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 2025-06-22 12:28:33	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 492 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-22 12:28:03	card stringlengths 11 1.01M
tyavika/Distilbert-QA-Pytorch-FULL	tyavika	2023-07-16T16:06:16Z	131	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-28T01:54:23Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Distilbert-QA-Pytorch-FULL 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-QA-Pytorch-FULL This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4175 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.297 \| 1.0 \| 3290 \| 1.1823 \| \| 0.9448 \| 2.0 \| 6580 \| 1.1464 \| \| 0.6704 \| 3.0 \| 9870 \| 1.2624 \| \| 0.4478 \| 4.0 \| 13160 \| 1.4175 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
tyavika/Bert-QA-Pytorch-FULL	tyavika	2023-07-16T16:05:57Z	7	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-28T02:19:57Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Bert-QA-Pytorch-FULL 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-QA-Pytorch-FULL This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2154 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1633 \| 1.0 \| 3290 \| 1.0515 \| \| 0.8061 \| 2.0 \| 6580 \| 1.0593 \| \| 0.533 \| 3.0 \| 9870 \| 1.2154 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ailabturkiye/NecmettinErbakan	ailabturkiye	2023-07-16T16:05:52Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T16:02:15Z	--- license: openrail language: - tr tags: - music --- Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
casque/Creampie_v11	casque	2023-07-16T16:05:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T16:03:25Z	--- license: creativeml-openrail-m ---
ailabturkiye/orkundk	ailabturkiye	2023-07-16T16:03:20Z	0	0	null	[ "region:us" ]	null	2023-07-16T16:02:09Z	--- license: openrail language: - tr tags: - music Orkundk (500 Epoch)
tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid	tyavika	2023-07-16T16:02:27Z	7	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-07-12T15:52:35Z	--- tags: - generated_from_trainer model-index: - name: lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid 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. --> # lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid This model is a fine-tuned version of [tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid](https://huggingface.co/tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ailabturkiye/mabelmatiz	ailabturkiye	2023-07-16T16:00:13Z	0	0	null	[ "region:us" ]	null	2023-07-16T15:58:53Z	--- license: openrail language: - tr tags: - music Mabel Matiz (500 Epoch)
ailabturkiye/AhmetAga	ailabturkiye	2023-07-16T15:58:14Z	0	0	null	[ "tr", "license:openrail", "region:us" ]	null	2023-07-16T15:37:02Z	--- license: openrail language: - tr --- This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ### Model Details 400 epoch 14 minute dataset ### Model Description ahmet aga - Developed by: Flowness"seloistaken" - Shared by :Flowness"seloistaken" - Model type: rvc v2 - Language: Turkish ### Model Sources https://www.youtube.com/watch?v=hwDRaGwfvQI ## Uses if you use and share this model give credits for discord.gg/ailab
localmodels/Orca-Mini-v2-13B-GPTQ	localmodels	2023-07-16T15:57:59Z	6	0	transformers	[ "transformers", "llama", "text-generation", "arxiv:2306.02707", "arxiv:2302.13971", "arxiv:2304.12244", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T15:57:59Z	--- duplicated_from: localmodels/LLM --- # Orca Mini v2 13B GPTQ From: https://huggingface.co/psmathur/orca_mini_v2_13b ## Prompt template ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: {prompt} ### Input: {input} ### Response: ``` --- \| Model \| Bits \| Group Size \| Act Order (desc_act) \| File Size \| ExLlama Compatible? \| Made With \| Description \| \| ------ \| ---- \| ---------- \| -------------------- \| --------- \| ------------------- \| --------- \| ----------- \| \| orca_mini_v2_13b-GPTQ-4bit-128g.no-act.order \| 4 \| 128 \| False \| 7.45 GB \| True \| GPTQ-for-LLaMa \| Most compatible. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. \| --- # Orca Mini v2 13B An Uncensored LLaMA-13b model in collaboration with [Eric Hartford](https://huggingface.co/ehartford). trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. Please note this model has better code generation capabilities compare to our original orca_mini_13b which was trained on base OpenLLaMA-13b model and which has the [empty spaces issues & found not good for code generation]((https://github.com/openlm-research/open_llama#update-06072023)). # Evaluation I evaluated orca_mini_v2_13b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) \|\|\|\| \|:------:\|:-------------:\|:---------:\| \|Task\|Value\|Stderr\| \|arc_challenge\|0.5478\|0.0145\| \|hellaswag\|0.7023\|0.0040\| \|mmlu\|0.4969\|0.035\| \|truthfulqa_mc\|0.44\|0.0158\| \|Total Average\|0.54675\|0.0114\| # Dataset We used uncensored script on top of the previous explain tuned datasets we build which are [WizardLM dataset ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) & [Dolly-V2 dataset ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. This helps student model aka this model to learn *thought* process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). Please see below example usage how the System prompt is added before each instruction. # Training The training configurations are provided in the table below. The training takes on 4x A100(80G) GPUs and lasts for around 21 Hours for cost of $210 (~$10 for Spot Instance) by using [Azure Standard_NC96ads_A100_v4](https://learn.microsoft.com/en-us/azure/virtual-machines/nc-a100-v4-series#supported-features). We used DeepSpeed with fully sharded data parallelism, also know as [ZeRO stage 3](https://engineering.fb.com/2021/07/15/open-source/fsdp/) by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [FastChat](https://github.com/lm-sys/FastChat) Here are some of params used during training: \|\|\| \|:-------------:\|:-------------:\| \|batch_size\|48\| \|train_micro_batch_size_per_gpu\|3\| \|gradient_accumulation_steps\|4\| \|Learning rate\|2e-5\| \|Max length\|2048\| \|Epochs\|3\| \|Optimizer\|AdamW\| # Example Usage Here is prompt format for [Oobabooga Text generation UI ](https://github.com/oobabooga/text-generation-webui) ``` ### System: {system} ### User: {instruction} ### Input: {input} ### Response: ``` Limitations & Biases: This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Disclaimer: The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. Citiation: If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX: ``` @misc{orca_mini_v2_13b, author = {Pankaj Mathur}, title = {orca_mini_v2_13b: An explain tuned LLaMA-13b model on uncensored wizardlm, alpaca, & dolly datasets}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v2_13b}, } ``` ``` @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ``` ``` @misc{openalpaca, author = {Yixuan Su and Tian Lan and Deng Cai}, title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, } ``` ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` ``` @misc{xu2023wizardlm, title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang}, year={2023}, eprint={2304.12244}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
ailabturkiye/MehmetAliErbil	ailabturkiye	2023-07-16T15:57:00Z	0	1	null	[ "region:us" ]	null	2023-07-16T15:23:06Z	--- Lisans: openrail Sunucu ve oyuncu Mehmet Ali Erbil'in Türkçe sesidir, Rvc V2 500 epoch olarak eğitilmiştir. _Dataset ve train jawbone0 tarafından yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: jawbone0 - YouTube: JawBone0 (https://www.youtube.com/@JawBone0) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)
NasimB/rarity-all-guten-2p5k-cbt-p5k-mixed	NasimB	2023-07-16T15:56:16Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T14:02:13Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: rarity-all-guten-2p5k-cbt-p5k-mixed 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. --> # rarity-all-guten-2p5k-cbt-p5k-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3206 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6916 \| 0.29 \| 500 \| 5.6242 \| \| 5.3287 \| 0.59 \| 1000 \| 5.1956 \| \| 4.9961 \| 0.88 \| 1500 \| 4.9421 \| \| 4.7198 \| 1.17 \| 2000 \| 4.8015 \| \| 4.5643 \| 1.47 \| 2500 \| 4.6835 \| \| 4.4523 \| 1.76 \| 3000 \| 4.5745 \| \| 4.3273 \| 2.06 \| 3500 \| 4.4993 \| \| 4.1372 \| 2.35 \| 4000 \| 4.4498 \| \| 4.1052 \| 2.64 \| 4500 \| 4.3880 \| \| 4.0721 \| 2.94 \| 5000 \| 4.3409 \| \| 3.8586 \| 3.23 \| 5500 \| 4.3325 \| \| 3.8079 \| 3.52 \| 6000 \| 4.3061 \| \| 3.7897 \| 3.82 \| 6500 \| 4.2690 \| \| 3.678 \| 4.11 \| 7000 \| 4.2702 \| \| 3.5266 \| 4.4 \| 7500 \| 4.2641 \| \| 3.5165 \| 4.7 \| 8000 \| 4.2488 \| \| 3.5069 \| 4.99 \| 8500 \| 4.2361 \| \| 3.3367 \| 5.28 \| 9000 \| 4.2512 \| \| 3.3295 \| 5.58 \| 9500 \| 4.2494 \| \| 3.3275 \| 5.87 \| 10000 \| 4.2480 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
ailabturkiye/SimsekMcqueen	ailabturkiye	2023-07-16T15:55:13Z	0	1	null	[ "region:us" ]	null	2023-07-16T15:32:48Z	--- Lisans: openrail Şimşek Mcqueen Türkçe seslendirmeni Yakta Kopan'ın sesidir, Rvc V2 1000 epoch olarak eğitilmiştir. _Dataset ve train jawbone0 tarafından yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: jawbone0 - YouTube: JawBone0 (https://www.youtube.com/@JawBone0) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)
ailabturkiye/FahrettinAltun	ailabturkiye	2023-07-16T15:54:26Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T15:51:36Z	--- license: openrail language: - tr tags: - music --- İletişim Başkanımız Sayın Fahrettin Altun. Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
helojo/wav2vec2-large-mms-1b-zh-colab	helojo	2023-07-16T15:54:22Z	6	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_6_1", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-12T02:58:25Z	--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer datasets: - common_voice_6_1 model-index: - name: wav2vec2-large-mms-1b-zh-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-mms-1b-zh-colab This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 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: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.1.0.dev20230711 - Datasets 2.13.1 - Tokenizers 0.13.3
casque/vacuum_fellatio1.1	casque	2023-07-16T15:54:05Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T15:52:41Z	--- license: creativeml-openrail-m ---
Mehmetakif/MinecraftZombiSesi	Mehmetakif	2023-07-16T15:53:31Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T15:47:14Z	--- license: openrail language: - tr tags: - music ---
casque/licking_my_dick.sd.v1.2	casque	2023-07-16T15:51:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T15:48:28Z	--- license: creativeml-openrail-m ---
giocs2017/Reinforce-cartPolev1	giocs2017	2023-07-16T15:49:01Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T15:48:52Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartPolev1 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
0sunfire0/LunarLander-v2_new_00	0sunfire0	2023-07-16T15:48:29Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T15:48:21Z	--- 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: -81.95 +/- 58.67 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': '0sunfire0/LunarLander-v2_new_00' 'batch_size': 512 'minibatch_size': 128} ```
casque/Spooning_Posititon	casque	2023-07-16T15:42:25Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T15:40:02Z	--- license: creativeml-openrail-m ---
TokenBender/falcon-7b-chat-oasst1	TokenBender	2023-07-16T15:40:15Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-16T15:39:30Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
ailabturkiye/Baso	ailabturkiye	2023-07-16T15:37:45Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-07-16T15:37:39Z	Temporary Redirect. Redirecting to /ailabturkiye/baso/resolve/main/README.md
NasimB/all-base-no-repetition-no-cut	NasimB	2023-07-16T15:34:04Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T13:44:52Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: all-base-no-repetition-no-cut results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-base-no-repetition-no-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.7605 \| 0.31 \| 500 \| 5.6506 \| \| 5.4119 \| 0.62 \| 1000 \| 5.2199 \| \| 5.0579 \| 0.94 \| 1500 \| 4.9665 \| \| 4.767 \| 1.25 \| 2000 \| 4.8190 \| \| 4.6276 \| 1.56 \| 2500 \| 4.6923 \| \| 4.5202 \| 1.87 \| 3000 \| 4.5802 \| \| 4.312 \| 2.19 \| 3500 \| 4.5219 \| \| 4.2135 \| 2.5 \| 4000 \| 4.4518 \| \| 4.1664 \| 2.81 \| 4500 \| 4.3926 \| \| 4.033 \| 3.12 \| 5000 \| 4.3652 \| \| 3.8843 \| 3.44 \| 5500 \| 4.3407 \| \| 3.8737 \| 3.75 \| 6000 \| 4.3029 \| \| 3.8047 \| 4.06 \| 6500 \| 4.2883 \| \| 3.5939 \| 4.37 \| 7000 \| 4.2854 \| \| 3.582 \| 4.68 \| 7500 \| 4.2692 \| \| 3.5745 \| 5.0 \| 8000 \| 4.2540 \| \| 3.3934 \| 5.31 \| 8500 \| 4.2671 \| \| 3.3874 \| 5.62 \| 9000 \| 4.2653 \| \| 3.3924 \| 5.93 \| 9500 \| 4.2645 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
lxyuan/distilbart-finetuned-summarization	lxyuan	2023-07-16T15:32:42Z	159	5	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "distilbart", "en", "dataset:cnn_dailymail", "dataset:xsum", "dataset:samsum", "dataset:ccdv/pubmed-summarization", "arxiv:2010.13002", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-09T05:23:35Z	--- tags: - generated_from_trainer - distilbart model-index: - name: distilbart-finetuned-summarization results: [] license: apache-2.0 datasets: - cnn_dailymail - xsum - samsum - ccdv/pubmed-summarization language: - en metrics: - rouge --- <!-- 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. --> # distilbart-finetuned-summarization This model is a further fine-tuned version of [distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the the combination of 4 different summarisation datasets: - [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) - [samsum](https://huggingface.co/datasets/samsum) - [xsum](https://huggingface.co/datasets/xsum) - [ccdv/pubmed-summarization](https://huggingface.co/datasets/ccdv/pubmed-summarization) Please check out the offical model page and paper: - [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) - [Pre-trained Summarization Distillation](https://arxiv.org/abs/2010.13002) ## Training and evaluation data One can reproduce the dataset using the following code: ```python from datasets import DatasetDict, load_dataset from datasets import concatenate_datasets xsum_dataset = load_dataset("xsum") pubmed_dataset = load_dataset("ccdv/pubmed-summarization").rename_column("article", "document").rename_column("abstract", "summary") cnn_dataset = load_dataset("cnn_dailymail", '3.0.0').rename_column("article", "document").rename_column("highlights", "summary") samsum_dataset = load_dataset("samsum").rename_column("dialogue", "document") summary_train = concatenate_datasets([xsum_dataset["train"], pubmed_dataset["train"], cnn_dataset["train"], samsum_dataset["train"]]) summary_validation = concatenate_datasets([xsum_dataset["validation"], pubmed_dataset["validation"], cnn_dataset["validation"], samsum_dataset["validation"]]) summary_test = concatenate_datasets([xsum_dataset["test"], pubmed_dataset["test"], cnn_dataset["test"], samsum_dataset["test"]]) raw_datasets = DatasetDict() raw_datasets["train"] = summary_train raw_datasets["validation"] = summary_validation raw_datasets["test"] = summary_test ``` ## Inference example ```python from transformers import pipeline pipe = pipeline("text2text-generation", model="lxyuan/distilbart-finetuned-summarization") text = """SINGAPORE: The Singapore Police Force on Sunday (Jul 16) issued a warning over a fake SMS impersonating as its "anti-scam centre (ASC)". "In this scam variant, members of the public would receive a scam SMS from 'ASC', requesting them to download and install an “anti-scam” app to ensure the security of their devices," said the police. "The fake SMS would direct members of the public to a URL link leading to an Android Package Kit (APK) file, an application created for Android’s operating system purportedly from 'ASC'." The fake website has an icon to download the “anti-scam” app and once downloaded, Android users are asked to allow accessibility services to enable the service. While the fake app purportedly claims to help identify and prevent scams by providing comprehensive protection and security, downloading it may enable scammers to gain remote access to devices. "Members of the public are advised not to download any suspicious APK files on their devices as they may contain malware which will allow scammers to access and take control of the device remotely as well as to steal passwords stored in the device," said the police. Members of the public are advised to adopt the following precautionary measures, including adding anti-virus or anti-malware apps to their devices. They should also disable “install unknown app” or “unknown sources” in their phone settings. Users should check the developer information on the app listing as well as the number of downloads and user reviews to ensure it is a reputable and legitimate app, the police said. Any fraudulent transactions should be immediately reported to the banks. """ pipe(text) >>>"""The Singapore Police Force has issued a warning over a fake SMS impersonating as its "anti-scam centre" that asks members of the public to download an Android app to ensure the security of their devices, the force said on Sunday. The fake SMS would direct people to a URL link leading to an Android Package Kit (APK) file, an application created for Android’s operating system purportedly from "ASC". """ ``` ## Training procedure Notebook link: [here](https://github.com/LxYuan0420/nlp/blob/main/notebooks/distilbart-finetune-summarisation.ipynb) ### Training hyperparameters The following hyperparameters were used during training: - evaluation_strategy="epoch", - save_strategy="epoch", - logging_strategy="epoch", - learning_rate=2e-5, - per_device_train_batch_size=2, - per_device_eval_batch_size=2, - gradient_accumulation_steps=64, - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - weight_decay=0.01, - save_total_limit=2, - num_train_epochs=4, - predict_with_generate=True, - fp16=True, - push_to_hub=True ### Training results _Training is still in progress_ \| Epoch \| Training Loss \| Validation Loss \| Rouge1 \| Rouge2 \| RougeL \| RougeLsum \| Gen Len \| \|-------\|---------------\|-----------------\|--------\|--------\|--------\|-----------\|---------\| \| 0 \| 1.779700 \| 1.719054 \| 40.003900 \| 17.907100 \| 27.882500 \| 34.888600 \| 88.893600 \| \| 1 \| 1.633800 \| 1.710876 \| 40.628800 \| 18.470200 \| 28.428100 \| 35.577500 \| 88.885000 \| \| 2 \| 1.566100 \| 1.694476 \| 40.928500 \| 18.695300 \| 28.613300 \| 35.813300 \| 88.993700 \| \| 3 \| 1.515700 \| 1.691141 \| 40.860500 \| 18.696500 \| 28.672700 \| 35.734600 \| 88.457300 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
ailabturkiye/CavsKarahanli	ailabturkiye	2023-07-16T15:31:16Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-16T15:27:29Z	--- license: openrail language: - tr tags: - music --- Yayıncı Cavs Karahanli.Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.
manuu01/q-FrozenLake-v1-4x4-noSlippery	manuu01	2023-07-16T14:57:47Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T14:57:25Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="manuu01/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Jonathaniu/alpaca-breast-cancer-13b-mix_data_2	Jonathaniu	2023-07-16T14:52:46Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-16T14:52:26Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0
Saideva/title_generation	Saideva	2023-07-16T14:38:55Z	107	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-16T14:10:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: title_generation 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. --> # title_generation This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 41.5236 - Rouge2: 17.5894 - Rougel: 37.2852 - Rougelsum: 37.2749 - Gen Len: 13.3542 ## Model description More information needed ## Intended uses & 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: 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.0 \| 1.0 \| 3748 \| nan \| 41.5236 \| 17.5894 \| 37.2852 \| 37.2749 \| 13.3542 \| \| 0.0 \| 2.0 \| 7496 \| nan \| 41.5236 \| 17.5894 \| 37.2852 \| 37.2749 \| 13.3542 \| \| 0.0 \| 3.0 \| 11244 \| nan \| 41.5236 \| 17.5894 \| 37.2852 \| 37.2749 \| 13.3542 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
PaulineJamin/ppo-SnowballTarget	PaulineJamin	2023-07-16T14:38:23Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-16T14:38:14Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: PaulineJamin/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
margosabry/food_classifier	margosabry	2023-07-16T14:28:12Z	63	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-16T13:50:22Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: margosabry/food_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # margosabry/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3853 - Validation Loss: 0.3150 - Train Accuracy: 0.928 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.8055 \| 1.6705 \| 0.808 \| 0 \| \| 1.2418 \| 0.8233 \| 0.883 \| 1 \| \| 0.7004 \| 0.5248 \| 0.912 \| 2 \| \| 0.5037 \| 0.3802 \| 0.926 \| 3 \| \| 0.3853 \| 0.3150 \| 0.928 \| 4 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
huggingFacing/ddpm-butterflies-128	huggingFacing	2023-07-16T14:11:21Z	0	0	diffusers	[ "diffusers", "en", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2023-07-16T14:09:03Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: /content/drive/MyDrive/image_and_text metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `/content/drive/MyDrive/image_and_text` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Tian7/ddpm-butterflies-128/tensorboard?#scalars)
olegs/distil-ast-audioset-finetuned-gtzan	olegs	2023-07-16T14:09:35Z	165	0	transformers	[ "transformers", "pytorch", "tensorboard", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:bookbot/distil-ast-audioset", "base_model:finetune:bookbot/distil-ast-audioset", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-16T13:02:16Z	--- license: apache-2.0 base_model: bookbot/distil-ast-audioset tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distil-ast-audioset-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.93 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distil-ast-audioset-finetuned-gtzan This model is a fine-tuned version of [bookbot/distil-ast-audioset](https://huggingface.co/bookbot/distil-ast-audioset) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5022 - Accuracy: 0.93 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8727 \| 1.0 \| 113 \| 0.6650 \| 0.81 \| \| 0.6665 \| 2.0 \| 226 \| 0.7639 \| 0.74 \| \| 0.5306 \| 3.0 \| 339 \| 0.6683 \| 0.76 \| \| 0.2793 \| 4.0 \| 452 \| 0.7423 \| 0.82 \| \| 0.0867 \| 5.0 \| 565 \| 0.6301 \| 0.85 \| \| 0.0156 \| 6.0 \| 678 \| 0.8905 \| 0.83 \| \| 0.2298 \| 7.0 \| 791 \| 0.4492 \| 0.92 \| \| 0.0073 \| 8.0 \| 904 \| 0.9028 \| 0.83 \| \| 0.0664 \| 9.0 \| 1017 \| 0.6387 \| 0.85 \| \| 0.0001 \| 10.0 \| 1130 \| 0.5022 \| 0.87 \| \| 0.0001 \| 11.0 \| 1243 \| 0.4047 \| 0.91 \| \| 0.0 \| 12.0 \| 1356 \| 0.3988 \| 0.92 \| \| 0.0 \| 13.0 \| 1469 \| 0.6225 \| 0.91 \| \| 0.0 \| 14.0 \| 1582 \| 0.6075 \| 0.86 \| \| 0.0 \| 15.0 \| 1695 \| 0.5259 \| 0.89 \| \| 0.0 \| 16.0 \| 1808 \| 0.5014 \| 0.92 \| \| 0.0 \| 17.0 \| 1921 \| 0.5004 \| 0.93 \| \| 0.0 \| 18.0 \| 2034 \| 0.5008 \| 0.93 \| \| 0.0 \| 19.0 \| 2147 \| 0.5022 \| 0.93 \| \| 0.0 \| 20.0 \| 2260 \| 0.5022 \| 0.93 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
lucasbertola/ppo-SnowballTarget	lucasbertola	2023-07-16T14:08:18Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-16T14:08:12Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: lucasbertola/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
antoniodee/fin-tench	antoniodee	2023-07-16T13:48:41Z	0	0	null	[ "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-16T13:43:49Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### fin_tench Dreambooth model trained by antoniodee 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:
SwampMan/a2c-AntBulletEnv-v0	SwampMan	2023-07-16T13:44:52Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T13:43:45Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1250.47 +/- 141.94 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 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 ... ```
NasimB/all-base-rarity-all-guten-rarity-all-2p5k-iorder-est-5p5k-mostf	NasimB	2023-07-16T13:17:43Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T11:29:42Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: all-base-rarity-all-guten-rarity-all-2p5k-iorder-est-5p5k-mostf results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-base-rarity-all-guten-rarity-all-2p5k-iorder-est-5p5k-mostf This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3428 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.7586 \| 0.32 \| 500 \| 5.6557 \| \| 5.4132 \| 0.63 \| 1000 \| 5.2227 \| \| 5.0611 \| 0.95 \| 1500 \| 4.9660 \| \| 4.7718 \| 1.26 \| 2000 \| 4.8202 \| \| 4.64 \| 1.58 \| 2500 \| 4.6997 \| \| 4.5178 \| 1.89 \| 3000 \| 4.5866 \| \| 4.308 \| 2.21 \| 3500 \| 4.5285 \| \| 4.223 \| 2.52 \| 4000 \| 4.4602 \| \| 4.1757 \| 2.84 \| 4500 \| 4.3982 \| \| 4.0214 \| 3.15 \| 5000 \| 4.3825 \| \| 3.8976 \| 3.47 \| 5500 \| 4.3455 \| \| 3.8816 \| 3.78 \| 6000 \| 4.3106 \| \| 3.7798 \| 4.1 \| 6500 \| 4.3020 \| \| 3.6074 \| 4.41 \| 7000 \| 4.2988 \| \| 3.5991 \| 4.73 \| 7500 \| 4.2789 \| \| 3.56 \| 5.04 \| 8000 \| 4.2749 \| \| 3.408 \| 5.36 \| 8500 \| 4.2785 \| \| 3.407 \| 5.67 \| 9000 \| 4.2775 \| \| 3.401 \| 5.99 \| 9500 \| 4.2772 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
madoe001/a2c-AntBulletEnv-v0	madoe001	2023-07-16T12:58:37Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T12:56:52Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1411.03 +/- 55.48 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 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 ... ```
WALIDALI/lyrieldiff	WALIDALI	2023-07-16T12:55:45Z	2	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T12:50:56Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### LyrielDiff Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
larry-jiang/RL	larry-jiang	2023-07-16T12:48:55Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T12:47:54Z	--- 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: 256.32 +/- 20.65 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 ... ```
UholoDala/tweet_sentiments_analysis	UholoDala	2023-07-16T12:30:23Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-16T12:11:33Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: tweet_sentiments_analysis 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. --> # tweet_sentiments_analysis This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1411 - eval_f1-score: 0.9585 - eval_runtime: 62.6587 - eval_samples_per_second: 31.919 - eval_steps_per_second: 3.99 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Rihong/ppo-LunarLander-v2	Rihong	2023-07-16T12:20:44Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T12:19:16Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 272.93 +/- 18.31 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 ... ```
ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20	ALM-AHME	2023-07-16T12:15:04Z	199	1	transformers	[ "transformers", "pytorch", "tensorboard", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-16T09:38:10Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Splitted-Resized split: train args: Splitted-Resized metrics: - name: Accuracy type: accuracy value: 0.9943422913719944 --- <!-- 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. --> # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20 This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0229 - Accuracy: 0.9943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 0.5 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2053 \| 1.0 \| 199 \| 0.1227 \| 0.9496 \| \| 0.1302 \| 2.0 \| 398 \| 0.0665 \| 0.9736 \| \| 0.0784 \| 3.0 \| 597 \| 0.0600 \| 0.9778 \| \| 0.1181 \| 4.0 \| 796 \| 0.0449 \| 0.9849 \| \| 0.208 \| 5.0 \| 995 \| 0.0393 \| 0.9887 \| \| 0.0057 \| 6.0 \| 1194 \| 0.0229 \| 0.9943 \| \| 0.0017 \| 7.0 \| 1393 \| 0.0263 \| 0.9939 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
sjdata/speecht5_finetuned_single_speaker_en_test_librivox	sjdata	2023-07-16T12:09:19Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "en", "dataset:speecht5_finetuned_single_speaker_en_test_librivox", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-13T12:31:39Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - speecht5_finetuned_single_speaker_en_test_librivox model-index: - name: SpeechT5 Single Speaker test 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 Single Speaker test This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the single_speaker_en_test_librivox dataset. It achieves the following results on the evaluation set: - Loss: 0.4215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4809 \| 1.78 \| 1000 \| 0.4215 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v1	hafidikhsan	2023-07-16T11:14:40Z	103	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-16T11:12:03Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v1 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4017 - Accuracy: 0.25 - F1: 0.1 - Precision: 0.0625 - Recall: 0.25 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---:\|:---------:\|:------:\| \| 1.3826 \| 1.0 \| 500 \| 1.4017 \| 0.25 \| 0.1 \| 0.0625 \| 0.25 \| \| 1.4074 \| 2.0 \| 1000 \| 1.3922 \| 0.25 \| 0.1 \| 0.0625 \| 0.25 \| \| 1.3984 \| 3.0 \| 1500 \| 1.3868 \| 0.25 \| 0.1 \| 0.0625 \| 0.25 \| \| 1.387 \| 4.0 \| 2000 \| 1.3863 \| 0.25 \| 0.1 \| 0.0625 \| 0.25 \| \| 1.3861 \| 5.0 \| 2500 \| 1.3863 \| 0.25 \| 0.1 \| 0.0625 \| 0.25 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
gfs0508/AIron-Trans-PT2EN	gfs0508	2023-07-16T11:10:39Z	0	1	keras	[ "keras", "translation", "pt", "en", "license:mit", "region:us" ]	translation	2023-07-16T11:02:01Z	--- license: mit language: - pt - en library_name: keras pipeline_tag: translation --- # AIron-Trans-PT2EN ## License - MIT ## Overview AIron-Trans-PT2EN is a Portuguese to English translation model developed using the Keras library. ## Description AIron-Trans-PT2EN is a translation model that allows you to translate phrases and texts from Portuguese to English. It has been trained using the Long Short-Term Memory (LSTM) neural network architecture and implemented using the Keras library. ## Features - Translation from Portuguese to English - Model trained using the Keras library - LSTM architecture for better contextual understanding - Text preprocessing for improved translation quality ## Usage You can use this translation model in your own projects by following the instructions below: 1. Install the necessary dependencies (Keras, TensorFlow, etc.). 2. Load the trained model using the `load_model()` function from Keras. 3. Preprocess input sentences using the same preprocessing steps used during training. 4. Call the `translate_sentence()` function to get the translation of the input sentence. Code example: ```python from tensorflow import keras # Load the model model = keras.models.load_model('path/to/model.h5') # Preprocess the input sentence preprocessed_sentence = preprocess_sentence('Olá, como vai?') # Translate the sentence translated_sentence = translate_sentence(preprocessed_sentence, model) print(translated_sentence) ``` ## Contribution If you encounter any issues, have ideas for improvements, or would like to contribute to this project, feel free to open an issue or submit a pull request. We welcome contributions! ## Acknowledgments We would like to thank all contributors who helped develop and improve this translation model.
sagarsdesai/ppo-Huggy	sagarsdesai	2023-07-16T11:10:05Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-16T11:09:59Z	--- 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: sagarsdesai/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Beams24/nzyi	Beams24	2023-07-16T11:08:38Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T11:06:38Z	--- license: creativeml-openrail-m ---
hyunussarioglu/ppo-Huggy	hyunussarioglu	2023-07-16T11:04:45Z	42	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-16T11:04:39Z	--- 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: hyunussarioglu/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
NasimB/all-base-rarity-all-bnc-rarity-iorder-est-5p5k-mostf	NasimB	2023-07-16T11:04:22Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T09:15:19Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: all-base-rarity-all-bnc-rarity-iorder-est-5p5k-mostf results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-base-rarity-all-bnc-rarity-iorder-est-5p5k-mostf This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3552 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.7751 \| 0.31 \| 500 \| 5.6534 \| \| 5.4091 \| 0.63 \| 1000 \| 5.2238 \| \| 5.0666 \| 0.94 \| 1500 \| 4.9739 \| \| 4.7773 \| 1.25 \| 2000 \| 4.8259 \| \| 4.6406 \| 1.56 \| 2500 \| 4.7086 \| \| 4.5289 \| 1.88 \| 3000 \| 4.6001 \| \| 4.3302 \| 2.19 \| 3500 \| 4.5391 \| \| 4.2295 \| 2.5 \| 4000 \| 4.4722 \| \| 4.1833 \| 2.82 \| 4500 \| 4.4085 \| \| 4.0396 \| 3.13 \| 5000 \| 4.3880 \| \| 3.9019 \| 3.44 \| 5500 \| 4.3625 \| \| 3.8912 \| 3.75 \| 6000 \| 4.3198 \| \| 3.8042 \| 4.07 \| 6500 \| 4.3143 \| \| 3.6122 \| 4.38 \| 7000 \| 4.3069 \| \| 3.6013 \| 4.69 \| 7500 \| 4.2897 \| \| 3.5881 \| 5.01 \| 8000 \| 4.2790 \| \| 3.4114 \| 5.32 \| 8500 \| 4.2918 \| \| 3.4083 \| 5.63 \| 9000 \| 4.2889 \| \| 3.4077 \| 5.94 \| 9500 \| 4.2889 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
vlkn/falcon_instruct_6	vlkn	2023-07-16T10:57:41Z	0	0	null	[ "tensorboard", "generated_from_trainer", "region:us" ]	null	2023-07-16T10:50:11Z	--- tags: - generated_from_trainer model-index: - name: falcon_instruct_6 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. --> # falcon_instruct_6 This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 30 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Kapiche/msmarco-MiniLM-L6-cos-v5	Kapiche	2023-07-16T10:31:33Z	7	0	sentence-transformers	[ "sentence-transformers", "pytorch", "tf", "jax", "rust", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "arxiv:1908.10084", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-02-02T22:27:10Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # msmarco-MiniLM-L6-cos-v5 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L6-cos-v5') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take average of all tokens def mean_pooling(model_output, attention_mask): token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") model = AutoModel.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Technical Details In the following some technical details how this model must be used: \| Setting \| Value \| \| --- \| :---: \| \| Dimensions \| 384 \| \| Produces normalized embeddings \| Yes \| \| Pooling-Method \| Mean pooling \| \| Suitable score functions \| dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance \| Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used. ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
neurae/albert-dnd-intents	neurae	2023-07-16T09:38:16Z	108	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "en", "dataset:neurae/dnd_style_intents", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-16T09:58:57Z	--- datasets: - neurae/dnd_style_intents language: - en pipeline_tag: text-classification license: apache-2.0 metrics: - accuracy - f1 --- This is albert base tuned with optimal lr, lr scheduler and weight decay on dnd-style-intents dataset. \| parametrs \| value \| \|---------------\|----------\| \| learning rate \| 5e-5 \| \| lr scheduler \| linear \| \| weight decay \| 0 \| Model has next metrics on test data from dataset \| metric \| value \| \|----------\|-------\| \| accuracy \| 0.981 \| \| Macro F1 \| 0.979 \| \| Micro F1 \| 0.985 \|
Xxmlala/q-FrozenLake-v1-4x4-noSlippery	Xxmlala	2023-07-16T09:29:27Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T09:29:19Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Xxmlala/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
NasimB/guten-rarity-all-end-19k-ctx-512-finegrained-eval	NasimB	2023-07-16T09:07:24Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T07:08:53Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-rarity-all-end-19k-ctx-512-finegrained-eval results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # guten-rarity-all-end-19k-ctx-512-finegrained-eval This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.2215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.9362 \| 0.24 \| 100 \| 7.3168 \| \| 6.5524 \| 0.48 \| 200 \| 6.1279 \| \| 5.9236 \| 0.71 \| 300 \| 5.7874 \| \| 5.6556 \| 0.95 \| 400 \| 5.5952 \| \| 5.4733 \| 1.19 \| 500 \| 5.4416 \| \| 5.2958 \| 1.43 \| 600 \| 5.2824 \| \| 5.1307 \| 1.66 \| 700 \| 5.1223 \| \| 4.9829 \| 1.9 \| 800 \| 4.9860 \| \| 4.8024 \| 2.14 \| 900 \| 4.8963 \| \| 4.6927 \| 2.38 \| 1000 \| 4.7992 \| \| 4.6095 \| 2.61 \| 1100 \| 4.6988 \| \| 4.516 \| 2.85 \| 1200 \| 4.6015 \| \| 4.3713 \| 3.09 \| 1300 \| 4.5147 \| \| 4.2277 \| 3.33 \| 1400 \| 4.4417 \| \| 4.1862 \| 3.56 \| 1500 \| 4.3820 \| \| 4.1371 \| 3.8 \| 1600 \| 4.3342 \| \| 4.059 \| 4.04 \| 1700 \| 4.2893 \| \| 3.8884 \| 4.28 \| 1800 \| 4.2612 \| \| 3.8665 \| 4.51 \| 1900 \| 4.2299 \| \| 3.8437 \| 4.75 \| 2000 \| 4.1981 \| \| 3.815 \| 4.99 \| 2100 \| 4.1766 \| \| 3.6574 \| 5.23 \| 2200 \| 4.1724 \| \| 3.6435 \| 5.46 \| 2300 \| 4.1629 \| \| 3.6348 \| 5.7 \| 2400 \| 4.1584 \| \| 3.6424 \| 5.94 \| 2500 \| 4.1557 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
rakaaa/tree-lora	rakaaa	2023-07-16T09:01:55Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-16T07:30:55Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - rakaaa/tree-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-80-10-10	hafidikhsan	2023-07-16T08:55:00Z	123	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-16T08:51:55Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-80-10-10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-80-10-10 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7905 - Accuracy: 0.816 - F1: 0.8141 - Precision: 0.8148 - Recall: 0.816 ## Model description More information needed ## Intended uses & 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: 8 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.7212 \| 1.0 \| 500 \| 0.8608 \| 0.622 \| 0.6089 \| 0.6237 \| 0.622 \| \| 0.7321 \| 2.0 \| 1000 \| 0.7336 \| 0.688 \| 0.6819 \| 0.6884 \| 0.688 \| \| 0.4413 \| 3.0 \| 1500 \| 0.6422 \| 0.774 \| 0.7727 \| 0.7729 \| 0.774 \| \| 0.3669 \| 4.0 \| 2000 \| 1.0008 \| 0.754 \| 0.7521 \| 0.7606 \| 0.754 \| \| 0.0219 \| 5.0 \| 2500 \| 0.9872 \| 0.782 \| 0.7808 \| 0.7801 \| 0.782 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
SADAF-IMAMU/train	SADAF-IMAMU	2023-07-16T08:54:59Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-25T09:54:23Z	--- tags: - generated_from_trainer metrics: - precision - recall - accuracy model-index: - name: train 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. --> # train This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9948 - Macro F1: 0.7856 - Precision: 0.7820 - Recall: 0.7956 - Kappa: 0.6940 - Accuracy: 0.7956 ## Model description More information needed ## Intended uses & 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: 128 - seed: 25 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Macro F1 \| Precision \| Recall \| Kappa \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 101 \| 1.1562 \| 0.6031 \| 0.5561 \| 0.7044 \| 0.4967 \| 0.7044 \| \| No log \| 2.0 \| 203 \| 0.9119 \| 0.7151 \| 0.7107 \| 0.7672 \| 0.6236 \| 0.7672 \| \| No log \| 3.0 \| 304 \| 0.8493 \| 0.7280 \| 0.7139 \| 0.7734 \| 0.6381 \| 0.7734 \| \| No log \| 4.0 \| 406 \| 0.8087 \| 0.7455 \| 0.7632 \| 0.7648 \| 0.6421 \| 0.7648 \| \| 0.9431 \| 5.0 \| 507 \| 0.7735 \| 0.7779 \| 0.7741 \| 0.7931 \| 0.6858 \| 0.7931 \| \| 0.9431 \| 6.0 \| 609 \| 0.8201 \| 0.7753 \| 0.7735 \| 0.7869 \| 0.6797 \| 0.7869 \| \| 0.9431 \| 7.0 \| 710 \| 0.8564 \| 0.7886 \| 0.7883 \| 0.8017 \| 0.7004 \| 0.8017 \| \| 0.9431 \| 8.0 \| 812 \| 0.8712 \| 0.7799 \| 0.7754 \| 0.7894 \| 0.6854 \| 0.7894 \| \| 0.9431 \| 9.0 \| 913 \| 0.9142 \| 0.7775 \| 0.7751 \| 0.7869 \| 0.6811 \| 0.7869 \| \| 0.2851 \| 10.0 \| 1015 \| 0.9007 \| 0.7820 \| 0.7764 \| 0.7943 \| 0.6913 \| 0.7943 \| \| 0.2851 \| 11.0 \| 1116 \| 0.9425 \| 0.7859 \| 0.7825 \| 0.7956 \| 0.6940 \| 0.7956 \| \| 0.2851 \| 12.0 \| 1218 \| 0.9798 \| 0.7815 \| 0.7797 \| 0.7906 \| 0.6869 \| 0.7906 \| \| 0.2851 \| 13.0 \| 1319 \| 0.9895 \| 0.7895 \| 0.7860 \| 0.7993 \| 0.7003 \| 0.7993 \| \| 0.2851 \| 14.0 \| 1421 \| 0.9872 \| 0.7854 \| 0.7813 \| 0.7943 \| 0.6935 \| 0.7943 \| \| 0.1273 \| 14.93 \| 1515 \| 0.9948 \| 0.7856 \| 0.7820 \| 0.7956 \| 0.6940 \| 0.7956 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
BaleChen/REINFORCE-pixelcopter-test	BaleChen	2023-07-16T08:51:54Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T07:58:26Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: REINFORCE-pixelcopter-test results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.80 +/- 26.25 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . 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
manmyung/a2c-PandaReachDense-v2	manmyung	2023-07-16T08:43:12Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T08:40:02Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.88 +/- 0.21 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
Hans14/PPO-LunarLander-v2	Hans14	2023-07-16T08:42:14Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T08:41:32Z	--- 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: 254.99 +/- 12.40 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 ... ```
li-ping/falcon_300csv_to_sheng	li-ping	2023-07-16T08:39:26Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T08:28:11Z	--- 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: float32 ### Framework versions - PEFT 0.4.0.dev0
esculapeso/biogpt-finetuned-twspookytest	esculapeso	2023-07-16T08:33:13Z	130	0	transformers	[ "transformers", "pytorch", "tensorboard", "biogpt", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T07:47:54Z	--- license: mit tags: - generated_from_trainer model-index: - name: biogpt-finetuned-twspookytest 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. --> # biogpt-finetuned-twspookytest This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9607 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 3 \| 3.1962 \| \| No log \| 2.0 \| 6 \| 3.0132 \| \| No log \| 3.0 \| 9 \| 2.9607 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
openlm-research/open_llama_3b_v2	openlm-research	2023-07-16T08:32:00Z	25,360	149	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:tiiuae/falcon-refinedweb", "dataset:bigcode/starcoderdata", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T00:39:43Z	--- license: apache-2.0 datasets: - tiiuae/falcon-refinedweb - bigcode/starcoderdata - togethercomputer/RedPajama-Data-1T --- # OpenLLaMA: An Open Reproduction of LLaMA TL;DR: we are releasing our public preview of OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA. We are releasing a series of 3B, 7B and 13B models trained on different data mixtures. Our model weights can serve as the drop in replacement of LLaMA in existing implementations. In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a series of 3B, 7B and 13B models trained on 1T tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. The v2 model is better than the old v1 model trained on a different data mixture. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details. ## Weights Release, License and Usage We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license. ### Loading the Weights with Hugging Face Transformers Preview checkpoints can be directly loaded from Hugging Face Hub. Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that [the auto-converted fast tokenizer sometimes gives incorrect tokenizations](https://github.com/huggingface/transformers/issues/24233). This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage. ```python import torch from transformers import LlamaTokenizer, LlamaForCausalLM ## v2 models model_path = 'openlm-research/open_llama_3b_v2' # model_path = 'openlm-research/open_llama_7b_v2' ## v1 models # model_path = 'openlm-research/open_llama_3b' # model_path = 'openlm-research/open_llama_7b' # model_path = 'openlm-research/open_llama_13b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) prompt = 'Q: What is the largest animal?\nA:' input_ids = tokenizer(prompt, return_tensors="pt").input_ids generation_output = model.generate( input_ids=input_ids, max_new_tokens=32 ) print(tokenizer.decode(generation_output[0])) ``` For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama). ### Evaluating with LM-Eval-Harness The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below: ```python tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained( pretrained if tokenizer is None else tokenizer, revision=revision + ("/" + subfolder if subfolder is not None else ""), use_fast=False ) ``` ### Loading the Weights with EasyLM For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. ## Dataset and Training The v1 models are trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). The v2 models are trained on a mixture of the [Falcon refined-web dataset](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), the [StarCoder dataset](https://huggingface.co/datasets/bigcode/starcoderdata) and the wikipedia, arxiv, book and stackexchange part of the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs open datasets rather than the one utilized by the original LLaMA. We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and fully sharded data parallelism [](https://engineering.fb.com/2021/07/15/open-source/fsdp/)(also know as ZeRO stage 3) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model. ## Evaluation We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. \| Task/Metric \| GPT-J 6B \| LLaMA 7B \| LLaMA 13B \| OpenLLaMA 3Bv2 \| OpenLLaMA 7Bv2 \| OpenLLaMA 3B \| OpenLLaMA 7B \| OpenLLaMA 13B \| \| ---------------------- \| -------- \| -------- \| --------- \| -------------- \| -------------- \| ------------ \| ------------ \| ------------- \| \| anli_r1/acc \| 0.32 \| 0.35 \| 0.35 \| 0.33 \| 0.34 \| 0.33 \| 0.33 \| 0.33 \| \| anli_r2/acc \| 0.34 \| 0.34 \| 0.36 \| 0.36 \| 0.35 \| 0.32 \| 0.36 \| 0.33 \| \| anli_r3/acc \| 0.35 \| 0.37 \| 0.39 \| 0.38 \| 0.39 \| 0.35 \| 0.38 \| 0.40 \| \| arc_challenge/acc \| 0.34 \| 0.39 \| 0.44 \| 0.34 \| 0.39 \| 0.34 \| 0.37 \| 0.41 \| \| arc_challenge/acc_norm \| 0.37 \| 0.41 \| 0.44 \| 0.36 \| 0.41 \| 0.37 \| 0.38 \| 0.44 \| \| arc_easy/acc \| 0.67 \| 0.68 \| 0.75 \| 0.68 \| 0.73 \| 0.69 \| 0.72 \| 0.75 \| \| arc_easy/acc_norm \| 0.62 \| 0.52 \| 0.59 \| 0.63 \| 0.70 \| 0.65 \| 0.68 \| 0.70 \| \| boolq/acc \| 0.66 \| 0.75 \| 0.71 \| 0.66 \| 0.72 \| 0.68 \| 0.71 \| 0.75 \| \| hellaswag/acc \| 0.50 \| 0.56 \| 0.59 \| 0.52 \| 0.56 \| 0.49 \| 0.53 \| 0.56 \| \| hellaswag/acc_norm \| 0.66 \| 0.73 \| 0.76 \| 0.70 \| 0.75 \| 0.67 \| 0.72 \| 0.76 \| \| openbookqa/acc \| 0.29 \| 0.29 \| 0.31 \| 0.26 \| 0.30 \| 0.27 \| 0.30 \| 0.31 \| \| openbookqa/acc_norm \| 0.38 \| 0.41 \| 0.42 \| 0.38 \| 0.41 \| 0.40 \| 0.40 \| 0.43 \| \| piqa/acc \| 0.75 \| 0.78 \| 0.79 \| 0.77 \| 0.79 \| 0.75 \| 0.76 \| 0.77 \| \| piqa/acc_norm \| 0.76 \| 0.78 \| 0.79 \| 0.78 \| 0.80 \| 0.76 \| 0.77 \| 0.79 \| \| record/em \| 0.88 \| 0.91 \| 0.92 \| 0.87 \| 0.89 \| 0.88 \| 0.89 \| 0.91 \| \| record/f1 \| 0.89 \| 0.91 \| 0.92 \| 0.88 \| 0.89 \| 0.89 \| 0.90 \| 0.91 \| \| rte/acc \| 0.54 \| 0.56 \| 0.69 \| 0.55 \| 0.57 \| 0.58 \| 0.60 \| 0.64 \| \| truthfulqa_mc/mc1 \| 0.20 \| 0.21 \| 0.25 \| 0.22 \| 0.23 \| 0.22 \| 0.23 \| 0.25 \| \| truthfulqa_mc/mc2 \| 0.36 \| 0.34 \| 0.40 \| 0.35 \| 0.35 \| 0.35 \| 0.35 \| 0.38 \| \| wic/acc \| 0.50 \| 0.50 \| 0.50 \| 0.50 \| 0.50 \| 0.48 \| 0.51 \| 0.47 \| \| winogrande/acc \| 0.64 \| 0.68 \| 0.70 \| 0.63 \| 0.66 \| 0.62 \| 0.67 \| 0.70 \| \| Average \| 0.52 \| 0.55 \| 0.57 \| 0.53 \| 0.56 \| 0.53 \| 0.55 \| 0.57 \| We removed the task CB and WSC from our benchmark, as our model performs suspiciously high on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. ## Contact We would love to get feedback from the community. If you have any questions, please open an issue or contact us. OpenLLaMA is developed by: [Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research. *Equal Contribution ## Acknowledgment We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback. The OpenLLaMA 13B v1 model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support. ## Reference If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX: ``` @software{openlm2023openllama, author = {Geng, Xinyang and Liu, Hao}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ``` @article{touvron2023llama, title={Llama: Open and efficient foundation language models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```
watcharakorn/whisper-small-th-v2	watcharakorn	2023-07-16T08:24:22Z	77	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "th-asr-leaderboard", "generated_from_trainer", "th", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-16T08:21:55Z	--- language: - th license: apache-2.0 base_model: openai/whisper-small tags: - th-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small th - mix dataset v.2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: th split: test args: 'config: th, split: test' metrics: - name: Wer type: wer value: 0.37791454289122656 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small th - mix dataset v.2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2980 - Wer: 0.3779 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.3654 \| 0.26 \| 1000 \| 0.2980 \| 0.3779 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
digiplay/polla_mix_2.4D	digiplay	2023-07-16T08:23:45Z	334	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T06:56:58Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/110130?modelVersionId=118734 Simple image I made thru Huggingface's API : ![827c852a-171d-4875-a5d1-f226ca9e82ae.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/XBES3_gljSSXufKpgJNoE.jpeg) prompt : > pink spider with pink heart symbol *Original Author's DEMO images :* ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d3bcc3e4-5612-4947-9f9c-c76e5347b67d/width=1024/00055-114656479-1boy,%20armor,%20arthur_pendragon_(fate),%20blonde_hair,%20commentary_request,%20fate_prototype,%20fate_(series),%20green_eyes,%20hood,%20male_foc.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6aa33f59-4e0d-48a3-84e9-ef9c2aa1ba4d/width=1024/00125-1371705934-1boy,%20bad_id,%20bad_pixiv_id,%20bandage_on_face,%20bandages,%20black_hair,%20blue_eyes,%20chain,%20hat,%20jojo_no_kimyou_na_bouken,%20kujo_jotaro,.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/05ded902-bd43-4ad5-8aaa-53ca6b2b1f7d/width=1024/00052-114656476-1girl,%20black_hair,%20blush,%20closed_eyes,%20closed_mouth,%20commentary_request,%20drawn_wings,%20from_side,%20haikyuu!!,%20happy_birthday,%20jack.jpeg)
Ricky1981/Lgmx	Ricky1981	2023-07-16T08:19:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T07:33:57Z	--- license: creativeml-openrail-m ---
gpcarl123/resnet18_mnist	gpcarl123	2023-07-16T08:16:35Z	0	0	timm	[ "timm", "en", "dataset:mnist", "model-index", "region:us" ]	null	2023-07-16T07:48:41Z	--- language: - en library_name: timm datasets: - mnist metrics: - accuracy model-index: - name: resnet18_mnist results: - task: type: image-classification dataset: name: MNIST type: mnist metrics: - type: accuracy value: 0.9936 --- # Usage ```python import timm import torchvision MNIST_PATH = './datasets/mnist' net = timm.create_model("resnet18", pretrained=False, num_classes=10) net.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) net.load_state_dict( torch.hub.load_state_dict_from_url( "https://huggingface.co/gpcarl123/resnet18_mnist/resolve/main/resnet18_mnist.pth", map_location="cpu", file_name="resnet18_mnist.pth", ) ) preprocessor = torchvision.transforms.Normalize((0.1307,), (0.3081,)) transform = transforms.Compose([transforms.ToTensor()]) test_set = datasets.MNIST(root=MNIST_PATH, train=False, download=True, transform=transform) test_loader = data.DataLoader(test_set, batch_size=5, shuffle=False, num_workers=2) for data, target in test_loader: print(net(preprocessor(data))) print(target) break ```
digiplay/polla_mix_2.5D	digiplay	2023-07-16T07:56:07Z	50	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T06:57:17Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/110130?modelVersionId=118741 Sample image I made thru Huggingface's API : ![012d0578-2ea4-4041-986c-411c3ebd2460.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/pyWHxPdufQnASl8CNW5qT.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/486ce102-be6c-4ea6-9a2c-3790a8e034b7/00010-1892571106-1girl,%20black_gloves,%20blonde_hair,%20blue_eyes,%20breasts,%20dress,%20fang,%20gloves,%20hair_ribbon,%20hat,%20holding,%20long_hair,%20open_mouth,%20pan.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9ad286ca-8c62-4d45-980a-c0993ed05259/00297-1401259144-1boy,%20bag,%20baseball_cap,%20black_background,%20black_bag,%20black_pants,%20blue_jacket,%20brown_eyes,%20brown_hair,%20bubble,%20commentary_reque.jpeg)
shihab17/bengali-bn-to-en	shihab17	2023-07-16T07:51:36Z	25	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "marian", "text2text-generation", "translation", "bn", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-05-23T10:43:30Z	--- library_name: transformers pipeline_tag: translation language: - bn --- ### How to use You can use this model directly with a pipeline: ```python from transformers import AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("shihab17/bengali-bn-to-en") model = AutoModelForSeq2SeqLM.from_pretrained("shihab17/bengali-bn-to-en") sentence = 'ম্যাচ শেষে পুরস্কার বিতরণের মঞ্চে তামিমের মুখে মোস্তাফিজের প্রশংসা শোনা গেল' translator = pipeline("translation_bn_to_en", model=model, tokenizer=tokenizer) output = translator(sentence) print(output) ```
murakami-dev/distilbert-base-uncased-finetuned-emotion	murakami-dev	2023-07-16T07:47:19Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-16T07:36:53Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.922976796795522 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2127 - Accuracy: 0.923 - F1: 0.9230 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7809 \| 1.0 \| 250 \| 0.3023 \| 0.9045 \| 0.9005 \| \| 0.2412 \| 2.0 \| 500 \| 0.2127 \| 0.923 \| 0.9230 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
digiplay/zodiac_eclipse_DAY1	digiplay	2023-07-16T07:40:46Z	285	2	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-14T08:32:01Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/108417/zodiac-eclipse-day1 Sample image I made thru Huggingface's API : ``` dog eat mango icecream ``` ![ebf8952b-9a34-4ddf-bacd-91cf85948371.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/e-i8jFIezPLNgYy_aRlao.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/21729b08-1f65-4029-bda3-e48d1ec00d3c/02569-2195959801-((best%20quality)),%20((masterpiece)),%20(detailed),%20alluring%20succubus,%20ethereal%20beauty,%20perched%20on%20a%20cloud,%20(fantasy%20illustration_1.3.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9a15ae22-804b-41e0-ba00-54fd2533d49b/02548-1697534186-monster,(((lake))),.jpeg)
tuanhnh/Reinforce-0	tuanhnh	2023-07-16T07:30:47Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T07:30:40Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 453.60 +/- 109.04 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
li-ping/falcon_0csv_to_sheng	li-ping	2023-07-16T07:12:27Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-16T06:56:45Z	--- 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: float32 ### Framework versions - PEFT 0.4.0.dev0
laserchalk/kangaroo-training-part-10	laserchalk	2023-07-16T06:53:40Z	6	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T06:39:24Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### kangaroo-training-part-10 Dreambooth model trained by laserchalk 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:
NasimB/guten-rarity-all-cut-20k	NasimB	2023-07-16T06:43:28Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T04:47:07Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-rarity-all-cut-20k 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. --> # guten-rarity-all-cut-20k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3156 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6961 \| 0.29 \| 500 \| 5.6410 \| \| 5.3325 \| 0.58 \| 1000 \| 5.1967 \| \| 4.9884 \| 0.88 \| 1500 \| 4.9500 \| \| 4.7101 \| 1.17 \| 2000 \| 4.8016 \| \| 4.5563 \| 1.46 \| 2500 \| 4.6746 \| \| 4.446 \| 1.75 \| 3000 \| 4.5705 \| \| 4.3244 \| 2.05 \| 3500 \| 4.4929 \| \| 4.1291 \| 2.34 \| 4000 \| 4.4489 \| \| 4.0992 \| 2.63 \| 4500 \| 4.3891 \| \| 4.0577 \| 2.92 \| 5000 \| 4.3368 \| \| 3.8593 \| 3.21 \| 5500 \| 4.3329 \| \| 3.8077 \| 3.51 \| 6000 \| 4.3001 \| \| 3.778 \| 3.8 \| 6500 \| 4.2669 \| \| 3.6848 \| 4.09 \| 7000 \| 4.2684 \| \| 3.513 \| 4.38 \| 7500 \| 4.2630 \| \| 3.5142 \| 4.68 \| 8000 \| 4.2467 \| \| 3.4975 \| 4.97 \| 8500 \| 4.2338 \| \| 3.3389 \| 5.26 \| 9000 \| 4.2463 \| \| 3.3207 \| 5.55 \| 9500 \| 4.2462 \| \| 3.3201 \| 5.84 \| 10000 \| 4.2453 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
bochen0909/ppo-LunarLander-v2	bochen0909	2023-07-16T06:41:23Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T06:41:08Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.97 +/- 20.22 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 ... ```
BaleChen/REINFORCE-cartpolev1-test	BaleChen	2023-07-16T06:39:08Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T06:38:57Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: REINFORCE-cartpolev1-test results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 456.90 +/- 89.34 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
NasimB/bnc-rarity-no-cut-shuffled	NasimB	2023-07-16T06:24:06Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T04:27:02Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-rarity-no-cut-shuffled 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. --> # bnc-rarity-no-cut-shuffled This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3207 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7157 \| 0.29 \| 500 \| 5.6437 \| \| 5.3513 \| 0.58 \| 1000 \| 5.2021 \| \| 5.0016 \| 0.88 \| 1500 \| 4.9595 \| \| 4.7286 \| 1.17 \| 2000 \| 4.8122 \| \| 4.5693 \| 1.46 \| 2500 \| 4.6857 \| \| 4.4647 \| 1.75 \| 3000 \| 4.5770 \| \| 4.3308 \| 2.05 \| 3500 \| 4.5068 \| \| 4.1402 \| 2.34 \| 4000 \| 4.4574 \| \| 4.1123 \| 2.63 \| 4500 \| 4.3983 \| \| 4.0711 \| 2.92 \| 5000 \| 4.3468 \| \| 3.8657 \| 3.22 \| 5500 \| 4.3414 \| \| 3.8086 \| 3.51 \| 6000 \| 4.3099 \| \| 3.7977 \| 3.8 \| 6500 \| 4.2728 \| \| 3.6947 \| 4.09 \| 7000 \| 4.2729 \| \| 3.5188 \| 4.39 \| 7500 \| 4.2684 \| \| 3.5211 \| 4.68 \| 8000 \| 4.2523 \| \| 3.5159 \| 4.97 \| 8500 \| 4.2387 \| \| 3.3414 \| 5.26 \| 9000 \| 4.2532 \| \| 3.3357 \| 5.56 \| 9500 \| 4.2520 \| \| 3.328 \| 5.85 \| 10000 \| 4.2517 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
lovelyxs/rl_course_vizdoom_health_gathering_supreme	lovelyxs	2023-07-16T05:56:49Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T05:56:44Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 13.28 +/- 4.85 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r lovelyxs/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
ardhies/vira	ardhies	2023-07-16T05:48:55Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-23T00:46:30Z	--- license: creativeml-openrail-m ---
Vasanth/distilbert-stock-tweet-sentiment-analysis	Vasanth	2023-07-16T05:26:06Z	185	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-16T05:15:36Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-stock-tweet-sentiment-analysis 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-stock-tweet-sentiment-analysis This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6075 - Accuracy: 0.782 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.686 \| 1.0 \| 1000 \| 0.5916 \| 0.7745 \| \| 0.4804 \| 2.0 \| 2000 \| 0.5635 \| 0.7812 \| \| 0.3644 \| 3.0 \| 3000 \| 0.6075 \| 0.782 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ltmai/morgan-embed-bio-clinical-bert-ddi	ltmai	2023-07-16T05:24:59Z	31	0	transformers	[ "transformers", "pytorch", "bert", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-07-15T18:38:02Z	--- tags: - generated_from_trainer model-index: - name: morgan-embed-bio-clinical-bert-ddi 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. --> # morgan-embed-bio-clinical-bert-ddi This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.000628 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3
hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-60-20-20	hafidikhsan	2023-07-16T05:24:57Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-16T05:22:44Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-60-20-20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-60-20-20 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9015 - Accuracy: 0.743 - F1: 0.7432 - Precision: 0.7495 - Recall: 0.743 ## Model description More information needed ## Intended uses & 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: 8 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.8727 \| 1.0 \| 375 \| 0.8777 \| 0.574 \| 0.5558 \| 0.5611 \| 0.574 \| \| 0.834 \| 2.0 \| 750 \| 0.8086 \| 0.652 \| 0.6470 \| 0.6558 \| 0.652 \| \| 0.6609 \| 3.0 \| 1125 \| 0.8289 \| 0.695 \| 0.6926 \| 0.6945 \| 0.695 \| \| 0.329 \| 4.0 \| 1500 \| 0.9585 \| 0.755 \| 0.7558 \| 0.7607 \| 0.755 \| \| 0.1628 \| 5.0 \| 1875 \| 1.1191 \| 0.751 \| 0.7488 \| 0.7479 \| 0.751 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Denilah/distilbert-base-uncased-finetuned-emotion	Denilah	2023-07-16T05:15:46Z	106	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-16T03:24:16Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.937 - name: F1 type: f1 value: 0.9373121473490384 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1565 - Accuracy: 0.937 - F1: 0.9373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4774 \| 1.0 \| 1000 \| 0.1971 \| 0.923 \| 0.9226 \| \| 0.147 \| 2.0 \| 2000 \| 0.1565 \| 0.937 \| 0.9373 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
j-hyeok/taxi-v3	j-hyeok	2023-07-16T04:27:10Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T04:27:06Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 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="j-hyeok/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"]) ```
j-hyeok/q-FrozenLake-v1-4x4-noSlippery	j-hyeok	2023-07-16T04:22:13Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-16T04:22:07Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="j-hyeok/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
SerCe/tortoise-tts-ruslan	SerCe	2023-07-16T04:21:51Z	0	9	null	[ "license:apache-2.0", "region:us" ]	null	2023-06-07T10:12:18Z	--- license: apache-2.0 --- The _tortoise-tts-ruslan_ model is a tortoise model capable of speaking Russian language. The model was first finetuned on public_youtube700_val+buriy_audiobooks_2_val from [Russian Open Speech To Text](https://learn.microsoft.com/en-us/azure/open-datasets/dataset-open-speech-text?tabs=azure-storage). The model was then finetuned on [RUSLAN: Russian Spoken Language Corpus For Speech Synthesis](https://ruslan-corpus.github.io/). The model is able to generate generic male voices, see [examples](https://huggingface.co/SerCe/tortoise-tts-ruslan/tree/main/examples/random). Additionally, the model is suitable for further finetuning on any Russian male voice, e.g. see a [finetuned](https://huggingface.co/SerCe/tortoise-tts-ruslan/tree/main/examples/finetuned) voice of Yury Dud (the finetuned model weights are not included). The code from [ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning) was used to train the model.
NasimB/children-rarity-all-guten-log-rarity-all	NasimB	2023-07-16T04:21:14Z	9	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-16T02:19:49Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: children-rarity-all-guten-log-rarity-all 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. --> # children-rarity-all-guten-log-rarity-all This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3116 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7036 \| 0.29 \| 500 \| 5.6365 \| \| 5.348 \| 0.58 \| 1000 \| 5.2064 \| \| 4.99 \| 0.87 \| 1500 \| 4.9589 \| \| 4.7208 \| 1.16 \| 2000 \| 4.8071 \| \| 4.5602 \| 1.46 \| 2500 \| 4.6761 \| \| 4.4513 \| 1.75 \| 3000 \| 4.5690 \| \| 4.3332 \| 2.04 \| 3500 \| 4.4907 \| \| 4.1308 \| 2.33 \| 4000 \| 4.4479 \| \| 4.1002 \| 2.62 \| 4500 \| 4.3912 \| \| 4.0711 \| 2.91 \| 5000 \| 4.3370 \| \| 3.8621 \| 3.2 \| 5500 \| 4.3334 \| \| 3.803 \| 3.49 \| 6000 \| 4.3002 \| \| 3.7865 \| 3.79 \| 6500 \| 4.2683 \| \| 3.6992 \| 4.08 \| 7000 \| 4.2633 \| \| 3.5158 \| 4.37 \| 7500 \| 4.2591 \| \| 3.5163 \| 4.66 \| 8000 \| 4.2433 \| \| 3.501 \| 4.95 \| 8500 \| 4.2300 \| \| 3.3525 \| 5.24 \| 9000 \| 4.2437 \| \| 3.3213 \| 5.53 \| 9500 \| 4.2424 \| \| 3.3235 \| 5.82 \| 10000 \| 4.2416 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
laserchalk/kangaroo-training-part-7	laserchalk	2023-07-16T04:15:03Z	2	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-16T04:04:01Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Kangaroo-training-part-7 Dreambooth model trained by laserchalk 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:
1112lee/setfit-model	1112lee	2023-07-16T03:42:36Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-16T03:28:00Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # 1112lee/setfit-model 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("1112lee/setfit-model") # 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} } ```
Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5	Evan-Lin	2023-07-16T03:40:20Z	48	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-07-15T19:51:18Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
OptimalScale/robin-13b-v2-delta	OptimalScale	2023-07-16T03:14:08Z	1,546	7	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2302.13971", "arxiv:2306.12420", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-05-28T05:55:54Z	--- inference: false --- # Robin Model Card ## Model Details Robin is a series of models finetuned from LLaMA on several high-quality data. - Developed by: [LMFlow](https://github.com/OptimalScale/LMFlow/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/OptimalScale/LMFlow/ - Blog: https://medium.com/@hkust.ml/robin-v2-launches-achieves-unparalleled-performance-on-openllm-4f6886e822c1 - Paper: https://arxiv.org/abs/2306.12420 - Demo: https://lmflow.com/ ## Uses Robin is primarily utilized for conducting research on extensive language models and chatbots, catering to users specializing in natural language processing, machine learning, and artificial intelligence research. ## How to Get Started with the Model We provide four kinds of demos including: - Online Service: If you don't want to run any code and just want to try our models, we deploy our instruction-tuned LLaMA you to have a try. - Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab. - Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab. - Local Deploy: We also provide a way for you to deploy your model/chatbot locally, which means you can deploy much larger model than previous three methods if you have enough resource. Please refer to https://github.com/OptimalScale/LMFlow#demos ## Training Details Expanding upon the initial idea of self-instruct techniques, we incorporated several different data sources and build a new dataset called [LMFlow Dataset](http://lmflow.org:5000/lmflow_data.tar.gz). The new training split is created by merging the following datasets: - ShareGPT: randomly sample 50K English data and 10K Chinese data from ShareGPT. - GPT-4-LLM: 52K English data from GPT-4-LLM. - BELLE: randomly sample 80K Chinese data from BELLE. See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Evaluation Robin is evaluated with [LMFlow Benchmark](https://blog.gopenai.com/lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms-ef5c6f142418). See more details in this [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Citation If you find this repository useful, please consider giving ⭐ and citing our [paper](https://arxiv.org/abs/2306.12420): ``` @misc{lmflow, author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang}, title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://optimalscale.github.io/LMFlow/}}, } ```
ALM-AHME/convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20	ALM-AHME	2023-07-16T03:13:16Z	12	1	transformers	[ "transformers", "pytorch", "tensorboard", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-15T00:35:53Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Splitted-Resized split: train args: Splitted-Resized metrics: - name: Accuracy type: accuracy value: 0.9900990099009901 --- <!-- 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. --> # convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0353 - 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-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5207 \| 1.0 \| 199 \| 0.4745 \| 0.8887 \| \| 0.2029 \| 2.0 \| 398 \| 0.2072 \| 0.9401 \| \| 0.1615 \| 3.0 \| 597 \| 0.1489 \| 0.9547 \| \| 0.1662 \| 4.0 \| 796 \| 0.1312 \| 0.9562 \| \| 0.1986 \| 5.0 \| 995 \| 0.1026 \| 0.9698 \| \| 0.0854 \| 6.0 \| 1194 \| 0.0583 \| 0.9802 \| \| 0.0538 \| 7.0 \| 1393 \| 0.0568 \| 0.9835 \| \| 0.0977 \| 8.0 \| 1592 \| 0.0654 \| 0.9793 \| \| 0.6971 \| 9.0 \| 1791 \| 0.6821 \| 0.5450 \| \| 0.211 \| 10.0 \| 1990 \| 0.1654 \| 0.9326 \| \| 0.1775 \| 11.0 \| 2189 \| 0.0859 \| 0.9665 \| \| 0.0042 \| 12.0 \| 2388 \| 0.0353 \| 0.9901 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
GarbageCollector/EFX2	GarbageCollector	2023-07-16T03:07:37Z	0	0	null	[ "stable-diffusion", "safetensors", "text-to-image", "license:unknown", "region:us" ]	text-to-image	2023-07-16T02:27:12Z	--- tags: - stable-diffusion - safetensors pipeline_tag: text-to-image license: unknown --- <p>this place is my garbage collection.<br> some models are not better than others.</p> <p>___SAMPLES___</p> <p>LOOMER<br> <img src="https://huggingface.co/GarbageCollector/EFX2/resolve/main/samples/LOOMER.jpg"/> </p>
OptimalScale/robin-65b-v2-delta	OptimalScale	2023-07-16T02:48:33Z	1,534	12	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2302.13971", "arxiv:2306.12420", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-06-11T06:48:38Z	--- inference: false --- # Robin Model Card ## Model Details Robin is a series of models finetuned from LLaMA on several high-quality data. - Developed by: [LMFlow](https://github.com/OptimalScale/LMFlow/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/OptimalScale/LMFlow/ - Blog: https://medium.com/@hkust.ml/robin-v2-launches-achieves-unparalleled-performance-on-openllm-4f6886e822c1 - Paper: https://arxiv.org/abs/2306.12420 - Demo: https://lmflow.com/ ## Uses Robin is primarily utilized for conducting research on extensive language models and chatbots, catering to users specializing in natural language processing, machine learning, and artificial intelligence research. ## How to Get Started with the Model We provide four kinds of demos including: - Online Service: If you don't want to run any code and just want to try our models, we deploy our instruction-tuned LLaMA you to have a try. - Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab. - Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab. - Local Deploy: We also provide a way for you to deploy your model/chatbot locally, which means you can deploy much larger model than previous three methods if you have enough resource. Please refer to https://github.com/OptimalScale/LMFlow#demos ## Training Details Expanding upon the initial idea of self-instruct techniques, we incorporated several different data sources and build a new dataset called [LMFlow Dataset](http://lmflow.org:5000/lmflow_data.tar.gz). The new training split is created by merging the following datasets: - ShareGPT: randomly sample 50K English data and 10K Chinese data from ShareGPT. - GPT-4-LLM: 52K English data from GPT-4-LLM. - BELLE: randomly sample 80K Chinese data from BELLE. See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Evaluation Robin is evaluated with [LMFlow Benchmark](https://blog.gopenai.com/lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms-ef5c6f142418). See more details in this [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Citation If you find this repository useful, please consider giving ⭐ and citing our [paper](https://arxiv.org/abs/2306.12420): ``` @misc{lmflow, author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang}, title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://optimalscale.github.io/LMFlow/}}, } ```
PeterBrendan/pbjsGPT2v2	PeterBrendan	2023-07-16T02:32:02Z	144	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-12T15:07:20Z	--- license: mit widget: - text: bidderTimeout - text: Usebidcache - text: bidderSequence - text: customPriceBucket --- ## Model: GPT-2 ### Model name: pbjsGPT2v2 ### Model description: This fine-tuned version of the GPT-2 model was trained on a subset of 1100+ publisher domains' Prebid config files. Its focus is on sophisticated Prebid publishers. The model provides insights into how these publishers configure their Prebid settings. By inputting a Prebid config setting, such as *bidderTimeout, the model generates sample Prebid configuration settings based on the collected data. It aims to assist publishers in understanding different configurations used by sophisticated publishers. ### Intended uses: This model is intended to assist publishers in understanding and exploring how other publishers configure their Prebid settings. It serves as a reference for gaining insights into common configurations, best practices, and different approaches used by top publishers across various domains. ### Limitations: The generated Prebid configuration settings are based on the data from the training set and may not cover all possible configurations or reflect the specific requirements of a particular domain. Publishers should carefully review and adapt the generated configurations to their specific needs and business rules. ### How to use: To use this model, provide a Prebid config setting, such as bidderSequence*. The model will generate a sample Prebid configuration related to that input based on the collected data. ### Training data: This model was trained on a subset of 1100+ publisher domains Prebid config files. The dataset was collected from a variety of publishers and represents a wide range of Prebid settings used in the industry. ### Training procedure: The model was fine-tuned using the GPT-2 base model with the aforementioned dataset. ### Evaluation results: The evaluation of this model focuses on its ability to generate coherent and valid Prebid configuration settings based on the provided Prebid config setting. Human evaluators reviewed the generated configurations for relevance and accuracy. ### Safety and bias considerations: The model is trained on data from actual Prebid config files and aims to provide accurate insights into publishers' configurations. However, it's important to note that biases may exist in the original data itself, as the training data is based on real-world configurations. Users should review and validate the generated configurations to ensure they align with their specific requirements and guidelines. Users are encouraged to exercise caution and use their expertise in interpreting and adapting the generated Prebid configurations for their own use. The model should be seen as a helpful tool to gain inspiration and understanding of common Prebid settings but not as a substitute for thorough testing and manual review of the final configurations.
PeterBrendan/pbjs_gpt2	PeterBrendan	2023-07-16T02:14:23Z	144	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-08T02:03:58Z	--- license: mit widget: - text: bidderTimeout - text: Usebidcache - text: bidderSequence - text: customPriceBucket --- Model: GPT-2 Model name: pbjs_gpt2 Model description: This fine-tuned version of the GPT-2 model was trained on a dataset of 1100+ publisher domains' Prebid config files. It aims to provide insights into how other publishers configure their Prebid settings. Given a Prebid config setting, such as *bidderTimeout, the model can generate sample Prebid configuration settings based on the collected data. It helps publishers gain an understanding of how different publishers configure their Prebid settings. Intended uses:* This model is intended to assist publishers in understanding and exploring how other publishers configure their Prebid settings. It serves as a reference to gain insights into common configurations, best practices, and different approaches used by publishers across various domains. Limitations: It's important to note that the generated Prebid configuration settings are based on the data from the training set and may not cover all possible configurations or reflect the specific requirements of a particular domain. Publishers should carefully review and adapt the generated configurations to their specific needs and business rules. How to use: To use this model, provide a Prebid config setting, such as *bidderSequence. The model will generate a sample Prebid configuration related to that input based on the collected data. Training data:* This model was trained on a dataset consisting of over 1100+ publisher domains Prebid config files. The dataset was collected from a variety of publishers and represents a wide range of Prebid settings used in the industry. Training procedure: The model was fine-tuned using the GPT-2 base model with the aforementioned dataset. The training loss achieved was 0.43277667846199475. Evaluation results: The evaluation of this model focuses on its ability to generate coherent and valid Prebid configuration settings based on the provided Prebid config setting. Human evaluators reviewed the generated configurations for relevance and accuracy. Safety and bias considerations: The model is trained on data from actual Prebid config files and aims to provide accurate insights into publishers' configurations. However, it's important to note that biases may exist in the original data itself, as the training data is based on real-world configurations. Users should review and validate the generated configurations to ensure they align with their specific requirements and guidelines. Users are encouraged to exercise caution and use their expertise in interpreting and adapting the generated Prebid configurations for their own use. The model should be seen as a helpful tool to gain inspiration and understanding of common Prebid settings but not as a substitute for thorough testing and manual review of the final configurations.
monideep2255/spell_correction_M04_V3	monideep2255	2023-07-16T02:10:18Z	3	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-16T00:59:14Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: spell_correction_M04_V3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # spell_correction_M04_V3 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0178 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 269 \| 0.2687 \| \| 1.8467 \| 2.0 \| 538 \| 0.0361 \| \| 1.8467 \| 3.0 \| 807 \| 0.0241 \| \| 0.0357 \| 4.0 \| 1076 \| 0.0198 \| \| 0.0357 \| 5.0 \| 1345 \| 0.0199 \| \| 0.0159 \| 6.0 \| 1614 \| 0.0175 \| \| 0.0159 \| 7.0 \| 1883 \| 0.0179 \| \| 0.0077 \| 8.0 \| 2152 \| 0.0189 \| \| 0.0077 \| 9.0 \| 2421 \| 0.0183 \| \| 0.006 \| 10.0 \| 2690 \| 0.0183 \| \| 0.006 \| 11.0 \| 2959 \| 0.0191 \| \| 0.0044 \| 12.0 \| 3228 \| 0.0186 \| \| 0.0044 \| 13.0 \| 3497 \| 0.0192 \| \| 0.0033 \| 14.0 \| 3766 \| 0.0189 \| \| 0.0024 \| 15.0 \| 4035 \| 0.0173 \| \| 0.0024 \| 16.0 \| 4304 \| 0.0171 \| \| 0.0026 \| 17.0 \| 4573 \| 0.0183 \| \| 0.0026 \| 18.0 \| 4842 \| 0.0181 \| \| 0.0021 \| 19.0 \| 5111 \| 0.0177 \| \| 0.0021 \| 20.0 \| 5380 \| 0.0174 \| \| 0.0015 \| 21.0 \| 5649 \| 0.0173 \| \| 0.0015 \| 22.0 \| 5918 \| 0.0174 \| \| 0.0016 \| 23.0 \| 6187 \| 0.0178 \| \| 0.0016 \| 24.0 \| 6456 \| 0.0180 \| \| 0.0018 \| 25.0 \| 6725 \| 0.0175 \| \| 0.0018 \| 26.0 \| 6994 \| 0.0171 \| \| 0.0017 \| 27.0 \| 7263 \| 0.0175 \| \| 0.0014 \| 28.0 \| 7532 \| 0.0177 \| \| 0.0014 \| 29.0 \| 7801 \| 0.0178 \| \| 0.0013 \| 30.0 \| 8070 \| 0.0178 \| ### Framework versions - Transformers 4.28.0 - Pytorch 1.12.1+cu102 - Datasets 2.13.1 - Tokenizers 0.13.3
manmyung/ppo-SnowballTarget	manmyung	2023-07-16T02:08:22Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-16T02:08:19Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: manmyung/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Hex820000/anime_v10	Hex820000	2023-07-16T01:57:47Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-16T01:46:09Z	--- license: creativeml-openrail-m ---

tyavika/Distilbert-QA-Pytorch-FULL

tyavika

2023-07-16T16:06:16Z

131

0

transformers

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

question-answering

2023-06-28T01:54:23Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Distilbert-QA-Pytorch-FULL results: [] ---  # Distilbert-QA-Pytorch-FULL This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4175 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.297 | 1.0 | 3290 | 1.1823 | | 0.9448 | 2.0 | 6580 | 1.1464 | | 0.6704 | 3.0 | 9870 | 1.2624 | | 0.4478 | 4.0 | 13160 | 1.4175 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

tyavika/Bert-QA-Pytorch-FULL

tyavika

2023-07-16T16:05:57Z

7

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-28T02:19:57Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Bert-QA-Pytorch-FULL results: [] ---  # Bert-QA-Pytorch-FULL This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2154 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1633 | 1.0 | 3290 | 1.0515 | | 0.8061 | 2.0 | 6580 | 1.0593 | | 0.533 | 3.0 | 9870 | 1.2154 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ailabturkiye/NecmettinErbakan

ailabturkiye

2023-07-16T16:05:52Z

0

null

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

null

2023-07-16T16:02:15Z

--- license: openrail language: - tr tags: - music --- Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.

casque/Creampie_v11

casque

2023-07-16T16:05:41Z

0

null

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

null

2023-07-16T16:03:25Z

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

ailabturkiye/orkundk

ailabturkiye

2023-07-16T16:03:20Z

0

null

[ "region:us" ]

null

2023-07-16T16:02:09Z

--- license: openrail language: - tr tags: - music Orkundk (500 Epoch)

tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid

tyavika

2023-07-16T16:02:27Z

7

0

transformers

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

question-answering

2023-07-12T15:52:35Z

--- tags: - generated_from_trainer model-index: - name: lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid results: [] ---  # lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid This model is a fine-tuned version of [tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid](https://huggingface.co/tyavika/lr1e5_bs16_layer1_Bert_CNN128LSTM64NoBid) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ailabturkiye/mabelmatiz

ailabturkiye

2023-07-16T16:00:13Z

0

null

[ "region:us" ]

null

2023-07-16T15:58:53Z

--- license: openrail language: - tr tags: - music Mabel Matiz (500 Epoch)

ailabturkiye/AhmetAga

ailabturkiye

2023-07-16T15:58:14Z

0

null

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

null

2023-07-16T15:37:02Z

--- license: openrail language: - tr --- This model card aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ### Model Details 400 epoch 14 minute dataset ### Model Description ahmet aga - **Developed by:** Flowness"seloistaken" - **Shared by :**Flowness"seloistaken" - **Model type:** rvc v2 - **Language:** Turkish ### Model Sources https://www.youtube.com/watch?v=hwDRaGwfvQI ## Uses if you use and share this model give credits for discord.gg/ailab

localmodels/Orca-Mini-v2-13B-GPTQ

localmodels

2023-07-16T15:57:59Z

6

0

transformers

[ "transformers", "llama", "text-generation", "arxiv:2306.02707", "arxiv:2302.13971", "arxiv:2304.12244", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-16T15:57:59Z

--- duplicated_from: localmodels/LLM --- # Orca Mini v2 13B GPTQ From: https://huggingface.co/psmathur/orca_mini_v2_13b ## Prompt template ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: {prompt} ### Input: {input} ### Response: ``` --- | Model | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description | | ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- | | orca_mini_v2_13b-GPTQ-4bit-128g.no-act.order | 4 | 128 | False | 7.45 GB | True | GPTQ-for-LLaMa | Most compatible. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. | --- # Orca Mini v2 13B An **Uncensored** LLaMA-13b model in collaboration with [Eric Hartford](https://huggingface.co/ehartford). trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. Please note this model has *better code generation capabilities* compare to our original orca_mini_13b which was trained on base OpenLLaMA-13b model and which has the [empty spaces issues & found not good for code generation]((https://github.com/openlm-research/open_llama#update-06072023)). # Evaluation I evaluated orca_mini_v2_13b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |||| |:------:|:-------------:|:---------:| |**Task**|**Value**|**Stderr**| |*arc_challenge*|0.5478|0.0145| |*hellaswag*|0.7023|0.0040| |*mmlu*|0.4969|0.035| |*truthfulqa_mc*|0.44|0.0158| |*Total Average*|0.54675|0.0114| # Dataset We used uncensored script on top of the previous explain tuned datasets we build which are [WizardLM dataset ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) & [Dolly-V2 dataset ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. This helps student model aka this model to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). Please see below example usage how the **System** prompt is added before each **instruction**. # Training The training configurations are provided in the table below. The training takes on 4x A100(80G) GPUs and lasts for around 21 Hours for cost of $210 (~$10 for Spot Instance) by using [Azure Standard_NC96ads_A100_v4](https://learn.microsoft.com/en-us/azure/virtual-machines/nc-a100-v4-series#supported-features). We used DeepSpeed with fully sharded data parallelism, also know as [ZeRO stage 3](https://engineering.fb.com/2021/07/15/open-source/fsdp/) by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [FastChat](https://github.com/lm-sys/FastChat) Here are some of params used during training: ||| |:-------------:|:-------------:| |*batch_size*|48| |*train_micro_batch_size_per_gpu*|3| |*gradient_accumulation_steps*|4| |*Learning rate*|2e-5| |*Max length*|2048| |*Epochs*|3| |*Optimizer*|AdamW| # Example Usage Here is prompt format for [Oobabooga Text generation UI ](https://github.com/oobabooga/text-generation-webui) ``` ### System: {system} ### User: {instruction} ### Input: {input} ### Response: ``` Limitations & Biases: This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Disclaimer: The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. Citiation: If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX: ``` @misc{orca_mini_v2_13b, author = {Pankaj Mathur}, title = {orca_mini_v2_13b: An explain tuned LLaMA-13b model on uncensored wizardlm, alpaca, & dolly datasets}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v2_13b}, } ``` ``` @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ``` ``` @misc{openalpaca, author = {Yixuan Su and Tian Lan and Deng Cai}, title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, } ``` ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` ``` @misc{xu2023wizardlm, title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang}, year={2023}, eprint={2304.12244}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

ailabturkiye/MehmetAliErbil

ailabturkiye

2023-07-16T15:57:00Z

0

1

null

[ "region:us" ]

null

2023-07-16T15:23:06Z

--- Lisans: openrail **Sunucu ve oyuncu Mehmet Ali Erbil'in Türkçe sesidir, Rvc V2 500 epoch olarak eğitilmiştir.** _Dataset ve train jawbone0 tarafından yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits **Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.** - Discord: jawbone0 - YouTube: JawBone0 (https://www.youtube.com/@JawBone0) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)

NasimB/rarity-all-guten-2p5k-cbt-p5k-mixed

NasimB

2023-07-16T15:56:16Z

5

0

transformers

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

text-generation

2023-07-16T14:02:13Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: rarity-all-guten-2p5k-cbt-p5k-mixed results: [] ---  # rarity-all-guten-2p5k-cbt-p5k-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3206 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6916 | 0.29 | 500 | 5.6242 | | 5.3287 | 0.59 | 1000 | 5.1956 | | 4.9961 | 0.88 | 1500 | 4.9421 | | 4.7198 | 1.17 | 2000 | 4.8015 | | 4.5643 | 1.47 | 2500 | 4.6835 | | 4.4523 | 1.76 | 3000 | 4.5745 | | 4.3273 | 2.06 | 3500 | 4.4993 | | 4.1372 | 2.35 | 4000 | 4.4498 | | 4.1052 | 2.64 | 4500 | 4.3880 | | 4.0721 | 2.94 | 5000 | 4.3409 | | 3.8586 | 3.23 | 5500 | 4.3325 | | 3.8079 | 3.52 | 6000 | 4.3061 | | 3.7897 | 3.82 | 6500 | 4.2690 | | 3.678 | 4.11 | 7000 | 4.2702 | | 3.5266 | 4.4 | 7500 | 4.2641 | | 3.5165 | 4.7 | 8000 | 4.2488 | | 3.5069 | 4.99 | 8500 | 4.2361 | | 3.3367 | 5.28 | 9000 | 4.2512 | | 3.3295 | 5.58 | 9500 | 4.2494 | | 3.3275 | 5.87 | 10000 | 4.2480 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

ailabturkiye/SimsekMcqueen

ailabturkiye

2023-07-16T15:55:13Z

0

1

null

[ "region:us" ]

null

2023-07-16T15:32:48Z

--- Lisans: openrail **Şimşek Mcqueen Türkçe seslendirmeni Yakta Kopan'ın sesidir, Rvc V2 1000 epoch olarak eğitilmiştir.** _Dataset ve train jawbone0 tarafından yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits **Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur.** - Discord: jawbone0 - YouTube: JawBone0 (https://www.youtube.com/@JawBone0) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)

ailabturkiye/FahrettinAltun

ailabturkiye

2023-07-16T15:54:26Z

0

null

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

null

2023-07-16T15:51:36Z

--- license: openrail language: - tr tags: - music --- İletişim Başkanımız Sayın Fahrettin Altun. Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.

helojo/wav2vec2-large-mms-1b-zh-colab

helojo

2023-07-16T15:54:22Z

6

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_6_1", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-12T02:58:25Z

--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer datasets: - common_voice_6_1 model-index: - name: wav2vec2-large-mms-1b-zh-colab results: [] ---  # wav2vec2-large-mms-1b-zh-colab This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 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: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.1.0.dev20230711 - Datasets 2.13.1 - Tokenizers 0.13.3

casque/vacuum_fellatio1.1

casque

2023-07-16T15:54:05Z

0

1

null

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

null

2023-07-16T15:52:41Z

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

Mehmetakif/MinecraftZombiSesi

Mehmetakif

2023-07-16T15:53:31Z

0

null

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

null

2023-07-16T15:47:14Z

--- license: openrail language: - tr tags: - music ---

casque/licking_my_dick.sd.v1.2

casque

2023-07-16T15:51:07Z

0

null

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

null

2023-07-16T15:48:28Z

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

giocs2017/Reinforce-cartPolev1

giocs2017

2023-07-16T15:49:01Z

0

null

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

reinforcement-learning

2023-07-16T15:48:52Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartPolev1 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

0sunfire0/LunarLander-v2_new_00

0sunfire0

2023-07-16T15:48:29Z

0

null

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

reinforcement-learning

2023-07-16T15:48:21Z

--- 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: -81.95 +/- 58.67 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': '0sunfire0/LunarLander-v2_new_00' 'batch_size': 512 'minibatch_size': 128} ```

casque/Spooning_Posititon

casque

2023-07-16T15:42:25Z

0

1

null

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

null

2023-07-16T15:40:02Z

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

TokenBender/falcon-7b-chat-oasst1

TokenBender

2023-07-16T15:40:15Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-16T15:39:30Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

ailabturkiye/Baso

ailabturkiye

2023-07-16T15:37:45Z

0

null

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

null

2023-07-16T15:37:39Z

Temporary Redirect. Redirecting to /ailabturkiye/baso/resolve/main/README.md

NasimB/all-base-no-repetition-no-cut

NasimB

2023-07-16T15:34:04Z

5

0

transformers

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

text-generation

2023-07-16T13:44:52Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: all-base-no-repetition-no-cut results: [] ---  # all-base-no-repetition-no-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.7605 | 0.31 | 500 | 5.6506 | | 5.4119 | 0.62 | 1000 | 5.2199 | | 5.0579 | 0.94 | 1500 | 4.9665 | | 4.767 | 1.25 | 2000 | 4.8190 | | 4.6276 | 1.56 | 2500 | 4.6923 | | 4.5202 | 1.87 | 3000 | 4.5802 | | 4.312 | 2.19 | 3500 | 4.5219 | | 4.2135 | 2.5 | 4000 | 4.4518 | | 4.1664 | 2.81 | 4500 | 4.3926 | | 4.033 | 3.12 | 5000 | 4.3652 | | 3.8843 | 3.44 | 5500 | 4.3407 | | 3.8737 | 3.75 | 6000 | 4.3029 | | 3.8047 | 4.06 | 6500 | 4.2883 | | 3.5939 | 4.37 | 7000 | 4.2854 | | 3.582 | 4.68 | 7500 | 4.2692 | | 3.5745 | 5.0 | 8000 | 4.2540 | | 3.3934 | 5.31 | 8500 | 4.2671 | | 3.3874 | 5.62 | 9000 | 4.2653 | | 3.3924 | 5.93 | 9500 | 4.2645 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

lxyuan/distilbart-finetuned-summarization

lxyuan

2023-07-16T15:32:42Z

159

5

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "distilbart", "en", "dataset:cnn_dailymail", "dataset:xsum", "dataset:samsum", "dataset:ccdv/pubmed-summarization", "arxiv:2010.13002", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-09T05:23:35Z

--- tags: - generated_from_trainer - distilbart model-index: - name: distilbart-finetuned-summarization results: [] license: apache-2.0 datasets: - cnn_dailymail - xsum - samsum - ccdv/pubmed-summarization language: - en metrics: - rouge ---  # distilbart-finetuned-summarization This model is a further fine-tuned version of [distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the the combination of 4 different summarisation datasets: - [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) - [samsum](https://huggingface.co/datasets/samsum) - [xsum](https://huggingface.co/datasets/xsum) - [ccdv/pubmed-summarization](https://huggingface.co/datasets/ccdv/pubmed-summarization) Please check out the offical model page and paper: - [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) - [Pre-trained Summarization Distillation](https://arxiv.org/abs/2010.13002) ## Training and evaluation data One can reproduce the dataset using the following code: ```python from datasets import DatasetDict, load_dataset from datasets import concatenate_datasets xsum_dataset = load_dataset("xsum") pubmed_dataset = load_dataset("ccdv/pubmed-summarization").rename_column("article", "document").rename_column("abstract", "summary") cnn_dataset = load_dataset("cnn_dailymail", '3.0.0').rename_column("article", "document").rename_column("highlights", "summary") samsum_dataset = load_dataset("samsum").rename_column("dialogue", "document") summary_train = concatenate_datasets([xsum_dataset["train"], pubmed_dataset["train"], cnn_dataset["train"], samsum_dataset["train"]]) summary_validation = concatenate_datasets([xsum_dataset["validation"], pubmed_dataset["validation"], cnn_dataset["validation"], samsum_dataset["validation"]]) summary_test = concatenate_datasets([xsum_dataset["test"], pubmed_dataset["test"], cnn_dataset["test"], samsum_dataset["test"]]) raw_datasets = DatasetDict() raw_datasets["train"] = summary_train raw_datasets["validation"] = summary_validation raw_datasets["test"] = summary_test ``` ## Inference example ```python from transformers import pipeline pipe = pipeline("text2text-generation", model="lxyuan/distilbart-finetuned-summarization") text = """SINGAPORE: The Singapore Police Force on Sunday (Jul 16) issued a warning over a fake SMS impersonating as its "anti-scam centre (ASC)". "In this scam variant, members of the public would receive a scam SMS from 'ASC', requesting them to download and install an “anti-scam” app to ensure the security of their devices," said the police. "The fake SMS would direct members of the public to a URL link leading to an Android Package Kit (APK) file, an application created for Android’s operating system purportedly from 'ASC'." The fake website has an icon to download the “anti-scam” app and once downloaded, Android users are asked to allow accessibility services to enable the service. While the fake app purportedly claims to help identify and prevent scams by providing comprehensive protection and security, downloading it may enable scammers to gain remote access to devices. "Members of the public are advised not to download any suspicious APK files on their devices as they may contain malware which will allow scammers to access and take control of the device remotely as well as to steal passwords stored in the device," said the police. Members of the public are advised to adopt the following precautionary measures, including adding anti-virus or anti-malware apps to their devices. They should also disable “install unknown app” or “unknown sources” in their phone settings. Users should check the developer information on the app listing as well as the number of downloads and user reviews to ensure it is a reputable and legitimate app, the police said. Any fraudulent transactions should be immediately reported to the banks. """ pipe(text) >>>"""The Singapore Police Force has issued a warning over a fake SMS impersonating as its "anti-scam centre" that asks members of the public to download an Android app to ensure the security of their devices, the force said on Sunday. The fake SMS would direct people to a URL link leading to an Android Package Kit (APK) file, an application created for Android’s operating system purportedly from "ASC". """ ``` ## Training procedure Notebook link: [here](https://github.com/LxYuan0420/nlp/blob/main/notebooks/distilbart-finetune-summarisation.ipynb) ### Training hyperparameters The following hyperparameters were used during training: - evaluation_strategy="epoch", - save_strategy="epoch", - logging_strategy="epoch", - learning_rate=2e-5, - per_device_train_batch_size=2, - per_device_eval_batch_size=2, - gradient_accumulation_steps=64, - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - weight_decay=0.01, - save_total_limit=2, - num_train_epochs=4, - predict_with_generate=True, - fp16=True, - push_to_hub=True ### Training results _Training is still in progress_ | Epoch | Training Loss | Validation Loss | Rouge1 | Rouge2 | RougeL | RougeLsum | Gen Len | |-------|---------------|-----------------|--------|--------|--------|-----------|---------| | 0 | 1.779700 | 1.719054 | 40.003900 | 17.907100 | 27.882500 | 34.888600 | 88.893600 | | 1 | 1.633800 | 1.710876 | 40.628800 | 18.470200 | 28.428100 | 35.577500 | 88.885000 | | 2 | 1.566100 | 1.694476 | 40.928500 | 18.695300 | 28.613300 | 35.813300 | 88.993700 | | 3 | 1.515700 | 1.691141 | 40.860500 | 18.696500 | 28.672700 | 35.734600 | 88.457300 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

ailabturkiye/CavsKarahanli

ailabturkiye

2023-07-16T15:31:16Z

0

null

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

null

2023-07-16T15:27:29Z

--- license: openrail language: - tr tags: - music --- Yayıncı Cavs Karahanli.Modeli kullanarak oluşturulan hiç bir ses hakkında sorumluluk bana ait değildir.

manuu01/q-FrozenLake-v1-4x4-noSlippery

manuu01

2023-07-16T14:57:47Z

0

null

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

reinforcement-learning

2023-07-16T14:57:25Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="manuu01/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Jonathaniu/alpaca-breast-cancer-13b-mix_data_2

Jonathaniu

2023-07-16T14:52:46Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-16T14:52:26Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0

Saideva/title_generation

Saideva

2023-07-16T14:38:55Z

107

0

transformers

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

text2text-generation

2023-07-16T14:10:04Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: title_generation results: [] ---  # title_generation This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 41.5236 - Rouge2: 17.5894 - Rougel: 37.2852 - Rougelsum: 37.2749 - Gen Len: 13.3542 ## Model description More information needed ## Intended uses & 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: 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.0 | 1.0 | 3748 | nan | 41.5236 | 17.5894 | 37.2852 | 37.2749 | 13.3542 | | 0.0 | 2.0 | 7496 | nan | 41.5236 | 17.5894 | 37.2852 | 37.2749 | 13.3542 | | 0.0 | 3.0 | 11244 | nan | 41.5236 | 17.5894 | 37.2852 | 37.2749 | 13.3542 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

PaulineJamin/ppo-SnowballTarget

PaulineJamin

2023-07-16T14:38:23Z

1

0

ml-agents

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

reinforcement-learning

2023-07-16T14:38:14Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** 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: PaulineJamin/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

margosabry/food_classifier

margosabry

2023-07-16T14:28:12Z

63

0

transformers

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

image-classification

2023-07-16T13:50:22Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: margosabry/food_classifier results: [] ---  # margosabry/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3853 - Validation Loss: 0.3150 - Train Accuracy: 0.928 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.8055 | 1.6705 | 0.808 | 0 | | 1.2418 | 0.8233 | 0.883 | 1 | | 0.7004 | 0.5248 | 0.912 | 2 | | 0.5037 | 0.3802 | 0.926 | 3 | | 0.3853 | 0.3150 | 0.928 | 4 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

huggingFacing/ddpm-butterflies-128

huggingFacing

2023-07-16T14:11:21Z

0

diffusers

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

null

2023-07-16T14:09:03Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: /content/drive/MyDrive/image_and_text metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `/content/drive/MyDrive/image_and_text` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Tian7/ddpm-butterflies-128/tensorboard?#scalars)

olegs/distil-ast-audioset-finetuned-gtzan

olegs

2023-07-16T14:09:35Z

165

0

transformers

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

audio-classification

2023-07-16T13:02:16Z

--- license: apache-2.0 base_model: bookbot/distil-ast-audioset tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distil-ast-audioset-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.93 ---  # distil-ast-audioset-finetuned-gtzan This model is a fine-tuned version of [bookbot/distil-ast-audioset](https://huggingface.co/bookbot/distil-ast-audioset) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5022 - Accuracy: 0.93 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8727 | 1.0 | 113 | 0.6650 | 0.81 | | 0.6665 | 2.0 | 226 | 0.7639 | 0.74 | | 0.5306 | 3.0 | 339 | 0.6683 | 0.76 | | 0.2793 | 4.0 | 452 | 0.7423 | 0.82 | | 0.0867 | 5.0 | 565 | 0.6301 | 0.85 | | 0.0156 | 6.0 | 678 | 0.8905 | 0.83 | | 0.2298 | 7.0 | 791 | 0.4492 | 0.92 | | 0.0073 | 8.0 | 904 | 0.9028 | 0.83 | | 0.0664 | 9.0 | 1017 | 0.6387 | 0.85 | | 0.0001 | 10.0 | 1130 | 0.5022 | 0.87 | | 0.0001 | 11.0 | 1243 | 0.4047 | 0.91 | | 0.0 | 12.0 | 1356 | 0.3988 | 0.92 | | 0.0 | 13.0 | 1469 | 0.6225 | 0.91 | | 0.0 | 14.0 | 1582 | 0.6075 | 0.86 | | 0.0 | 15.0 | 1695 | 0.5259 | 0.89 | | 0.0 | 16.0 | 1808 | 0.5014 | 0.92 | | 0.0 | 17.0 | 1921 | 0.5004 | 0.93 | | 0.0 | 18.0 | 2034 | 0.5008 | 0.93 | | 0.0 | 19.0 | 2147 | 0.5022 | 0.93 | | 0.0 | 20.0 | 2260 | 0.5022 | 0.93 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

lucasbertola/ppo-SnowballTarget

lucasbertola

2023-07-16T14:08:18Z

3

0

ml-agents

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

reinforcement-learning

2023-07-16T14:08:12Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** 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: lucasbertola/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

antoniodee/fin-tench

antoniodee

2023-07-16T13:48:41Z

0

null

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

text-to-image

2023-07-16T13:43:49Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### fin_tench Dreambooth model trained by antoniodee 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:

SwampMan/a2c-AntBulletEnv-v0

SwampMan

2023-07-16T13:44:52Z

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-16T13:43:45Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1250.47 +/- 141.94 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```

NasimB/all-base-rarity-all-guten-rarity-all-2p5k-iorder-est-5p5k-mostf

NasimB

2023-07-16T13:17:43Z

4

0

transformers

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

text-generation

2023-07-16T11:29:42Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: all-base-rarity-all-guten-rarity-all-2p5k-iorder-est-5p5k-mostf results: [] ---  # all-base-rarity-all-guten-rarity-all-2p5k-iorder-est-5p5k-mostf This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3428 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.7586 | 0.32 | 500 | 5.6557 | | 5.4132 | 0.63 | 1000 | 5.2227 | | 5.0611 | 0.95 | 1500 | 4.9660 | | 4.7718 | 1.26 | 2000 | 4.8202 | | 4.64 | 1.58 | 2500 | 4.6997 | | 4.5178 | 1.89 | 3000 | 4.5866 | | 4.308 | 2.21 | 3500 | 4.5285 | | 4.223 | 2.52 | 4000 | 4.4602 | | 4.1757 | 2.84 | 4500 | 4.3982 | | 4.0214 | 3.15 | 5000 | 4.3825 | | 3.8976 | 3.47 | 5500 | 4.3455 | | 3.8816 | 3.78 | 6000 | 4.3106 | | 3.7798 | 4.1 | 6500 | 4.3020 | | 3.6074 | 4.41 | 7000 | 4.2988 | | 3.5991 | 4.73 | 7500 | 4.2789 | | 3.56 | 5.04 | 8000 | 4.2749 | | 3.408 | 5.36 | 8500 | 4.2785 | | 3.407 | 5.67 | 9000 | 4.2775 | | 3.401 | 5.99 | 9500 | 4.2772 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

madoe001/a2c-AntBulletEnv-v0

madoe001

2023-07-16T12:58:37Z

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-16T12:56:52Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1411.03 +/- 55.48 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```

WALIDALI/lyrieldiff

WALIDALI

2023-07-16T12:55:45Z

2

1

diffusers

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

text-to-image

2023-07-16T12:50:56Z

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

larry-jiang/RL

larry-jiang

2023-07-16T12:48:55Z

0

stable-baselines3

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

reinforcement-learning

2023-07-16T12:47:54Z

--- 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: 256.32 +/- 20.65 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 ... ```

UholoDala/tweet_sentiments_analysis

UholoDala

2023-07-16T12:30:23Z

163

0

transformers

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

text-classification

2023-07-16T12:11:33Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: tweet_sentiments_analysis results: [] ---  # tweet_sentiments_analysis This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1411 - eval_f1-score: 0.9585 - eval_runtime: 62.6587 - eval_samples_per_second: 31.919 - eval_steps_per_second: 3.99 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Rihong/ppo-LunarLander-v2

Rihong

2023-07-16T12:20:44Z

0

stable-baselines3

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

reinforcement-learning

2023-07-16T12:19:16Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 272.93 +/- 18.31 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 ... ```

ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20

ALM-AHME

2023-07-16T12:15:04Z

199

1

transformers

[ "transformers", "pytorch", "tensorboard", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-16T09:38:10Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Splitted-Resized split: train args: Splitted-Resized metrics: - name: Accuracy type: accuracy value: 0.9943422913719944 ---  # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-60-20-20 This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0229 - Accuracy: 0.9943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 0.5 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2053 | 1.0 | 199 | 0.1227 | 0.9496 | | 0.1302 | 2.0 | 398 | 0.0665 | 0.9736 | | 0.0784 | 3.0 | 597 | 0.0600 | 0.9778 | | 0.1181 | 4.0 | 796 | 0.0449 | 0.9849 | | 0.208 | 5.0 | 995 | 0.0393 | 0.9887 | | 0.0057 | 6.0 | 1194 | 0.0229 | 0.9943 | | 0.0017 | 7.0 | 1393 | 0.0263 | 0.9939 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

sjdata/speecht5_finetuned_single_speaker_en_test_librivox

sjdata

2023-07-16T12:09:19Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "en", "dataset:speecht5_finetuned_single_speaker_en_test_librivox", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-13T12:31:39Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - speecht5_finetuned_single_speaker_en_test_librivox model-index: - name: SpeechT5 Single Speaker test results: [] ---  # SpeechT5 Single Speaker test This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the single_speaker_en_test_librivox dataset. It achieves the following results on the evaluation set: - Loss: 0.4215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4809 | 1.78 | 1000 | 0.4215 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v1

hafidikhsan

2023-07-16T11:14:40Z

103

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-16T11:12:03Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v1 results: [] ---  # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-lr-v1 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4017 - Accuracy: 0.25 - F1: 0.1 - Precision: 0.0625 - Recall: 0.25 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:| | 1.3826 | 1.0 | 500 | 1.4017 | 0.25 | 0.1 | 0.0625 | 0.25 | | 1.4074 | 2.0 | 1000 | 1.3922 | 0.25 | 0.1 | 0.0625 | 0.25 | | 1.3984 | 3.0 | 1500 | 1.3868 | 0.25 | 0.1 | 0.0625 | 0.25 | | 1.387 | 4.0 | 2000 | 1.3863 | 0.25 | 0.1 | 0.0625 | 0.25 | | 1.3861 | 5.0 | 2500 | 1.3863 | 0.25 | 0.1 | 0.0625 | 0.25 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

gfs0508/AIron-Trans-PT2EN

gfs0508

2023-07-16T11:10:39Z

0

1

keras

[ "keras", "translation", "pt", "en", "license:mit", "region:us" ]

translation

2023-07-16T11:02:01Z

--- license: mit language: - pt - en library_name: keras pipeline_tag: translation --- # AIron-Trans-PT2EN ## License - MIT ## Overview AIron-Trans-PT2EN is a Portuguese to English translation model developed using the Keras library. ## Description AIron-Trans-PT2EN is a translation model that allows you to translate phrases and texts from Portuguese to English. It has been trained using the Long Short-Term Memory (LSTM) neural network architecture and implemented using the Keras library. ## Features - Translation from Portuguese to English - Model trained using the Keras library - LSTM architecture for better contextual understanding - Text preprocessing for improved translation quality ## Usage You can use this translation model in your own projects by following the instructions below: 1. Install the necessary dependencies (Keras, TensorFlow, etc.). 2. Load the trained model using the `load_model()` function from Keras. 3. Preprocess input sentences using the same preprocessing steps used during training. 4. Call the `translate_sentence()` function to get the translation of the input sentence. Code example: ```python from tensorflow import keras # Load the model model = keras.models.load_model('path/to/model.h5') # Preprocess the input sentence preprocessed_sentence = preprocess_sentence('Olá, como vai?') # Translate the sentence translated_sentence = translate_sentence(preprocessed_sentence, model) print(translated_sentence) ``` ## Contribution If you encounter any issues, have ideas for improvements, or would like to contribute to this project, feel free to open an issue or submit a pull request. We welcome contributions! ## Acknowledgments We would like to thank all contributors who helped develop and improve this translation model.

sagarsdesai/ppo-Huggy

sagarsdesai

2023-07-16T11:10:05Z

0

ml-agents

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

reinforcement-learning

2023-07-16T11:09:59Z

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

Beams24/nzyi

Beams24

2023-07-16T11:08:38Z

0

null

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

null

2023-07-16T11:06:38Z

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

hyunussarioglu/ppo-Huggy

hyunussarioglu

2023-07-16T11:04:45Z

42

0

ml-agents

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

reinforcement-learning

2023-07-16T11:04:39Z

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

NasimB/all-base-rarity-all-bnc-rarity-iorder-est-5p5k-mostf

NasimB

2023-07-16T11:04:22Z

5

0

transformers

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

text-generation

2023-07-16T09:15:19Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: all-base-rarity-all-bnc-rarity-iorder-est-5p5k-mostf results: [] ---  # all-base-rarity-all-bnc-rarity-iorder-est-5p5k-mostf This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3552 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.7751 | 0.31 | 500 | 5.6534 | | 5.4091 | 0.63 | 1000 | 5.2238 | | 5.0666 | 0.94 | 1500 | 4.9739 | | 4.7773 | 1.25 | 2000 | 4.8259 | | 4.6406 | 1.56 | 2500 | 4.7086 | | 4.5289 | 1.88 | 3000 | 4.6001 | | 4.3302 | 2.19 | 3500 | 4.5391 | | 4.2295 | 2.5 | 4000 | 4.4722 | | 4.1833 | 2.82 | 4500 | 4.4085 | | 4.0396 | 3.13 | 5000 | 4.3880 | | 3.9019 | 3.44 | 5500 | 4.3625 | | 3.8912 | 3.75 | 6000 | 4.3198 | | 3.8042 | 4.07 | 6500 | 4.3143 | | 3.6122 | 4.38 | 7000 | 4.3069 | | 3.6013 | 4.69 | 7500 | 4.2897 | | 3.5881 | 5.01 | 8000 | 4.2790 | | 3.4114 | 5.32 | 8500 | 4.2918 | | 3.4083 | 5.63 | 9000 | 4.2889 | | 3.4077 | 5.94 | 9500 | 4.2889 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

vlkn/falcon_instruct_6

vlkn

2023-07-16T10:57:41Z

0

null

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

null

2023-07-16T10:50:11Z

--- tags: - generated_from_trainer model-index: - name: falcon_instruct_6 results: [] ---  # falcon_instruct_6 This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 30 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Kapiche/msmarco-MiniLM-L6-cos-v5

Kapiche

2023-07-16T10:31:33Z

7

0

sentence-transformers

[ "sentence-transformers", "pytorch", "tf", "jax", "rust", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "arxiv:1908.10084", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-02-02T22:27:10Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # msmarco-MiniLM-L6-cos-v5 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L6-cos-v5') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take average of all tokens def mean_pooling(model_output, attention_mask): token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") model = AutoModel.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Technical Details In the following some technical details how this model must be used: | Setting | Value | | --- | :---: | | Dimensions | 384 | | Produces normalized embeddings | Yes | | Pooling-Method | Mean pooling | | Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance | Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used. ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

neurae/albert-dnd-intents

neurae

2023-07-16T09:38:16Z

108

0

transformers

[ "transformers", "pytorch", "albert", "text-classification", "en", "dataset:neurae/dnd_style_intents", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-16T09:58:57Z

--- datasets: - neurae/dnd_style_intents language: - en pipeline_tag: text-classification license: apache-2.0 metrics: - accuracy - f1 --- This is albert base tuned with optimal lr, lr scheduler and weight decay on dnd-style-intents dataset. | parametrs | value | |---------------|----------| | learning rate | 5e-5 | | lr scheduler | linear | | weight decay | 0 | Model has next metrics on test data from dataset | metric | value | |----------|-------| | accuracy | 0.981 | | Macro F1 | 0.979 | | Micro F1 | 0.985 |

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

Xxmlala

2023-07-16T09:29:27Z

0

null

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

reinforcement-learning

2023-07-16T09:29:19Z

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

NasimB/guten-rarity-all-end-19k-ctx-512-finegrained-eval

NasimB

2023-07-16T09:07:24Z

3

0

transformers

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

text-generation

2023-07-16T07:08:53Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-rarity-all-end-19k-ctx-512-finegrained-eval results: [] ---  # guten-rarity-all-end-19k-ctx-512-finegrained-eval This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.2215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.9362 | 0.24 | 100 | 7.3168 | | 6.5524 | 0.48 | 200 | 6.1279 | | 5.9236 | 0.71 | 300 | 5.7874 | | 5.6556 | 0.95 | 400 | 5.5952 | | 5.4733 | 1.19 | 500 | 5.4416 | | 5.2958 | 1.43 | 600 | 5.2824 | | 5.1307 | 1.66 | 700 | 5.1223 | | 4.9829 | 1.9 | 800 | 4.9860 | | 4.8024 | 2.14 | 900 | 4.8963 | | 4.6927 | 2.38 | 1000 | 4.7992 | | 4.6095 | 2.61 | 1100 | 4.6988 | | 4.516 | 2.85 | 1200 | 4.6015 | | 4.3713 | 3.09 | 1300 | 4.5147 | | 4.2277 | 3.33 | 1400 | 4.4417 | | 4.1862 | 3.56 | 1500 | 4.3820 | | 4.1371 | 3.8 | 1600 | 4.3342 | | 4.059 | 4.04 | 1700 | 4.2893 | | 3.8884 | 4.28 | 1800 | 4.2612 | | 3.8665 | 4.51 | 1900 | 4.2299 | | 3.8437 | 4.75 | 2000 | 4.1981 | | 3.815 | 4.99 | 2100 | 4.1766 | | 3.6574 | 5.23 | 2200 | 4.1724 | | 3.6435 | 5.46 | 2300 | 4.1629 | | 3.6348 | 5.7 | 2400 | 4.1584 | | 3.6424 | 5.94 | 2500 | 4.1557 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

rakaaa/tree-lora

rakaaa

2023-07-16T09:01:55Z

1

0

diffusers

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

text-to-image

2023-07-16T07:30:55Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - rakaaa/tree-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-80-10-10

hafidikhsan

2023-07-16T08:55:00Z

123

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-16T08:51:55Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-80-10-10 results: [] ---  # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-80-10-10 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7905 - Accuracy: 0.816 - F1: 0.8141 - Precision: 0.8148 - Recall: 0.816 ## Model description More information needed ## Intended uses & 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: 8 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.7212 | 1.0 | 500 | 0.8608 | 0.622 | 0.6089 | 0.6237 | 0.622 | | 0.7321 | 2.0 | 1000 | 0.7336 | 0.688 | 0.6819 | 0.6884 | 0.688 | | 0.4413 | 3.0 | 1500 | 0.6422 | 0.774 | 0.7727 | 0.7729 | 0.774 | | 0.3669 | 4.0 | 2000 | 1.0008 | 0.754 | 0.7521 | 0.7606 | 0.754 | | 0.0219 | 5.0 | 2500 | 0.9872 | 0.782 | 0.7808 | 0.7801 | 0.782 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

SADAF-IMAMU/train

SADAF-IMAMU

2023-07-16T08:54:59Z

4

0

transformers

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

text-classification

2023-05-25T09:54:23Z

--- tags: - generated_from_trainer metrics: - precision - recall - accuracy model-index: - name: train results: [] ---  # train This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9948 - Macro F1: 0.7856 - Precision: 0.7820 - Recall: 0.7956 - Kappa: 0.6940 - Accuracy: 0.7956 ## Model description More information needed ## Intended uses & 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: 128 - seed: 25 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall | Kappa | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 101 | 1.1562 | 0.6031 | 0.5561 | 0.7044 | 0.4967 | 0.7044 | | No log | 2.0 | 203 | 0.9119 | 0.7151 | 0.7107 | 0.7672 | 0.6236 | 0.7672 | | No log | 3.0 | 304 | 0.8493 | 0.7280 | 0.7139 | 0.7734 | 0.6381 | 0.7734 | | No log | 4.0 | 406 | 0.8087 | 0.7455 | 0.7632 | 0.7648 | 0.6421 | 0.7648 | | 0.9431 | 5.0 | 507 | 0.7735 | 0.7779 | 0.7741 | 0.7931 | 0.6858 | 0.7931 | | 0.9431 | 6.0 | 609 | 0.8201 | 0.7753 | 0.7735 | 0.7869 | 0.6797 | 0.7869 | | 0.9431 | 7.0 | 710 | 0.8564 | 0.7886 | 0.7883 | 0.8017 | 0.7004 | 0.8017 | | 0.9431 | 8.0 | 812 | 0.8712 | 0.7799 | 0.7754 | 0.7894 | 0.6854 | 0.7894 | | 0.9431 | 9.0 | 913 | 0.9142 | 0.7775 | 0.7751 | 0.7869 | 0.6811 | 0.7869 | | 0.2851 | 10.0 | 1015 | 0.9007 | 0.7820 | 0.7764 | 0.7943 | 0.6913 | 0.7943 | | 0.2851 | 11.0 | 1116 | 0.9425 | 0.7859 | 0.7825 | 0.7956 | 0.6940 | 0.7956 | | 0.2851 | 12.0 | 1218 | 0.9798 | 0.7815 | 0.7797 | 0.7906 | 0.6869 | 0.7906 | | 0.2851 | 13.0 | 1319 | 0.9895 | 0.7895 | 0.7860 | 0.7993 | 0.7003 | 0.7993 | | 0.2851 | 14.0 | 1421 | 0.9872 | 0.7854 | 0.7813 | 0.7943 | 0.6935 | 0.7943 | | 0.1273 | 14.93 | 1515 | 0.9948 | 0.7856 | 0.7820 | 0.7956 | 0.6940 | 0.7956 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3

BaleChen/REINFORCE-pixelcopter-test

BaleChen

2023-07-16T08:51:54Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-16T07:58:26Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: REINFORCE-pixelcopter-test results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.80 +/- 26.25 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . 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

manmyung/a2c-PandaReachDense-v2

manmyung

2023-07-16T08:43:12Z

0

stable-baselines3

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

reinforcement-learning

2023-07-16T08:40:02Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.88 +/- 0.21 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```

Hans14/PPO-LunarLander-v2

Hans14

2023-07-16T08:42:14Z

0

stable-baselines3

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

reinforcement-learning

2023-07-16T08:41:32Z

--- 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: 254.99 +/- 12.40 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 ... ```

li-ping/falcon_300csv_to_sheng

li-ping

2023-07-16T08:39:26Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T08:28:11Z

--- 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: float32 ### Framework versions - PEFT 0.4.0.dev0

esculapeso/biogpt-finetuned-twspookytest

esculapeso

2023-07-16T08:33:13Z

130

0

transformers

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

text-generation

2023-07-16T07:47:54Z

--- license: mit tags: - generated_from_trainer model-index: - name: biogpt-finetuned-twspookytest results: [] ---  # biogpt-finetuned-twspookytest This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9607 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 3 | 3.1962 | | No log | 2.0 | 6 | 3.0132 | | No log | 3.0 | 9 | 2.9607 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

openlm-research/open_llama_3b_v2

openlm-research

2023-07-16T08:32:00Z

25,360

149

transformers

[ "transformers", "pytorch", "llama", "text-generation", "dataset:tiiuae/falcon-refinedweb", "dataset:bigcode/starcoderdata", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-16T00:39:43Z

--- license: apache-2.0 datasets: - tiiuae/falcon-refinedweb - bigcode/starcoderdata - togethercomputer/RedPajama-Data-1T --- # OpenLLaMA: An Open Reproduction of LLaMA **TL;DR**: we are releasing our public preview of OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA. We are releasing a series of 3B, 7B and 13B models trained on different data mixtures. Our model weights can serve as the drop in replacement of LLaMA in existing implementations. In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a series of 3B, 7B and 13B models trained on 1T tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. The v2 model is better than the old v1 model trained on a different data mixture. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details. ## Weights Release, License and Usage We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license. ### Loading the Weights with Hugging Face Transformers Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that** [**the auto-converted fast tokenizer sometimes gives incorrect tokenizations**](https://github.com/huggingface/transformers/issues/24233)**.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage. ```python import torch from transformers import LlamaTokenizer, LlamaForCausalLM ## v2 models model_path = 'openlm-research/open_llama_3b_v2' # model_path = 'openlm-research/open_llama_7b_v2' ## v1 models # model_path = 'openlm-research/open_llama_3b' # model_path = 'openlm-research/open_llama_7b' # model_path = 'openlm-research/open_llama_13b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) prompt = 'Q: What is the largest animal?\nA:' input_ids = tokenizer(prompt, return_tensors="pt").input_ids generation_output = model.generate( input_ids=input_ids, max_new_tokens=32 ) print(tokenizer.decode(generation_output[0])) ``` For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama). ### Evaluating with LM-Eval-Harness The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below: ```python tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained( pretrained if tokenizer is None else tokenizer, revision=revision + ("/" + subfolder if subfolder is not None else ""), use_fast=False ) ``` ### Loading the Weights with EasyLM For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. ## Dataset and Training The v1 models are trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). The v2 models are trained on a mixture of the [Falcon refined-web dataset](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), the [StarCoder dataset](https://huggingface.co/datasets/bigcode/starcoderdata) and the wikipedia, arxiv, book and stackexchange part of the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs open datasets rather than the one utilized by the original LLaMA. We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and fully sharded data parallelism [](https://engineering.fb.com/2021/07/15/open-source/fsdp/)(also know as ZeRO stage 3) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model. ## Evaluation We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. | **Task/Metric** | GPT-J 6B | LLaMA 7B | LLaMA 13B | OpenLLaMA 3Bv2 | OpenLLaMA 7Bv2 | OpenLLaMA 3B | OpenLLaMA 7B | OpenLLaMA 13B | | ---------------------- | -------- | -------- | --------- | -------------- | -------------- | ------------ | ------------ | ------------- | | anli_r1/acc | 0.32 | 0.35 | 0.35 | 0.33 | 0.34 | 0.33 | 0.33 | 0.33 | | anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.36 | 0.35 | 0.32 | 0.36 | 0.33 | | anli_r3/acc | 0.35 | 0.37 | 0.39 | 0.38 | 0.39 | 0.35 | 0.38 | 0.40 | | arc_challenge/acc | 0.34 | 0.39 | 0.44 | 0.34 | 0.39 | 0.34 | 0.37 | 0.41 | | arc_challenge/acc_norm | 0.37 | 0.41 | 0.44 | 0.36 | 0.41 | 0.37 | 0.38 | 0.44 | | arc_easy/acc | 0.67 | 0.68 | 0.75 | 0.68 | 0.73 | 0.69 | 0.72 | 0.75 | | arc_easy/acc_norm | 0.62 | 0.52 | 0.59 | 0.63 | 0.70 | 0.65 | 0.68 | 0.70 | | boolq/acc | 0.66 | 0.75 | 0.71 | 0.66 | 0.72 | 0.68 | 0.71 | 0.75 | | hellaswag/acc | 0.50 | 0.56 | 0.59 | 0.52 | 0.56 | 0.49 | 0.53 | 0.56 | | hellaswag/acc_norm | 0.66 | 0.73 | 0.76 | 0.70 | 0.75 | 0.67 | 0.72 | 0.76 | | openbookqa/acc | 0.29 | 0.29 | 0.31 | 0.26 | 0.30 | 0.27 | 0.30 | 0.31 | | openbookqa/acc_norm | 0.38 | 0.41 | 0.42 | 0.38 | 0.41 | 0.40 | 0.40 | 0.43 | | piqa/acc | 0.75 | 0.78 | 0.79 | 0.77 | 0.79 | 0.75 | 0.76 | 0.77 | | piqa/acc_norm | 0.76 | 0.78 | 0.79 | 0.78 | 0.80 | 0.76 | 0.77 | 0.79 | | record/em | 0.88 | 0.91 | 0.92 | 0.87 | 0.89 | 0.88 | 0.89 | 0.91 | | record/f1 | 0.89 | 0.91 | 0.92 | 0.88 | 0.89 | 0.89 | 0.90 | 0.91 | | rte/acc | 0.54 | 0.56 | 0.69 | 0.55 | 0.57 | 0.58 | 0.60 | 0.64 | | truthfulqa_mc/mc1 | 0.20 | 0.21 | 0.25 | 0.22 | 0.23 | 0.22 | 0.23 | 0.25 | | truthfulqa_mc/mc2 | 0.36 | 0.34 | 0.40 | 0.35 | 0.35 | 0.35 | 0.35 | 0.38 | | wic/acc | 0.50 | 0.50 | 0.50 | 0.50 | 0.50 | 0.48 | 0.51 | 0.47 | | winogrande/acc | 0.64 | 0.68 | 0.70 | 0.63 | 0.66 | 0.62 | 0.67 | 0.70 | | Average | 0.52 | 0.55 | 0.57 | 0.53 | 0.56 | 0.53 | 0.55 | 0.57 | We removed the task CB and WSC from our benchmark, as our model performs suspiciously high on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. ## Contact We would love to get feedback from the community. If you have any questions, please open an issue or contact us. OpenLLaMA is developed by: [Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research. *Equal Contribution ## Acknowledgment We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback. The OpenLLaMA 13B v1 model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support. ## Reference If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX: ``` @software{openlm2023openllama, author = {Geng, Xinyang and Liu, Hao}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ``` @article{touvron2023llama, title={Llama: Open and efficient foundation language models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```

watcharakorn/whisper-small-th-v2

watcharakorn

2023-07-16T08:24:22Z

77

0

transformers

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

automatic-speech-recognition

2023-07-16T08:21:55Z

--- language: - th license: apache-2.0 base_model: openai/whisper-small tags: - th-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small th - mix dataset v.2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: th split: test args: 'config: th, split: test' metrics: - name: Wer type: wer value: 0.37791454289122656 ---  # Whisper Small th - mix dataset v.2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2980 - Wer: 0.3779 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3654 | 0.26 | 1000 | 0.2980 | 0.3779 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

digiplay/polla_mix_2.4D

digiplay

2023-07-16T08:23:45Z

334

4

diffusers

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

text-to-image

2023-07-16T06:56:58Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/110130?modelVersionId=118734 Simple image I made thru Huggingface's API : ![827c852a-171d-4875-a5d1-f226ca9e82ae.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/XBES3_gljSSXufKpgJNoE.jpeg) prompt : > pink spider with pink heart symbol ***Original Author's DEMO images :*** ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d3bcc3e4-5612-4947-9f9c-c76e5347b67d/width=1024/00055-114656479-1boy,%20armor,%20arthur_pendragon_(fate),%20blonde_hair,%20commentary_request,%20fate_prototype,%20fate_(series),%20green_eyes,%20hood,%20male_foc.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6aa33f59-4e0d-48a3-84e9-ef9c2aa1ba4d/width=1024/00125-1371705934-1boy,%20bad_id,%20bad_pixiv_id,%20bandage_on_face,%20bandages,%20black_hair,%20blue_eyes,%20chain,%20hat,%20jojo_no_kimyou_na_bouken,%20kujo_jotaro,.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/05ded902-bd43-4ad5-8aaa-53ca6b2b1f7d/width=1024/00052-114656476-1girl,%20black_hair,%20blush,%20closed_eyes,%20closed_mouth,%20commentary_request,%20drawn_wings,%20from_side,%20haikyuu!!,%20happy_birthday,%20jack.jpeg)

Ricky1981/Lgmx

Ricky1981

2023-07-16T08:19:20Z

0

null

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

null

2023-07-16T07:33:57Z

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

gpcarl123/resnet18_mnist

gpcarl123

2023-07-16T08:16:35Z

0

timm

[ "timm", "en", "dataset:mnist", "model-index", "region:us" ]

null

2023-07-16T07:48:41Z

--- language: - en library_name: timm datasets: - mnist metrics: - accuracy model-index: - name: resnet18_mnist results: - task: type: image-classification dataset: name: MNIST type: mnist metrics: - type: accuracy value: 0.9936 --- # Usage ```python import timm import torchvision MNIST_PATH = './datasets/mnist' net = timm.create_model("resnet18", pretrained=False, num_classes=10) net.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) net.load_state_dict( torch.hub.load_state_dict_from_url( "https://huggingface.co/gpcarl123/resnet18_mnist/resolve/main/resnet18_mnist.pth", map_location="cpu", file_name="resnet18_mnist.pth", ) ) preprocessor = torchvision.transforms.Normalize((0.1307,), (0.3081,)) transform = transforms.Compose([transforms.ToTensor()]) test_set = datasets.MNIST(root=MNIST_PATH, train=False, download=True, transform=transform) test_loader = data.DataLoader(test_set, batch_size=5, shuffle=False, num_workers=2) for data, target in test_loader: print(net(preprocessor(data))) print(target) break ```

digiplay/polla_mix_2.5D

digiplay

2023-07-16T07:56:07Z

50

3

diffusers

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

text-to-image

2023-07-16T06:57:17Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/110130?modelVersionId=118741 Sample image I made thru Huggingface's API : ![012d0578-2ea4-4041-986c-411c3ebd2460.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/pyWHxPdufQnASl8CNW5qT.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/486ce102-be6c-4ea6-9a2c-3790a8e034b7/00010-1892571106-1girl,%20black_gloves,%20blonde_hair,%20blue_eyes,%20breasts,%20dress,%20fang,%20gloves,%20hair_ribbon,%20hat,%20holding,%20long_hair,%20open_mouth,%20pan.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9ad286ca-8c62-4d45-980a-c0993ed05259/00297-1401259144-1boy,%20bag,%20baseball_cap,%20black_background,%20black_bag,%20black_pants,%20blue_jacket,%20brown_eyes,%20brown_hair,%20bubble,%20commentary_reque.jpeg)

shihab17/bengali-bn-to-en

shihab17

2023-07-16T07:51:36Z

25

1

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "marian", "text2text-generation", "translation", "bn", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-05-23T10:43:30Z

--- library_name: transformers pipeline_tag: translation language: - bn --- ### How to use You can use this model directly with a pipeline: ```python from transformers import AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("shihab17/bengali-bn-to-en") model = AutoModelForSeq2SeqLM.from_pretrained("shihab17/bengali-bn-to-en") sentence = 'ম্যাচ শেষে পুরস্কার বিতরণের মঞ্চে তামিমের মুখে মোস্তাফিজের প্রশংসা শোনা গেল' translator = pipeline("translation_bn_to_en", model=model, tokenizer=tokenizer) output = translator(sentence) print(output) ```

murakami-dev/distilbert-base-uncased-finetuned-emotion

murakami-dev

2023-07-16T07:47:19Z

105

0

transformers

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

text-classification

2023-07-16T07:36:53Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.922976796795522 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2127 - Accuracy: 0.923 - F1: 0.9230 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7809 | 1.0 | 250 | 0.3023 | 0.9045 | 0.9005 | | 0.2412 | 2.0 | 500 | 0.2127 | 0.923 | 0.9230 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

digiplay/zodiac_eclipse_DAY1

digiplay

2023-07-16T07:40:46Z

285

2

diffusers

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

text-to-image

2023-07-14T08:32:01Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/108417/zodiac-eclipse-day1 Sample image I made thru Huggingface's API : ``` dog eat mango icecream ``` ![ebf8952b-9a34-4ddf-bacd-91cf85948371.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/e-i8jFIezPLNgYy_aRlao.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/21729b08-1f65-4029-bda3-e48d1ec00d3c/02569-2195959801-((best%20quality)),%20((masterpiece)),%20(detailed),%20alluring%20succubus,%20ethereal%20beauty,%20perched%20on%20a%20cloud,%20(fantasy%20illustration_1.3.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9a15ae22-804b-41e0-ba00-54fd2533d49b/02548-1697534186-monster,(((lake))),.jpeg)

tuanhnh/Reinforce-0

tuanhnh

2023-07-16T07:30:47Z

0

null

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

reinforcement-learning

2023-07-16T07:30:40Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 453.60 +/- 109.04 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

li-ping/falcon_0csv_to_sheng

li-ping

2023-07-16T07:12:27Z

0

peft

[ "peft", "region:us" ]

null

2023-07-16T06:56:45Z

--- 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: float32 ### Framework versions - PEFT 0.4.0.dev0

laserchalk/kangaroo-training-part-10

laserchalk

2023-07-16T06:53:40Z

6

1

diffusers

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

text-to-image

2023-07-16T06:39:24Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### kangaroo-training-part-10 Dreambooth model trained by laserchalk 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:

NasimB/guten-rarity-all-cut-20k

NasimB

2023-07-16T06:43:28Z

6

0

transformers

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

text-generation

2023-07-16T04:47:07Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-rarity-all-cut-20k results: [] ---  # guten-rarity-all-cut-20k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3156 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6961 | 0.29 | 500 | 5.6410 | | 5.3325 | 0.58 | 1000 | 5.1967 | | 4.9884 | 0.88 | 1500 | 4.9500 | | 4.7101 | 1.17 | 2000 | 4.8016 | | 4.5563 | 1.46 | 2500 | 4.6746 | | 4.446 | 1.75 | 3000 | 4.5705 | | 4.3244 | 2.05 | 3500 | 4.4929 | | 4.1291 | 2.34 | 4000 | 4.4489 | | 4.0992 | 2.63 | 4500 | 4.3891 | | 4.0577 | 2.92 | 5000 | 4.3368 | | 3.8593 | 3.21 | 5500 | 4.3329 | | 3.8077 | 3.51 | 6000 | 4.3001 | | 3.778 | 3.8 | 6500 | 4.2669 | | 3.6848 | 4.09 | 7000 | 4.2684 | | 3.513 | 4.38 | 7500 | 4.2630 | | 3.5142 | 4.68 | 8000 | 4.2467 | | 3.4975 | 4.97 | 8500 | 4.2338 | | 3.3389 | 5.26 | 9000 | 4.2463 | | 3.3207 | 5.55 | 9500 | 4.2462 | | 3.3201 | 5.84 | 10000 | 4.2453 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

bochen0909/ppo-LunarLander-v2

bochen0909

2023-07-16T06:41:23Z

0

stable-baselines3

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

reinforcement-learning

2023-07-16T06:41:08Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.97 +/- 20.22 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 ... ```

BaleChen/REINFORCE-cartpolev1-test

BaleChen

2023-07-16T06:39:08Z

0

null

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

reinforcement-learning

2023-07-16T06:38:57Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: REINFORCE-cartpolev1-test results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 456.90 +/- 89.34 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

NasimB/bnc-rarity-no-cut-shuffled

NasimB

2023-07-16T06:24:06Z

4

0

transformers

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

text-generation

2023-07-16T04:27:02Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-rarity-no-cut-shuffled results: [] ---  # bnc-rarity-no-cut-shuffled This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3207 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7157 | 0.29 | 500 | 5.6437 | | 5.3513 | 0.58 | 1000 | 5.2021 | | 5.0016 | 0.88 | 1500 | 4.9595 | | 4.7286 | 1.17 | 2000 | 4.8122 | | 4.5693 | 1.46 | 2500 | 4.6857 | | 4.4647 | 1.75 | 3000 | 4.5770 | | 4.3308 | 2.05 | 3500 | 4.5068 | | 4.1402 | 2.34 | 4000 | 4.4574 | | 4.1123 | 2.63 | 4500 | 4.3983 | | 4.0711 | 2.92 | 5000 | 4.3468 | | 3.8657 | 3.22 | 5500 | 4.3414 | | 3.8086 | 3.51 | 6000 | 4.3099 | | 3.7977 | 3.8 | 6500 | 4.2728 | | 3.6947 | 4.09 | 7000 | 4.2729 | | 3.5188 | 4.39 | 7500 | 4.2684 | | 3.5211 | 4.68 | 8000 | 4.2523 | | 3.5159 | 4.97 | 8500 | 4.2387 | | 3.3414 | 5.26 | 9000 | 4.2532 | | 3.3357 | 5.56 | 9500 | 4.2520 | | 3.328 | 5.85 | 10000 | 4.2517 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

lovelyxs/rl_course_vizdoom_health_gathering_supreme

lovelyxs

2023-07-16T05:56:49Z

0

sample-factory

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

reinforcement-learning

2023-07-16T05:56:44Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 13.28 +/- 4.85 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r lovelyxs/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

ardhies/vira

ardhies

2023-07-16T05:48:55Z

0

null

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

null

2023-06-23T00:46:30Z

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

Vasanth/distilbert-stock-tweet-sentiment-analysis

Vasanth

2023-07-16T05:26:06Z

185

0

transformers

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

text-classification

2023-07-16T05:15:36Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-stock-tweet-sentiment-analysis results: [] ---  # distilbert-stock-tweet-sentiment-analysis This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6075 - Accuracy: 0.782 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.686 | 1.0 | 1000 | 0.5916 | 0.7745 | | 0.4804 | 2.0 | 2000 | 0.5635 | 0.7812 | | 0.3644 | 3.0 | 3000 | 0.6075 | 0.782 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ltmai/morgan-embed-bio-clinical-bert-ddi

ltmai

2023-07-16T05:24:59Z

31

0

transformers

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

null

2023-07-15T18:38:02Z

--- tags: - generated_from_trainer model-index: - name: morgan-embed-bio-clinical-bert-ddi results: [] ---  # morgan-embed-bio-clinical-bert-ddi This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.000628 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3

hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-60-20-20

hafidikhsan

2023-07-16T05:24:57Z

105

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-16T05:22:44Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-60-20-20 results: [] ---  # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-dp-60-20-20 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9015 - Accuracy: 0.743 - F1: 0.7432 - Precision: 0.7495 - Recall: 0.743 ## Model description More information needed ## Intended uses & 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: 8 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.8727 | 1.0 | 375 | 0.8777 | 0.574 | 0.5558 | 0.5611 | 0.574 | | 0.834 | 2.0 | 750 | 0.8086 | 0.652 | 0.6470 | 0.6558 | 0.652 | | 0.6609 | 3.0 | 1125 | 0.8289 | 0.695 | 0.6926 | 0.6945 | 0.695 | | 0.329 | 4.0 | 1500 | 0.9585 | 0.755 | 0.7558 | 0.7607 | 0.755 | | 0.1628 | 5.0 | 1875 | 1.1191 | 0.751 | 0.7488 | 0.7479 | 0.751 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Denilah/distilbert-base-uncased-finetuned-emotion

Denilah

2023-07-16T05:15:46Z

106

0

transformers

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

text-classification

2023-07-16T03:24:16Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.937 - name: F1 type: f1 value: 0.9373121473490384 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1565 - Accuracy: 0.937 - F1: 0.9373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4774 | 1.0 | 1000 | 0.1971 | 0.923 | 0.9226 | | 0.147 | 2.0 | 2000 | 0.1565 | 0.937 | 0.9373 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

j-hyeok/taxi-v3

j-hyeok

2023-07-16T04:27:10Z

0

null

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

reinforcement-learning

2023-07-16T04:27:06Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 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="j-hyeok/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"]) ```

j-hyeok/q-FrozenLake-v1-4x4-noSlippery

j-hyeok

2023-07-16T04:22:13Z

0

null

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

reinforcement-learning

2023-07-16T04:22:07Z

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

SerCe/tortoise-tts-ruslan

SerCe

2023-07-16T04:21:51Z

0

9

null

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

null

2023-06-07T10:12:18Z

--- license: apache-2.0 --- The _tortoise-tts-ruslan_ model is a tortoise model capable of speaking Russian language. The model was first finetuned on public_youtube700_val+buriy_audiobooks_2_val from [Russian Open Speech To Text](https://learn.microsoft.com/en-us/azure/open-datasets/dataset-open-speech-text?tabs=azure-storage). The model was then finetuned on [RUSLAN: Russian Spoken Language Corpus For Speech Synthesis](https://ruslan-corpus.github.io/). The model is able to generate generic male voices, see [examples](https://huggingface.co/SerCe/tortoise-tts-ruslan/tree/main/examples/random). Additionally, the model is suitable for further finetuning on any Russian male voice, e.g. see a [finetuned](https://huggingface.co/SerCe/tortoise-tts-ruslan/tree/main/examples/finetuned) voice of Yury Dud (the finetuned model weights are not included). The code from [ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning) was used to train the model.

NasimB/children-rarity-all-guten-log-rarity-all

NasimB

2023-07-16T04:21:14Z

9

0

transformers

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

text-generation

2023-07-16T02:19:49Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: children-rarity-all-guten-log-rarity-all results: [] ---  # children-rarity-all-guten-log-rarity-all This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3116 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7036 | 0.29 | 500 | 5.6365 | | 5.348 | 0.58 | 1000 | 5.2064 | | 4.99 | 0.87 | 1500 | 4.9589 | | 4.7208 | 1.16 | 2000 | 4.8071 | | 4.5602 | 1.46 | 2500 | 4.6761 | | 4.4513 | 1.75 | 3000 | 4.5690 | | 4.3332 | 2.04 | 3500 | 4.4907 | | 4.1308 | 2.33 | 4000 | 4.4479 | | 4.1002 | 2.62 | 4500 | 4.3912 | | 4.0711 | 2.91 | 5000 | 4.3370 | | 3.8621 | 3.2 | 5500 | 4.3334 | | 3.803 | 3.49 | 6000 | 4.3002 | | 3.7865 | 3.79 | 6500 | 4.2683 | | 3.6992 | 4.08 | 7000 | 4.2633 | | 3.5158 | 4.37 | 7500 | 4.2591 | | 3.5163 | 4.66 | 8000 | 4.2433 | | 3.501 | 4.95 | 8500 | 4.2300 | | 3.3525 | 5.24 | 9000 | 4.2437 | | 3.3213 | 5.53 | 9500 | 4.2424 | | 3.3235 | 5.82 | 10000 | 4.2416 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

laserchalk/kangaroo-training-part-7

laserchalk

2023-07-16T04:15:03Z

2

0

diffusers

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

text-to-image

2023-07-16T04:04:01Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Kangaroo-training-part-7 Dreambooth model trained by laserchalk 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:

1112lee/setfit-model

1112lee

2023-07-16T03:42:36Z

4

0

sentence-transformers

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

text-classification

2023-07-16T03:28:00Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # 1112lee/setfit-model 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("1112lee/setfit-model") # 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} } ```

Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5

Evan-Lin

2023-07-16T03:40:20Z

48

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-07-15T19:51:18Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpi3mfbi5q/Evan-Lin/Bart-RL-many-keywordmax-entailment-attractive-reward5") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

OptimalScale/robin-13b-v2-delta

OptimalScale

2023-07-16T03:14:08Z

1,546

7

transformers

[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2302.13971", "arxiv:2306.12420", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-05-28T05:55:54Z

--- inference: false --- # Robin Model Card ## Model Details Robin is a series of models finetuned from LLaMA on several high-quality data. - **Developed by:** [LMFlow](https://github.com/OptimalScale/LMFlow/) - **Model type:** An auto-regressive language model based on the transformer architecture. - **License:** Non-commercial license - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - **Repository:** https://github.com/OptimalScale/LMFlow/ - **Blog:** https://medium.com/@hkust.ml/robin-v2-launches-achieves-unparalleled-performance-on-openllm-4f6886e822c1 - **Paper:** https://arxiv.org/abs/2306.12420 - **Demo:** https://lmflow.com/ ## Uses Robin is primarily utilized for conducting research on extensive language models and chatbots, catering to users specializing in natural language processing, machine learning, and artificial intelligence research. ## How to Get Started with the Model We provide four kinds of demos including: - Online Service: If you don't want to run any code and just want to try our models, we deploy our instruction-tuned LLaMA you to have a try. - Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab. - Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab. - Local Deploy: We also provide a way for you to deploy your model/chatbot locally, which means you can deploy much larger model than previous three methods if you have enough resource. Please refer to https://github.com/OptimalScale/LMFlow#demos ## Training Details Expanding upon the initial idea of self-instruct techniques, we incorporated several different data sources and build a new dataset called [LMFlow Dataset](http://lmflow.org:5000/lmflow_data.tar.gz). The new training split is created by merging the following datasets: - ShareGPT: randomly sample 50K English data and 10K Chinese data from ShareGPT. - GPT-4-LLM: 52K English data from GPT-4-LLM. - BELLE: randomly sample 80K Chinese data from BELLE. See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Evaluation Robin is evaluated with [LMFlow Benchmark](https://blog.gopenai.com/lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms-ef5c6f142418). See more details in this [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Citation If you find this repository useful, please consider giving ⭐ and citing our [paper](https://arxiv.org/abs/2306.12420): ``` @misc{lmflow, author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang}, title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://optimalscale.github.io/LMFlow/}}, } ```

ALM-AHME/convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20

ALM-AHME

2023-07-16T03:13:16Z

12

1

transformers

[ "transformers", "pytorch", "tensorboard", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-15T00:35:53Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Splitted-Resized split: train args: Splitted-Resized metrics: - name: Accuracy type: accuracy value: 0.9900990099009901 ---  # convnextv2-large-1k-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20 This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0353 - 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-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5207 | 1.0 | 199 | 0.4745 | 0.8887 | | 0.2029 | 2.0 | 398 | 0.2072 | 0.9401 | | 0.1615 | 3.0 | 597 | 0.1489 | 0.9547 | | 0.1662 | 4.0 | 796 | 0.1312 | 0.9562 | | 0.1986 | 5.0 | 995 | 0.1026 | 0.9698 | | 0.0854 | 6.0 | 1194 | 0.0583 | 0.9802 | | 0.0538 | 7.0 | 1393 | 0.0568 | 0.9835 | | 0.0977 | 8.0 | 1592 | 0.0654 | 0.9793 | | 0.6971 | 9.0 | 1791 | 0.6821 | 0.5450 | | 0.211 | 10.0 | 1990 | 0.1654 | 0.9326 | | 0.1775 | 11.0 | 2189 | 0.0859 | 0.9665 | | 0.0042 | 12.0 | 2388 | 0.0353 | 0.9901 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

GarbageCollector/EFX2

GarbageCollector

2023-07-16T03:07:37Z

0

null

[ "stable-diffusion", "safetensors", "text-to-image", "license:unknown", "region:us" ]

text-to-image

2023-07-16T02:27:12Z

--- tags: - stable-diffusion - safetensors pipeline_tag: text-to-image license: unknown --- this place is my garbage collection. some models are not better than others. ___SAMPLES___ LOOMER <img src="https://huggingface.co/GarbageCollector/EFX2/resolve/main/samples/LOOMER.jpg"/>

OptimalScale/robin-65b-v2-delta

OptimalScale

2023-07-16T02:48:33Z

1,534

12

transformers

[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2302.13971", "arxiv:2306.12420", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-06-11T06:48:38Z

--- inference: false --- # Robin Model Card ## Model Details Robin is a series of models finetuned from LLaMA on several high-quality data. - **Developed by:** [LMFlow](https://github.com/OptimalScale/LMFlow/) - **Model type:** An auto-regressive language model based on the transformer architecture. - **License:** Non-commercial license - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - **Repository:** https://github.com/OptimalScale/LMFlow/ - **Blog:** https://medium.com/@hkust.ml/robin-v2-launches-achieves-unparalleled-performance-on-openllm-4f6886e822c1 - **Paper:** https://arxiv.org/abs/2306.12420 - **Demo:** https://lmflow.com/ ## Uses Robin is primarily utilized for conducting research on extensive language models and chatbots, catering to users specializing in natural language processing, machine learning, and artificial intelligence research. ## How to Get Started with the Model We provide four kinds of demos including: - Online Service: If you don't want to run any code and just want to try our models, we deploy our instruction-tuned LLaMA you to have a try. - Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab. - Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab. - Local Deploy: We also provide a way for you to deploy your model/chatbot locally, which means you can deploy much larger model than previous three methods if you have enough resource. Please refer to https://github.com/OptimalScale/LMFlow#demos ## Training Details Expanding upon the initial idea of self-instruct techniques, we incorporated several different data sources and build a new dataset called [LMFlow Dataset](http://lmflow.org:5000/lmflow_data.tar.gz). The new training split is created by merging the following datasets: - ShareGPT: randomly sample 50K English data and 10K Chinese data from ShareGPT. - GPT-4-LLM: 52K English data from GPT-4-LLM. - BELLE: randomly sample 80K Chinese data from BELLE. See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Evaluation Robin is evaluated with [LMFlow Benchmark](https://blog.gopenai.com/lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms-ef5c6f142418). See more details in this [paper](https://arxiv.org/pdf/2306.12420.pdf). ## Citation If you find this repository useful, please consider giving ⭐ and citing our [paper](https://arxiv.org/abs/2306.12420): ``` @misc{lmflow, author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang}, title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://optimalscale.github.io/LMFlow/}}, } ```

PeterBrendan/pbjsGPT2v2

PeterBrendan

2023-07-16T02:32:02Z

144

0

transformers

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

text-generation

2023-07-12T15:07:20Z

--- license: mit widget: - text: bidderTimeout - text: Usebidcache - text: bidderSequence - text: customPriceBucket --- ## Model: GPT-2 ### Model name: pbjsGPT2v2 ### Model description: This fine-tuned version of the GPT-2 model was trained on a subset of 1100+ publisher domains' Prebid config files. Its focus is on sophisticated Prebid publishers. The model provides insights into how these publishers configure their Prebid settings. By inputting a Prebid config setting, such as ***bidderTimeout***, the model generates sample Prebid configuration settings based on the collected data. It aims to assist publishers in understanding different configurations used by sophisticated publishers. ### Intended uses: This model is intended to assist publishers in understanding and exploring how other publishers configure their Prebid settings. It serves as a reference for gaining insights into common configurations, best practices, and different approaches used by top publishers across various domains. ### Limitations: The generated Prebid configuration settings are based on the data from the training set and may not cover all possible configurations or reflect the specific requirements of a particular domain. Publishers should carefully review and adapt the generated configurations to their specific needs and business rules. ### How to use: To use this model, provide a Prebid config setting, such as ***bidderSequence***. The model will generate a sample Prebid configuration related to that input based on the collected data. ### Training data: This model was trained on a subset of 1100+ publisher domains Prebid config files. The dataset was collected from a variety of publishers and represents a wide range of Prebid settings used in the industry. ### Training procedure: The model was fine-tuned using the GPT-2 base model with the aforementioned dataset. ### Evaluation results: The evaluation of this model focuses on its ability to generate coherent and valid Prebid configuration settings based on the provided Prebid config setting. Human evaluators reviewed the generated configurations for relevance and accuracy. ### Safety and bias considerations: The model is trained on data from actual Prebid config files and aims to provide accurate insights into publishers' configurations. However, it's important to note that biases may exist in the original data itself, as the training data is based on real-world configurations. Users should review and validate the generated configurations to ensure they align with their specific requirements and guidelines. Users are encouraged to exercise caution and use their expertise in interpreting and adapting the generated Prebid configurations for their own use. The model should be seen as a helpful tool to gain inspiration and understanding of common Prebid settings but not as a substitute for thorough testing and manual review of the final configurations.

PeterBrendan/pbjs_gpt2

PeterBrendan

2023-07-16T02:14:23Z

144

1

transformers

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

text-generation

2023-07-08T02:03:58Z

--- license: mit widget: - text: bidderTimeout - text: Usebidcache - text: bidderSequence - text: customPriceBucket --- **Model:** GPT-2 **Model name:** pbjs_gpt2 **Model description:** This fine-tuned version of the GPT-2 model was trained on a dataset of 1100+ publisher domains' Prebid config files. It aims to provide insights into how other publishers configure their Prebid settings. Given a Prebid config setting, such as ***bidderTimeout***, the model can generate sample Prebid configuration settings based on the collected data. It helps publishers gain an understanding of how different publishers configure their Prebid settings. **Intended uses:** This model is intended to assist publishers in understanding and exploring how other publishers configure their Prebid settings. It serves as a reference to gain insights into common configurations, best practices, and different approaches used by publishers across various domains. **Limitations:** It's important to note that the generated Prebid configuration settings are based on the data from the training set and may not cover all possible configurations or reflect the specific requirements of a particular domain. Publishers should carefully review and adapt the generated configurations to their specific needs and business rules. **How to use:** To use this model, provide a Prebid config setting, such as ***bidderSequence***. The model will generate a sample Prebid configuration related to that input based on the collected data. **Training data:** This model was trained on a dataset consisting of over 1100+ publisher domains Prebid config files. The dataset was collected from a variety of publishers and represents a wide range of Prebid settings used in the industry. **Training procedure:** The model was fine-tuned using the GPT-2 base model with the aforementioned dataset. The training loss achieved was 0.43277667846199475. **Evaluation results:** The evaluation of this model focuses on its ability to generate coherent and valid Prebid configuration settings based on the provided Prebid config setting. Human evaluators reviewed the generated configurations for relevance and accuracy. **Safety and bias considerations:** The model is trained on data from actual Prebid config files and aims to provide accurate insights into publishers' configurations. However, it's important to note that biases may exist in the original data itself, as the training data is based on real-world configurations. Users should review and validate the generated configurations to ensure they align with their specific requirements and guidelines. Users are encouraged to exercise caution and use their expertise in interpreting and adapting the generated Prebid configurations for their own use. The model should be seen as a helpful tool to gain inspiration and understanding of common Prebid settings but not as a substitute for thorough testing and manual review of the final configurations.

monideep2255/spell_correction_M04_V3

monideep2255

2023-07-16T02:10:18Z

3

0

transformers

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

text2text-generation

2023-07-16T00:59:14Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: spell_correction_M04_V3 results: [] ---  # spell_correction_M04_V3 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0178 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 269 | 0.2687 | | 1.8467 | 2.0 | 538 | 0.0361 | | 1.8467 | 3.0 | 807 | 0.0241 | | 0.0357 | 4.0 | 1076 | 0.0198 | | 0.0357 | 5.0 | 1345 | 0.0199 | | 0.0159 | 6.0 | 1614 | 0.0175 | | 0.0159 | 7.0 | 1883 | 0.0179 | | 0.0077 | 8.0 | 2152 | 0.0189 | | 0.0077 | 9.0 | 2421 | 0.0183 | | 0.006 | 10.0 | 2690 | 0.0183 | | 0.006 | 11.0 | 2959 | 0.0191 | | 0.0044 | 12.0 | 3228 | 0.0186 | | 0.0044 | 13.0 | 3497 | 0.0192 | | 0.0033 | 14.0 | 3766 | 0.0189 | | 0.0024 | 15.0 | 4035 | 0.0173 | | 0.0024 | 16.0 | 4304 | 0.0171 | | 0.0026 | 17.0 | 4573 | 0.0183 | | 0.0026 | 18.0 | 4842 | 0.0181 | | 0.0021 | 19.0 | 5111 | 0.0177 | | 0.0021 | 20.0 | 5380 | 0.0174 | | 0.0015 | 21.0 | 5649 | 0.0173 | | 0.0015 | 22.0 | 5918 | 0.0174 | | 0.0016 | 23.0 | 6187 | 0.0178 | | 0.0016 | 24.0 | 6456 | 0.0180 | | 0.0018 | 25.0 | 6725 | 0.0175 | | 0.0018 | 26.0 | 6994 | 0.0171 | | 0.0017 | 27.0 | 7263 | 0.0175 | | 0.0014 | 28.0 | 7532 | 0.0177 | | 0.0014 | 29.0 | 7801 | 0.0178 | | 0.0013 | 30.0 | 8070 | 0.0178 | ### Framework versions - Transformers 4.28.0 - Pytorch 1.12.1+cu102 - Datasets 2.13.1 - Tokenizers 0.13.3

manmyung/ppo-SnowballTarget

manmyung

2023-07-16T02:08:22Z

2

0

ml-agents

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

reinforcement-learning

2023-07-16T02:08:19Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** 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: manmyung/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Hex820000/anime_v10

Hex820000

2023-07-16T01:57:47Z

0

null

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

null

2023-07-16T01:46:09Z

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