \| 45.0 \| 7020 \| 0.4841 \| 0.7545 \| \| 0.3329 \| 46.0 \| 7176 \| 0.4815 \| 0.7365 \| \| 0.3329 \| 47.0 \| 7332 \| 0.4678 \| 0.7509 \| \| 0.3329 \| 48.0 \| 7488 \| 0.4918 \| 0.7473 \| \| 0.3235 \| 49.0 \| 7644 \| 0.4592 \| 0.7581 \| \| 0.3235 \| 50.0 \| 7800 \| 0.5005 \| 0.7437 \| \| 0.3235 \| 51.0 \| 7956 \| 0.4777 \| 0.7545 \| \| 0.3193 \| 52.0 \| 8112 \| 0.4558 \| 0.7545 \| \| 0.3193 \| 53.0 \| 8268 \| 0.4870 \| 0.7437 \| \| 0.3193 \| 54.0 \| 8424 \| 0.4792 \| 0.7437 \| \| 0.3132 \| 55.0 \| 8580 \| 0.4673 \| 0.7437 \| \| 0.3132 \| 56.0 \| 8736 \| 0.4943 \| 0.7437 \| \| 0.3132 \| 57.0 \| 8892 \| 0.4970 \| 0.7437 \| \| 0.311 \| 58.0 \| 9048 \| 0.4914 \| 0.7401 \| \| 0.311 \| 59.0 \| 9204 \| 0.4887 \| 0.7437 \| \| 0.311 \| 60.0 \| 9360 \| 0.4836 \| 0.7437 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230822202056	dkqjrm	2023-08-22T13:11:55Z	106	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	2023-08-22T11:21:14Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202056' 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. --> # 20230822202056 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: 0.1724 - Accuracy: 0.7112 ## Model description More information needed ## 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 \| 0.1785 \| 0.5307 \| \| 0.2552 \| 2.0 \| 624 \| 0.1826 \| 0.5054 \| \| 0.2552 \| 3.0 \| 936 \| 0.3328 \| 0.4729 \| \| 0.24 \| 4.0 \| 1248 \| 0.2050 \| 0.4729 \| \| 0.2369 \| 5.0 \| 1560 \| 0.1750 \| 0.6065 \| \| 0.2369 \| 6.0 \| 1872 \| 0.1752 \| 0.4765 \| \| 0.2199 \| 7.0 \| 2184 \| 0.1799 \| 0.5921 \| \| 0.2199 \| 8.0 \| 2496 \| 0.1896 \| 0.4729 \| \| 0.1955 \| 9.0 \| 2808 \| 0.1727 \| 0.6245 \| \| 0.185 \| 10.0 \| 3120 \| 0.1734 \| 0.5668 \| \| 0.185 \| 11.0 \| 3432 \| 0.1781 \| 0.5812 \| \| 0.184 \| 12.0 \| 3744 \| 0.1711 \| 0.6318 \| \| 0.1819 \| 13.0 \| 4056 \| 0.1783 \| 0.4910 \| \| 0.1819 \| 14.0 \| 4368 \| 0.1703 \| 0.6534 \| \| 0.1793 \| 15.0 \| 4680 \| 0.1697 \| 0.6931 \| \| 0.1793 \| 16.0 \| 4992 \| 0.1710 \| 0.6643 \| \| 0.179 \| 17.0 \| 5304 \| 0.1728 \| 0.6534 \| \| 0.1784 \| 18.0 \| 5616 \| 0.1712 \| 0.6498 \| \| 0.1784 \| 19.0 \| 5928 \| 0.1726 \| 0.6065 \| \| 0.1778 \| 20.0 \| 6240 \| 0.1720 \| 0.6679 \| \| 0.1761 \| 21.0 \| 6552 \| 0.1724 \| 0.6606 \| \| 0.1761 \| 22.0 \| 6864 \| 0.1792 \| 0.6534 \| \| 0.1761 \| 23.0 \| 7176 \| 0.1700 \| 0.6715 \| \| 0.1761 \| 24.0 \| 7488 \| 0.1698 \| 0.6679 \| \| 0.1748 \| 25.0 \| 7800 \| 0.1697 \| 0.6968 \| \| 0.1744 \| 26.0 \| 8112 \| 0.1729 \| 0.6859 \| \| 0.1744 \| 27.0 \| 8424 \| 0.1702 \| 0.6570 \| \| 0.1736 \| 28.0 \| 8736 \| 0.1708 \| 0.6931 \| \| 0.1723 \| 29.0 \| 9048 \| 0.1698 \| 0.6787 \| \| 0.1723 \| 30.0 \| 9360 \| 0.1799 \| 0.6462 \| \| 0.1735 \| 31.0 \| 9672 \| 0.1727 \| 0.6751 \| \| 0.1735 \| 32.0 \| 9984 \| 0.1732 \| 0.6498 \| \| 0.1722 \| 33.0 \| 10296 \| 0.1702 \| 0.6751 \| \| 0.1709 \| 34.0 \| 10608 \| 0.1707 \| 0.6968 \| \| 0.1709 \| 35.0 \| 10920 \| 0.1714 \| 0.6968 \| \| 0.1697 \| 36.0 \| 11232 \| 0.1712 \| 0.6751 \| \| 0.1696 \| 37.0 \| 11544 \| 0.1788 \| 0.6570 \| \| 0.1696 \| 38.0 \| 11856 \| 0.1703 \| 0.6787 \| \| 0.1697 \| 39.0 \| 12168 \| 0.1735 \| 0.6751 \| \| 0.1697 \| 40.0 \| 12480 \| 0.1740 \| 0.6787 \| \| 0.1683 \| 41.0 \| 12792 \| 0.1710 \| 0.6895 \| \| 0.1688 \| 42.0 \| 13104 \| 0.1724 \| 0.7076 \| \| 0.1688 \| 43.0 \| 13416 \| 0.1718 \| 0.7004 \| \| 0.1679 \| 44.0 \| 13728 \| 0.1736 \| 0.7040 \| \| 0.1681 \| 45.0 \| 14040 \| 0.1720 \| 0.7040 \| \| 0.1681 \| 46.0 \| 14352 \| 0.1717 \| 0.7076 \| \| 0.1664 \| 47.0 \| 14664 \| 0.1710 \| 0.6895 \| \| 0.1664 \| 48.0 \| 14976 \| 0.1766 \| 0.6895 \| \| 0.1662 \| 49.0 \| 15288 \| 0.1729 \| 0.7040 \| \| 0.1655 \| 50.0 \| 15600 \| 0.1704 \| 0.7076 \| \| 0.1655 \| 51.0 \| 15912 \| 0.1711 \| 0.7184 \| \| 0.1665 \| 52.0 \| 16224 \| 0.1709 \| 0.7040 \| \| 0.1651 \| 53.0 \| 16536 \| 0.1711 \| 0.6931 \| \| 0.1651 \| 54.0 \| 16848 \| 0.1736 \| 0.7040 \| \| 0.1646 \| 55.0 \| 17160 \| 0.1712 \| 0.7112 \| \| 0.1646 \| 56.0 \| 17472 \| 0.1740 \| 0.7076 \| \| 0.1647 \| 57.0 \| 17784 \| 0.1723 \| 0.7076 \| \| 0.1642 \| 58.0 \| 18096 \| 0.1715 \| 0.7004 \| \| 0.1642 \| 59.0 \| 18408 \| 0.1727 \| 0.7076 \| \| 0.1643 \| 60.0 \| 18720 \| 0.1724 \| 0.7112 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230822202110	dkqjrm	2023-08-22T13:09:57Z	106	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	2023-08-22T11:21:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202110' 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. --> # 20230822202110 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: 0.1679 - Accuracy: 0.7148 ## Model description More information needed ## 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: 16 - 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 \| 156 \| 0.4220 \| 0.5271 \| \| No log \| 2.0 \| 312 \| 0.2767 \| 0.4729 \| \| No log \| 3.0 \| 468 \| 0.4345 \| 0.4729 \| \| 0.2507 \| 4.0 \| 624 \| 0.2006 \| 0.5343 \| \| 0.2507 \| 5.0 \| 780 \| 0.1797 \| 0.4729 \| \| 0.2507 \| 6.0 \| 936 \| 0.2180 \| 0.5271 \| \| 0.2023 \| 7.0 \| 1092 \| 0.1726 \| 0.5054 \| \| 0.2023 \| 8.0 \| 1248 \| 0.1811 \| 0.4729 \| \| 0.2023 \| 9.0 \| 1404 \| 0.1828 \| 0.5451 \| \| 0.2077 \| 10.0 \| 1560 \| 0.1921 \| 0.5343 \| \| 0.2077 \| 11.0 \| 1716 \| 0.1772 \| 0.4838 \| \| 0.2077 \| 12.0 \| 1872 \| 0.1724 \| 0.6462 \| \| 0.189 \| 13.0 \| 2028 \| 0.1718 \| 0.5379 \| \| 0.189 \| 14.0 \| 2184 \| 0.1728 \| 0.5126 \| \| 0.189 \| 15.0 \| 2340 \| 0.1775 \| 0.5126 \| \| 0.189 \| 16.0 \| 2496 \| 0.1813 \| 0.5596 \| \| 0.1803 \| 17.0 \| 2652 \| 0.1739 \| 0.6318 \| \| 0.1803 \| 18.0 \| 2808 \| 0.1718 \| 0.6137 \| \| 0.1803 \| 19.0 \| 2964 \| 0.1711 \| 0.6390 \| \| 0.1791 \| 20.0 \| 3120 \| 0.1797 \| 0.5957 \| \| 0.1791 \| 21.0 \| 3276 \| 0.1710 \| 0.6859 \| \| 0.1791 \| 22.0 \| 3432 \| 0.1729 \| 0.6643 \| \| 0.1781 \| 23.0 \| 3588 \| 0.1701 \| 0.6823 \| \| 0.1781 \| 24.0 \| 3744 \| 0.1706 \| 0.6390 \| \| 0.1781 \| 25.0 \| 3900 \| 0.1708 \| 0.6859 \| \| 0.1765 \| 26.0 \| 4056 \| 0.1697 \| 0.6643 \| \| 0.1765 \| 27.0 \| 4212 \| 0.1698 \| 0.6715 \| \| 0.1765 \| 28.0 \| 4368 \| 0.1710 \| 0.6426 \| \| 0.176 \| 29.0 \| 4524 \| 0.1710 \| 0.6931 \| \| 0.176 \| 30.0 \| 4680 \| 0.1703 \| 0.6968 \| \| 0.176 \| 31.0 \| 4836 \| 0.1725 \| 0.6570 \| \| 0.176 \| 32.0 \| 4992 \| 0.1699 \| 0.6715 \| \| 0.1749 \| 33.0 \| 5148 \| 0.1710 \| 0.6895 \| \| 0.1749 \| 34.0 \| 5304 \| 0.1694 \| 0.7220 \| \| 0.1749 \| 35.0 \| 5460 \| 0.1700 \| 0.6534 \| \| 0.1739 \| 36.0 \| 5616 \| 0.1690 \| 0.7112 \| \| 0.1739 \| 37.0 \| 5772 \| 0.1685 \| 0.7220 \| \| 0.1739 \| 38.0 \| 5928 \| 0.1696 \| 0.7040 \| \| 0.1738 \| 39.0 \| 6084 \| 0.1688 \| 0.7148 \| \| 0.1738 \| 40.0 \| 6240 \| 0.1692 \| 0.7220 \| \| 0.1738 \| 41.0 \| 6396 \| 0.1683 \| 0.7365 \| \| 0.1726 \| 42.0 \| 6552 \| 0.1690 \| 0.6679 \| \| 0.1726 \| 43.0 \| 6708 \| 0.1679 \| 0.7076 \| \| 0.1726 \| 44.0 \| 6864 \| 0.1691 \| 0.7184 \| \| 0.1719 \| 45.0 \| 7020 \| 0.1692 \| 0.7292 \| \| 0.1719 \| 46.0 \| 7176 \| 0.1685 \| 0.7329 \| \| 0.1719 \| 47.0 \| 7332 \| 0.1684 \| 0.7184 \| \| 0.1719 \| 48.0 \| 7488 \| 0.1690 \| 0.7112 \| \| 0.1712 \| 49.0 \| 7644 \| 0.1690 \| 0.7292 \| \| 0.1712 \| 50.0 \| 7800 \| 0.1685 \| 0.6931 \| \| 0.1712 \| 51.0 \| 7956 \| 0.1680 \| 0.7256 \| \| 0.1705 \| 52.0 \| 8112 \| 0.1687 \| 0.7076 \| \| 0.1705 \| 53.0 \| 8268 \| 0.1685 \| 0.7184 \| \| 0.1705 \| 54.0 \| 8424 \| 0.1689 \| 0.7365 \| \| 0.1705 \| 55.0 \| 8580 \| 0.1677 \| 0.7148 \| \| 0.1705 \| 56.0 \| 8736 \| 0.1694 \| 0.7220 \| \| 0.1705 \| 57.0 \| 8892 \| 0.1682 \| 0.7256 \| \| 0.1692 \| 58.0 \| 9048 \| 0.1684 \| 0.7148 \| \| 0.1692 \| 59.0 \| 9204 \| 0.1679 \| 0.7148 \| \| 0.1692 \| 60.0 \| 9360 \| 0.1679 \| 0.7148 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
kaanhho/speecht5_finetuned_voxpopuli_it	kaanhho	2023-08-22T13:07:13Z	86	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-08-22T12:08:05Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_it 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. --> # speecht5_finetuned_voxpopuli_it This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.5724 ## Model description More information needed ## 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: 1 - eval_batch_size: 2 - 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 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5644 \| 1.53 \| 1000 \| 0.5845 \| \| 0.5521 \| 3.07 \| 2000 \| 0.5724 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
anshudaur/chair_model_with_prior_preservation	anshudaur	2023-08-22T13:05:37Z	12	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-08-22T12:48:20Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: photo of a <new1> chair tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - anshudaur/chair_model_with_prior_preservation These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> chair using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
akar49/detr-crack-II	akar49	2023-08-22T12:54:57Z	34	0	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:crack_detection-merged-ii", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-08-22T11:09:26Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - crack_detection-merged-ii model-index: - name: detr-crack-II 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. --> # detr-crack-II This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the crack_detection-merged-ii 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
barbieheimer/MND_TweetEvalBert_model	barbieheimer	2023-08-22T12:40:58Z	107	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "en", "dataset:tweet_eval", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-19T05:33:08Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - tweet_eval model-index: - name: MND_TweetEvalBert_model results: [] language: - en pipeline_tag: text-classification metrics: - accuracy widget: - text: 'I loved Barbie and Oppenheimer' example_title: Barbenheimer --- <!-- 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. --> # MND_TweetEvalBert_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.7241 ## Model description This is how to use the model with the transformer library to do a text classification task. This model was trained and built for sentiment analysis with a text classification model architecture. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model") model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model") # We can now use the model in the pipeline. classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) # Get some text to fool around with for a basic test. text = "I loved Oppenheimer and Barbie " classifier(text) # Let's see if the model works on our example text. ``` ``` [{'label': 'JOY', 'score': 0.9845513701438904}] ``` ## Training Evalutation Results ```python {'eval_loss': 0.7240552306175232, 'eval_runtime': 3.7803, 'eval_samples_per_second': 375.896, 'eval_steps_per_second': 23.543, 'epoch': 5.0} ``` ## Overall Model Evaluation Results ```python {'accuracy': {'confidence_interval': (0.783, 0.832), 'standard_error': 0.01241992329458207, 'score': 0.808}, 'total_time_in_seconds': 150.93268656500004, 'samples_per_second': 6.625470087086432, 'latency_in_seconds': 0.15093268656500003} ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ```python {'training_loss'=0.3821827131159165} {'train_runtime': 174.1546, 'train_samples_per_second': 93.509, 'train_steps_per_second': 5.857, 'total_flos': 351397804992312.0, 'train_loss': 0.3821827131159165, 'epoch': 5.0} ``` ``` Step: 500 {training loss: 0.607100} Step: 1000 {training loss: 0.169000} ``` ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
GrantW65/ppo-Huggy	GrantW65	2023-08-22T12:40:40Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-08-22T12:40:35Z	--- 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: GrantW65/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
natsusakiyomi/SakuraMix	natsusakiyomi	2023-08-22T12:30:44Z	115	70	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "ja", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-03-17T17:37:21Z	--- license: creativeml-openrail-m language: - ja pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image library_name: diffusers --- <div class="flex justify-center"> <div class="container p-0 w-100"> <img class="mt-0 object-cover rounded-t-lg w-100" style="height: 320px;" src="https://pbs.twimg.com/media/Fwzt7HZaEAAkX2U?format=jpg" width="100%"/> <div class="flex px-4"> <div class="flex-auto"> <h1 class="mb-2 text-3xl font-bold leading-tight" style="color: rgb(252, 238, 235/var(--tw-text-opacity));"> SakuraMixSeries </h1> <p class="mb-4 text-base text-neutral-600 dark:text-neutral-200"> 背景とキャラクタークオリティーを両立させたVAE内蔵型モデル<br> Model with built-in VAE for both background and character quality </p> </div> <div> <a href="https://twitter.com/min__san" class="mb-2 inline-block rounded px-6 py-2.5 text-white shadow-md" style="background-color: #1da1f2"> <svg xmlns="http://www.w3.org/2000/svg" class="h-3.5 w-3.5" fill="currentColor" viewBox="0 0 24 24"> <path d="M24 4.557c-.883.392-1.832.656-2.828.775 1.017-.609 1.798-1.574 2.165-2.724-.951.564-2.005.974-3.127 1.195-.897-.957-2.178-1.555-3.594-1.555-3.179 0-5.515 2.966-4.797 6.045-4.091-.205-7.719-2.165-10.148-5.144-1.29 2.213-.669 5.108 1.523 6.574-.806-.026-1.566-.247-2.229-.616-.054 2.281 1.581 4.415 3.949 4.89-.693.188-1.452.232-2.224.084.626 1.956 2.444 3.379 4.6 3.419-2.07 1.623-4.678 2.348-7.29 2.04 2.179 1.397 4.768 2.212 7.548 2.212 9.142 0 14.307-7.721 13.995-14.646.962-.695 1.797-1.562 2.457-2.549z" /> </svg> </a> </div> </div> </div> </div> --- <h4>📄 ライセンス / License</h4> <div class="px-2"> <table class="table-fixed border mt-0 text-xs"> <tbody> <tr> <td class="px-4 text-base" colspan="2"> <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license"> 修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license </a> </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルのクレジットを入れずに使用する<br> Use the model without crediting the creator </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルで生成した画像を商用利用する<br> Sell images they generate </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルを商用の画像生成サービスで利用する</br> Run on services that generate images for money </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルを使用したマージモデルを共有する<br> Share merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデル、またはこのモデルをマージしたモデルを販売する</br> Sell this model or merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルをマージしたモデルに異なる権限を設定する</br> Have different permissions when sharing merges </td> </tr> </tbody> </table> </div> <h3 id="blue_pencil-v7" class="mt-0 text-2xl"> <code>SakuraMix-v4</code> <small></small> </h3> <div> v3の改修モデル全体的に手や破綻の少なくなったモデル<br> 若干書き込み量が減ったような気がするので昔からSakuraMix好きな人はflat loraを使うことを推奨 <hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));"> <h3 id="blue_pencil-v7" class="mt-0 text-2xl"> <code>SakuraMix-v3</code> <small></small> </h3> <div> v2の改修モデル服装や構図が前よりも増えた気がする破綻しやすいがいいものが生成できるときはとてもいいものが生成できる<br> 個人的にはv2をお勧めします <hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));"> <h3 id="SakuraMix-v2" class="mt-0 text-2xl"> <code>SakuraMix-v2</code> <small></small> </h3> <div> HimawariMix-v2B(没案)を改造したモデル<br> HimawariMix-v2自体character自体を強化したモデルだがさらにキャラを強くしたモデル <hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));"> <h3 id="SakuraMix-v1" class="mt-0 text-2xl"> <code>SakuraMix-v1</code> <small></small> </h3> <div> 初代SakuraMix 特徴とか知らん忘れた<br> --- # 作者&連絡先 Twiter: [@min__san](https://twitter.com/min__san)<br> mail: ([email protected])
asenella/ms_config_1_alpha_10_beta_250_seed_1	asenella	2023-08-22T12:29:55Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-08-22T12:29:53Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
kevinassemi/WaterWizard	kevinassemi	2023-08-22T12:19:21Z	0	0	null	[ "text-generation", "en", "license:llama2", "region:us" ]	text-generation	2023-08-22T12:17:16Z	--- license: llama2 language: - en pipeline_tag: text-generation ---
Muhammadreza/mann-e-bitmap-revised-2	Muhammadreza	2023-08-22T12:14:16Z	4	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-22T12:01:29Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mann-e_bitmap_revised-2 Dreambooth model trained by Muhammadreza with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
Saurabh16100/distilgpt2-finetuned-wikitext2	Saurabh16100	2023-08-22T12:13:32Z	204	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-22T11:28:20Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7602 \| 1.0 \| 2334 \| 3.6669 \| \| 3.653 \| 2.0 \| 4668 \| 3.6472 \| \| 3.6006 \| 3.0 \| 7002 \| 3.6421 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
loicspigeleer/PPO-LunarLander-v2	loicspigeleer	2023-08-22T11:56:15Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T11:55:51Z	--- 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: 272.06 +/- 16.19 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 ... ```
EliKet/lora-trained-xl-colab	EliKet	2023-08-22T11:53:44Z	2	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-0.9", "base_model:adapter:stabilityai/stable-diffusion-xl-base-0.9", "license:openrail++", "region:us" ]	text-to-image	2023-08-17T09:17:36Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-0.9 instance_prompt: a photo of model tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - EliKet/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-0.9. The weights were trained on a photo of model using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Arup-Dutta-Bappy/bert-large-cased-whole-word-masking-finetuned-squad	Arup-Dutta-Bappy	2023-08-22T11:50:57Z	121	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:google-bert/bert-large-cased-whole-word-masking", "base_model:finetune:google-bert/bert-large-cased-whole-word-masking", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-13T17:53:28Z	--- license: apache-2.0 base_model: bert-large-cased-whole-word-masking tags: - generated_from_trainer datasets: - squad model-index: - name: bert-large-cased-whole-word-masking-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-whole-word-masking-finetuned-squad This model is a fine-tuned version of [bert-large-cased-whole-word-masking](https://huggingface.co/bert-large-cased-whole-word-masking) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
dkqjrm/20230822185044	dkqjrm	2023-08-22T11:47:15Z	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	2023-08-22T09:51:01Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822185044' 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. --> # 20230822185044 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: 0.3482 - Accuracy: 0.4729 ## Model description More information needed ## 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 \| 0.3580 \| 0.5379 \| \| 0.5102 \| 2.0 \| 624 \| 0.3670 \| 0.5415 \| \| 0.5102 \| 3.0 \| 936 \| 0.4888 \| 0.4765 \| \| 0.4569 \| 4.0 \| 1248 \| 0.3742 \| 0.4982 \| \| 0.4403 \| 5.0 \| 1560 \| 0.3796 \| 0.5379 \| \| 0.4403 \| 6.0 \| 1872 \| 0.3602 \| 0.5776 \| \| 0.4215 \| 7.0 \| 2184 \| 0.4013 \| 0.5415 \| \| 0.4215 \| 8.0 \| 2496 \| 0.3596 \| 0.5884 \| \| 0.4166 \| 9.0 \| 2808 \| 0.3447 \| 0.5487 \| \| 0.3885 \| 10.0 \| 3120 \| 0.3395 \| 0.6101 \| \| 0.3885 \| 11.0 \| 3432 \| 0.3395 \| 0.6354 \| \| 0.3776 \| 12.0 \| 3744 \| 0.3568 \| 0.5343 \| \| 0.4274 \| 13.0 \| 4056 \| 0.5923 \| 0.4729 \| \| 0.4274 \| 14.0 \| 4368 \| 0.3503 \| 0.5668 \| \| 0.4138 \| 15.0 \| 4680 \| 0.3605 \| 0.5523 \| \| 0.4138 \| 16.0 \| 4992 \| 0.3491 \| 0.5451 \| \| 0.4025 \| 17.0 \| 5304 \| 0.3728 \| 0.5379 \| \| 0.394 \| 18.0 \| 5616 \| 0.4029 \| 0.4729 \| \| 0.394 \| 19.0 \| 5928 \| 0.3682 \| 0.4729 \| \| 0.3892 \| 20.0 \| 6240 \| 0.3484 \| 0.5054 \| \| 0.3839 \| 21.0 \| 6552 \| 0.3485 \| 0.4765 \| \| 0.3839 \| 22.0 \| 6864 \| 0.3467 \| 0.5343 \| \| 0.3782 \| 23.0 \| 7176 \| 0.3471 \| 0.5307 \| \| 0.3782 \| 24.0 \| 7488 \| 0.3565 \| 0.4693 \| \| 0.3757 \| 25.0 \| 7800 \| 0.3483 \| 0.5343 \| \| 0.3737 \| 26.0 \| 8112 \| 0.3495 \| 0.5271 \| \| 0.3737 \| 27.0 \| 8424 \| 0.3550 \| 0.4729 \| \| 0.3724 \| 28.0 \| 8736 \| 0.3544 \| 0.4729 \| \| 0.3696 \| 29.0 \| 9048 \| 0.3478 \| 0.5307 \| \| 0.3696 \| 30.0 \| 9360 \| 0.3519 \| 0.5271 \| \| 0.3693 \| 31.0 \| 9672 \| 0.3515 \| 0.5271 \| \| 0.3693 \| 32.0 \| 9984 \| 0.3487 \| 0.4729 \| \| 0.3674 \| 33.0 \| 10296 \| 0.3492 \| 0.5379 \| \| 0.3628 \| 34.0 \| 10608 \| 0.3555 \| 0.4729 \| \| 0.3628 \| 35.0 \| 10920 \| 0.3550 \| 0.4729 \| \| 0.3635 \| 36.0 \| 11232 \| 0.3686 \| 0.4729 \| \| 0.3636 \| 37.0 \| 11544 \| 0.3488 \| 0.4801 \| \| 0.3636 \| 38.0 \| 11856 \| 0.3484 \| 0.4874 \| \| 0.3595 \| 39.0 \| 12168 \| 0.3477 \| 0.4910 \| \| 0.3595 \| 40.0 \| 12480 \| 0.3486 \| 0.5307 \| \| 0.3598 \| 41.0 \| 12792 \| 0.3488 \| 0.4801 \| \| 0.3594 \| 42.0 \| 13104 \| 0.3614 \| 0.4729 \| \| 0.3594 \| 43.0 \| 13416 \| 0.3476 \| 0.5199 \| \| 0.3586 \| 44.0 \| 13728 \| 0.3482 \| 0.4729 \| \| 0.3581 \| 45.0 \| 14040 \| 0.3519 \| 0.4729 \| \| 0.3581 \| 46.0 \| 14352 \| 0.3494 \| 0.4729 \| \| 0.3579 \| 47.0 \| 14664 \| 0.3613 \| 0.4729 \| \| 0.3579 \| 48.0 \| 14976 \| 0.3480 \| 0.4729 \| \| 0.3573 \| 49.0 \| 15288 \| 0.3480 \| 0.4729 \| \| 0.3564 \| 50.0 \| 15600 \| 0.3487 \| 0.4729 \| \| 0.3564 \| 51.0 \| 15912 \| 0.3529 \| 0.4729 \| \| 0.3561 \| 52.0 \| 16224 \| 0.3515 \| 0.4729 \| \| 0.3554 \| 53.0 \| 16536 \| 0.3475 \| 0.4946 \| \| 0.3554 \| 54.0 \| 16848 \| 0.3489 \| 0.5271 \| \| 0.3535 \| 55.0 \| 17160 \| 0.3488 \| 0.4729 \| \| 0.3535 \| 56.0 \| 17472 \| 0.3478 \| 0.5018 \| \| 0.3542 \| 57.0 \| 17784 \| 0.3491 \| 0.4729 \| \| 0.354 \| 58.0 \| 18096 \| 0.3485 \| 0.4729 \| \| 0.354 \| 59.0 \| 18408 \| 0.3483 \| 0.4729 \| \| 0.3529 \| 60.0 \| 18720 \| 0.3482 \| 0.4729 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230822185237	dkqjrm	2023-08-22T11:44:15Z	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	2023-08-22T09:52:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822185237' 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. --> # 20230822185237 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: 0.3335 - Accuracy: 0.6498 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 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 \| 0.3589 \| 0.5415 \| \| 0.4381 \| 2.0 \| 624 \| 0.3585 \| 0.5560 \| \| 0.4381 \| 3.0 \| 936 \| 0.4824 \| 0.4729 \| \| 0.4251 \| 4.0 \| 1248 \| 0.3497 \| 0.5740 \| \| 0.4013 \| 5.0 \| 1560 \| 0.5515 \| 0.5307 \| \| 0.4013 \| 6.0 \| 1872 \| 0.5300 \| 0.5343 \| \| 0.4064 \| 7.0 \| 2184 \| 0.3515 \| 0.4982 \| \| 0.4064 \| 8.0 \| 2496 \| 0.3456 \| 0.5704 \| \| 0.4121 \| 9.0 \| 2808 \| 0.3522 \| 0.5632 \| \| 0.4048 \| 10.0 \| 3120 \| 0.3437 \| 0.5632 \| \| 0.4048 \| 11.0 \| 3432 \| 0.3483 \| 0.5668 \| \| 0.4035 \| 12.0 \| 3744 \| 0.3952 \| 0.4657 \| \| 0.3797 \| 13.0 \| 4056 \| 0.3535 \| 0.4801 \| \| 0.3797 \| 14.0 \| 4368 \| 0.3443 \| 0.5993 \| \| 0.3657 \| 15.0 \| 4680 \| 0.3431 \| 0.5379 \| \| 0.3657 \| 16.0 \| 4992 \| 0.3478 \| 0.5993 \| \| 0.3615 \| 17.0 \| 5304 \| 0.3475 \| 0.6173 \| \| 0.3573 \| 18.0 \| 5616 \| 0.3539 \| 0.6101 \| \| 0.3573 \| 19.0 \| 5928 \| 0.3384 \| 0.6101 \| \| 0.3552 \| 20.0 \| 6240 \| 0.3483 \| 0.6245 \| \| 0.3545 \| 21.0 \| 6552 \| 0.3359 \| 0.6173 \| \| 0.3545 \| 22.0 \| 6864 \| 0.3844 \| 0.5740 \| \| 0.349 \| 23.0 \| 7176 \| 0.3436 \| 0.6498 \| \| 0.349 \| 24.0 \| 7488 \| 0.3422 \| 0.6209 \| \| 0.351 \| 25.0 \| 7800 \| 0.3495 \| 0.6318 \| \| 0.3471 \| 26.0 \| 8112 \| 0.3498 \| 0.6101 \| \| 0.3471 \| 27.0 \| 8424 \| 0.3316 \| 0.6462 \| \| 0.3468 \| 28.0 \| 8736 \| 0.3322 \| 0.6751 \| \| 0.3459 \| 29.0 \| 9048 \| 0.3354 \| 0.6390 \| \| 0.3459 \| 30.0 \| 9360 \| 0.3353 \| 0.6390 \| \| 0.344 \| 31.0 \| 9672 \| 0.3383 \| 0.6354 \| \| 0.344 \| 32.0 \| 9984 \| 0.3329 \| 0.6245 \| \| 0.3435 \| 33.0 \| 10296 \| 0.3411 \| 0.6390 \| \| 0.3408 \| 34.0 \| 10608 \| 0.3414 \| 0.6354 \| \| 0.3408 \| 35.0 \| 10920 \| 0.3319 \| 0.6534 \| \| 0.3401 \| 36.0 \| 11232 \| 0.3347 \| 0.6282 \| \| 0.3406 \| 37.0 \| 11544 \| 0.3382 \| 0.6137 \| \| 0.3406 \| 38.0 \| 11856 \| 0.3355 \| 0.6245 \| \| 0.3378 \| 39.0 \| 12168 \| 0.3416 \| 0.6245 \| \| 0.3378 \| 40.0 \| 12480 \| 0.3422 \| 0.6209 \| \| 0.3386 \| 41.0 \| 12792 \| 0.3388 \| 0.6390 \| \| 0.3362 \| 42.0 \| 13104 \| 0.3330 \| 0.6390 \| \| 0.3362 \| 43.0 \| 13416 \| 0.3393 \| 0.6282 \| \| 0.3373 \| 44.0 \| 13728 \| 0.3340 \| 0.6282 \| \| 0.3337 \| 45.0 \| 14040 \| 0.3318 \| 0.6390 \| \| 0.3337 \| 46.0 \| 14352 \| 0.3323 \| 0.6354 \| \| 0.3332 \| 47.0 \| 14664 \| 0.3301 \| 0.6643 \| \| 0.3332 \| 48.0 \| 14976 \| 0.3422 \| 0.6282 \| \| 0.3315 \| 49.0 \| 15288 \| 0.3348 \| 0.6570 \| \| 0.33 \| 50.0 \| 15600 \| 0.3366 \| 0.6462 \| \| 0.33 \| 51.0 \| 15912 \| 0.3308 \| 0.6570 \| \| 0.331 \| 52.0 \| 16224 \| 0.3298 \| 0.6606 \| \| 0.3295 \| 53.0 \| 16536 \| 0.3377 \| 0.6498 \| \| 0.3295 \| 54.0 \| 16848 \| 0.3439 \| 0.6462 \| \| 0.3282 \| 55.0 \| 17160 \| 0.3326 \| 0.6570 \| \| 0.3282 \| 56.0 \| 17472 \| 0.3356 \| 0.6498 \| \| 0.3291 \| 57.0 \| 17784 \| 0.3309 \| 0.6570 \| \| 0.3278 \| 58.0 \| 18096 \| 0.3333 \| 0.6498 \| \| 0.3278 \| 59.0 \| 18408 \| 0.3324 \| 0.6498 \| \| 0.3292 \| 60.0 \| 18720 \| 0.3335 \| 0.6498 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230822185221	dkqjrm	2023-08-22T11:41:38Z	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	2023-08-22T09:52:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822185221' 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. --> # 20230822185221 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: 0.3289 - 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: 0.002 - 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 \| 0.5077 \| 0.5307 \| \| 0.4439 \| 2.0 \| 624 \| 0.3971 \| 0.4874 \| \| 0.4439 \| 3.0 \| 936 \| 0.3574 \| 0.5379 \| \| 0.4231 \| 4.0 \| 1248 \| 0.3625 \| 0.5776 \| \| 0.4071 \| 5.0 \| 1560 \| 0.4937 \| 0.5343 \| \| 0.4071 \| 6.0 \| 1872 \| 0.3738 \| 0.5668 \| \| 0.3956 \| 7.0 \| 2184 \| 0.4081 \| 0.4729 \| \| 0.3956 \| 8.0 \| 2496 \| 0.3386 \| 0.6209 \| \| 0.3905 \| 9.0 \| 2808 \| 0.4147 \| 0.4729 \| \| 0.3888 \| 10.0 \| 3120 \| 0.3353 \| 0.6354 \| \| 0.3888 \| 11.0 \| 3432 \| 0.3540 \| 0.6282 \| \| 0.3992 \| 12.0 \| 3744 \| 0.3453 \| 0.5848 \| \| 0.372 \| 13.0 \| 4056 \| 0.3265 \| 0.6895 \| \| 0.372 \| 14.0 \| 4368 \| 0.3575 \| 0.6426 \| \| 0.3643 \| 15.0 \| 4680 \| 0.3304 \| 0.6498 \| \| 0.3643 \| 16.0 \| 4992 \| 0.3633 \| 0.6715 \| \| 0.3666 \| 17.0 \| 5304 \| 0.5230 \| 0.5343 \| \| 0.3517 \| 18.0 \| 5616 \| 0.3384 \| 0.6462 \| \| 0.3517 \| 19.0 \| 5928 \| 0.3293 \| 0.6823 \| \| 0.3519 \| 20.0 \| 6240 \| 0.3613 \| 0.6823 \| \| 0.338 \| 21.0 \| 6552 \| 0.3242 \| 0.7256 \| \| 0.338 \| 22.0 \| 6864 \| 0.3399 \| 0.7184 \| \| 0.3316 \| 23.0 \| 7176 \| 0.3392 \| 0.7004 \| \| 0.3316 \| 24.0 \| 7488 \| 0.3343 \| 0.6534 \| \| 0.3266 \| 25.0 \| 7800 \| 0.3467 \| 0.7112 \| \| 0.3213 \| 26.0 \| 8112 \| 0.3419 \| 0.7040 \| \| 0.3213 \| 27.0 \| 8424 \| 0.3190 \| 0.7112 \| \| 0.3177 \| 28.0 \| 8736 \| 0.3205 \| 0.6931 \| \| 0.3187 \| 29.0 \| 9048 \| 0.3303 \| 0.7076 \| \| 0.3187 \| 30.0 \| 9360 \| 0.3268 \| 0.7148 \| \| 0.3162 \| 31.0 \| 9672 \| 0.3274 \| 0.7148 \| \| 0.3162 \| 32.0 \| 9984 \| 0.3311 \| 0.7112 \| \| 0.3132 \| 33.0 \| 10296 \| 0.3454 \| 0.7148 \| \| 0.3087 \| 34.0 \| 10608 \| 0.3250 \| 0.7076 \| \| 0.3087 \| 35.0 \| 10920 \| 0.3266 \| 0.7076 \| \| 0.3076 \| 36.0 \| 11232 \| 0.3347 \| 0.7292 \| \| 0.3071 \| 37.0 \| 11544 \| 0.3308 \| 0.7112 \| \| 0.3071 \| 38.0 \| 11856 \| 0.3272 \| 0.7220 \| \| 0.3061 \| 39.0 \| 12168 \| 0.3301 \| 0.7148 \| \| 0.3061 \| 40.0 \| 12480 \| 0.3226 \| 0.7256 \| \| 0.3006 \| 41.0 \| 12792 \| 0.3285 \| 0.7365 \| \| 0.3016 \| 42.0 \| 13104 \| 0.3226 \| 0.7148 \| \| 0.3016 \| 43.0 \| 13416 \| 0.3291 \| 0.7220 \| \| 0.2984 \| 44.0 \| 13728 \| 0.3377 \| 0.7112 \| \| 0.2976 \| 45.0 \| 14040 \| 0.3326 \| 0.7220 \| \| 0.2976 \| 46.0 \| 14352 \| 0.3341 \| 0.7292 \| \| 0.2967 \| 47.0 \| 14664 \| 0.3187 \| 0.7184 \| \| 0.2967 \| 48.0 \| 14976 \| 0.3322 \| 0.7148 \| \| 0.2953 \| 49.0 \| 15288 \| 0.3269 \| 0.7365 \| \| 0.2911 \| 50.0 \| 15600 \| 0.3256 \| 0.7365 \| \| 0.2911 \| 51.0 \| 15912 \| 0.3252 \| 0.7256 \| \| 0.2929 \| 52.0 \| 16224 \| 0.3251 \| 0.7292 \| \| 0.2904 \| 53.0 \| 16536 \| 0.3258 \| 0.7256 \| \| 0.2904 \| 54.0 \| 16848 \| 0.3358 \| 0.7220 \| \| 0.2895 \| 55.0 \| 17160 \| 0.3219 \| 0.7329 \| \| 0.2895 \| 56.0 \| 17472 \| 0.3322 \| 0.7329 \| \| 0.2887 \| 57.0 \| 17784 \| 0.3259 \| 0.7365 \| \| 0.2883 \| 58.0 \| 18096 \| 0.3260 \| 0.7292 \| \| 0.2883 \| 59.0 \| 18408 \| 0.3276 \| 0.7365 \| \| 0.2874 \| 60.0 \| 18720 \| 0.3289 \| 0.7329 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
AhmedTaha012/finance-ner-v0.0.2-finetuned-ner	AhmedTaha012	2023-08-22T11:39:45Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-22T10:33:22Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finance-ner-v0.0.2-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finance-ner-v0.0.2-finetuned-ner This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0004 - Precision: 0.9945 - Recall: 1.0 - F1: 0.9972 - Accuracy: 0.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0002 \| 1.0 \| 551 \| 0.0011 \| 0.9850 \| 0.9940 \| 0.9895 \| 0.9997 \| \| 0.0 \| 2.0 \| 1102 \| 0.0006 \| 0.9900 \| 0.9991 \| 0.9945 \| 0.9999 \| \| 0.0 \| 3.0 \| 1653 \| 0.0005 \| 0.9953 \| 0.9991 \| 0.9972 \| 0.9999 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
viprav/llama2-quote-1-row	viprav	2023-08-22T11:38:59Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-22T11:38:51Z	--- 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 ### Framework versions - PEFT 0.5.0.dev0
lengoctuong/gpt2-finetuned-chatbot	lengoctuong	2023-08-22T11:38:39Z	139	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-22T11:34:55Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-chatbot 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. --> # gpt2-chatbot This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
anshudaur/cat_model_small_lr	anshudaur	2023-08-22T11:29:13Z	5	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-08-22T09:52:51Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: photo of a <new1> cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - anshudaur/cat_model_small_lr These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
Nebyx/LunarLanderunit8	Nebyx	2023-08-22T11:25:12Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T11:25:07Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -128.72 +/- 76.26 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Nebyx/LunarLanderunit8' 'batch_size': 512 'minibatch_size': 128} ```
stalker331333/my-pet-cat	stalker331333	2023-08-22T11:19:37Z	41	0	diffusers	[ "diffusers", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-22T11:16:01Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat Dreambooth model trained by stalker331333 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/stalker331333/my-pet-cat/resolve/main/sample_images/a4056cc6-bf04-450e-9ed4-eb57a73ae4cb.jpeg)
elit333/newstable	elit333	2023-08-22T11:15:25Z	5	1	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-22T10:20:12Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true extra_gated_prompt: \|- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - Resources for more information: [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` after generation of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training Training Data The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) Training Procedure Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - Hardware: 32 x 8 x A100 GPUs - Optimizer: AdamW - Gradient Accumulations: 2 - Batch: 32 x 8 x 2 x 4 = 2048 - Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 150000 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).
deepsdh99/llama2-qlora-finetunined-8	deepsdh99	2023-08-22T11:05:58Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-22T11:05:30Z	--- 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
MattStammers/dqn-SpaceInvadersNoFrameskip-v4	MattStammers	2023-08-22T11:04:08Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T11:03:00Z	--- 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: 710.50 +/- 398.67 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 MattStammers -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 MattStammers -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 MattStammers ``` ## 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', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
OpenBuddy/openbuddy-openllama-3b-v10-bf16	OpenBuddy	2023-08-22T10:51:04Z	1,568	8	transformers	[ "transformers", "pytorch", "llama", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-08-10T13:37:46Z	--- language: - zh - en - fr - de - ja - ko - it - ru pipeline_tag: text-generation inference: false library_name: transformers license: apache-2.0 --- # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Copyright Notice License: Apache 2.0. ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
roa7n/gpt2-human_nontata_promoters-rng_ep8	roa7n	2023-08-22T10:51:03Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-22T10:51:00Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
kasperchen/a2c-PandaReachDense-v3	kasperchen	2023-08-22T10:49:40Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T03:13:36Z	--- 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 ... ```
ankur24022002/test1	ankur24022002	2023-08-22T10:44:57Z	0	0	peft	[ "peft", "pytorch", "opt", "region:us" ]	null	2023-08-22T08:27:08Z	--- 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 ### Framework versions - PEFT 0.5.0.dev0
jwhandley/setfit-gb-manifestos	jwhandley	2023-08-22T10:38:48Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-08-21T23:56:51Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # jwhandley/setfit-gb-manifestos 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("jwhandley/setfit-gb-manifestos") # 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} } ```
MayankAmrit/my-pet-dog	MayankAmrit	2023-08-22T10:26:54Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-22T10:13:31Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by MayankAmrit following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/MayankAmrit/my-pet-dog/resolve/main/sample_images/ad0676a7-604d-435f-a0e9-9a781de0ae2f.jpeg)
dkqjrm/20230822173808	dkqjrm	2023-08-22T10:26:48Z	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	2023-08-22T08:38:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822173808' 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. --> # 20230822173808 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: 0.3493 - Accuracy: 0.6968 ## Model description More information needed ## 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: 0.004 - 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 \| 0.3774 \| 0.5162 \| \| 0.5343 \| 2.0 \| 624 \| 0.3506 \| 0.5018 \| \| 0.5343 \| 3.0 \| 936 \| 0.4575 \| 0.4729 \| \| 0.4659 \| 4.0 \| 1248 \| 0.3759 \| 0.5307 \| \| 0.4691 \| 5.0 \| 1560 \| 0.3500 \| 0.5812 \| \| 0.4691 \| 6.0 \| 1872 \| 0.3457 \| 0.5993 \| \| 0.4442 \| 7.0 \| 2184 \| 0.3500 \| 0.6101 \| \| 0.4442 \| 8.0 \| 2496 \| 0.3403 \| 0.6173 \| \| 0.4366 \| 9.0 \| 2808 \| 0.3840 \| 0.5776 \| \| 0.4097 \| 10.0 \| 3120 \| 0.4391 \| 0.5487 \| \| 0.4097 \| 11.0 \| 3432 \| 0.3584 \| 0.6029 \| \| 0.3922 \| 12.0 \| 3744 \| 0.3356 \| 0.6498 \| \| 0.3564 \| 13.0 \| 4056 \| 0.3275 \| 0.6931 \| \| 0.3564 \| 14.0 \| 4368 \| 0.3283 \| 0.7076 \| \| 0.3343 \| 15.0 \| 4680 \| 0.3377 \| 0.6462 \| \| 0.3343 \| 16.0 \| 4992 \| 0.3550 \| 0.6390 \| \| 0.335 \| 17.0 \| 5304 \| 0.3370 \| 0.6895 \| \| 0.3233 \| 18.0 \| 5616 \| 0.3256 \| 0.6787 \| \| 0.3233 \| 19.0 \| 5928 \| 0.3174 \| 0.7112 \| \| 0.3232 \| 20.0 \| 6240 \| 0.3440 \| 0.6643 \| \| 0.3102 \| 21.0 \| 6552 \| 0.3375 \| 0.6895 \| \| 0.3102 \| 22.0 \| 6864 \| 0.3433 \| 0.6787 \| \| 0.3064 \| 23.0 \| 7176 \| 0.3690 \| 0.6715 \| \| 0.3064 \| 24.0 \| 7488 \| 0.3394 \| 0.6931 \| \| 0.3004 \| 25.0 \| 7800 \| 0.3377 \| 0.7256 \| \| 0.2962 \| 26.0 \| 8112 \| 0.3435 \| 0.6751 \| \| 0.2962 \| 27.0 \| 8424 \| 0.3182 \| 0.7329 \| \| 0.2937 \| 28.0 \| 8736 \| 0.3306 \| 0.7112 \| \| 0.2905 \| 29.0 \| 9048 \| 0.3362 \| 0.7148 \| \| 0.2905 \| 30.0 \| 9360 \| 0.3675 \| 0.6751 \| \| 0.2865 \| 31.0 \| 9672 \| 0.3406 \| 0.7076 \| \| 0.2865 \| 32.0 \| 9984 \| 0.3343 \| 0.7040 \| \| 0.2812 \| 33.0 \| 10296 \| 0.3472 \| 0.6859 \| \| 0.2727 \| 34.0 \| 10608 \| 0.3372 \| 0.7292 \| \| 0.2727 \| 35.0 \| 10920 \| 0.3575 \| 0.7076 \| \| 0.2735 \| 36.0 \| 11232 \| 0.3300 \| 0.7076 \| \| 0.2701 \| 37.0 \| 11544 \| 0.3585 \| 0.6968 \| \| 0.2701 \| 38.0 \| 11856 \| 0.3422 \| 0.7148 \| \| 0.2688 \| 39.0 \| 12168 \| 0.3579 \| 0.6931 \| \| 0.2688 \| 40.0 \| 12480 \| 0.3326 \| 0.7148 \| \| 0.2644 \| 41.0 \| 12792 \| 0.3464 \| 0.7256 \| \| 0.2637 \| 42.0 \| 13104 \| 0.3579 \| 0.6931 \| \| 0.2637 \| 43.0 \| 13416 \| 0.3489 \| 0.7040 \| \| 0.26 \| 44.0 \| 13728 \| 0.3439 \| 0.7076 \| \| 0.2582 \| 45.0 \| 14040 \| 0.3585 \| 0.7004 \| \| 0.2582 \| 46.0 \| 14352 \| 0.3535 \| 0.7076 \| \| 0.2533 \| 47.0 \| 14664 \| 0.3440 \| 0.7148 \| \| 0.2533 \| 48.0 \| 14976 \| 0.3506 \| 0.7040 \| \| 0.2535 \| 49.0 \| 15288 \| 0.3519 \| 0.7040 \| \| 0.2498 \| 50.0 \| 15600 \| 0.3457 \| 0.6931 \| \| 0.2498 \| 51.0 \| 15912 \| 0.3494 \| 0.7112 \| \| 0.2504 \| 52.0 \| 16224 \| 0.3431 \| 0.7040 \| \| 0.2499 \| 53.0 \| 16536 \| 0.3450 \| 0.7040 \| \| 0.2499 \| 54.0 \| 16848 \| 0.3485 \| 0.6895 \| \| 0.2488 \| 55.0 \| 17160 \| 0.3437 \| 0.7004 \| \| 0.2488 \| 56.0 \| 17472 \| 0.3465 \| 0.7004 \| \| 0.2479 \| 57.0 \| 17784 \| 0.3479 \| 0.6895 \| \| 0.247 \| 58.0 \| 18096 \| 0.3447 \| 0.7004 \| \| 0.247 \| 59.0 \| 18408 \| 0.3521 \| 0.7004 \| \| 0.2468 \| 60.0 \| 18720 \| 0.3493 \| 0.6968 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
thanhnew2001/bloom560m_grade7_2000_10kstep	thanhnew2001	2023-08-22T10:22:01Z	31	0	peft	[ "peft", "text-generation", "region:us" ]	text-generation	2023-08-22T09:50:28Z	--- library_name: peft pipeline_tag: text-generation --- ## 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 0.5.0.dev0 - PEFT 0.5.0.dev0
asenella/ms_config_1_alpha_10_beta_250_seed_0	asenella	2023-08-22T10:18:27Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-08-22T10:18:24Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
AddisuSeteye/speecht5_tts_amharic2	AddisuSeteye	2023-08-22T10:17:13Z	77	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "tags", "generated_from_trainer", "am", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-08-11T20:06:06Z	--- language: - am license: mit base_model: microsoft/speecht5_tts tags: - tags - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS amharic 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. --> # SpeechT5 TTS amharic This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the alfaa dataset. It achieves the following results on the evaluation set: - Loss: 0.3855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4304 \| 3.32 \| 1000 \| 0.3855 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
TobiTob/decision_transformer_merged3	TobiTob	2023-08-22T10:12:41Z	31	1	transformers	[ "transformers", "pytorch", "tensorboard", "decision_transformer", "generated_from_trainer", "dataset:city_learn", "endpoints_compatible", "region:us" ]	null	2023-07-07T12:10:55Z	--- tags: - generated_from_trainer datasets: - city_learn model-index: - name: decision_transformer_merged3 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. --> # decision_transformer_merged3 This model is a fine-tuned version of [](https://huggingface.co/) on the city_learn 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_ratio: 0.1 - num_epochs: 200 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
asenella/ms_config_1_alpha_10_beta_1_seed_2	asenella	2023-08-22T10:02:53Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-08-22T10:02:51Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
Tina-2005/sunset	Tina-2005	2023-08-22T09:50:39Z	1	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-22T09:37:33Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Sunset Dreambooth model trained by Tina-2005 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: TSEC230 Sample pictures of this concept: ![0](https://huggingface.co/Tina-2005/sunset/resolve/main/sample_images/04.jpg)
qgallouedec/window-close-v2	qgallouedec	2023-08-22T09:48:51Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:03:03Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: window-close-v2 type: window-close-v2 metrics: - type: mean_reward value: 593.18 +/- 40.45 name: mean_reward verified: false --- A(n) APPO model trained on the window-close-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/window-close-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=window-close-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=window-close-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Samuael/llama-2-7b-tebot-sharded	Samuael	2023-08-22T09:44:04Z	0	0	peft	[ "peft", "pytorch", "llama", "region:us" ]	null	2023-08-17T16:00:53Z	--- 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 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
qgallouedec/shelf-place-v2	qgallouedec	2023-08-22T09:43:18Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:01:57Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: shelf-place-v2 type: shelf-place-v2 metrics: - type: mean_reward value: 274.68 +/- 29.20 name: mean_reward verified: false --- A(n) APPO model trained on the shelf-place-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/shelf-place-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=shelf-place-v2 --train_dir=./train_dir --experiment=shelf-place-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=shelf-place-v2 --train_dir=./train_dir --experiment=shelf-place-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/reach-wall-v2	qgallouedec	2023-08-22T09:42:23Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:01:45Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: reach-wall-v2 type: reach-wall-v2 metrics: - type: mean_reward value: 794.75 +/- 72.50 name: mean_reward verified: false --- A(n) APPO model trained on the reach-wall-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/reach-wall-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=reach-wall-v2 --train_dir=./train_dir --experiment=reach-wall-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=reach-wall-v2 --train_dir=./train_dir --experiment=reach-wall-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/reach-v2	qgallouedec	2023-08-22T09:41:29Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:01:35Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: reach-v2 type: reach-v2 metrics: - type: mean_reward value: 686.43 +/- 166.95 name: mean_reward verified: false --- A(n) APPO model trained on the reach-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/reach-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=reach-v2 --train_dir=./train_dir --experiment=reach-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=reach-v2 --train_dir=./train_dir --experiment=reach-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/push-v2	qgallouedec	2023-08-22T09:39:39Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:01:14Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: push-v2 type: push-v2 metrics: - type: mean_reward value: 742.07 +/- 37.84 name: mean_reward verified: false --- A(n) APPO model trained on the push-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/push-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=push-v2 --train_dir=./train_dir --experiment=push-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=push-v2 --train_dir=./train_dir --experiment=push-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/plate-slide-side-v2	qgallouedec	2023-08-22T09:36:55Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:00:43Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: plate-slide-side-v2 type: plate-slide-side-v2 metrics: - type: mean_reward value: 711.01 +/- 56.29 name: mean_reward verified: false --- A(n) APPO model trained on the plate-slide-side-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/plate-slide-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=plate-slide-side-v2 --train_dir=./train_dir --experiment=plate-slide-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=plate-slide-side-v2 --train_dir=./train_dir --experiment=plate-slide-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/plate-slide-back-v2	qgallouedec	2023-08-22T09:35:57Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:00:34Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: plate-slide-back-v2 type: plate-slide-back-v2 metrics: - type: mean_reward value: 709.93 +/- 82.31 name: mean_reward verified: false --- A(n) APPO model trained on the plate-slide-back-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/plate-slide-back-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=plate-slide-back-v2 --train_dir=./train_dir --experiment=plate-slide-back-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=plate-slide-back-v2 --train_dir=./train_dir --experiment=plate-slide-back-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/pick-place-wall-v2	qgallouedec	2023-08-22T09:34:08Z	0	1	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T10:00:14Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: pick-place-wall-v2 type: pick-place-wall-v2 metrics: - type: mean_reward value: 449.64 +/- 63.43 name: mean_reward verified: false --- A(n) APPO model trained on the pick-place-wall-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/pick-place-wall-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=pick-place-wall-v2 --train_dir=./train_dir --experiment=pick-place-wall-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=pick-place-wall-v2 --train_dir=./train_dir --experiment=pick-place-wall-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/peg-unplug-side-v2	qgallouedec	2023-08-22T09:31:20Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:59:45Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: peg-unplug-side-v2 type: peg-unplug-side-v2 metrics: - type: mean_reward value: 499.20 +/- 95.68 name: mean_reward verified: false --- A(n) APPO model trained on the peg-unplug-side-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/peg-unplug-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=peg-unplug-side-v2 --train_dir=./train_dir --experiment=peg-unplug-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=peg-unplug-side-v2 --train_dir=./train_dir --experiment=peg-unplug-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/peg-insert-side-v2	qgallouedec	2023-08-22T09:30:22Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:59:35Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: peg-insert-side-v2 type: peg-insert-side-v2 metrics: - type: mean_reward value: 308.94 +/- 175.97 name: mean_reward verified: false --- A(n) APPO model trained on the peg-insert-side-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/peg-insert-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=peg-insert-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=peg-insert-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/handle-pull-v2	qgallouedec	2023-08-22T09:28:29Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:59:15Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: handle-pull-v2 type: handle-pull-v2 metrics: - type: mean_reward value: 698.01 +/- 21.12 name: mean_reward verified: false --- A(n) APPO model trained on the handle-pull-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/handle-pull-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=handle-pull-v2 --train_dir=./train_dir --experiment=handle-pull-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=handle-pull-v2 --train_dir=./train_dir --experiment=handle-pull-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/handle-pull-side-v2	qgallouedec	2023-08-22T09:27:34Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:59:05Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: handle-pull-side-v2 type: handle-pull-side-v2 metrics: - type: mean_reward value: 462.12 +/- 95.86 name: mean_reward verified: false --- A(n) APPO model trained on the handle-pull-side-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/handle-pull-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=handle-pull-side-v2 --train_dir=./train_dir --experiment=handle-pull-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=handle-pull-side-v2 --train_dir=./train_dir --experiment=handle-pull-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/handle-press-v2	qgallouedec	2023-08-22T09:26:37Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:58:54Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: handle-press-v2 type: handle-press-v2 metrics: - type: mean_reward value: 862.58 +/- 32.94 name: mean_reward verified: false --- A(n) APPO model trained on the handle-press-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/handle-press-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=handle-press-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=handle-press-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/hand-insert-v2	qgallouedec	2023-08-22T09:24:46Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:58:36Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: hand-insert-v2 type: hand-insert-v2 metrics: - type: mean_reward value: 742.89 +/- 26.31 name: mean_reward verified: false --- A(n) APPO model trained on the hand-insert-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/hand-insert-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=hand-insert-v2 --train_dir=./train_dir --experiment=hand-insert-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=hand-insert-v2 --train_dir=./train_dir --experiment=hand-insert-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
VK246/IC_ver6I_coco_swin_gpt2_50A_1e	VK246	2023-08-22T09:24:05Z	45	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:coco", "base_model:VK246/IC_ver6H_coco_swin_gpt2_50B_1e", "base_model:finetune:VK246/IC_ver6H_coco_swin_gpt2_50B_1e", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-08-22T07:16:44Z	--- base_model: VK246/IC_ver6H_coco_swin_gpt2_50B_1e tags: - generated_from_trainer datasets: - coco metrics: - rouge model-index: - name: IC_ver6I_coco_swin_gpt2_50A_1e 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. --> # IC_ver6I_coco_swin_gpt2_50A_1e This model is a fine-tuned version of [VK246/IC_ver6H_coco_swin_gpt2_50B_1e](https://huggingface.co/VK246/IC_ver6H_coco_swin_gpt2_50B_1e) on the coco dataset. It achieves the following results on the evaluation set: - Loss: 0.8003 - Cider: 36.4847 - Rouge1: 41.9392 - Rouge2: 16.4156 - Rougel: 38.0808 - Rougelsum: 38.0721 - Bleu-1: 42.8624 - Bleu-2: 24.8647 - Bleu-3: 15.7144 - Bleu-4: 10.4434 - Gen Len: 11.2806 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - 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 \| Cider \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bleu-1 \| Bleu-2 \| Bleu-3 \| Bleu-4 \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\|:-------:\|:-------:\|:-------:\|:-------:\| \| 0.5622 \| 0.34 \| 1000 \| 0.8598 \| 16.5035 \| 41.0303 \| 15.4795 \| 37.2917 \| 37.2896 \| 41.7661 \| 23.7724 \| 14.7804 \| 9.5941 \| 11.2806 \| \| 0.639 \| 0.68 \| 2000 \| 0.8003 \| 36.4847 \| 41.9392 \| 16.4156 \| 38.0808 \| 38.0721 \| 42.8624 \| 24.8647 \| 15.7144 \| 10.4434 \| 11.2806 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
qgallouedec/drawer-open-v2	qgallouedec	2023-08-22T09:21:02Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:57:55Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: drawer-open-v2 type: drawer-open-v2 metrics: - type: mean_reward value: 493.34 +/- 2.61 name: mean_reward verified: false --- A(n) APPO model trained on the drawer-open-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/drawer-open-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=drawer-open-v2 --train_dir=./train_dir --experiment=drawer-open-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=drawer-open-v2 --train_dir=./train_dir --experiment=drawer-open-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/door-open-v2	qgallouedec	2023-08-22T09:18:18Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:57:27Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: door-open-v2 type: door-open-v2 metrics: - type: mean_reward value: 579.89 +/- 31.92 name: mean_reward verified: false --- A(n) APPO model trained on the door-open-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/door-open-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=door-open-v2 --train_dir=./train_dir --experiment=door-open-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=door-open-v2 --train_dir=./train_dir --experiment=door-open-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
IngeniousArtist/distilbert-finance	IngeniousArtist	2023-08-22T09:15:43Z	161	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:financial_phrasebank", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-31T00:31:08Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy model-index: - name: distilbert-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: 0.7386363636363636 --- <!-- 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-finance This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.9962 - Accuracy: 0.7386 ## Model description More information needed ## 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: cosine - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.904 \| 0.33 \| 20 \| 1.5959 \| 0.4205 \| \| 0.6562 \| 0.66 \| 40 \| 1.6665 \| 0.4143 \| \| 0.539 \| 0.98 \| 60 \| 1.6067 \| 0.3936 \| \| 0.4759 \| 1.31 \| 80 \| 1.5079 \| 0.4236 \| \| 0.3882 \| 1.64 \| 100 \| 1.4719 \| 0.4298 \| \| 0.3782 \| 1.97 \| 120 \| 1.2392 \| 0.4267 \| \| 0.2729 \| 2.3 \| 140 \| 1.0114 \| 0.4928 \| \| 0.2607 \| 2.62 \| 160 \| 0.9514 \| 0.5930 \| \| 0.2889 \| 2.95 \| 180 \| 0.8661 \| 0.6477 \| \| 0.181 \| 3.28 \| 200 \| 0.7093 \| 0.7417 \| \| 0.1742 \| 3.61 \| 220 \| 1.1042 \| 0.5764 \| \| 0.1904 \| 3.93 \| 240 \| 0.7439 \| 0.7510 \| \| 0.1186 \| 4.26 \| 260 \| 0.8587 \| 0.7469 \| \| 0.137 \| 4.59 \| 280 \| 0.7408 \| 0.7603 \| \| 0.1166 \| 4.92 \| 300 \| 1.0107 \| 0.6705 \| \| 0.0938 \| 5.25 \| 320 \| 0.7883 \| 0.7624 \| \| 0.0881 \| 5.57 \| 340 \| 1.0339 \| 0.7056 \| \| 0.0812 \| 5.9 \| 360 \| 0.8409 \| 0.7490 \| \| 0.0586 \| 6.23 \| 380 \| 0.9146 \| 0.7345 \| \| 0.0572 \| 6.56 \| 400 \| 0.9000 \| 0.7366 \| \| 0.0527 \| 6.89 \| 420 \| 0.9782 \| 0.7335 \| \| 0.045 \| 7.21 \| 440 \| 1.0102 \| 0.7262 \| \| 0.0471 \| 7.54 \| 460 \| 1.0322 \| 0.7324 \| \| 0.0508 \| 7.87 \| 480 \| 0.9381 \| 0.7448 \| \| 0.039 \| 8.2 \| 500 \| 0.9489 \| 0.7459 \| \| 0.0419 \| 8.52 \| 520 \| 0.9779 \| 0.7469 \| \| 0.0256 \| 8.85 \| 540 \| 0.9834 \| 0.7407 \| \| 0.0264 \| 9.18 \| 560 \| 0.9963 \| 0.7376 \| \| 0.0378 \| 9.51 \| 580 \| 0.9981 \| 0.7376 \| \| 0.0421 \| 9.84 \| 600 \| 0.9962 \| 0.7386 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
qgallouedec/coffee-push-v2	qgallouedec	2023-08-22T09:13:37Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:56:36Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: coffee-push-v2 type: coffee-push-v2 metrics: - type: mean_reward value: 526.27 +/- 120.28 name: mean_reward verified: false --- A(n) APPO model trained on the coffee-push-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/coffee-push-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=coffee-push-v2 --train_dir=./train_dir --experiment=coffee-push-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=coffee-push-v2 --train_dir=./train_dir --experiment=coffee-push-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/coffee-pull-v2	qgallouedec	2023-08-22T09:12:42Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-08T09:56:25Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: coffee-pull-v2 type: coffee-pull-v2 metrics: - type: mean_reward value: 262.59 +/- 63.08 name: mean_reward verified: false --- A(n) APPO model trained on the coffee-pull-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/coffee-pull-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=coffee-pull-v2 --train_dir=./train_dir --experiment=coffee-pull-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=coffee-pull-v2 --train_dir=./train_dir --experiment=coffee-pull-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
922-CA/Modded-Berry	922-CA	2023-08-22T09:10:37Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-11-18T11:48:37Z	--- license: creativeml-openrail-m --- # INFO (11/18/2022) Old stable diffusion 1.5 merge of berry mix + anythingv3 at around ~30/70 ratio + further merges. (If can recall correctly...)
qgallouedec/button-press-topdown-wall-v2	qgallouedec	2023-08-22T09:09:02Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T16:13:14Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: button-press-topdown-wall-v2 type: button-press-topdown-wall-v2 metrics: - type: mean_reward value: 497.31 +/- 37.73 name: mean_reward verified: false --- A(n) APPO model trained on the button-press-topdown-wall-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/button-press-topdown-wall-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=button-press-topdown-wall-v2 --train_dir=./train_dir --experiment=button-press-topdown-wall-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=button-press-topdown-wall-v2 --train_dir=./train_dir --experiment=button-press-topdown-wall-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/button-press-topdown-v2	qgallouedec	2023-08-22T09:08:08Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T16:12:47Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: button-press-topdown-v2 type: button-press-topdown-v2 metrics: - type: mean_reward value: 486.12 +/- 38.04 name: mean_reward verified: false --- A(n) APPO model trained on the button-press-topdown-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/button-press-topdown-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=button-press-topdown-v2 --train_dir=./train_dir --experiment=button-press-topdown-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=button-press-topdown-v2 --train_dir=./train_dir --experiment=button-press-topdown-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/bin-picking-v2	qgallouedec	2023-08-22T09:06:20Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T16:11:52Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: bin-picking-v2 type: bin-picking-v2 metrics: - type: mean_reward value: 452.37 +/- 36.53 name: mean_reward verified: false --- A(n) APPO model trained on the bin-picking-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/bin-picking-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=bin-picking-v2 --train_dir=./train_dir --experiment=bin-picking-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=bin-picking-v2 --train_dir=./train_dir --experiment=bin-picking-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
marksverdhei/t5-base-define	marksverdhei	2023-08-22T09:05:55Z	123	6	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "en", "dataset:marksverdhei/wordnet-definitions-en-2021", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-02T09:50:37Z	--- language: en widget: - text: 'define "toecoin": toecoin rose by 200% after Elon Musk mentioned it in his tweet' datasets: - 'marksverdhei/wordnet-definitions-en-2021' --- # T5-define (This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy) This model is trained to generate word definitions based on the word and a context, using a subset of wordnet for all words that have an example and definition. The model uses task prompts on the format 'define "[word]": [example sentence]' This model in particular is a one-shot learner for unseen words, as it has to infer the definition by only one example How to run: ```python from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("marksverdhei/t5-base-define") model = T5ForConditionalGeneration.from_pretrained("marksverdhei/t5-base-define") prompt = "define \"noseplow\": The children hid as the noseplow drove across the street" ids = tokenizer(prompt, return_tensors="pt").input_ids generated_tokens = model.generate(ids)[0][1:-1] print(tokenizer.decode(generated_tokens)) ``` See the gist for the source code to used to train the model: https://gist.github.com/marksverdhei/0a13f67e65460b71c05fcf558a6a91ae
qgallouedec/basketball-v2	qgallouedec	2023-08-22T09:05:27Z	0	1	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T16:11:22Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: basketball-v2 type: basketball-v2 metrics: - type: mean_reward value: 584.02 +/- 49.43 name: mean_reward verified: false --- A(n) APPO model trained on the basketball-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/basketball-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=basketball-v2 --train_dir=./train_dir --experiment=basketball-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=basketball-v2 --train_dir=./train_dir --experiment=basketball-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/assembly-v2	qgallouedec	2023-08-22T09:04:34Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T13:44:05Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: assembly-v2 type: assembly-v2 metrics: - type: mean_reward value: 245.47 +/- 4.68 name: mean_reward verified: false --- A(n) APPO model trained on the assembly-v2 environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/assembly-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=assembly-v2 --train_dir=./train_dir --experiment=assembly-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=assembly-v2 --train_dir=./train_dir --experiment=assembly-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
newronai/lma2-7b-Chat-Adapter-N	newronai	2023-08-22T08:56:11Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-22T08:56:04Z	--- 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 ### Framework versions - PEFT 0.5.0.dev0
Abhishek-Pathak/llama2_finetuned_demo	Abhishek-Pathak	2023-08-22T08:51:12Z	0	0	null	[ "tensorboard", "generated_from_trainer", "region:us" ]	null	2023-08-22T06:28:44Z	--- tags: - generated_from_trainer model-index: - name: llama2_finetuned_demo 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. --> # llama2_finetuned_demo This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
grv805/llama2-qlora-finetunined-13b-gcp	grv805	2023-08-22T08:47:03Z	6	0	peft	[ "peft", "region:us" ]	null	2023-08-22T08:46:51Z	--- 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 ### Framework versions - PEFT 0.5.0.dev0
tianpf/chinese-alpaca-2-qlora-finetunined-law2	tianpf	2023-08-22T08:46:11Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-22T08:46:07Z	--- 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 ### Framework versions - PEFT 0.5.0.dev0
linoyts/lora-xl-3d_icons-0.0001-5e-05-2000-1-5	linoyts	2023-08-22T08:45:51Z	5	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-08-22T07:54:07Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: blb 3d icon tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - LinoyTsaban/lora-xl-3d_icons-0.0001-5e-05-2000-1-5 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on blb 3d icon using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
dkqjrm/20230822155557	dkqjrm	2023-08-22T08:44:13Z	106	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	2023-08-22T06:56:15Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822155557' 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. --> # 20230822155557 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: 0.3488 - Accuracy: 0.5307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 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 \| 0.3548 \| 0.4729 \| \| 0.3737 \| 2.0 \| 624 \| 0.3480 \| 0.5199 \| \| 0.3737 \| 3.0 \| 936 \| 0.3486 \| 0.5162 \| \| 0.3718 \| 4.0 \| 1248 \| 0.3495 \| 0.5235 \| \| 0.3714 \| 5.0 \| 1560 \| 0.3505 \| 0.4729 \| \| 0.3714 \| 6.0 \| 1872 \| 0.3487 \| 0.5235 \| \| 0.3686 \| 7.0 \| 2184 \| 0.3496 \| 0.4729 \| \| 0.3686 \| 8.0 \| 2496 \| 0.3505 \| 0.4729 \| \| 0.3684 \| 9.0 \| 2808 \| 0.3502 \| 0.5235 \| \| 0.3679 \| 10.0 \| 3120 \| 0.3491 \| 0.5054 \| \| 0.3679 \| 11.0 \| 3432 \| 0.3515 \| 0.4729 \| \| 0.3659 \| 12.0 \| 3744 \| 0.3496 \| 0.5162 \| \| 0.3649 \| 13.0 \| 4056 \| 0.3517 \| 0.4729 \| \| 0.3649 \| 14.0 \| 4368 \| 0.3543 \| 0.4729 \| \| 0.3651 \| 15.0 \| 4680 \| 0.3513 \| 0.4729 \| \| 0.3651 \| 16.0 \| 4992 \| 0.3489 \| 0.5235 \| \| 0.363 \| 17.0 \| 5304 \| 0.3537 \| 0.5235 \| \| 0.3613 \| 18.0 \| 5616 \| 0.3487 \| 0.5307 \| \| 0.3613 \| 19.0 \| 5928 \| 0.3495 \| 0.5126 \| \| 0.3645 \| 20.0 \| 6240 \| 0.3530 \| 0.5199 \| \| 0.359 \| 21.0 \| 6552 \| 0.3497 \| 0.5235 \| \| 0.359 \| 22.0 \| 6864 \| 0.3487 \| 0.5235 \| \| 0.3614 \| 23.0 \| 7176 \| 0.3511 \| 0.5235 \| \| 0.3614 \| 24.0 \| 7488 \| 0.3491 \| 0.5271 \| \| 0.3617 \| 25.0 \| 7800 \| 0.3493 \| 0.5199 \| \| 0.3611 \| 26.0 \| 8112 \| 0.3491 \| 0.5271 \| \| 0.3611 \| 27.0 \| 8424 \| 0.3581 \| 0.4729 \| \| 0.3583 \| 28.0 \| 8736 \| 0.3496 \| 0.5343 \| \| 0.3583 \| 29.0 \| 9048 \| 0.3492 \| 0.5162 \| \| 0.3583 \| 30.0 \| 9360 \| 0.3493 \| 0.4404 \| \| 0.3564 \| 31.0 \| 9672 \| 0.3494 \| 0.5343 \| \| 0.3564 \| 32.0 \| 9984 \| 0.3489 \| 0.5199 \| \| 0.3567 \| 33.0 \| 10296 \| 0.3490 \| 0.5343 \| \| 0.3561 \| 34.0 \| 10608 \| 0.3486 \| 0.5271 \| \| 0.3561 \| 35.0 \| 10920 \| 0.3492 \| 0.5307 \| \| 0.3556 \| 36.0 \| 11232 \| 0.3503 \| 0.4765 \| \| 0.3556 \| 37.0 \| 11544 \| 0.3497 \| 0.5307 \| \| 0.3556 \| 38.0 \| 11856 \| 0.3494 \| 0.5379 \| \| 0.3561 \| 39.0 \| 12168 \| 0.3488 \| 0.5235 \| \| 0.3561 \| 40.0 \| 12480 \| 0.3503 \| 0.5271 \| \| 0.3558 \| 41.0 \| 12792 \| 0.3489 \| 0.5343 \| \| 0.3579 \| 42.0 \| 13104 \| 0.3508 \| 0.4729 \| \| 0.3579 \| 43.0 \| 13416 \| 0.3505 \| 0.5271 \| \| 0.3547 \| 44.0 \| 13728 \| 0.3493 \| 0.5379 \| \| 0.3567 \| 45.0 \| 14040 \| 0.3519 \| 0.4729 \| \| 0.3567 \| 46.0 \| 14352 \| 0.3497 \| 0.4729 \| \| 0.3548 \| 47.0 \| 14664 \| 0.3499 \| 0.4729 \| \| 0.3548 \| 48.0 \| 14976 \| 0.3492 \| 0.5343 \| \| 0.3563 \| 49.0 \| 15288 \| 0.3491 \| 0.5307 \| \| 0.3552 \| 50.0 \| 15600 \| 0.3489 \| 0.5235 \| \| 0.3552 \| 51.0 \| 15912 \| 0.3487 \| 0.5162 \| \| 0.3557 \| 52.0 \| 16224 \| 0.3496 \| 0.4513 \| \| 0.3555 \| 53.0 \| 16536 \| 0.3488 \| 0.5307 \| \| 0.3555 \| 54.0 \| 16848 \| 0.3489 \| 0.5271 \| \| 0.3542 \| 55.0 \| 17160 \| 0.3488 \| 0.5162 \| \| 0.3542 \| 56.0 \| 17472 \| 0.3488 \| 0.5343 \| \| 0.3545 \| 57.0 \| 17784 \| 0.3494 \| 0.5379 \| \| 0.3543 \| 58.0 \| 18096 \| 0.3489 \| 0.5126 \| \| 0.3543 \| 59.0 \| 18408 \| 0.3489 \| 0.5162 \| \| 0.3553 \| 60.0 \| 18720 \| 0.3488 \| 0.5307 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
xray1111/ppo-LunarLander-v2	xray1111	2023-08-22T08:41:09Z	5	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-22T08:40:42Z	--- 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: 273.95 +/- 16.80 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 ... ```
newronai/llama-2-7b-Chat-QLoRA-New-1.0	newronai	2023-08-22T08:36:35Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-22T08:36:31Z	--- 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 ### Framework versions - PEFT 0.5.0.dev0
kaanhho/whisper-tiny-01	kaanhho	2023-08-22T08:29:58Z	76	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	2023-08-22T01:07:48Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-01 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.33884297520661155 --- <!-- 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-01 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: 0.6410 - Wer Ortho: 0.3430 - Wer: 0.3388 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.001 \| 17.24 \| 500 \| 0.6410 \| 0.3430 \| 0.3388 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
CodyNichol14/M_Shadows_HTTK	CodyNichol14	2023-08-22T08:28:22Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2023-08-22T08:25:25Z	--- license: artistic-2.0 Model Maker: CodyNichol14 Epoch: 250 Model: M. SHadows From Avenged Sevenfold ---

NethamTSG/ppo-LunarLander-v5

NethamTSG

2023-08-22T15:09:22Z

0

stable-baselines3

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

reinforcement-learning

2023-08-22T15:08:58Z

--- 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: 287.98 +/- 17.19 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 ... ```

pbeyens/mydataset-repo

pbeyens

2023-08-22T15:08:45Z

78

0

transformers

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

token-classification

2023-08-21T14:03:22Z

--- license: mit tags: - generated_from_trainer datasets: - mydataset model-index: - name: mydataset-repo results: [] ---  # mydataset-repo This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the mydataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Total-str: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} - Total-val: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} - Overall Precision: 1.0 - Overall Recall: 1.0 - Overall F1: 1.0 - Overall Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Total-str | Total-val | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:------:|:----:|:---------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.0674 | 28.57 | 200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0001 | 57.14 | 400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 85.71 | 600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 114.29 | 800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 142.86 | 1000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 171.43 | 1200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 200.0 | 1400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 228.57 | 1600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 257.14 | 1800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 285.71 | 2000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 314.29 | 2200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 342.86 | 2400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 371.43 | 2600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 400.0 | 2800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 428.57 | 3000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 457.14 | 3200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 485.71 | 3400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 514.29 | 3600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 542.86 | 3800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 571.43 | 4000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 600.0 | 4200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 628.57 | 4400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 657.14 | 4600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 685.71 | 4800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0 | 714.29 | 5000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.13.2

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

Tarti

2023-08-22T14:58:26Z

134

0

transformers

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

token-classification

2023-08-22T10:41:31Z

--- 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 args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8602627537962806 ---  # 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: 0.1355 - F1: 0.8603 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2574 | 1.0 | 525 | 0.1627 | 0.8221 | | 0.1295 | 2.0 | 1050 | 0.1435 | 0.8467 | | 0.0815 | 3.0 | 1575 | 0.1355 | 0.8603 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu117 - Datasets 1.16.1 - Tokenizers 0.13.3

agarc15/gpt2-finetuned-PRC

agarc15

2023-08-22T14:52:00Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-classification", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2023-08-21T07:48:25Z

--- license: mit base_model: gpt2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: gpt2-finetuned-PRC results: [] ---  # gpt2-finetuned-PRC This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4564 - Accuracy: 0.8711 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 391 | 0.5038 | 0.8420 | | 0.7758 | 2.0 | 782 | 0.4486 | 0.8629 | | 0.4031 | 3.0 | 1173 | 0.4664 | 0.8678 | | 0.3225 | 4.0 | 1564 | 0.4564 | 0.8711 | | 0.3225 | 5.0 | 1955 | 0.4637 | 0.8693 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

camenduru/seamless-m4t-medium

camenduru

2023-08-22T14:50:58Z

0

1

null

[ "SeamlessM4T", "license:cc-by-nc-4.0", "region:us" ]

null

2023-08-22T14:45:53Z

--- inference: false tags: - SeamlessM4T license: cc-by-nc-4.0 --- # SeamlessM4T Medium SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. SeamlessM4T covers: - 📥 101 languages for speech input - ⌨️ 96 Languages for text input/output - 🗣️ 35 languages for speech output. This is the "medium" variant of the unified model, which enables multiple tasks without relying on multiple separate models: - Speech-to-speech translation (S2ST) - Speech-to-text translation (S2TT) - Text-to-speech translation (T2ST) - Text-to-text translation (T2TT) - Automatic speech recognition (ASR) ## SeamlessM4T models The SeamlessM4T models come in two checkpoints of different size: | Model Name | #params | checkpoint | metrics | | - | - | - | - | | [SeamlessM4T-Medium]((https://huggingface.co/facebook/seamless-m4t-medium) | 1.2B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) | [metrics]() | | [SeamlessM4T-Large](https://huggingface.co/facebook/seamless-m4t-large) | 2.3B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt) | [metrics]() | We provide extensive evaluation results of SeamlessM4T-Medium and SeamlessM4T-Large in the SeamlessM4T paper (as averages) in the `metrics` files above. ## Instructions to run inference with SeamlessM4T models The SeamlessM4T models are currently available through the `seamless_communication` package. The `seamless_communication` package can be installed by following the instructions outlined here: [Installation](https://github.com/fairinternal/seamless_communication/tree/main#installation). Once installed, a [`Translator`](https://github.com/fairinternal/seamless_communication/blob/590547965b343b590d15847a0aa25a6779fc3753/src/seamless_communication/models/inference/translator.py#L47) object can be instantiated to perform all five of the spoken langauge tasks. The `Translator` is instantiated with three arguments: 1. **model_name_or_card**: SeamlessM4T checkpoint. Can be either `seamlessM4T_medium` for the medium model, or `seamlessM4T_large` for the large model 2. **vocoder_name_or_card**: vocoder checkpoint (`vocoder_36langs`) 3. **device**: Torch device ```python import torch from seamless_communication.models.inference import Translator # Initialize a Translator object with a multitask model, vocoder on the GPU. translator = Translator("seamlessM4T_medium", vocoder_name_or_card="vocoder_36langs", device=torch.device("cuda:0")) ``` Once instantiated, the `predict()` method can be used to run inference as many times on any of the supported tasks. Given an input audio with `<path_to_input_audio>` or an input text `<input_text>` in `<src_lang>`, we can translate into `<tgt_lang>` as follows. ### S2ST and T2ST: ```python # S2ST translated_text, wav, sr = translator.predict(<path_to_input_audio>, "s2st", <tgt_lang>) # T2ST translated_text, wav, sr = translator.predict(<input_text>, "t2st", <tgt_lang>, src_lang=<src_lang>) ``` Note that `<src_lang>` must be specified for T2ST. The generated units are synthesized and the output audio file is saved with: ```python wav, sr = translator.synthesize_speech(<speech_units>, <tgt_lang>) # Save the translated audio generation. torchaudio.save( <path_to_save_audio>, wav[0].cpu(), sample_rate=sr, ) ``` ### S2TT, T2TT and ASR: ```python # S2TT translated_text, _, _ = translator.predict(<path_to_input_audio>, "s2tt", <tgt_lang>) # ASR # This is equivalent to S2TT with `<tgt_lang>=<src_lang>`. transcribed_text, _, _ = translator.predict(<path_to_input_audio>, "asr", <src_lang>) # T2TT translated_text, _, _ = translator.predict(<input_text>, "t2tt", <tgt_lang>, src_lang=<src_lang>) ``` Note that `<src_lang>` must be specified for T2TT. ## Citation If you plan to use SeamlessM4T in your work or any models/datasets/artifacts published in SeamlessM4T, please cite: ```bibtex @article{seamlessm4t2023, title={"SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation"}, author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang}, journal={ArXiv}, year={2023} } ``` ## License The Seamless Communication code and weights are CC-BY-NC 4.0 licensed.

sarankup-newgen/llama2-13b-email-trained

sarankup-newgen

2023-08-22T14:49:42Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-22T14:49:16Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0

pknayak/speecht5_finetuned_voxpopuli_es

pknayak

2023-08-22T14:46:12Z

82

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-08-22T14:06:01Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_es results: [] ---  # speecht5_finetuned_voxpopuli_es This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4620 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.4902 | 153.85 | 500 | 0.4620 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Amirhnrn/Reinforce-Cartpole

Amirhnrn

2023-08-22T14:40:23Z

0

null

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

reinforcement-learning

2023-08-22T07:30:33Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole 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

Luciferio/MiniLLM-finetuned

Luciferio

2023-08-22T14:39:58Z

105

0

transformers

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:microsoft/MiniLM-L12-H384-uncased", "base_model:finetune:microsoft/MiniLM-L12-H384-uncased", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-22T12:57:56Z

--- license: mit base_model: microsoft/MiniLM-L12-H384-uncased tags: - generated_from_trainer datasets: - emotion metrics: - f1 model-index: - name: MiniLLM-finetuned results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: F1 type: f1 value: 0.922353805579638 ---  # MiniLLM-finetuned This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2932 - F1: 0.9224 ## Model description More information needed ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 2000 | 0.4408 | 0.8888 | | No log | 2.0 | 4000 | 0.2932 | 0.9224 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

ALM-AHME/beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled-3rd

ALM-AHME

2023-08-22T14:31:20Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "base_model:microsoft/beit-large-patch16-224", "base_model:finetune:microsoft/beit-large-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-08-22T11:54:04Z

--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled-3rd results: [] ---  # beit-large-patch16-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled-3rd This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0488 - Accuracy: 0.9901 ## Model description More information needed ## 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-06 - 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 - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9835 | 1.0 | 114 | 1.9296 | 0.2315 | | 1.6045 | 2.0 | 229 | 1.4334 | 0.5172 | | 1.0525 | 3.0 | 343 | 0.9298 | 0.6962 | | 0.795 | 4.0 | 458 | 0.6580 | 0.7709 | | 0.5739 | 5.0 | 572 | 0.4717 | 0.8366 | | 0.5821 | 6.0 | 687 | 0.3511 | 0.8851 | | 0.4566 | 7.0 | 801 | 0.2705 | 0.9204 | | 0.2751 | 8.0 | 916 | 0.2114 | 0.9384 | | 0.2352 | 9.0 | 1030 | 0.1303 | 0.9688 | | 0.1831 | 10.0 | 1145 | 0.1194 | 0.9688 | | 0.1515 | 11.0 | 1259 | 0.0673 | 0.9869 | | 0.204 | 11.95 | 1368 | 0.0488 | 0.9901 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Toobese/lilt-invoices

Toobese

2023-08-22T14:24:48Z

113

0

transformers

[ "transformers", "pytorch", "tensorboard", "lilt", "token-classification", "generated_from_trainer", "base_model:SCUT-DLVCLab/lilt-roberta-en-base", "base_model:finetune:SCUT-DLVCLab/lilt-roberta-en-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-21T13:32:34Z

--- license: mit base_model: SCUT-DLVCLab/lilt-roberta-en-base tags: - generated_from_trainer model-index: - name: lilt-invoices results: [] ---  # lilt-invoices This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Endorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} - Escription: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} - Illingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} - Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} - Nitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} - Nvoicedate: {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} - Nvoicetotal: {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} - Otaltax: {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} - Uantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} - Ubtotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} - Overall Precision: 0.9988 - Overall Recall: 0.9982 - Overall F1: 0.9985 - Overall Accuracy: 0.9994 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Endorname | Escription | Illingaddress | Mount | Nitprice | Nvoicedate | Nvoicetotal | Otaltax | Uantity | Ubtotal | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.1736 | 21.74 | 500 | 0.0031 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 177} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 183} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 175} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 156} | {'precision': 0.9941520467836257, 'recall': 1.0, 'f1': 0.9970674486803519, 'number': 170} | {'precision': 0.9946808510638298, 'recall': 0.9946808510638298, 'f1': 0.9946808510638298, 'number': 188} | {'precision': 1.0, 'recall': 0.9927007299270073, 'f1': 0.9963369963369962, 'number': 137} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 167} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 151} | 0.9988 | 0.9982 | 0.9985 | 0.9994 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

wjbmattingly/distilbert-base-uncased-finetuned-imdb

wjbmattingly

2023-08-22T14:18:48Z

115

0

transformers

[ "transformers", "pytorch", "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

2023-08-22T14:00:15Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # distilbert-base-uncased-finetuned-imdb 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: 2.9420 ## Model description More information needed ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1689 | 1.0 | 8 | 3.0928 | | 2.9706 | 2.0 | 16 | 2.7093 | | 2.9273 | 3.0 | 24 | 2.7136 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3

Tarti/xlm-roberta-base-finetuned-panx-de-fr

Tarti

2023-08-22T14:14:38Z

105

0

transformers

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

token-classification

2023-08-22T14:02:58Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1600 - F1: 0.8591 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.288 | 1.0 | 715 | 0.1823 | 0.8218 | | 0.1458 | 2.0 | 1430 | 0.1533 | 0.8503 | | 0.0934 | 3.0 | 2145 | 0.1600 | 0.8591 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu117 - Datasets 1.16.1 - Tokenizers 0.13.3

loicspigeleer/q-Taxi-v3

loicspigeleer

2023-08-22T14:11:38Z

0

null

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

reinforcement-learning

2023-08-22T14:11:35Z

--- 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: 7.50 +/- 2.78 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="loicspigeleer/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"]) ```

facebook/blaser-2.0-ref

facebook

2023-08-22T14:07:37Z

0

5

null

[ "license:cc-by-nc-4.0", "region:us" ]

null

2023-08-21T13:25:31Z

--- license: cc-by-nc-4.0 --- # BLASER 2.0 [[Blog]](https://ai.meta.com/resources/models-and-libraries/seamless-communication/) [[Code](https://github.com/facebookresearch/SONAR)] BLASER 2.0 is the new version of BLASER ([Chen et al., 2023](https://aclanthology.org/2023.acl-long.504/)), a family of models for automatic evaluation of machine translation quality. BLASER 2.0 is based on [SONAR](https://huggingface.co/facebook/SONAR) sentence embeddings and works with both speech and text modalities. The actual model predicts a similarity score for the translated sentence based on the the translation, the source sentence, and the reference translation. Its sibling model, [BLASER 2.0 QE](https://huggingface.co/facebook/blaser-2.0-qe), does not use references. Supervised BLASER models are trained to predict cross-lingual semantic similarity scores, XSTS ([Licht et al., 2022](https://aclanthology.org/2022.amta-research.24/)), on a scale where 1 corresponds to completely unrelated sentences and 5 corresponds to fully semantically equivalent sentences. The models predictions, though, are unbounded and can occasionally surpass these limits. ## Installation See the SONAR github [repo](https://github.com/facebookresearch/SONAR) for the installation instructions. ## Usage BLASER 2.0 models accept 1024-dimensional SONAR sentence embeddings as inputs, and produce a single score as an output. The code below illustrates their usage with text embeddings: ```Python from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline from sonar.models.blaser.loader import load_blaser_model blaser = load_blaser_model("blaser_2_0_ref").eval() text_embedder = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder", tokenizer="text_sonar_basic_encoder") src_embs = text_embedder.predict(["Le chat s'assit sur le tapis."], source_lang="fra_Latn") ref_embs = text_embedder.predict(["The cat sat on the mat."], source_lang="eng_Latn") mt_embs = text_embedder.predict(["The cat sat down on the carpet."], source_lang="eng_Latn") print(blaser(src=src_embs, ref=ref_embs, mt=mt_embs).item()) # 4.688 ``` With BLASER 2.0 models, SONAR text and speech embeddings can be used interchangeably. ## Model details - **Developed by:** Seamless Communication et al. - **License:** CC-BY-NC 4.0 license - **Citation:** If you use BLASER 2.0 in your work, please cite [the paper](https://ai.meta.com/resources/models-and-libraries/seamless-communication/): ```bibtex @article{seamlessm4t2023, title={SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation}, author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang}, journal={ArXiv}, year={2023} } ```

facebook/blaser-2.0-qe

facebook

2023-08-22T14:07:31Z

0

7

null

[ "license:cc-by-nc-4.0", "region:us" ]

null

2023-08-21T15:01:40Z

--- license: cc-by-nc-4.0 --- # BLASER 2.0 [[Blog]](https://ai.meta.com/resources/models-and-libraries/seamless-communication/) [[Code](https://github.com/facebookresearch/SONAR)] BLASER 2.0 is the new version of BLASER ([Chen et al., 2023](https://aclanthology.org/2023.acl-long.504/)), a family of models for automatic evaluation of machine translation quality. BLASER 2.0 is based on [SONAR](https://huggingface.co/facebook/SONAR) sentence embeddings and works with both speech and text modalities. The actual model predicts a similarity score for the translated sentence based on the translation and the source sentence. This, it can be applied in settings where reference translations are missing or if their quality is questionable. In contrast, its sibling model, [BLASER 2.0-referenced](https://huggingface.co/facebook/blaser-2.0-ref), requires also a reference translation. Supervised BLASER models are trained to predict cross-lingual semantic similarity scores, XSTS ([Licht et al., 2022](https://aclanthology.org/2022.amta-research.24/)), on a scale where 1 corresponds to completely unrelated sentences and 5 corresponds to fully semantically equivalent sentences. The models predictions, though, are unbounded and can occasionally surpass these limits. ## Installation See the SONAR github [repo](https://github.com/facebookresearch/SONAR) for the installation instructions. ## Usage BLASER 2.0 models accept 1024-dimensional SONAR sentence embeddings as inputs, and produce a single score as an output. The code below illustrates their usage with text embeddings: ```Python from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline from sonar.models.blaser.loader import load_blaser_model blaser = load_blaser_model("blaser_2_0_qe").eval() text_embedder = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder", tokenizer="text_sonar_basic_encoder") src_embs = text_embedder.predict(["Le chat s'assit sur le tapis."], source_lang="fra_Latn") mt_embs = text_embedder.predict(["The cat sat down on the carpet."], source_lang="eng_Latn") print(blaser(src=src_embs, mt=mt_embs).item()) # 4.708 ``` With BLASER 2.0 models, SONAR text and speech embeddings can be used interchangeably. ## Model details - **Developed by:** Seamless Communication et al. - **License:** CC-BY-NC 4.0 license - **Citation:** If you use BLASER 2.0 in your work, please cite [the paper](https://ai.meta.com/resources/models-and-libraries/seamless-communication/): ```bibtex @article{seamlessm4t2023, title={SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation}, author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye, Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang}, journal={ArXiv}, year={2023} } ```

ColdChair/hw_1

ColdChair

2023-08-22T13:53:18Z

0

null

[ "dataset:roneneldan/TinyStories", "license:openrail", "region:us" ]

null

2023-08-22T13:51:46Z

--- license: openrail datasets: - roneneldan/TinyStories ---

natfil/pmserver_bloom-3b

natfil

2023-08-22T13:45:44Z

3

0

peft

[ "peft", "region:us" ]

null

2023-08-22T13:45:33Z

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

Jbdddsai/lora-trained-xl-colab_gieskanne_500it_lr_1e-4

Jbdddsai

2023-08-22T13:20:10Z

4

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2023-08-22T10:04:27Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of datadrivers watering can tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Jbddai/lora-trained-xl-colab_gieskanne_500it_lr_1e-4 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of datadrivers watering can using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ![harbour1.png](harbour1.png) ![plant.png](plant.png) ![beach1](beach1.png) ![beach2](beach2.png) ![detail1.png](detail1.png) ![garden1](garden1.png) ![garden2](garden2.png) ![advertising_campaign](advertising_campaign.png) ![detail1.png](detail1.png) ![office1.png](office1.png) ![office2.png](office2.png) ![gieskanne_office.png](gieskanne_office.png) ![ny_black_white.png](ny_black_white.png) ![times_square.png](times_square.png) ![tv_add.png](tv_add.png)

dkqjrm/20230822202040

dkqjrm

2023-08-22T13:20:05Z

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

2023-08-22T11:20:58Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202040' results: [] ---  # 20230822202040 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: 0.5208 - Accuracy: 0.7365 ## Model description More information needed ## 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 | 0.7722 | 0.5271 | | 0.7133 | 2.0 | 624 | 0.5588 | 0.4982 | | 0.7133 | 3.0 | 936 | 0.6273 | 0.4729 | | 0.6364 | 4.0 | 1248 | 0.5976 | 0.4946 | | 0.6219 | 5.0 | 1560 | 0.7382 | 0.5415 | | 0.6219 | 6.0 | 1872 | 0.5328 | 0.6282 | | 0.5974 | 7.0 | 2184 | 0.5253 | 0.6282 | | 0.5974 | 8.0 | 2496 | 0.8677 | 0.5668 | | 0.5614 | 9.0 | 2808 | 0.5249 | 0.5884 | | 0.5732 | 10.0 | 3120 | 0.5113 | 0.6895 | | 0.5732 | 11.0 | 3432 | 0.5092 | 0.6931 | | 0.5559 | 12.0 | 3744 | 0.4693 | 0.7148 | | 0.5301 | 13.0 | 4056 | 0.4781 | 0.7256 | | 0.5301 | 14.0 | 4368 | 0.5693 | 0.6823 | | 0.4999 | 15.0 | 4680 | 0.4649 | 0.7256 | | 0.4999 | 16.0 | 4992 | 0.5702 | 0.6859 | | 0.4712 | 17.0 | 5304 | 0.4598 | 0.7401 | | 0.4431 | 18.0 | 5616 | 0.4750 | 0.7076 | | 0.4431 | 19.0 | 5928 | 0.4782 | 0.7184 | | 0.4348 | 20.0 | 6240 | 0.6236 | 0.6570 | | 0.4113 | 21.0 | 6552 | 0.5125 | 0.7473 | | 0.4113 | 22.0 | 6864 | 0.5703 | 0.6787 | | 0.4035 | 23.0 | 7176 | 0.5080 | 0.7112 | | 0.4035 | 24.0 | 7488 | 0.4619 | 0.7365 | | 0.3898 | 25.0 | 7800 | 0.5639 | 0.7076 | | 0.3736 | 26.0 | 8112 | 0.4968 | 0.7292 | | 0.3736 | 27.0 | 8424 | 0.4483 | 0.7509 | | 0.3708 | 28.0 | 8736 | 0.4929 | 0.7220 | | 0.3656 | 29.0 | 9048 | 0.5168 | 0.7401 | | 0.3656 | 30.0 | 9360 | 0.5618 | 0.7256 | | 0.3545 | 31.0 | 9672 | 0.4900 | 0.7365 | | 0.3545 | 32.0 | 9984 | 0.4676 | 0.7256 | | 0.3474 | 33.0 | 10296 | 0.5222 | 0.7220 | | 0.3326 | 34.0 | 10608 | 0.4861 | 0.7437 | | 0.3326 | 35.0 | 10920 | 0.4560 | 0.7401 | | 0.3313 | 36.0 | 11232 | 0.5375 | 0.7256 | | 0.3209 | 37.0 | 11544 | 0.5606 | 0.7329 | | 0.3209 | 38.0 | 11856 | 0.5173 | 0.7401 | | 0.3169 | 39.0 | 12168 | 0.5060 | 0.7329 | | 0.3169 | 40.0 | 12480 | 0.5250 | 0.7365 | | 0.3096 | 41.0 | 12792 | 0.5133 | 0.7256 | | 0.3097 | 42.0 | 13104 | 0.5012 | 0.7437 | | 0.3097 | 43.0 | 13416 | 0.5274 | 0.7401 | | 0.3049 | 44.0 | 13728 | 0.5086 | 0.7329 | | 0.2929 | 45.0 | 14040 | 0.4934 | 0.7329 | | 0.2929 | 46.0 | 14352 | 0.5667 | 0.7401 | | 0.293 | 47.0 | 14664 | 0.5047 | 0.7437 | | 0.293 | 48.0 | 14976 | 0.5353 | 0.7292 | | 0.291 | 49.0 | 15288 | 0.5280 | 0.7401 | | 0.2817 | 50.0 | 15600 | 0.5142 | 0.7365 | | 0.2817 | 51.0 | 15912 | 0.5141 | 0.7329 | | 0.2822 | 52.0 | 16224 | 0.4990 | 0.7329 | | 0.2758 | 53.0 | 16536 | 0.5074 | 0.7292 | | 0.2758 | 54.0 | 16848 | 0.5147 | 0.7329 | | 0.2763 | 55.0 | 17160 | 0.5138 | 0.7365 | | 0.2763 | 56.0 | 17472 | 0.5291 | 0.7365 | | 0.2782 | 57.0 | 17784 | 0.5204 | 0.7329 | | 0.272 | 58.0 | 18096 | 0.5093 | 0.7365 | | 0.272 | 59.0 | 18408 | 0.5217 | 0.7365 | | 0.2758 | 60.0 | 18720 | 0.5208 | 0.7365 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Polo123/llama2-qlora-finetunined-task

Polo123

2023-08-22T13:19:45Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-22T13:19:12Z

--- 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 ### Framework versions - PEFT 0.6.0.dev0

dkqjrm/20230822202124

dkqjrm

2023-08-22T13:12:14Z

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

2023-08-22T11:21:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202124' results: [] ---  # 20230822202124 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: 0.4836 - Accuracy: 0.7437 ## Model description More information needed ## 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: 16 - 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 | 156 | 0.5548 | 0.4693 | | No log | 2.0 | 312 | 0.5565 | 0.4838 | | No log | 3.0 | 468 | 0.5531 | 0.4729 | | 0.6259 | 4.0 | 624 | 0.5810 | 0.4729 | | 0.6259 | 5.0 | 780 | 0.6010 | 0.5596 | | 0.6259 | 6.0 | 936 | 0.4969 | 0.6462 | | 0.5907 | 7.0 | 1092 | 0.7982 | 0.5487 | | 0.5907 | 8.0 | 1248 | 0.4883 | 0.6318 | | 0.5907 | 9.0 | 1404 | 0.4714 | 0.6931 | | 0.5602 | 10.0 | 1560 | 0.9236 | 0.5560 | | 0.5602 | 11.0 | 1716 | 0.4972 | 0.6968 | | 0.5602 | 12.0 | 1872 | 0.5116 | 0.6895 | | 0.5015 | 13.0 | 2028 | 0.4913 | 0.7076 | | 0.5015 | 14.0 | 2184 | 0.4683 | 0.7112 | | 0.5015 | 15.0 | 2340 | 0.5265 | 0.6895 | | 0.5015 | 16.0 | 2496 | 0.4616 | 0.7040 | | 0.4782 | 17.0 | 2652 | 0.5788 | 0.6679 | | 0.4782 | 18.0 | 2808 | 0.4471 | 0.7292 | | 0.4782 | 19.0 | 2964 | 0.4588 | 0.7545 | | 0.4628 | 20.0 | 3120 | 0.6477 | 0.6426 | | 0.4628 | 21.0 | 3276 | 0.5305 | 0.6968 | | 0.4628 | 22.0 | 3432 | 0.4549 | 0.7292 | | 0.4248 | 23.0 | 3588 | 0.5101 | 0.7256 | | 0.4248 | 24.0 | 3744 | 0.4763 | 0.7184 | | 0.4248 | 25.0 | 3900 | 0.5809 | 0.6895 | | 0.4067 | 26.0 | 4056 | 0.4461 | 0.7473 | | 0.4067 | 27.0 | 4212 | 0.4460 | 0.7473 | | 0.4067 | 28.0 | 4368 | 0.4454 | 0.7509 | | 0.3941 | 29.0 | 4524 | 0.4664 | 0.7365 | | 0.3941 | 30.0 | 4680 | 0.5039 | 0.7292 | | 0.3941 | 31.0 | 4836 | 0.4548 | 0.7473 | | 0.3941 | 32.0 | 4992 | 0.4484 | 0.7437 | | 0.3749 | 33.0 | 5148 | 0.4924 | 0.7473 | | 0.3749 | 34.0 | 5304 | 0.4569 | 0.7473 | | 0.3749 | 35.0 | 5460 | 0.4604 | 0.7617 | | 0.3586 | 36.0 | 5616 | 0.4448 | 0.7653 | | 0.3586 | 37.0 | 5772 | 0.4768 | 0.7365 | | 0.3586 | 38.0 | 5928 | 0.5052 | 0.7473 | | 0.3521 | 39.0 | 6084 | 0.5167 | 0.7329 | | 0.3521 | 40.0 | 6240 | 0.4425 | 0.7509 | | 0.3521 | 41.0 | 6396 | 0.4730 | 0.7545 | | 0.3407 | 42.0 | 6552 | 0.4624 | 0.7509 | | 0.3407 | 43.0 | 6708 | 0.4847 | 0.7509 | | 0.3407 | 44.0 | 6864 | 0.5371 | 0.7329 | | 0.3329 | 45.0 | 7020 | 0.4841 | 0.7545 | | 0.3329 | 46.0 | 7176 | 0.4815 | 0.7365 | | 0.3329 | 47.0 | 7332 | 0.4678 | 0.7509 | | 0.3329 | 48.0 | 7488 | 0.4918 | 0.7473 | | 0.3235 | 49.0 | 7644 | 0.4592 | 0.7581 | | 0.3235 | 50.0 | 7800 | 0.5005 | 0.7437 | | 0.3235 | 51.0 | 7956 | 0.4777 | 0.7545 | | 0.3193 | 52.0 | 8112 | 0.4558 | 0.7545 | | 0.3193 | 53.0 | 8268 | 0.4870 | 0.7437 | | 0.3193 | 54.0 | 8424 | 0.4792 | 0.7437 | | 0.3132 | 55.0 | 8580 | 0.4673 | 0.7437 | | 0.3132 | 56.0 | 8736 | 0.4943 | 0.7437 | | 0.3132 | 57.0 | 8892 | 0.4970 | 0.7437 | | 0.311 | 58.0 | 9048 | 0.4914 | 0.7401 | | 0.311 | 59.0 | 9204 | 0.4887 | 0.7437 | | 0.311 | 60.0 | 9360 | 0.4836 | 0.7437 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230822202056

dkqjrm

2023-08-22T13:11:55Z

106

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

2023-08-22T11:21:14Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202056' results: [] ---  # 20230822202056 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: 0.1724 - Accuracy: 0.7112 ## Model description More information needed ## 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 | 0.1785 | 0.5307 | | 0.2552 | 2.0 | 624 | 0.1826 | 0.5054 | | 0.2552 | 3.0 | 936 | 0.3328 | 0.4729 | | 0.24 | 4.0 | 1248 | 0.2050 | 0.4729 | | 0.2369 | 5.0 | 1560 | 0.1750 | 0.6065 | | 0.2369 | 6.0 | 1872 | 0.1752 | 0.4765 | | 0.2199 | 7.0 | 2184 | 0.1799 | 0.5921 | | 0.2199 | 8.0 | 2496 | 0.1896 | 0.4729 | | 0.1955 | 9.0 | 2808 | 0.1727 | 0.6245 | | 0.185 | 10.0 | 3120 | 0.1734 | 0.5668 | | 0.185 | 11.0 | 3432 | 0.1781 | 0.5812 | | 0.184 | 12.0 | 3744 | 0.1711 | 0.6318 | | 0.1819 | 13.0 | 4056 | 0.1783 | 0.4910 | | 0.1819 | 14.0 | 4368 | 0.1703 | 0.6534 | | 0.1793 | 15.0 | 4680 | 0.1697 | 0.6931 | | 0.1793 | 16.0 | 4992 | 0.1710 | 0.6643 | | 0.179 | 17.0 | 5304 | 0.1728 | 0.6534 | | 0.1784 | 18.0 | 5616 | 0.1712 | 0.6498 | | 0.1784 | 19.0 | 5928 | 0.1726 | 0.6065 | | 0.1778 | 20.0 | 6240 | 0.1720 | 0.6679 | | 0.1761 | 21.0 | 6552 | 0.1724 | 0.6606 | | 0.1761 | 22.0 | 6864 | 0.1792 | 0.6534 | | 0.1761 | 23.0 | 7176 | 0.1700 | 0.6715 | | 0.1761 | 24.0 | 7488 | 0.1698 | 0.6679 | | 0.1748 | 25.0 | 7800 | 0.1697 | 0.6968 | | 0.1744 | 26.0 | 8112 | 0.1729 | 0.6859 | | 0.1744 | 27.0 | 8424 | 0.1702 | 0.6570 | | 0.1736 | 28.0 | 8736 | 0.1708 | 0.6931 | | 0.1723 | 29.0 | 9048 | 0.1698 | 0.6787 | | 0.1723 | 30.0 | 9360 | 0.1799 | 0.6462 | | 0.1735 | 31.0 | 9672 | 0.1727 | 0.6751 | | 0.1735 | 32.0 | 9984 | 0.1732 | 0.6498 | | 0.1722 | 33.0 | 10296 | 0.1702 | 0.6751 | | 0.1709 | 34.0 | 10608 | 0.1707 | 0.6968 | | 0.1709 | 35.0 | 10920 | 0.1714 | 0.6968 | | 0.1697 | 36.0 | 11232 | 0.1712 | 0.6751 | | 0.1696 | 37.0 | 11544 | 0.1788 | 0.6570 | | 0.1696 | 38.0 | 11856 | 0.1703 | 0.6787 | | 0.1697 | 39.0 | 12168 | 0.1735 | 0.6751 | | 0.1697 | 40.0 | 12480 | 0.1740 | 0.6787 | | 0.1683 | 41.0 | 12792 | 0.1710 | 0.6895 | | 0.1688 | 42.0 | 13104 | 0.1724 | 0.7076 | | 0.1688 | 43.0 | 13416 | 0.1718 | 0.7004 | | 0.1679 | 44.0 | 13728 | 0.1736 | 0.7040 | | 0.1681 | 45.0 | 14040 | 0.1720 | 0.7040 | | 0.1681 | 46.0 | 14352 | 0.1717 | 0.7076 | | 0.1664 | 47.0 | 14664 | 0.1710 | 0.6895 | | 0.1664 | 48.0 | 14976 | 0.1766 | 0.6895 | | 0.1662 | 49.0 | 15288 | 0.1729 | 0.7040 | | 0.1655 | 50.0 | 15600 | 0.1704 | 0.7076 | | 0.1655 | 51.0 | 15912 | 0.1711 | 0.7184 | | 0.1665 | 52.0 | 16224 | 0.1709 | 0.7040 | | 0.1651 | 53.0 | 16536 | 0.1711 | 0.6931 | | 0.1651 | 54.0 | 16848 | 0.1736 | 0.7040 | | 0.1646 | 55.0 | 17160 | 0.1712 | 0.7112 | | 0.1646 | 56.0 | 17472 | 0.1740 | 0.7076 | | 0.1647 | 57.0 | 17784 | 0.1723 | 0.7076 | | 0.1642 | 58.0 | 18096 | 0.1715 | 0.7004 | | 0.1642 | 59.0 | 18408 | 0.1727 | 0.7076 | | 0.1643 | 60.0 | 18720 | 0.1724 | 0.7112 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230822202110

dkqjrm

2023-08-22T13:09:57Z

106

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

2023-08-22T11:21:27Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822202110' results: [] ---  # 20230822202110 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: 0.1679 - Accuracy: 0.7148 ## Model description More information needed ## 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: 16 - 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 | 156 | 0.4220 | 0.5271 | | No log | 2.0 | 312 | 0.2767 | 0.4729 | | No log | 3.0 | 468 | 0.4345 | 0.4729 | | 0.2507 | 4.0 | 624 | 0.2006 | 0.5343 | | 0.2507 | 5.0 | 780 | 0.1797 | 0.4729 | | 0.2507 | 6.0 | 936 | 0.2180 | 0.5271 | | 0.2023 | 7.0 | 1092 | 0.1726 | 0.5054 | | 0.2023 | 8.0 | 1248 | 0.1811 | 0.4729 | | 0.2023 | 9.0 | 1404 | 0.1828 | 0.5451 | | 0.2077 | 10.0 | 1560 | 0.1921 | 0.5343 | | 0.2077 | 11.0 | 1716 | 0.1772 | 0.4838 | | 0.2077 | 12.0 | 1872 | 0.1724 | 0.6462 | | 0.189 | 13.0 | 2028 | 0.1718 | 0.5379 | | 0.189 | 14.0 | 2184 | 0.1728 | 0.5126 | | 0.189 | 15.0 | 2340 | 0.1775 | 0.5126 | | 0.189 | 16.0 | 2496 | 0.1813 | 0.5596 | | 0.1803 | 17.0 | 2652 | 0.1739 | 0.6318 | | 0.1803 | 18.0 | 2808 | 0.1718 | 0.6137 | | 0.1803 | 19.0 | 2964 | 0.1711 | 0.6390 | | 0.1791 | 20.0 | 3120 | 0.1797 | 0.5957 | | 0.1791 | 21.0 | 3276 | 0.1710 | 0.6859 | | 0.1791 | 22.0 | 3432 | 0.1729 | 0.6643 | | 0.1781 | 23.0 | 3588 | 0.1701 | 0.6823 | | 0.1781 | 24.0 | 3744 | 0.1706 | 0.6390 | | 0.1781 | 25.0 | 3900 | 0.1708 | 0.6859 | | 0.1765 | 26.0 | 4056 | 0.1697 | 0.6643 | | 0.1765 | 27.0 | 4212 | 0.1698 | 0.6715 | | 0.1765 | 28.0 | 4368 | 0.1710 | 0.6426 | | 0.176 | 29.0 | 4524 | 0.1710 | 0.6931 | | 0.176 | 30.0 | 4680 | 0.1703 | 0.6968 | | 0.176 | 31.0 | 4836 | 0.1725 | 0.6570 | | 0.176 | 32.0 | 4992 | 0.1699 | 0.6715 | | 0.1749 | 33.0 | 5148 | 0.1710 | 0.6895 | | 0.1749 | 34.0 | 5304 | 0.1694 | 0.7220 | | 0.1749 | 35.0 | 5460 | 0.1700 | 0.6534 | | 0.1739 | 36.0 | 5616 | 0.1690 | 0.7112 | | 0.1739 | 37.0 | 5772 | 0.1685 | 0.7220 | | 0.1739 | 38.0 | 5928 | 0.1696 | 0.7040 | | 0.1738 | 39.0 | 6084 | 0.1688 | 0.7148 | | 0.1738 | 40.0 | 6240 | 0.1692 | 0.7220 | | 0.1738 | 41.0 | 6396 | 0.1683 | 0.7365 | | 0.1726 | 42.0 | 6552 | 0.1690 | 0.6679 | | 0.1726 | 43.0 | 6708 | 0.1679 | 0.7076 | | 0.1726 | 44.0 | 6864 | 0.1691 | 0.7184 | | 0.1719 | 45.0 | 7020 | 0.1692 | 0.7292 | | 0.1719 | 46.0 | 7176 | 0.1685 | 0.7329 | | 0.1719 | 47.0 | 7332 | 0.1684 | 0.7184 | | 0.1719 | 48.0 | 7488 | 0.1690 | 0.7112 | | 0.1712 | 49.0 | 7644 | 0.1690 | 0.7292 | | 0.1712 | 50.0 | 7800 | 0.1685 | 0.6931 | | 0.1712 | 51.0 | 7956 | 0.1680 | 0.7256 | | 0.1705 | 52.0 | 8112 | 0.1687 | 0.7076 | | 0.1705 | 53.0 | 8268 | 0.1685 | 0.7184 | | 0.1705 | 54.0 | 8424 | 0.1689 | 0.7365 | | 0.1705 | 55.0 | 8580 | 0.1677 | 0.7148 | | 0.1705 | 56.0 | 8736 | 0.1694 | 0.7220 | | 0.1705 | 57.0 | 8892 | 0.1682 | 0.7256 | | 0.1692 | 58.0 | 9048 | 0.1684 | 0.7148 | | 0.1692 | 59.0 | 9204 | 0.1679 | 0.7148 | | 0.1692 | 60.0 | 9360 | 0.1679 | 0.7148 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

kaanhho/speecht5_finetuned_voxpopuli_it

kaanhho

2023-08-22T13:07:13Z

86

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-08-22T12:08:05Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_it results: [] ---  # speecht5_finetuned_voxpopuli_it This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.5724 ## Model description More information needed ## 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: 1 - eval_batch_size: 2 - 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 - training_steps: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5644 | 1.53 | 1000 | 0.5845 | | 0.5521 | 3.07 | 2000 | 0.5724 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

anshudaur/chair_model_with_prior_preservation

anshudaur

2023-08-22T13:05:37Z

12

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-08-22T12:48:20Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: photo of a <new1> chair tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - anshudaur/chair_model_with_prior_preservation These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> chair using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

akar49/detr-crack-II

akar49

2023-08-22T12:54:57Z

34

0

transformers

[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:crack_detection-merged-ii", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-08-22T11:09:26Z

--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - crack_detection-merged-ii model-index: - name: detr-crack-II results: [] ---  # detr-crack-II This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the crack_detection-merged-ii 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

barbieheimer/MND_TweetEvalBert_model

barbieheimer

2023-08-22T12:40:58Z

107

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "en", "dataset:tweet_eval", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-19T05:33:08Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - tweet_eval model-index: - name: MND_TweetEvalBert_model results: [] language: - en pipeline_tag: text-classification metrics: - accuracy widget: - text: 'I loved Barbie and Oppenheimer' example_title: Barbenheimer ---  # MND_TweetEvalBert_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.7241 ## Model description This is how to use the model with the transformer library to do a text classification task. This model was trained and built for sentiment analysis with a text classification model architecture. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model") model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model") # We can now use the model in the pipeline. classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) # Get some text to fool around with for a basic test. text = "I loved Oppenheimer and Barbie " classifier(text) # Let's see if the model works on our example text. ``` ``` [{'label': 'JOY', 'score': 0.9845513701438904}] ``` ## Training Evalutation Results ```python {'eval_loss': 0.7240552306175232, 'eval_runtime': 3.7803, 'eval_samples_per_second': 375.896, 'eval_steps_per_second': 23.543, 'epoch': 5.0} ``` ## Overall Model Evaluation Results ```python {'accuracy': {'confidence_interval': (0.783, 0.832), 'standard_error': 0.01241992329458207, 'score': 0.808}, 'total_time_in_seconds': 150.93268656500004, 'samples_per_second': 6.625470087086432, 'latency_in_seconds': 0.15093268656500003} ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ```python {'training_loss'=0.3821827131159165} {'train_runtime': 174.1546, 'train_samples_per_second': 93.509, 'train_steps_per_second': 5.857, 'total_flos': 351397804992312.0, 'train_loss': 0.3821827131159165, 'epoch': 5.0} ``` ``` Step: 500 {training loss: 0.607100} Step: 1000 {training loss: 0.169000} ``` ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

GrantW65/ppo-Huggy

GrantW65

2023-08-22T12:40:40Z

0

ml-agents

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

reinforcement-learning

2023-08-22T12:40:35Z

--- 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: GrantW65/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

natsusakiyomi/SakuraMix

natsusakiyomi

2023-08-22T12:30:44Z

115

70

diffusers

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

text-to-image

2023-03-17T17:37:21Z

--- license: creativeml-openrail-m language: - ja pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image library_name: diffusers --- <div class="flex justify-center"> <div class="container p-0 w-100"> <img class="mt-0 object-cover rounded-t-lg w-100" style="height: 320px;" src="https://pbs.twimg.com/media/Fwzt7HZaEAAkX2U?format=jpg" width="100%"/> <div class="flex px-4"> <div class="flex-auto"> <h1 class="mb-2 text-3xl font-bold leading-tight" style="color: rgb(252, 238, 235/var(--tw-text-opacity));"> SakuraMixSeries </h1> <p class="mb-4 text-base text-neutral-600 dark:text-neutral-200"> 背景とキャラクタークオリティーを両立させたVAE内蔵型モデル<br> Model with built-in VAE for both background and character quality </p> </div> <div> <a href="https://twitter.com/min__san" class="mb-2 inline-block rounded px-6 py-2.5 text-white shadow-md" style="background-color: #1da1f2"> <svg xmlns="http://www.w3.org/2000/svg" class="h-3.5 w-3.5" fill="currentColor" viewBox="0 0 24 24"> <path d="M24 4.557c-.883.392-1.832.656-2.828.775 1.017-.609 1.798-1.574 2.165-2.724-.951.564-2.005.974-3.127 1.195-.897-.957-2.178-1.555-3.594-1.555-3.179 0-5.515 2.966-4.797 6.045-4.091-.205-7.719-2.165-10.148-5.144-1.29 2.213-.669 5.108 1.523 6.574-.806-.026-1.566-.247-2.229-.616-.054 2.281 1.581 4.415 3.949 4.89-.693.188-1.452.232-2.224.084.626 1.956 2.444 3.379 4.6 3.419-2.07 1.623-4.678 2.348-7.29 2.04 2.179 1.397 4.768 2.212 7.548 2.212 9.142 0 14.307-7.721 13.995-14.646.962-.695 1.797-1.562 2.457-2.549z" /> </svg> </a> </div> </div> </div> </div> --- <h4>📄 ライセンス / License</h4> <div class="px-2"> <table class="table-fixed border mt-0 text-xs"> <tbody> <tr> <td class="px-4 text-base" colspan="2"> <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license"> 修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license </a> </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルのクレジットを入れずに使用する<br> Use the model without crediting the creator </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルで生成した画像を商用利用する<br> Sell images they generate </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルを商用の画像生成サービスで利用する</br> Run on services that generate images for money </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルを使用したマージモデルを共有する<br> Share merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデル、またはこのモデルをマージしたモデルを販売する</br> Sell this model or merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルをマージしたモデルに異なる権限を設定する</br> Have different permissions when sharing merges </td> </tr> </tbody> </table> </div> <h3 id="blue_pencil-v7" class="mt-0 text-2xl"> <code>SakuraMix-v4</code> <small></small> </h3> <div> v3の改修モデル全体的に手や破綻の少なくなったモデル<br> 若干書き込み量が減ったような気がするので昔からSakuraMix好きな人はflat loraを使うことを推奨 <hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));"> <h3 id="blue_pencil-v7" class="mt-0 text-2xl"> <code>SakuraMix-v3</code> <small></small> </h3> <div> v2の改修モデル服装や構図が前よりも増えた気がする破綻しやすいがいいものが生成できるときはとてもいいものが生成できる<br> 個人的にはv2をお勧めします <hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));"> <h3 id="SakuraMix-v2" class="mt-0 text-2xl"> <code>SakuraMix-v2</code> <small></small> </h3> <div> HimawariMix-v2B(没案)を改造したモデル<br> HimawariMix-v2自体character自体を強化したモデルだがさらにキャラを強くしたモデル <hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));"> <h3 id="SakuraMix-v1" class="mt-0 text-2xl"> <code>SakuraMix-v1</code> <small></small> </h3> <div> 初代SakuraMix 特徴とか知らん忘れた<br> --- # 作者&連絡先 Twiter: [@min__san](https://twitter.com/min__san)<br> mail: ([email protected])

asenella/ms_config_1_alpha_10_beta_250_seed_1

asenella

2023-08-22T12:29:55Z

0

null

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

null

2023-08-22T12:29:53Z

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

kevinassemi/WaterWizard

kevinassemi

2023-08-22T12:19:21Z

0

null

[ "text-generation", "en", "license:llama2", "region:us" ]

text-generation

2023-08-22T12:17:16Z

--- license: llama2 language: - en pipeline_tag: text-generation ---

Muhammadreza/mann-e-bitmap-revised-2

Muhammadreza

2023-08-22T12:14:16Z

4

0

diffusers

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

text-to-image

2023-08-22T12:01:29Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mann-e_bitmap_revised-2 Dreambooth model trained by Muhammadreza with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

Saurabh16100/distilgpt2-finetuned-wikitext2

Saurabh16100

2023-08-22T12:13:32Z

204

0

transformers

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

text-generation

2023-08-22T11:28:20Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7602 | 1.0 | 2334 | 3.6669 | | 3.653 | 2.0 | 4668 | 3.6472 | | 3.6006 | 3.0 | 7002 | 3.6421 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

loicspigeleer/PPO-LunarLander-v2

loicspigeleer

2023-08-22T11:56:15Z

4

0

stable-baselines3

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

reinforcement-learning

2023-08-22T11:55:51Z

--- 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: 272.06 +/- 16.19 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 ... ```

EliKet/lora-trained-xl-colab

EliKet

2023-08-22T11:53:44Z

2

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-0.9", "base_model:adapter:stabilityai/stable-diffusion-xl-base-0.9", "license:openrail++", "region:us" ]

text-to-image

2023-08-17T09:17:36Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-0.9 instance_prompt: a photo of model tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - EliKet/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-0.9. The weights were trained on a photo of model using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

Arup-Dutta-Bappy/bert-large-cased-whole-word-masking-finetuned-squad

Arup-Dutta-Bappy

2023-08-22T11:50:57Z

121

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:google-bert/bert-large-cased-whole-word-masking", "base_model:finetune:google-bert/bert-large-cased-whole-word-masking", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-13T17:53:28Z

--- license: apache-2.0 base_model: bert-large-cased-whole-word-masking tags: - generated_from_trainer datasets: - squad model-index: - name: bert-large-cased-whole-word-masking-finetuned-squad results: [] ---  # bert-large-cased-whole-word-masking-finetuned-squad This model is a fine-tuned version of [bert-large-cased-whole-word-masking](https://huggingface.co/bert-large-cased-whole-word-masking) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

dkqjrm/20230822185044

dkqjrm

2023-08-22T11:47:15Z

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

2023-08-22T09:51:01Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822185044' results: [] ---  # 20230822185044 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: 0.3482 - Accuracy: 0.4729 ## Model description More information needed ## 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 | 0.3580 | 0.5379 | | 0.5102 | 2.0 | 624 | 0.3670 | 0.5415 | | 0.5102 | 3.0 | 936 | 0.4888 | 0.4765 | | 0.4569 | 4.0 | 1248 | 0.3742 | 0.4982 | | 0.4403 | 5.0 | 1560 | 0.3796 | 0.5379 | | 0.4403 | 6.0 | 1872 | 0.3602 | 0.5776 | | 0.4215 | 7.0 | 2184 | 0.4013 | 0.5415 | | 0.4215 | 8.0 | 2496 | 0.3596 | 0.5884 | | 0.4166 | 9.0 | 2808 | 0.3447 | 0.5487 | | 0.3885 | 10.0 | 3120 | 0.3395 | 0.6101 | | 0.3885 | 11.0 | 3432 | 0.3395 | 0.6354 | | 0.3776 | 12.0 | 3744 | 0.3568 | 0.5343 | | 0.4274 | 13.0 | 4056 | 0.5923 | 0.4729 | | 0.4274 | 14.0 | 4368 | 0.3503 | 0.5668 | | 0.4138 | 15.0 | 4680 | 0.3605 | 0.5523 | | 0.4138 | 16.0 | 4992 | 0.3491 | 0.5451 | | 0.4025 | 17.0 | 5304 | 0.3728 | 0.5379 | | 0.394 | 18.0 | 5616 | 0.4029 | 0.4729 | | 0.394 | 19.0 | 5928 | 0.3682 | 0.4729 | | 0.3892 | 20.0 | 6240 | 0.3484 | 0.5054 | | 0.3839 | 21.0 | 6552 | 0.3485 | 0.4765 | | 0.3839 | 22.0 | 6864 | 0.3467 | 0.5343 | | 0.3782 | 23.0 | 7176 | 0.3471 | 0.5307 | | 0.3782 | 24.0 | 7488 | 0.3565 | 0.4693 | | 0.3757 | 25.0 | 7800 | 0.3483 | 0.5343 | | 0.3737 | 26.0 | 8112 | 0.3495 | 0.5271 | | 0.3737 | 27.0 | 8424 | 0.3550 | 0.4729 | | 0.3724 | 28.0 | 8736 | 0.3544 | 0.4729 | | 0.3696 | 29.0 | 9048 | 0.3478 | 0.5307 | | 0.3696 | 30.0 | 9360 | 0.3519 | 0.5271 | | 0.3693 | 31.0 | 9672 | 0.3515 | 0.5271 | | 0.3693 | 32.0 | 9984 | 0.3487 | 0.4729 | | 0.3674 | 33.0 | 10296 | 0.3492 | 0.5379 | | 0.3628 | 34.0 | 10608 | 0.3555 | 0.4729 | | 0.3628 | 35.0 | 10920 | 0.3550 | 0.4729 | | 0.3635 | 36.0 | 11232 | 0.3686 | 0.4729 | | 0.3636 | 37.0 | 11544 | 0.3488 | 0.4801 | | 0.3636 | 38.0 | 11856 | 0.3484 | 0.4874 | | 0.3595 | 39.0 | 12168 | 0.3477 | 0.4910 | | 0.3595 | 40.0 | 12480 | 0.3486 | 0.5307 | | 0.3598 | 41.0 | 12792 | 0.3488 | 0.4801 | | 0.3594 | 42.0 | 13104 | 0.3614 | 0.4729 | | 0.3594 | 43.0 | 13416 | 0.3476 | 0.5199 | | 0.3586 | 44.0 | 13728 | 0.3482 | 0.4729 | | 0.3581 | 45.0 | 14040 | 0.3519 | 0.4729 | | 0.3581 | 46.0 | 14352 | 0.3494 | 0.4729 | | 0.3579 | 47.0 | 14664 | 0.3613 | 0.4729 | | 0.3579 | 48.0 | 14976 | 0.3480 | 0.4729 | | 0.3573 | 49.0 | 15288 | 0.3480 | 0.4729 | | 0.3564 | 50.0 | 15600 | 0.3487 | 0.4729 | | 0.3564 | 51.0 | 15912 | 0.3529 | 0.4729 | | 0.3561 | 52.0 | 16224 | 0.3515 | 0.4729 | | 0.3554 | 53.0 | 16536 | 0.3475 | 0.4946 | | 0.3554 | 54.0 | 16848 | 0.3489 | 0.5271 | | 0.3535 | 55.0 | 17160 | 0.3488 | 0.4729 | | 0.3535 | 56.0 | 17472 | 0.3478 | 0.5018 | | 0.3542 | 57.0 | 17784 | 0.3491 | 0.4729 | | 0.354 | 58.0 | 18096 | 0.3485 | 0.4729 | | 0.354 | 59.0 | 18408 | 0.3483 | 0.4729 | | 0.3529 | 60.0 | 18720 | 0.3482 | 0.4729 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230822185237

dkqjrm

2023-08-22T11:44:15Z

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

2023-08-22T09:52:55Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822185237' results: [] ---  # 20230822185237 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: 0.3335 - Accuracy: 0.6498 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 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 | 0.3589 | 0.5415 | | 0.4381 | 2.0 | 624 | 0.3585 | 0.5560 | | 0.4381 | 3.0 | 936 | 0.4824 | 0.4729 | | 0.4251 | 4.0 | 1248 | 0.3497 | 0.5740 | | 0.4013 | 5.0 | 1560 | 0.5515 | 0.5307 | | 0.4013 | 6.0 | 1872 | 0.5300 | 0.5343 | | 0.4064 | 7.0 | 2184 | 0.3515 | 0.4982 | | 0.4064 | 8.0 | 2496 | 0.3456 | 0.5704 | | 0.4121 | 9.0 | 2808 | 0.3522 | 0.5632 | | 0.4048 | 10.0 | 3120 | 0.3437 | 0.5632 | | 0.4048 | 11.0 | 3432 | 0.3483 | 0.5668 | | 0.4035 | 12.0 | 3744 | 0.3952 | 0.4657 | | 0.3797 | 13.0 | 4056 | 0.3535 | 0.4801 | | 0.3797 | 14.0 | 4368 | 0.3443 | 0.5993 | | 0.3657 | 15.0 | 4680 | 0.3431 | 0.5379 | | 0.3657 | 16.0 | 4992 | 0.3478 | 0.5993 | | 0.3615 | 17.0 | 5304 | 0.3475 | 0.6173 | | 0.3573 | 18.0 | 5616 | 0.3539 | 0.6101 | | 0.3573 | 19.0 | 5928 | 0.3384 | 0.6101 | | 0.3552 | 20.0 | 6240 | 0.3483 | 0.6245 | | 0.3545 | 21.0 | 6552 | 0.3359 | 0.6173 | | 0.3545 | 22.0 | 6864 | 0.3844 | 0.5740 | | 0.349 | 23.0 | 7176 | 0.3436 | 0.6498 | | 0.349 | 24.0 | 7488 | 0.3422 | 0.6209 | | 0.351 | 25.0 | 7800 | 0.3495 | 0.6318 | | 0.3471 | 26.0 | 8112 | 0.3498 | 0.6101 | | 0.3471 | 27.0 | 8424 | 0.3316 | 0.6462 | | 0.3468 | 28.0 | 8736 | 0.3322 | 0.6751 | | 0.3459 | 29.0 | 9048 | 0.3354 | 0.6390 | | 0.3459 | 30.0 | 9360 | 0.3353 | 0.6390 | | 0.344 | 31.0 | 9672 | 0.3383 | 0.6354 | | 0.344 | 32.0 | 9984 | 0.3329 | 0.6245 | | 0.3435 | 33.0 | 10296 | 0.3411 | 0.6390 | | 0.3408 | 34.0 | 10608 | 0.3414 | 0.6354 | | 0.3408 | 35.0 | 10920 | 0.3319 | 0.6534 | | 0.3401 | 36.0 | 11232 | 0.3347 | 0.6282 | | 0.3406 | 37.0 | 11544 | 0.3382 | 0.6137 | | 0.3406 | 38.0 | 11856 | 0.3355 | 0.6245 | | 0.3378 | 39.0 | 12168 | 0.3416 | 0.6245 | | 0.3378 | 40.0 | 12480 | 0.3422 | 0.6209 | | 0.3386 | 41.0 | 12792 | 0.3388 | 0.6390 | | 0.3362 | 42.0 | 13104 | 0.3330 | 0.6390 | | 0.3362 | 43.0 | 13416 | 0.3393 | 0.6282 | | 0.3373 | 44.0 | 13728 | 0.3340 | 0.6282 | | 0.3337 | 45.0 | 14040 | 0.3318 | 0.6390 | | 0.3337 | 46.0 | 14352 | 0.3323 | 0.6354 | | 0.3332 | 47.0 | 14664 | 0.3301 | 0.6643 | | 0.3332 | 48.0 | 14976 | 0.3422 | 0.6282 | | 0.3315 | 49.0 | 15288 | 0.3348 | 0.6570 | | 0.33 | 50.0 | 15600 | 0.3366 | 0.6462 | | 0.33 | 51.0 | 15912 | 0.3308 | 0.6570 | | 0.331 | 52.0 | 16224 | 0.3298 | 0.6606 | | 0.3295 | 53.0 | 16536 | 0.3377 | 0.6498 | | 0.3295 | 54.0 | 16848 | 0.3439 | 0.6462 | | 0.3282 | 55.0 | 17160 | 0.3326 | 0.6570 | | 0.3282 | 56.0 | 17472 | 0.3356 | 0.6498 | | 0.3291 | 57.0 | 17784 | 0.3309 | 0.6570 | | 0.3278 | 58.0 | 18096 | 0.3333 | 0.6498 | | 0.3278 | 59.0 | 18408 | 0.3324 | 0.6498 | | 0.3292 | 60.0 | 18720 | 0.3335 | 0.6498 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230822185221

dkqjrm

2023-08-22T11:41:38Z

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

2023-08-22T09:52:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822185221' results: [] ---  # 20230822185221 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: 0.3289 - 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: 0.002 - 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 | 0.5077 | 0.5307 | | 0.4439 | 2.0 | 624 | 0.3971 | 0.4874 | | 0.4439 | 3.0 | 936 | 0.3574 | 0.5379 | | 0.4231 | 4.0 | 1248 | 0.3625 | 0.5776 | | 0.4071 | 5.0 | 1560 | 0.4937 | 0.5343 | | 0.4071 | 6.0 | 1872 | 0.3738 | 0.5668 | | 0.3956 | 7.0 | 2184 | 0.4081 | 0.4729 | | 0.3956 | 8.0 | 2496 | 0.3386 | 0.6209 | | 0.3905 | 9.0 | 2808 | 0.4147 | 0.4729 | | 0.3888 | 10.0 | 3120 | 0.3353 | 0.6354 | | 0.3888 | 11.0 | 3432 | 0.3540 | 0.6282 | | 0.3992 | 12.0 | 3744 | 0.3453 | 0.5848 | | 0.372 | 13.0 | 4056 | 0.3265 | 0.6895 | | 0.372 | 14.0 | 4368 | 0.3575 | 0.6426 | | 0.3643 | 15.0 | 4680 | 0.3304 | 0.6498 | | 0.3643 | 16.0 | 4992 | 0.3633 | 0.6715 | | 0.3666 | 17.0 | 5304 | 0.5230 | 0.5343 | | 0.3517 | 18.0 | 5616 | 0.3384 | 0.6462 | | 0.3517 | 19.0 | 5928 | 0.3293 | 0.6823 | | 0.3519 | 20.0 | 6240 | 0.3613 | 0.6823 | | 0.338 | 21.0 | 6552 | 0.3242 | 0.7256 | | 0.338 | 22.0 | 6864 | 0.3399 | 0.7184 | | 0.3316 | 23.0 | 7176 | 0.3392 | 0.7004 | | 0.3316 | 24.0 | 7488 | 0.3343 | 0.6534 | | 0.3266 | 25.0 | 7800 | 0.3467 | 0.7112 | | 0.3213 | 26.0 | 8112 | 0.3419 | 0.7040 | | 0.3213 | 27.0 | 8424 | 0.3190 | 0.7112 | | 0.3177 | 28.0 | 8736 | 0.3205 | 0.6931 | | 0.3187 | 29.0 | 9048 | 0.3303 | 0.7076 | | 0.3187 | 30.0 | 9360 | 0.3268 | 0.7148 | | 0.3162 | 31.0 | 9672 | 0.3274 | 0.7148 | | 0.3162 | 32.0 | 9984 | 0.3311 | 0.7112 | | 0.3132 | 33.0 | 10296 | 0.3454 | 0.7148 | | 0.3087 | 34.0 | 10608 | 0.3250 | 0.7076 | | 0.3087 | 35.0 | 10920 | 0.3266 | 0.7076 | | 0.3076 | 36.0 | 11232 | 0.3347 | 0.7292 | | 0.3071 | 37.0 | 11544 | 0.3308 | 0.7112 | | 0.3071 | 38.0 | 11856 | 0.3272 | 0.7220 | | 0.3061 | 39.0 | 12168 | 0.3301 | 0.7148 | | 0.3061 | 40.0 | 12480 | 0.3226 | 0.7256 | | 0.3006 | 41.0 | 12792 | 0.3285 | 0.7365 | | 0.3016 | 42.0 | 13104 | 0.3226 | 0.7148 | | 0.3016 | 43.0 | 13416 | 0.3291 | 0.7220 | | 0.2984 | 44.0 | 13728 | 0.3377 | 0.7112 | | 0.2976 | 45.0 | 14040 | 0.3326 | 0.7220 | | 0.2976 | 46.0 | 14352 | 0.3341 | 0.7292 | | 0.2967 | 47.0 | 14664 | 0.3187 | 0.7184 | | 0.2967 | 48.0 | 14976 | 0.3322 | 0.7148 | | 0.2953 | 49.0 | 15288 | 0.3269 | 0.7365 | | 0.2911 | 50.0 | 15600 | 0.3256 | 0.7365 | | 0.2911 | 51.0 | 15912 | 0.3252 | 0.7256 | | 0.2929 | 52.0 | 16224 | 0.3251 | 0.7292 | | 0.2904 | 53.0 | 16536 | 0.3258 | 0.7256 | | 0.2904 | 54.0 | 16848 | 0.3358 | 0.7220 | | 0.2895 | 55.0 | 17160 | 0.3219 | 0.7329 | | 0.2895 | 56.0 | 17472 | 0.3322 | 0.7329 | | 0.2887 | 57.0 | 17784 | 0.3259 | 0.7365 | | 0.2883 | 58.0 | 18096 | 0.3260 | 0.7292 | | 0.2883 | 59.0 | 18408 | 0.3276 | 0.7365 | | 0.2874 | 60.0 | 18720 | 0.3289 | 0.7329 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AhmedTaha012/finance-ner-v0.0.2-finetuned-ner

AhmedTaha012

2023-08-22T11:39:45Z

103

0

transformers

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

token-classification

2023-08-22T10:33:22Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finance-ner-v0.0.2-finetuned-ner results: [] ---  # finance-ner-v0.0.2-finetuned-ner This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0004 - Precision: 0.9945 - Recall: 1.0 - F1: 0.9972 - Accuracy: 0.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0002 | 1.0 | 551 | 0.0011 | 0.9850 | 0.9940 | 0.9895 | 0.9997 | | 0.0 | 2.0 | 1102 | 0.0006 | 0.9900 | 0.9991 | 0.9945 | 0.9999 | | 0.0 | 3.0 | 1653 | 0.0005 | 0.9953 | 0.9991 | 0.9972 | 0.9999 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

viprav/llama2-quote-1-row

viprav

2023-08-22T11:38:59Z

0

peft

[ "peft", "region:us" ]

null

2023-08-22T11:38:51Z

--- 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 ### Framework versions - PEFT 0.5.0.dev0

lengoctuong/gpt2-finetuned-chatbot

lengoctuong

2023-08-22T11:38:39Z

139

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-22T11:34:55Z

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-chatbot results: [] ---  # gpt2-chatbot This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

anshudaur/cat_model_small_lr

anshudaur

2023-08-22T11:29:13Z

5

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-08-22T09:52:51Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: photo of a <new1> cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - anshudaur/cat_model_small_lr These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).

Nebyx/LunarLanderunit8

Nebyx

2023-08-22T11:25:12Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-08-22T11:25:07Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -128.72 +/- 76.26 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Nebyx/LunarLanderunit8' 'batch_size': 512 'minibatch_size': 128} ```

stalker331333/my-pet-cat

stalker331333

2023-08-22T11:19:37Z

41

0

diffusers

[ "diffusers", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-22T11:16:01Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat Dreambooth model trained by stalker331333 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/stalker331333/my-pet-cat/resolve/main/sample_images/a4056cc6-bf04-450e-9ed4-eb57a73ae4cb.jpeg)

elit333/newstable

elit333

2023-08-22T11:15:25Z

5

1

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-22T10:20:12Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true extra_gated_prompt: |- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

deepsdh99/llama2-qlora-finetunined-8

deepsdh99

2023-08-22T11:05:58Z

0

peft

[ "peft", "region:us" ]

null

2023-08-22T11:05:30Z

--- 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

MattStammers/dqn-SpaceInvadersNoFrameskip-v4

MattStammers

2023-08-22T11:04:08Z

0

stable-baselines3

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

reinforcement-learning

2023-08-22T11:03:00Z

--- 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: 710.50 +/- 398.67 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 MattStammers -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 MattStammers -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 MattStammers ``` ## 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', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

OpenBuddy/openbuddy-openllama-3b-v10-bf16

OpenBuddy

2023-08-22T10:51:04Z

1,568

8

transformers

[ "transformers", "pytorch", "llama", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-08-10T13:37:46Z

--- language: - zh - en - fr - de - ja - ko - it - ru pipeline_tag: text-generation inference: false library_name: transformers license: apache-2.0 --- # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Copyright Notice License: Apache 2.0. ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。

roa7n/gpt2-human_nontata_promoters-rng_ep8

roa7n

2023-08-22T10:51:03Z

0

peft

[ "peft", "region:us" ]

null

2023-08-22T10:51:00Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

kasperchen/a2c-PandaReachDense-v3

kasperchen

2023-08-22T10:49:40Z

0

stable-baselines3

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

reinforcement-learning

2023-08-22T03:13:36Z

--- 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 ... ```

ankur24022002/test1

ankur24022002

2023-08-22T10:44:57Z

0

peft

[ "peft", "pytorch", "opt", "region:us" ]

null

2023-08-22T08:27:08Z

--- 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 ### Framework versions - PEFT 0.5.0.dev0

jwhandley/setfit-gb-manifestos

jwhandley

2023-08-22T10:38:48Z

3

0

sentence-transformers

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

text-classification

2023-08-21T23:56:51Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # jwhandley/setfit-gb-manifestos 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("jwhandley/setfit-gb-manifestos") # 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} } ```

MayankAmrit/my-pet-dog

MayankAmrit

2023-08-22T10:26:54Z

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-22T10:13:31Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by MayankAmrit following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/MayankAmrit/my-pet-dog/resolve/main/sample_images/ad0676a7-604d-435f-a0e9-9a781de0ae2f.jpeg)

dkqjrm/20230822173808

dkqjrm

2023-08-22T10:26:48Z

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

2023-08-22T08:38:26Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822173808' results: [] ---  # 20230822173808 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: 0.3493 - Accuracy: 0.6968 ## Model description More information needed ## 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: 0.004 - 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 | 0.3774 | 0.5162 | | 0.5343 | 2.0 | 624 | 0.3506 | 0.5018 | | 0.5343 | 3.0 | 936 | 0.4575 | 0.4729 | | 0.4659 | 4.0 | 1248 | 0.3759 | 0.5307 | | 0.4691 | 5.0 | 1560 | 0.3500 | 0.5812 | | 0.4691 | 6.0 | 1872 | 0.3457 | 0.5993 | | 0.4442 | 7.0 | 2184 | 0.3500 | 0.6101 | | 0.4442 | 8.0 | 2496 | 0.3403 | 0.6173 | | 0.4366 | 9.0 | 2808 | 0.3840 | 0.5776 | | 0.4097 | 10.0 | 3120 | 0.4391 | 0.5487 | | 0.4097 | 11.0 | 3432 | 0.3584 | 0.6029 | | 0.3922 | 12.0 | 3744 | 0.3356 | 0.6498 | | 0.3564 | 13.0 | 4056 | 0.3275 | 0.6931 | | 0.3564 | 14.0 | 4368 | 0.3283 | 0.7076 | | 0.3343 | 15.0 | 4680 | 0.3377 | 0.6462 | | 0.3343 | 16.0 | 4992 | 0.3550 | 0.6390 | | 0.335 | 17.0 | 5304 | 0.3370 | 0.6895 | | 0.3233 | 18.0 | 5616 | 0.3256 | 0.6787 | | 0.3233 | 19.0 | 5928 | 0.3174 | 0.7112 | | 0.3232 | 20.0 | 6240 | 0.3440 | 0.6643 | | 0.3102 | 21.0 | 6552 | 0.3375 | 0.6895 | | 0.3102 | 22.0 | 6864 | 0.3433 | 0.6787 | | 0.3064 | 23.0 | 7176 | 0.3690 | 0.6715 | | 0.3064 | 24.0 | 7488 | 0.3394 | 0.6931 | | 0.3004 | 25.0 | 7800 | 0.3377 | 0.7256 | | 0.2962 | 26.0 | 8112 | 0.3435 | 0.6751 | | 0.2962 | 27.0 | 8424 | 0.3182 | 0.7329 | | 0.2937 | 28.0 | 8736 | 0.3306 | 0.7112 | | 0.2905 | 29.0 | 9048 | 0.3362 | 0.7148 | | 0.2905 | 30.0 | 9360 | 0.3675 | 0.6751 | | 0.2865 | 31.0 | 9672 | 0.3406 | 0.7076 | | 0.2865 | 32.0 | 9984 | 0.3343 | 0.7040 | | 0.2812 | 33.0 | 10296 | 0.3472 | 0.6859 | | 0.2727 | 34.0 | 10608 | 0.3372 | 0.7292 | | 0.2727 | 35.0 | 10920 | 0.3575 | 0.7076 | | 0.2735 | 36.0 | 11232 | 0.3300 | 0.7076 | | 0.2701 | 37.0 | 11544 | 0.3585 | 0.6968 | | 0.2701 | 38.0 | 11856 | 0.3422 | 0.7148 | | 0.2688 | 39.0 | 12168 | 0.3579 | 0.6931 | | 0.2688 | 40.0 | 12480 | 0.3326 | 0.7148 | | 0.2644 | 41.0 | 12792 | 0.3464 | 0.7256 | | 0.2637 | 42.0 | 13104 | 0.3579 | 0.6931 | | 0.2637 | 43.0 | 13416 | 0.3489 | 0.7040 | | 0.26 | 44.0 | 13728 | 0.3439 | 0.7076 | | 0.2582 | 45.0 | 14040 | 0.3585 | 0.7004 | | 0.2582 | 46.0 | 14352 | 0.3535 | 0.7076 | | 0.2533 | 47.0 | 14664 | 0.3440 | 0.7148 | | 0.2533 | 48.0 | 14976 | 0.3506 | 0.7040 | | 0.2535 | 49.0 | 15288 | 0.3519 | 0.7040 | | 0.2498 | 50.0 | 15600 | 0.3457 | 0.6931 | | 0.2498 | 51.0 | 15912 | 0.3494 | 0.7112 | | 0.2504 | 52.0 | 16224 | 0.3431 | 0.7040 | | 0.2499 | 53.0 | 16536 | 0.3450 | 0.7040 | | 0.2499 | 54.0 | 16848 | 0.3485 | 0.6895 | | 0.2488 | 55.0 | 17160 | 0.3437 | 0.7004 | | 0.2488 | 56.0 | 17472 | 0.3465 | 0.7004 | | 0.2479 | 57.0 | 17784 | 0.3479 | 0.6895 | | 0.247 | 58.0 | 18096 | 0.3447 | 0.7004 | | 0.247 | 59.0 | 18408 | 0.3521 | 0.7004 | | 0.2468 | 60.0 | 18720 | 0.3493 | 0.6968 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

thanhnew2001/bloom560m_grade7_2000_10kstep

thanhnew2001

2023-08-22T10:22:01Z

31

0

peft

[ "peft", "text-generation", "region:us" ]

text-generation

2023-08-22T09:50:28Z

--- library_name: peft pipeline_tag: text-generation --- ## 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 0.5.0.dev0 - PEFT 0.5.0.dev0

asenella/ms_config_1_alpha_10_beta_250_seed_0

asenella

2023-08-22T10:18:27Z

0

null

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

null

2023-08-22T10:18:24Z

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

AddisuSeteye/speecht5_tts_amharic2

AddisuSeteye

2023-08-22T10:17:13Z

77

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "tags", "generated_from_trainer", "am", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-08-11T20:06:06Z

--- language: - am license: mit base_model: microsoft/speecht5_tts tags: - tags - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS amharic results: [] ---  # SpeechT5 TTS amharic This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the alfaa dataset. It achieves the following results on the evaluation set: - Loss: 0.3855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4304 | 3.32 | 1000 | 0.3855 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

TobiTob/decision_transformer_merged3

TobiTob

2023-08-22T10:12:41Z

31

1

transformers

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

null

2023-07-07T12:10:55Z

--- tags: - generated_from_trainer datasets: - city_learn model-index: - name: decision_transformer_merged3 results: [] ---  # decision_transformer_merged3 This model is a fine-tuned version of [](https://huggingface.co/) on the city_learn 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_ratio: 0.1 - num_epochs: 200 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

asenella/ms_config_1_alpha_10_beta_1_seed_2

asenella

2023-08-22T10:02:53Z

0

null

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

null

2023-08-22T10:02:51Z

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

Tina-2005/sunset

Tina-2005

2023-08-22T09:50:39Z

1

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-22T09:37:33Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Sunset Dreambooth model trained by Tina-2005 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: TSEC230 Sample pictures of this concept: ![0](https://huggingface.co/Tina-2005/sunset/resolve/main/sample_images/04.jpg)

qgallouedec/window-close-v2

qgallouedec

2023-08-22T09:48:51Z

0

sample-factory

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

reinforcement-learning

2023-07-08T10:03:03Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: window-close-v2 type: window-close-v2 metrics: - type: mean_reward value: 593.18 +/- 40.45 name: mean_reward verified: false --- A(n) **APPO** model trained on the **window-close-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/window-close-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=window-close-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=window-close-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

Samuael/llama-2-7b-tebot-sharded

Samuael

2023-08-22T09:44:04Z

0

peft

[ "peft", "pytorch", "llama", "region:us" ]

null

2023-08-17T16:00:53Z

--- 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 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0

qgallouedec/shelf-place-v2

qgallouedec

2023-08-22T09:43:18Z

0

sample-factory

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

reinforcement-learning

2023-07-08T10:01:57Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: shelf-place-v2 type: shelf-place-v2 metrics: - type: mean_reward value: 274.68 +/- 29.20 name: mean_reward verified: false --- A(n) **APPO** model trained on the **shelf-place-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/shelf-place-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=shelf-place-v2 --train_dir=./train_dir --experiment=shelf-place-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=shelf-place-v2 --train_dir=./train_dir --experiment=shelf-place-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/reach-wall-v2

qgallouedec

2023-08-22T09:42:23Z

0

sample-factory

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

reinforcement-learning

2023-07-08T10:01:45Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: reach-wall-v2 type: reach-wall-v2 metrics: - type: mean_reward value: 794.75 +/- 72.50 name: mean_reward verified: false --- A(n) **APPO** model trained on the **reach-wall-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/reach-wall-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=reach-wall-v2 --train_dir=./train_dir --experiment=reach-wall-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=reach-wall-v2 --train_dir=./train_dir --experiment=reach-wall-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/reach-v2

qgallouedec

2023-08-22T09:41:29Z

0

sample-factory

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

reinforcement-learning

2023-07-08T10:01:35Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: reach-v2 type: reach-v2 metrics: - type: mean_reward value: 686.43 +/- 166.95 name: mean_reward verified: false --- A(n) **APPO** model trained on the **reach-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/reach-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=reach-v2 --train_dir=./train_dir --experiment=reach-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=reach-v2 --train_dir=./train_dir --experiment=reach-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/push-v2

qgallouedec

2023-08-22T09:39:39Z

0

sample-factory

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

reinforcement-learning

2023-07-08T10:01:14Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: push-v2 type: push-v2 metrics: - type: mean_reward value: 742.07 +/- 37.84 name: mean_reward verified: false --- A(n) **APPO** model trained on the **push-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/push-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=push-v2 --train_dir=./train_dir --experiment=push-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=push-v2 --train_dir=./train_dir --experiment=push-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/plate-slide-side-v2

qgallouedec

2023-08-22T09:36:55Z

0

sample-factory

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

reinforcement-learning

2023-07-08T10:00:43Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: plate-slide-side-v2 type: plate-slide-side-v2 metrics: - type: mean_reward value: 711.01 +/- 56.29 name: mean_reward verified: false --- A(n) **APPO** model trained on the **plate-slide-side-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/plate-slide-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=plate-slide-side-v2 --train_dir=./train_dir --experiment=plate-slide-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=plate-slide-side-v2 --train_dir=./train_dir --experiment=plate-slide-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/plate-slide-back-v2

qgallouedec

2023-08-22T09:35:57Z

0

sample-factory

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

reinforcement-learning

2023-07-08T10:00:34Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: plate-slide-back-v2 type: plate-slide-back-v2 metrics: - type: mean_reward value: 709.93 +/- 82.31 name: mean_reward verified: false --- A(n) **APPO** model trained on the **plate-slide-back-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/plate-slide-back-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=plate-slide-back-v2 --train_dir=./train_dir --experiment=plate-slide-back-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=plate-slide-back-v2 --train_dir=./train_dir --experiment=plate-slide-back-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/pick-place-wall-v2

qgallouedec

2023-08-22T09:34:08Z

0

1

sample-factory

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

reinforcement-learning

2023-07-08T10:00:14Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: pick-place-wall-v2 type: pick-place-wall-v2 metrics: - type: mean_reward value: 449.64 +/- 63.43 name: mean_reward verified: false --- A(n) **APPO** model trained on the **pick-place-wall-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/pick-place-wall-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=pick-place-wall-v2 --train_dir=./train_dir --experiment=pick-place-wall-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=pick-place-wall-v2 --train_dir=./train_dir --experiment=pick-place-wall-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/peg-unplug-side-v2

qgallouedec

2023-08-22T09:31:20Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:59:45Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: peg-unplug-side-v2 type: peg-unplug-side-v2 metrics: - type: mean_reward value: 499.20 +/- 95.68 name: mean_reward verified: false --- A(n) **APPO** model trained on the **peg-unplug-side-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/peg-unplug-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=peg-unplug-side-v2 --train_dir=./train_dir --experiment=peg-unplug-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=peg-unplug-side-v2 --train_dir=./train_dir --experiment=peg-unplug-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/peg-insert-side-v2

qgallouedec

2023-08-22T09:30:22Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:59:35Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: peg-insert-side-v2 type: peg-insert-side-v2 metrics: - type: mean_reward value: 308.94 +/- 175.97 name: mean_reward verified: false --- A(n) **APPO** model trained on the **peg-insert-side-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/peg-insert-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=peg-insert-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=peg-insert-side-v2 --train_dir=./train_dir --experiment=peg-insert-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/handle-pull-v2

qgallouedec

2023-08-22T09:28:29Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:59:15Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: handle-pull-v2 type: handle-pull-v2 metrics: - type: mean_reward value: 698.01 +/- 21.12 name: mean_reward verified: false --- A(n) **APPO** model trained on the **handle-pull-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/handle-pull-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=handle-pull-v2 --train_dir=./train_dir --experiment=handle-pull-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=handle-pull-v2 --train_dir=./train_dir --experiment=handle-pull-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/handle-pull-side-v2

qgallouedec

2023-08-22T09:27:34Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:59:05Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: handle-pull-side-v2 type: handle-pull-side-v2 metrics: - type: mean_reward value: 462.12 +/- 95.86 name: mean_reward verified: false --- A(n) **APPO** model trained on the **handle-pull-side-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/handle-pull-side-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=handle-pull-side-v2 --train_dir=./train_dir --experiment=handle-pull-side-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=handle-pull-side-v2 --train_dir=./train_dir --experiment=handle-pull-side-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/handle-press-v2

qgallouedec

2023-08-22T09:26:37Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:58:54Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: handle-press-v2 type: handle-press-v2 metrics: - type: mean_reward value: 862.58 +/- 32.94 name: mean_reward verified: false --- A(n) **APPO** model trained on the **handle-press-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/handle-press-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=handle-press-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=handle-press-v2 --train_dir=./train_dir --experiment=handle-press-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/hand-insert-v2

qgallouedec

2023-08-22T09:24:46Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:58:36Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: hand-insert-v2 type: hand-insert-v2 metrics: - type: mean_reward value: 742.89 +/- 26.31 name: mean_reward verified: false --- A(n) **APPO** model trained on the **hand-insert-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/hand-insert-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=hand-insert-v2 --train_dir=./train_dir --experiment=hand-insert-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=hand-insert-v2 --train_dir=./train_dir --experiment=hand-insert-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

VK246/IC_ver6I_coco_swin_gpt2_50A_1e

VK246

2023-08-22T09:24:05Z

45

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:coco", "base_model:VK246/IC_ver6H_coco_swin_gpt2_50B_1e", "base_model:finetune:VK246/IC_ver6H_coco_swin_gpt2_50B_1e", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-08-22T07:16:44Z

--- base_model: VK246/IC_ver6H_coco_swin_gpt2_50B_1e tags: - generated_from_trainer datasets: - coco metrics: - rouge model-index: - name: IC_ver6I_coco_swin_gpt2_50A_1e results: [] ---  # IC_ver6I_coco_swin_gpt2_50A_1e This model is a fine-tuned version of [VK246/IC_ver6H_coco_swin_gpt2_50B_1e](https://huggingface.co/VK246/IC_ver6H_coco_swin_gpt2_50B_1e) on the coco dataset. It achieves the following results on the evaluation set: - Loss: 0.8003 - Cider: 36.4847 - Rouge1: 41.9392 - Rouge2: 16.4156 - Rougel: 38.0808 - Rougelsum: 38.0721 - Bleu-1: 42.8624 - Bleu-2: 24.8647 - Bleu-3: 15.7144 - Bleu-4: 10.4434 - Gen Len: 11.2806 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - 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 | Cider | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:-------:|:-------:|:-------:| | 0.5622 | 0.34 | 1000 | 0.8598 | 16.5035 | 41.0303 | 15.4795 | 37.2917 | 37.2896 | 41.7661 | 23.7724 | 14.7804 | 9.5941 | 11.2806 | | 0.639 | 0.68 | 2000 | 0.8003 | 36.4847 | 41.9392 | 16.4156 | 38.0808 | 38.0721 | 42.8624 | 24.8647 | 15.7144 | 10.4434 | 11.2806 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

qgallouedec/drawer-open-v2

qgallouedec

2023-08-22T09:21:02Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:57:55Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: drawer-open-v2 type: drawer-open-v2 metrics: - type: mean_reward value: 493.34 +/- 2.61 name: mean_reward verified: false --- A(n) **APPO** model trained on the **drawer-open-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/drawer-open-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=drawer-open-v2 --train_dir=./train_dir --experiment=drawer-open-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=drawer-open-v2 --train_dir=./train_dir --experiment=drawer-open-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/door-open-v2

qgallouedec

2023-08-22T09:18:18Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:57:27Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: door-open-v2 type: door-open-v2 metrics: - type: mean_reward value: 579.89 +/- 31.92 name: mean_reward verified: false --- A(n) **APPO** model trained on the **door-open-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/door-open-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=door-open-v2 --train_dir=./train_dir --experiment=door-open-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=door-open-v2 --train_dir=./train_dir --experiment=door-open-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

IngeniousArtist/distilbert-finance

IngeniousArtist

2023-08-22T09:15:43Z

161

0

transformers

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

text-classification

2023-07-31T00:31:08Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - accuracy model-index: - name: distilbert-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: 0.7386363636363636 ---  # distilbert-finance This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.9962 - Accuracy: 0.7386 ## Model description More information needed ## 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: cosine - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.904 | 0.33 | 20 | 1.5959 | 0.4205 | | 0.6562 | 0.66 | 40 | 1.6665 | 0.4143 | | 0.539 | 0.98 | 60 | 1.6067 | 0.3936 | | 0.4759 | 1.31 | 80 | 1.5079 | 0.4236 | | 0.3882 | 1.64 | 100 | 1.4719 | 0.4298 | | 0.3782 | 1.97 | 120 | 1.2392 | 0.4267 | | 0.2729 | 2.3 | 140 | 1.0114 | 0.4928 | | 0.2607 | 2.62 | 160 | 0.9514 | 0.5930 | | 0.2889 | 2.95 | 180 | 0.8661 | 0.6477 | | 0.181 | 3.28 | 200 | 0.7093 | 0.7417 | | 0.1742 | 3.61 | 220 | 1.1042 | 0.5764 | | 0.1904 | 3.93 | 240 | 0.7439 | 0.7510 | | 0.1186 | 4.26 | 260 | 0.8587 | 0.7469 | | 0.137 | 4.59 | 280 | 0.7408 | 0.7603 | | 0.1166 | 4.92 | 300 | 1.0107 | 0.6705 | | 0.0938 | 5.25 | 320 | 0.7883 | 0.7624 | | 0.0881 | 5.57 | 340 | 1.0339 | 0.7056 | | 0.0812 | 5.9 | 360 | 0.8409 | 0.7490 | | 0.0586 | 6.23 | 380 | 0.9146 | 0.7345 | | 0.0572 | 6.56 | 400 | 0.9000 | 0.7366 | | 0.0527 | 6.89 | 420 | 0.9782 | 0.7335 | | 0.045 | 7.21 | 440 | 1.0102 | 0.7262 | | 0.0471 | 7.54 | 460 | 1.0322 | 0.7324 | | 0.0508 | 7.87 | 480 | 0.9381 | 0.7448 | | 0.039 | 8.2 | 500 | 0.9489 | 0.7459 | | 0.0419 | 8.52 | 520 | 0.9779 | 0.7469 | | 0.0256 | 8.85 | 540 | 0.9834 | 0.7407 | | 0.0264 | 9.18 | 560 | 0.9963 | 0.7376 | | 0.0378 | 9.51 | 580 | 0.9981 | 0.7376 | | 0.0421 | 9.84 | 600 | 0.9962 | 0.7386 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

qgallouedec/coffee-push-v2

qgallouedec

2023-08-22T09:13:37Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:56:36Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: coffee-push-v2 type: coffee-push-v2 metrics: - type: mean_reward value: 526.27 +/- 120.28 name: mean_reward verified: false --- A(n) **APPO** model trained on the **coffee-push-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/coffee-push-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=coffee-push-v2 --train_dir=./train_dir --experiment=coffee-push-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=coffee-push-v2 --train_dir=./train_dir --experiment=coffee-push-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/coffee-pull-v2

qgallouedec

2023-08-22T09:12:42Z

0

sample-factory

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

reinforcement-learning

2023-07-08T09:56:25Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: coffee-pull-v2 type: coffee-pull-v2 metrics: - type: mean_reward value: 262.59 +/- 63.08 name: mean_reward verified: false --- A(n) **APPO** model trained on the **coffee-pull-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/coffee-pull-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=coffee-pull-v2 --train_dir=./train_dir --experiment=coffee-pull-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=coffee-pull-v2 --train_dir=./train_dir --experiment=coffee-pull-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

922-CA/Modded-Berry

922-CA

2023-08-22T09:10:37Z

0

null

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

null

2022-11-18T11:48:37Z

--- license: creativeml-openrail-m --- # INFO (11/18/2022) Old stable diffusion 1.5 merge of berry mix + anythingv3 at around ~30/70 ratio + further merges. (If can recall correctly...)

qgallouedec/button-press-topdown-wall-v2

qgallouedec

2023-08-22T09:09:02Z

0

sample-factory

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

reinforcement-learning

2023-07-07T16:13:14Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: button-press-topdown-wall-v2 type: button-press-topdown-wall-v2 metrics: - type: mean_reward value: 497.31 +/- 37.73 name: mean_reward verified: false --- A(n) **APPO** model trained on the **button-press-topdown-wall-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/button-press-topdown-wall-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=button-press-topdown-wall-v2 --train_dir=./train_dir --experiment=button-press-topdown-wall-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=button-press-topdown-wall-v2 --train_dir=./train_dir --experiment=button-press-topdown-wall-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/button-press-topdown-v2

qgallouedec

2023-08-22T09:08:08Z

0

sample-factory

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

reinforcement-learning

2023-07-07T16:12:47Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: button-press-topdown-v2 type: button-press-topdown-v2 metrics: - type: mean_reward value: 486.12 +/- 38.04 name: mean_reward verified: false --- A(n) **APPO** model trained on the **button-press-topdown-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/button-press-topdown-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=button-press-topdown-v2 --train_dir=./train_dir --experiment=button-press-topdown-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=button-press-topdown-v2 --train_dir=./train_dir --experiment=button-press-topdown-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/bin-picking-v2

qgallouedec

2023-08-22T09:06:20Z

0

sample-factory

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

reinforcement-learning

2023-07-07T16:11:52Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: bin-picking-v2 type: bin-picking-v2 metrics: - type: mean_reward value: 452.37 +/- 36.53 name: mean_reward verified: false --- A(n) **APPO** model trained on the **bin-picking-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/bin-picking-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=bin-picking-v2 --train_dir=./train_dir --experiment=bin-picking-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=bin-picking-v2 --train_dir=./train_dir --experiment=bin-picking-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

marksverdhei/t5-base-define

marksverdhei

2023-08-22T09:05:55Z

123

6

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "en", "dataset:marksverdhei/wordnet-definitions-en-2021", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-02T09:50:37Z

--- language: en widget: - text: 'define "toecoin": toecoin rose by 200% after Elon Musk mentioned it in his tweet' datasets: - 'marksverdhei/wordnet-definitions-en-2021' --- # T5-define (This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy) This model is trained to generate word definitions based on the word and a context, using a subset of wordnet for all words that have an example and definition. The model uses task prompts on the format 'define "[word]": [example sentence]' This model in particular is a one-shot learner for unseen words, as it has to infer the definition by only one example How to run: ```python from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("marksverdhei/t5-base-define") model = T5ForConditionalGeneration.from_pretrained("marksverdhei/t5-base-define") prompt = "define \"noseplow\": The children hid as the noseplow drove across the street" ids = tokenizer(prompt, return_tensors="pt").input_ids generated_tokens = model.generate(ids)[0][1:-1] print(tokenizer.decode(generated_tokens)) ``` See the gist for the source code to used to train the model: https://gist.github.com/marksverdhei/0a13f67e65460b71c05fcf558a6a91ae

qgallouedec/basketball-v2

qgallouedec

2023-08-22T09:05:27Z

0

1

sample-factory

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

reinforcement-learning

2023-07-07T16:11:22Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: basketball-v2 type: basketball-v2 metrics: - type: mean_reward value: 584.02 +/- 49.43 name: mean_reward verified: false --- A(n) **APPO** model trained on the **basketball-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/basketball-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=basketball-v2 --train_dir=./train_dir --experiment=basketball-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=basketball-v2 --train_dir=./train_dir --experiment=basketball-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

qgallouedec/assembly-v2

qgallouedec

2023-08-22T09:04:34Z

0

sample-factory

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

reinforcement-learning

2023-07-07T13:44:05Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: assembly-v2 type: assembly-v2 metrics: - type: mean_reward value: 245.47 +/- 4.68 name: mean_reward verified: false --- A(n) **APPO** model trained on the **assembly-v2** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r qgallouedec/assembly-v2 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m enjoy --algo=APPO --env=assembly-v2 --train_dir=./train_dir --experiment=assembly-v2 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m train --algo=APPO --env=assembly-v2 --train_dir=./train_dir --experiment=assembly-v2 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

newronai/lma2-7b-Chat-Adapter-N

newronai

2023-08-22T08:56:11Z

0

peft

[ "peft", "region:us" ]

null

2023-08-22T08:56:04Z

--- 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 ### Framework versions - PEFT 0.5.0.dev0

Abhishek-Pathak/llama2_finetuned_demo

Abhishek-Pathak

2023-08-22T08:51:12Z

0

null

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

null

2023-08-22T06:28:44Z

--- tags: - generated_from_trainer model-index: - name: llama2_finetuned_demo results: [] ---  # llama2_finetuned_demo This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

grv805/llama2-qlora-finetunined-13b-gcp

grv805

2023-08-22T08:47:03Z

6

0

peft

[ "peft", "region:us" ]

null

2023-08-22T08:46:51Z

--- 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 ### Framework versions - PEFT 0.5.0.dev0

tianpf/chinese-alpaca-2-qlora-finetunined-law2

tianpf

2023-08-22T08:46:11Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-22T08:46:07Z

--- 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 ### Framework versions - PEFT 0.5.0.dev0

linoyts/lora-xl-3d_icons-0.0001-5e-05-2000-1-5

linoyts

2023-08-22T08:45:51Z

5

1

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2023-08-22T07:54:07Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: blb 3d icon tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - LinoyTsaban/lora-xl-3d_icons-0.0001-5e-05-2000-1-5 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on blb 3d icon using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

dkqjrm/20230822155557

dkqjrm

2023-08-22T08:44:13Z

106

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

2023-08-22T06:56:15Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230822155557' results: [] ---  # 20230822155557 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: 0.3488 - Accuracy: 0.5307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 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 | 0.3548 | 0.4729 | | 0.3737 | 2.0 | 624 | 0.3480 | 0.5199 | | 0.3737 | 3.0 | 936 | 0.3486 | 0.5162 | | 0.3718 | 4.0 | 1248 | 0.3495 | 0.5235 | | 0.3714 | 5.0 | 1560 | 0.3505 | 0.4729 | | 0.3714 | 6.0 | 1872 | 0.3487 | 0.5235 | | 0.3686 | 7.0 | 2184 | 0.3496 | 0.4729 | | 0.3686 | 8.0 | 2496 | 0.3505 | 0.4729 | | 0.3684 | 9.0 | 2808 | 0.3502 | 0.5235 | | 0.3679 | 10.0 | 3120 | 0.3491 | 0.5054 | | 0.3679 | 11.0 | 3432 | 0.3515 | 0.4729 | | 0.3659 | 12.0 | 3744 | 0.3496 | 0.5162 | | 0.3649 | 13.0 | 4056 | 0.3517 | 0.4729 | | 0.3649 | 14.0 | 4368 | 0.3543 | 0.4729 | | 0.3651 | 15.0 | 4680 | 0.3513 | 0.4729 | | 0.3651 | 16.0 | 4992 | 0.3489 | 0.5235 | | 0.363 | 17.0 | 5304 | 0.3537 | 0.5235 | | 0.3613 | 18.0 | 5616 | 0.3487 | 0.5307 | | 0.3613 | 19.0 | 5928 | 0.3495 | 0.5126 | | 0.3645 | 20.0 | 6240 | 0.3530 | 0.5199 | | 0.359 | 21.0 | 6552 | 0.3497 | 0.5235 | | 0.359 | 22.0 | 6864 | 0.3487 | 0.5235 | | 0.3614 | 23.0 | 7176 | 0.3511 | 0.5235 | | 0.3614 | 24.0 | 7488 | 0.3491 | 0.5271 | | 0.3617 | 25.0 | 7800 | 0.3493 | 0.5199 | | 0.3611 | 26.0 | 8112 | 0.3491 | 0.5271 | | 0.3611 | 27.0 | 8424 | 0.3581 | 0.4729 | | 0.3583 | 28.0 | 8736 | 0.3496 | 0.5343 | | 0.3583 | 29.0 | 9048 | 0.3492 | 0.5162 | | 0.3583 | 30.0 | 9360 | 0.3493 | 0.4404 | | 0.3564 | 31.0 | 9672 | 0.3494 | 0.5343 | | 0.3564 | 32.0 | 9984 | 0.3489 | 0.5199 | | 0.3567 | 33.0 | 10296 | 0.3490 | 0.5343 | | 0.3561 | 34.0 | 10608 | 0.3486 | 0.5271 | | 0.3561 | 35.0 | 10920 | 0.3492 | 0.5307 | | 0.3556 | 36.0 | 11232 | 0.3503 | 0.4765 | | 0.3556 | 37.0 | 11544 | 0.3497 | 0.5307 | | 0.3556 | 38.0 | 11856 | 0.3494 | 0.5379 | | 0.3561 | 39.0 | 12168 | 0.3488 | 0.5235 | | 0.3561 | 40.0 | 12480 | 0.3503 | 0.5271 | | 0.3558 | 41.0 | 12792 | 0.3489 | 0.5343 | | 0.3579 | 42.0 | 13104 | 0.3508 | 0.4729 | | 0.3579 | 43.0 | 13416 | 0.3505 | 0.5271 | | 0.3547 | 44.0 | 13728 | 0.3493 | 0.5379 | | 0.3567 | 45.0 | 14040 | 0.3519 | 0.4729 | | 0.3567 | 46.0 | 14352 | 0.3497 | 0.4729 | | 0.3548 | 47.0 | 14664 | 0.3499 | 0.4729 | | 0.3548 | 48.0 | 14976 | 0.3492 | 0.5343 | | 0.3563 | 49.0 | 15288 | 0.3491 | 0.5307 | | 0.3552 | 50.0 | 15600 | 0.3489 | 0.5235 | | 0.3552 | 51.0 | 15912 | 0.3487 | 0.5162 | | 0.3557 | 52.0 | 16224 | 0.3496 | 0.4513 | | 0.3555 | 53.0 | 16536 | 0.3488 | 0.5307 | | 0.3555 | 54.0 | 16848 | 0.3489 | 0.5271 | | 0.3542 | 55.0 | 17160 | 0.3488 | 0.5162 | | 0.3542 | 56.0 | 17472 | 0.3488 | 0.5343 | | 0.3545 | 57.0 | 17784 | 0.3494 | 0.5379 | | 0.3543 | 58.0 | 18096 | 0.3489 | 0.5126 | | 0.3543 | 59.0 | 18408 | 0.3489 | 0.5162 | | 0.3553 | 60.0 | 18720 | 0.3488 | 0.5307 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

xray1111/ppo-LunarLander-v2

xray1111

2023-08-22T08:41:09Z

5

0

stable-baselines3

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

reinforcement-learning

2023-08-22T08:40:42Z

--- 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: 273.95 +/- 16.80 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 ... ```

newronai/llama-2-7b-Chat-QLoRA-New-1.0

newronai

2023-08-22T08:36:35Z

0

peft

[ "peft", "region:us" ]

null

2023-08-22T08:36:31Z

--- 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 ### Framework versions - PEFT 0.5.0.dev0

kaanhho/whisper-tiny-01

kaanhho

2023-08-22T08:29:58Z

76

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

2023-08-22T01:07:48Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-01 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.33884297520661155 ---  # whisper-tiny-01 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: 0.6410 - Wer Ortho: 0.3430 - Wer: 0.3388 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.001 | 17.24 | 500 | 0.6410 | 0.3430 | 0.3388 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

CodyNichol14/M_Shadows_HTTK

CodyNichol14

2023-08-22T08:28:22Z

0

null

[ "license:artistic-2.0", "region:us" ]

null

2023-08-22T08:25:25Z

--- license: artistic-2.0 Model Maker: CodyNichol14 Epoch: 250 Model: M. SHadows From Avenged Sevenfold ---