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 2025-06-28 12:28:24	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-28 12:27:53	card stringlengths 11 1.01M
EnterNameBros/DialoGPT-large-Senko-san-ver-2	EnterNameBros	2023-05-24T03:42:34Z	143	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-24T03:17:01Z	--- pipeline_tag: conversational language: - en metrics: - character - accuracy ---
AdonaiHS/unit_8_LunarLander-v2	AdonaiHS	2023-05-24T03:38:36Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-05-24T03:38:30Z	--- 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: -268.70 +/- 134.46 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': 'AdonaiHS/unit_8_LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
YakovElm/Jira5Classic_with_cleaning	YakovElm	2023-05-24T03:37:03Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-24T03:36:17Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Jira5Classic_with_cleaning 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. --> # Jira5Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2646 - Train Accuracy: 0.8919 - Validation Loss: 1.1625 - Validation Accuracy: 0.5584 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.5257 \| 0.7639 \| 0.6646 \| 0.5931 \| 0 \| \| 0.4200 \| 0.7901 \| 1.2433 \| 0.4890 \| 1 \| \| 0.2646 \| 0.8919 \| 1.1625 \| 0.5584 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
mio/chtholly	mio	2023-05-24T03:21:37Z	3	9	espnet	[ "espnet", "audio", "text-to-speech", "jp", "dataset:chtholly", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	text-to-speech	2023-05-22T13:06:29Z	--- tags: - espnet - audio - text-to-speech language: jp datasets: - chtholly license: cc-by-4.0 widget: - text: "こんにちは、クトリ・ノタ・セニオリスです。終末なにしてますか？忙しいですか？救ってもらっていいですか？" --- ## ESPnet2 TTS model ![](./珂朵莉.jpg) ### `mio/chtholly` This model was trained by mio using chtholly recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 0232f540a98ece921477b961db8ae019211da9af pip install -e . cd egs2/chtholly/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model mio/chtholly ``` ## TTS config <details><summary>expand</summary> ``` config: conf/tuning/finetune_vits.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_chtholly_vits_finetune_from_jsut ngpu: 1 seed: 777 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 50705 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - total_count - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: -1 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 50 use_matplotlib: true use_tensorboard: false create_graph_in_tensorboard: false use_wandb: true wandb_project: chtholly wandb_id: null wandb_entity: null wandb_name: vits_finetune_chtholly_from_jsut wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: - downloads/f3698edf589206588f58f5ec837fa516/exp/tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause/train.total_count.ave_10best.pth:tts:tts ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 1000 batch_size: 20 valid_batch_size: null batch_bins: 5000000 valid_batch_bins: null train_shape_file: - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/text_shape.phn - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/speech_shape valid_shape_file: - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/text_shape.phn - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/22k/raw/train/text - text - text - - dump/22k/raw/train/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/22k/raw/dev/text - text - text - - dump/22k/raw/dev/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adamw optim_conf: lr: 0.0001 betas: - 0.8 - 0.99 eps: 1.0e-09 weight_decay: 0.0 scheduler: exponentiallr scheduler_conf: gamma: 0.999875 optim2: adamw optim2_conf: lr: 0.0001 betas: - 0.8 - 0.99 eps: 1.0e-09 weight_decay: 0.0 scheduler2: exponentiallr scheduler2_conf: gamma: 0.999875 generator_first: false token_list: - <blank> - <unk> - '1' - '2' - '0' - '3' - '4' - '-1' - '5' - a - o - '-2' - i - '-3' - u - e - k - n - t - '6' - r - '-4' - s - N - m - pau - '7' - sh - d - g - w - '8' - U - '-5' - I - cl - h - y - b - '9' - j - ts - ch - '-6' - z - p - '-7' - f - ky - ry - '-8' - gy - '-9' - hy - ny - '-10' - by - my - '-11' - '-12' - '-13' - py - '-14' - '-15' - v - '10' - '-16' - '-17' - '11' - '-21' - '-20' - '12' - '-19' - '13' - '-18' - '14' - dy - '15' - ty - '-22' - '16' - '18' - '19' - '17' - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: jaconv g2p: pyopenjtalk_accent_with_pause feats_extract: linear_spectrogram feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null normalize: null normalize_conf: {} tts: vits tts_conf: generator_type: vits_generator generator_params: hidden_channels: 192 spks: -1 global_channels: -1 segment_size: 32 text_encoder_attention_heads: 2 text_encoder_ffn_expand: 4 text_encoder_blocks: 6 text_encoder_positionwise_layer_type: conv1d text_encoder_positionwise_conv_kernel_size: 3 text_encoder_positional_encoding_layer_type: rel_pos text_encoder_self_attention_layer_type: rel_selfattn text_encoder_activation_type: swish text_encoder_normalize_before: true text_encoder_dropout_rate: 0.1 text_encoder_positional_dropout_rate: 0.0 text_encoder_attention_dropout_rate: 0.1 use_macaron_style_in_text_encoder: true use_conformer_conv_in_text_encoder: false text_encoder_conformer_kernel_size: -1 decoder_kernel_size: 7 decoder_channels: 512 decoder_upsample_scales: - 8 - 8 - 2 - 2 decoder_upsample_kernel_sizes: - 16 - 16 - 4 - 4 decoder_resblock_kernel_sizes: - 3 - 7 - 11 decoder_resblock_dilations: - - 1 - 3 - 5 - - 1 - 3 - 5 - - 1 - 3 - 5 use_weight_norm_in_decoder: true posterior_encoder_kernel_size: 5 posterior_encoder_layers: 16 posterior_encoder_stacks: 1 posterior_encoder_base_dilation: 1 posterior_encoder_dropout_rate: 0.0 use_weight_norm_in_posterior_encoder: true flow_flows: 4 flow_kernel_size: 5 flow_base_dilation: 1 flow_layers: 4 flow_dropout_rate: 0.0 use_weight_norm_in_flow: true use_only_mean_in_flow: true stochastic_duration_predictor_kernel_size: 3 stochastic_duration_predictor_dropout_rate: 0.5 stochastic_duration_predictor_flows: 4 stochastic_duration_predictor_dds_conv_layers: 3 vocabs: 85 aux_channels: 513 discriminator_type: hifigan_multi_scale_multi_period_discriminator discriminator_params: scales: 1 scale_downsample_pooling: AvgPool1d scale_downsample_pooling_params: kernel_size: 4 stride: 2 padding: 2 scale_discriminator_params: in_channels: 1 out_channels: 1 kernel_sizes: - 15 - 41 - 5 - 3 channels: 128 max_downsample_channels: 1024 max_groups: 16 bias: true downsample_scales: - 2 - 2 - 4 - 4 - 1 nonlinear_activation: LeakyReLU nonlinear_activation_params: negative_slope: 0.1 use_weight_norm: true use_spectral_norm: false follow_official_norm: false periods: - 2 - 3 - 5 - 7 - 11 period_discriminator_params: in_channels: 1 out_channels: 1 kernel_sizes: - 5 - 3 channels: 32 downsample_scales: - 3 - 3 - 3 - 3 - 1 max_downsample_channels: 1024 bias: true nonlinear_activation: LeakyReLU nonlinear_activation_params: negative_slope: 0.1 use_weight_norm: true use_spectral_norm: false generator_adv_loss_params: average_by_discriminators: false loss_type: mse discriminator_adv_loss_params: average_by_discriminators: false loss_type: mse feat_match_loss_params: average_by_discriminators: false average_by_layers: false include_final_outputs: true mel_loss_params: fs: 22050 n_fft: 1024 hop_length: 256 win_length: null window: hann n_mels: 80 fmin: 0 fmax: null log_base: null lambda_adv: 1.0 lambda_mel: 45.0 lambda_feat_match: 2.0 lambda_dur: 1.0 lambda_kl: 1.0 sampling_rate: 22050 cache_generator_outputs: true pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: '202207' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
YakovElm/IntelDAOS20SetFitModel_clean_data	YakovElm	2023-05-24T03:20:40Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-05-24T03:20:00Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/IntelDAOS20SetFitModel_clean_data 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("YakovElm/IntelDAOS20SetFitModel_clean_data") # 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} } ```
sarahpuspdew/DeepRLCourse_Unit4-Reinforce-CartPole-v1	sarahpuspdew	2023-05-24T03:20:03Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T01:51:05Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: DeepRLCourse_Unit4-Reinforce-CartPole-v1 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
YakovElm/IntelDAOS15SetFitModel_clean_data	YakovElm	2023-05-24T03:06:54Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-05-24T03:06:18Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/IntelDAOS15SetFitModel_clean_data 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("YakovElm/IntelDAOS15SetFitModel_clean_data") # 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} } ```
arjunpatel/bloom-speed-check-small	arjunpatel	2023-05-24T02:54:32Z	0	0	transformers	[ "transformers", "text-generation", "en", "endpoints_compatible", "region:us" ]	text-generation	2023-05-15T03:27:00Z	--- language: - en pipeline_tag: text-generation library_name: transformers ---
Erdenebold/test-distilbert-base-multilingual-cased	Erdenebold	2023-05-24T02:53:05Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "mn", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-24T02:11:40Z	--- language: - mn license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: test-distilbert-base-multilingual-cased 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. --> # test-distilbert-base-multilingual-cased This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1533 - Precision: 0.8783 - Recall: 0.9010 - F1: 0.8895 - Accuracy: 0.9721 ## Model description More information needed ## Intended uses & 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2124 \| 1.0 \| 477 \| 0.1286 \| 0.8065 \| 0.8469 \| 0.8262 \| 0.9586 \| \| 0.103 \| 2.0 \| 954 \| 0.1113 \| 0.8374 \| 0.8772 \| 0.8568 \| 0.9663 \| \| 0.0673 \| 3.0 \| 1431 \| 0.1124 \| 0.8480 \| 0.8810 \| 0.8641 \| 0.9668 \| \| 0.0474 \| 4.0 \| 1908 \| 0.1165 \| 0.8658 \| 0.8922 \| 0.8788 \| 0.9710 \| \| 0.0338 \| 5.0 \| 2385 \| 0.1254 \| 0.8664 \| 0.8909 \| 0.8785 \| 0.9692 \| \| 0.0236 \| 6.0 \| 2862 \| 0.1349 \| 0.8686 \| 0.8954 \| 0.8818 \| 0.9707 \| \| 0.018 \| 7.0 \| 3339 \| 0.1428 \| 0.8772 \| 0.8991 \| 0.8880 \| 0.9715 \| \| 0.0133 \| 8.0 \| 3816 \| 0.1505 \| 0.8739 \| 0.8961 \| 0.8849 \| 0.9712 \| \| 0.0106 \| 9.0 \| 4293 \| 0.1529 \| 0.8812 \| 0.9012 \| 0.8911 \| 0.9720 \| \| 0.0082 \| 10.0 \| 4770 \| 0.1533 \| 0.8783 \| 0.9010 \| 0.8895 \| 0.9721 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
bbbdbbb/sd-class-butterflies-32_test	bbbdbbb	2023-05-24T02:43:12Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-05-24T02:40:57Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('bbbdbbb/sd-class-butterflies-32_test') image = pipeline().images[0] image ```
dclab/task-test	dclab	2023-05-24T02:37:37Z	0	0	null	[ "text-generation", "license:other", "region:us" ]	text-generation	2023-05-23T14:37:47Z	--- pipeline_tag: text-generation license: other ---
YakovElm/IntelDAOS20Classic_with_cleaning	YakovElm	2023-05-24T02:36:25Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-24T02:35:50Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: IntelDAOS20Classic_with_cleaning 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. --> # IntelDAOS20Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1286 - Train Accuracy: 0.9610 - Validation Loss: 0.3677 - Validation Accuracy: 0.9099 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.2178 \| 0.9600 \| 0.3604 \| 0.9099 \| 0 \| \| 0.1502 \| 0.9610 \| 0.3197 \| 0.9099 \| 1 \| \| 0.1286 \| 0.9610 \| 0.3677 \| 0.9099 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
cebernalc/ppo-LunarLander-v2	cebernalc	2023-05-24T02:28:13Z	5	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-24T02:27:53Z	--- 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: 248.03 +/- 23.78 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
YakovElm/IntelDAOS10Classic_with_cleaning	YakovElm	2023-05-24T02:08:53Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-24T02:08:18Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: IntelDAOS10Classic_with_cleaning 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. --> # IntelDAOS10Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2283 - Train Accuracy: 0.9210 - Validation Loss: 0.4310 - Validation Accuracy: 0.8739 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.3141 \| 0.9200 \| 0.3738 \| 0.8739 \| 0 \| \| 0.2612 \| 0.9200 \| 0.4105 \| 0.8739 \| 1 \| \| 0.2283 \| 0.9210 \| 0.4310 \| 0.8739 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
dev2bit/es2bash-mt5	dev2bit	2023-05-24T01:57:45Z	9	1	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "code", "bash", "es", "dataset:dev2bit/es2bash", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-23T23:23:17Z	--- license: apache-2.0 datasets: - dev2bit/es2bash language: - es pipeline_tag: text2text-generation tags: - code - bash widget: - text: Muestra el contenido de file.py que se encuentra en ~/project/ example_title: cat - text: Lista los 3 primeros archivos en /bin example_title: ls - text: Por favor, cambia al directorio /home/user/project/ example_title: cd - text: Lista todos los átomos del universo example_title: noCommand - text: ls -lh example_title: literal - text: file.txt example_title: simple --- # es2bash-mt5: Spanish to Bash Model <p align="center"> <img width="460" height="300" src="https://dev2bit.com/wp-content/themes/lovecraft_child/assets/icons/dev2bit_monitor2.svg"> </p> Developed by dev2bit, es2bash-mt5 is a language transformer model that is capable of predicting the optimal Bash command given a natural language request in Spanish. This model represents a major advancement in human-computer interaction, providing a natural language interface for Unix operating system commands. ## About the Model es2bash-mt5 is a fine-tuning model based on mt5-small. It has been trained on the dev2bit/es2bash dataset, which specializes in translating natural language in Spanish into Bash commands. This model is optimized for processing requests related to the commands: * `cat` * `ls` * `cd` ## Usage Below is an example of how to use es2bash-mt5 with the Hugging Face Transformers library: ```python from transformers import pipeline translator = pipeline('translation', model='dev2bit/es2bash-mt5') request = "listar los archivos en el directorio actual" translated = translator(request, max_length=512) print(translated[0]['translation_text']) ``` This will print the Bash command corresponding to the given Spanish request. ## Contributions We appreciate your contributions! You can help improve es2bash-mt5 in various ways, including: * Testing the model and reporting any issues or suggestions in the Issues section. * Improving the documentation. * Providing usage examples. --- # es2bash-mt5: Modelo de español a Bash Desarrollado por dev2bit, `es2bash-mt5` es un modelo transformador de lenguaje que tiene la capacidad de predecir el comando Bash óptimo dada una solicitud en lenguaje natural en español. Este modelo representa un gran avance en la interacción humano-computadora, proporcionando una interfaz de lenguaje natural para los comandos del sistema operativo Unix. ## Sobre el modelo `es2bash-mt5` es un modelo de ajuste fino basado en `mt5-small`. Ha sido entrenado en el conjunto de datos `dev2bit/es2bash`, especializado en la traducción de lenguaje natural en español a comandos Bash. Este modelo está optimizado para procesar solicitudes relacionadas con los comandos: * `cat` * `ls` * `cd` ## Uso A continuación, se muestra un ejemplo de cómo usar `es2bash-mt5` con la biblioteca Hugging Face Transformers: ```python from transformers import pipeline translator = pipeline('translation', model='dev2bit/es2bash-mt5') request = "listar los archivos en el directorio actual" translated = translator(request, max_length=512) print(translated[0]['translation_text']) ``` Esto imprimirá el comando Bash correspondiente a la solicitud dada en español. ## Contribuciones Agradecemos sus contribuciones! Puede ayudar a mejorar es2bash-mt5 de varias formas, incluyendo: * Probar el modelo y reportar cualquier problema o sugerencia en la sección de Issues. * Mejorando la documentación. * Proporcionando ejemplos de uso. --- This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the es2bash dataset. It achieves the following results on the evaluation set: - Loss: 0.0919 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 28 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 21.394 \| 1.0 \| 672 \| 1.7470 \| \| 2.5294 \| 2.0 \| 1344 \| 0.6350 \| \| 0.5873 \| 3.0 \| 2016 \| 0.2996 \| \| 0.3802 \| 4.0 \| 2688 \| 0.2142 \| \| 0.2951 \| 5.0 \| 3360 \| 0.1806 \| \| 0.225 \| 6.0 \| 4032 \| 0.1565 \| \| 0.2065 \| 7.0 \| 4704 \| 0.1461 \| \| 0.1944 \| 8.0 \| 5376 \| 0.1343 \| \| 0.174 \| 9.0 \| 6048 \| 0.1281 \| \| 0.1647 \| 10.0 \| 6720 \| 0.1207 \| \| 0.1566 \| 11.0 \| 7392 \| 0.1140 \| \| 0.1498 \| 12.0 \| 8064 \| 0.1106 \| \| 0.1382 \| 13.0 \| 8736 \| 0.1076 \| \| 0.1393 \| 14.0 \| 9408 \| 0.1042 \| \| 0.1351 \| 15.0 \| 10080 \| 0.1019 \| \| 0.13 \| 16.0 \| 10752 \| 0.0998 \| \| 0.1292 \| 17.0 \| 11424 \| 0.0983 \| \| 0.1265 \| 18.0 \| 12096 \| 0.0973 \| \| 0.1255 \| 19.0 \| 12768 \| 0.0969 \| \| 0.1216 \| 20.0 \| 13440 \| 0.0956 \| \| 0.1216 \| 21.0 \| 14112 \| 0.0946 \| \| 0.123 \| 22.0 \| 14784 \| 0.0938 \| \| 0.113 \| 23.0 \| 15456 \| 0.0931 \| \| 0.1185 \| 24.0 \| 16128 \| 0.0929 \| \| 0.1125 \| 25.0 \| 16800 \| 0.0927 \| \| 0.1213 \| 26.0 \| 17472 \| 0.0925 \| \| 0.1153 \| 27.0 \| 18144 \| 0.0921 \| \| 0.1134 \| 28.0 \| 18816 \| 0.0919 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
HippyHoppity/ppo-LunarLander-v2	HippyHoppity	2023-05-24T01:53:38Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-24T01:53:07Z	--- 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: 246.66 +/- 21.99 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 ... ```
ausboss/llama-30b-SuperHOT-4bit	ausboss	2023-05-24T01:35:50Z	7	5	transformers	[ "transformers", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-05-23T18:23:06Z	Merge of [SuperHOT-LoRA-prototype](https://huggingface.co/kaiokendev/SuperHOT-LoRA-prototype) and [llama-30b](https://huggingface.co/huggyllama/llama-30b) Llama30B-SuperHOT-4bit-128g.safetensors Quantization: ``` CUDA_VISIBLE_DEVICES=0 python llama.py ausboss/Llama30B-SuperHOT c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors Llama30B-SuperHOT-4bit-128g.safetensors ``` Llama30B-SuperHOT-4bit.safetensors Quantization: ``` CUDA_VISIBLE_DEVICES=0 python llama.py ausboss/Llama30B-SuperHOT c4 --wbits 4 --true-sequential --save_safetensors Llama30B-SuperHOT-4bit.safetensors ``` # From the SuperHot Page: ## Prototypes for SuperHOT No guarantees for output quality, simply uploading what I have so others can play around with it. Not even sure if the rank in cutoff-8192 is correct (think it should be 10 maybe.. can't remember) All prototypes are extremely early epochs (sub 0.5) ## Model/Training All trained with Flash Attention with conversation sequence lengths ranging from 8K to 16K tokens (No Alibi unless otherwise mentioned) All trained on LLaMa 13B 4-bit (no groupsize) (Personally, I like the 8K cutoff version better, so I would say start with that one) ## Data A combination of various datasets and cleaned logs converted into datasets including but not limited to: - Bluemoon Fanbased - Roleplaying Guild - Community-sourced outputs - [Dan's PocketDoc/RUCAIBox-Story-Generation-Alpaca](https://huggingface.co/datasets/PocketDoc/RUCAIBox-Story-Generation-Alpaca) - [IlyaGusev/gpt_roleplay_realm](https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm) - others ## Bias SuperHOT is a fiction-focused model. No alignment has been performed on the training data. Be mindful that this model may output harmful, violent, or otherwise problematic content ## Format Any format should work with such early checkpoints. However the training data is entirely in the following format: ``` --- mode: chat characters: <char1 name>: <descriptive tags for char1> <char2 name>: <descriptive tags for char2> summary: <summary of the story thus far or the purpose of the chat> (optional) <any other miscellaneous data> --- <chat history> ``` By "any other miscellaneous data", it means you should be able to put any additional metadata for the story or characters. I.e., ``` ... locations: location1: <tags for location1> inventory: item1: <tags for item1> ``` Again, format does not hold such a large weight on these early checkpoints. I have found success with the following setup for an RPG-like experience. Just play around with the format and see what works: ``` --- mode: rpg characters: You: a new player system: The system controls the RPG, handles character creation, world narration, and quest management. Also controls any NPCs and inventory tracking. Their first message provides a lengthy introduction for the player into the RPG world they are about to play in. After completing the character creation, the system will give a lengthy introduction into the world of ___. The first quest will follow right after rpg setting: The world of ___ rpg rules: Any rules typical of RPG games, including typical items, battle stats, etc --- ```
mauhcs/distilbert-base-uncased-finetuned-emotion	mauhcs	2023-05-24T01:35:18Z	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	2023-05-23T01:56:39Z	--- 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: 0.925 - name: F1 type: f1 value: 0.9249666408719047 --- <!-- 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: 0.2147 - Accuracy: 0.925 - F1: 0.9250 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8493 \| 1.0 \| 250 \| 0.3120 \| 0.9115 \| 0.9084 \| \| 0.2513 \| 2.0 \| 500 \| 0.2147 \| 0.925 \| 0.9250 \| ### Framework versions - Transformers 4.29.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
edhrdh/cathx	edhrdh	2023-05-24T00:49:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-24T00:47:02Z	--- license: creativeml-openrail-m ---
Erdenebold/testing_mongolian-roberta_base	Erdenebold	2023-05-24T00:33:57Z	101	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "mn", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-23T23:33:51Z	--- language: - mn tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: testing_mongolian-roberta_base 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. --> # testing_mongolian-roberta_base This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1244 - Precision: 0.9311 - Recall: 0.9399 - F1: 0.9355 - Accuracy: 0.9821 ## Model description More information needed ## Intended uses & 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1683 \| 1.0 \| 477 \| 0.0805 \| 0.8377 \| 0.8921 \| 0.8640 \| 0.9730 \| \| 0.0545 \| 2.0 \| 954 \| 0.0739 \| 0.9205 \| 0.9334 \| 0.9269 \| 0.9806 \| \| 0.0292 \| 3.0 \| 1431 \| 0.0778 \| 0.9270 \| 0.9354 \| 0.9312 \| 0.9817 \| \| 0.0164 \| 4.0 \| 1908 \| 0.0884 \| 0.9290 \| 0.9360 \| 0.9325 \| 0.9820 \| \| 0.008 \| 5.0 \| 2385 \| 0.1025 \| 0.9247 \| 0.9365 \| 0.9306 \| 0.9811 \| \| 0.0057 \| 6.0 \| 2862 \| 0.1093 \| 0.9294 \| 0.9369 \| 0.9331 \| 0.9815 \| \| 0.0037 \| 7.0 \| 3339 \| 0.1173 \| 0.9336 \| 0.9412 \| 0.9374 \| 0.9822 \| \| 0.0026 \| 8.0 \| 3816 \| 0.1217 \| 0.9281 \| 0.9374 \| 0.9327 \| 0.9817 \| \| 0.0016 \| 9.0 \| 4293 \| 0.1225 \| 0.9334 \| 0.9399 \| 0.9366 \| 0.9821 \| \| 0.0012 \| 10.0 \| 4770 \| 0.1244 \| 0.9311 \| 0.9399 \| 0.9355 \| 0.9821 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
sd-concepts-library/clothes	sd-concepts-library	2023-05-24T00:05:08Z	0	1	null	[ "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]	null	2023-05-24T00:05:05Z	--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### Clothes on Stable Diffusion This is the `<cat-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<cat-toy> 0](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_3.jpg) ![<cat-toy> 1](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_2.jpg) ![<cat-toy> 2](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_1.jpg) ![<cat-toy> 3](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_4.jpg)
D-Roberts/tf-efficientformer-l3-300-dev1	D-Roberts	2023-05-23T23:43:54Z	62	0	transformers	[ "transformers", "tf", "efficientformer", "image-feature-extraction", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-feature-extraction	2023-05-17T13:48:42Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tf-efficientformer-l3-300-dev1 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. --> # tf-efficientformer-l3-300-dev1 dev only ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - TensorFlow 2.11.1 - Datasets 2.12.0 - Tokenizers 0.13.3
D-Roberts/tf-efficientformer-l1-300-dev1	D-Roberts	2023-05-23T23:43:15Z	61	0	transformers	[ "transformers", "tf", "efficientformer", "image-feature-extraction", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-feature-extraction	2023-05-17T13:47:22Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tf-efficientformer-l1-300-dev1 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. --> # tf-efficientformer-l1-300-dev1 dev-only ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - TensorFlow 2.11.1 - Datasets 2.12.0 - Tokenizers 0.13.3
wiorz/legal_bert_small_summarized_defined	wiorz	2023-05-23T23:21:46Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-22T23:57:17Z	--- license: cc-by-sa-4.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: legal_bert_small_summarized_defined 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. --> # legal_bert_small_summarized_defined This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8897 - Accuracy: 0.835 - Precision: 0.5 - Recall: 0.1515 - F1: 0.2326 - D-index: 1.5181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1600 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| D-index \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:-------:\| \| No log \| 1.0 \| 200 \| 0.4467 \| 0.835 \| 0.0 \| 0.0 \| 0.0 \| 1.4607 \| \| No log \| 2.0 \| 400 \| 0.4909 \| 0.835 \| 0.0 \| 0.0 \| 0.0 \| 1.4607 \| \| 0.5409 \| 3.0 \| 600 \| 0.4941 \| 0.83 \| 0.4545 \| 0.1515 \| 0.2273 \| 1.5113 \| \| 0.5409 \| 4.0 \| 800 \| 0.5612 \| 0.84 \| 0.6 \| 0.0909 \| 0.1579 \| 1.5021 \| \| 0.4849 \| 5.0 \| 1000 \| 0.6301 \| 0.84 \| 0.5714 \| 0.1212 \| 0.2 \| 1.5135 \| \| 0.4849 \| 6.0 \| 1200 \| 0.8969 \| 0.84 \| 0.6 \| 0.0909 \| 0.1579 \| 1.5021 \| \| 0.4849 \| 7.0 \| 1400 \| 1.3171 \| 0.82 \| 0.3636 \| 0.1212 \| 0.1818 \| 1.4865 \| \| 0.2104 \| 8.0 \| 1600 \| 1.6653 \| 0.775 \| 0.2692 \| 0.2121 \| 0.2373 \| 1.4593 \| \| 0.2104 \| 9.0 \| 1800 \| 1.7041 \| 0.795 \| 0.3182 \| 0.2121 \| 0.2545 \| 1.4866 \| \| 0.0314 \| 10.0 \| 2000 \| 1.7495 \| 0.815 \| 0.3571 \| 0.1515 \| 0.2128 \| 1.4911 \| \| 0.0314 \| 11.0 \| 2200 \| 1.7627 \| 0.815 \| 0.3571 \| 0.1515 \| 0.2128 \| 1.4911 \| \| 0.0314 \| 12.0 \| 2400 \| 1.7892 \| 0.825 \| 0.375 \| 0.0909 \| 0.1463 \| 1.4819 \| \| 0.0067 \| 13.0 \| 2600 \| 1.8211 \| 0.83 \| 0.4444 \| 0.1212 \| 0.1905 \| 1.5000 \| \| 0.0067 \| 14.0 \| 2800 \| 1.8567 \| 0.83 \| 0.4444 \| 0.1212 \| 0.1905 \| 1.5000 \| \| 0.0 \| 15.0 \| 3000 \| 1.8817 \| 0.83 \| 0.4444 \| 0.1212 \| 0.1905 \| 1.5000 \| \| 0.0 \| 16.0 \| 3200 \| 1.8590 \| 0.825 \| 0.4167 \| 0.1515 \| 0.2222 \| 1.5046 \| \| 0.0 \| 17.0 \| 3400 \| 1.8619 \| 0.835 \| 0.5 \| 0.1515 \| 0.2326 \| 1.5181 \| \| 0.0014 \| 18.0 \| 3600 \| 1.8744 \| 0.835 \| 0.5 \| 0.1515 \| 0.2326 \| 1.5181 \| \| 0.0014 \| 19.0 \| 3800 \| 1.8849 \| 0.835 \| 0.5 \| 0.1515 \| 0.2326 \| 1.5181 \| \| 0.0 \| 20.0 \| 4000 \| 1.8897 \| 0.835 \| 0.5 \| 0.1515 \| 0.2326 \| 1.5181 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
asenella/reproducing_mvae_mmnist_seed_0	asenella	2023-05-23T23:01:10Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-05-23T23:01:00Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
kaminomisan/PhenixB7b	kaminomisan	2023-05-23T22:54:11Z	5	0	transformers	[ "transformers", "pytorch", "mpt", "text-generation", "Composer", "MosaicML", "llm-foundry", "StreamingDatasets", "custom_code", "dataset:mc4", "dataset:c4", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:bigcode/the-stack", "dataset:allenai/s2orc", "arxiv:2108.12409", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2010.04245", "arxiv:1909.08053", "arxiv:2302.06675", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-05-23T22:54:11Z	--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false duplicated_from: mosaicml/mpt-7b --- # MPT-7B MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B is * Licensed for the possibility of commercial use (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * Trained on a large amount of data (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * Prepared to handle extremely long inputs thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models). * Capable of fast training and inference (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * Equipped with highly efficient open-source training code via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B: The following models are finetuned on MPT-7B: * [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths. Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3). At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b). * License: Apache 2.0 * [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following. Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. * License: _CC-By-SA-3.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct) * [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets. * License: _CC-By-NC-SA-4.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-chat) ## Model Date May 5, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-1btms90mc-GipE2ufuPkKY0QBrmF3LSA)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`: ```python config = transformers.AutoConfig.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) config.attn_config['attn_impl'] = 'triton' model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', config=config, torch_dtype=torch.bfloat16, trust_remote_code=True ) model.to(device='cuda:0') ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python config = transformers.AutoConfig.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) config.update({"max_seq_len": 4096}) model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases \| Hyperparameter \| Value \| \|----------------\|-------\| \|n_parameters \| 6.7B \| \|n_layers \| 32 \| \| n_heads \| 32 \| \| d_model \| 4096 \| \| vocab size \| 50432 \| \| sequence length \| 2048 \| ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix: \| Data Source \| Number of Tokens in Source \| Proportion \| Effective Number of Tokens \| Epochs \| \|-------------\|----------------------------\|------------\|----------------------------\|--------\| \| mC4 3.1.0 - English \| 417.99 B \| 0.33 \| 330 B \| 0.14 \| \| C4 - English - SemDedup 80% \| 100.42 B \| 0.299 \| 299 B \| 2.98 \| \| RedPajama - CommonCrawl \| 878.45 B \| 0.1 \| 100 B \| 0.11 \| \| The Stack - Selected Languages \| 463.78 B \| 0.1 \| 100 B \| 0.22 \| \| RedPajama - Wikipedia - En \| 4.87 B \| 0.04 \| 40 B \| 8.21 \| \| The Stack - Markdown \| 107.07 B \| 0.035 \| 35 B \| 0.33 \| \| S2ORC \| 48.85 B \| 0.033 \| 33 B \| 0.68 \| \| RedPajama - Books \| 26.02 B \| 0.03 \| 30B \| 1.15 \| \| RedPajama - arXiv \| 28.10 B \| 0.019 \| 19 B \| 0.68 \| \| RedPajama - StackExchange \| 20.54 B \| 0.014 \| 14 B \|0.68 \| Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B (Base) is not intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B 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. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## 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. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, ly Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-03-28}, % change this date urldate = {2023-03-28} % change this date } ```
YakovElm/Hyperledger15Classic_with_cleaning	YakovElm	2023-05-23T22:44:21Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T22:43:21Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Hyperledger15Classic_with_cleaning 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. --> # Hyperledger15Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2347 - Train Accuracy: 0.9045 - Validation Loss: 0.3515 - Validation Accuracy: 0.8651 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.3126 \| 0.9031 \| 0.3318 \| 0.8807 \| 0 \| \| 0.2844 \| 0.9028 \| 0.3275 \| 0.8807 \| 1 \| \| 0.2347 \| 0.9045 \| 0.3515 \| 0.8651 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
dgalik/distilbert-finetuning-hate-speech-score-all-samples-3splits-seedv2-dropout005-epochs-10	dgalik	2023-05-23T22:31:48Z	31	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-05-23T21:28:22Z	--- tags: - generated_from_trainer model-index: - name: distilbert-finetuning-hate-speech-score-all-samples-3splits-seedv2-dropout005-epochs-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. --> # distilbert-finetuning-hate-speech-score-all-samples-3splits-seedv2-dropout005-epochs-10 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3501 - Mse: 0.3501 - Rmse: 0.5917 - Mae: 0.2542 - R2: 0.9374 ## Model description More information needed ## Intended uses & 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: 10 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
YakovElm/Qt20Classic_with_cleaning	YakovElm	2023-05-23T22:11:51Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T22:10:56Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt20Classic_with_cleaning 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. --> # Qt20Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1619 - Train Accuracy: 0.9500 - Validation Loss: 0.1838 - Validation Accuracy: 0.9554 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.2142 \| 0.9462 \| 0.1640 \| 0.9586 \| 0 \| \| 0.1934 \| 0.9462 \| 0.1576 \| 0.9586 \| 1 \| \| 0.1619 \| 0.9500 \| 0.1838 \| 0.9554 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
SenY/shoujo	SenY	2023-05-23T21:51:54Z	0	58	null	[ "art", "region:us" ]	null	2023-03-12T02:08:45Z	--- tags: - art --- # shoujo.safetensors Concepts: Shoujo Manga ![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo.png) ```prompt example <lora:shoujo:1> 1girl ``` ```prompt example <lora:shoujo:1.4> 1girl ``` \|<div style="width:16rem">name</div>\|90s\|00s\|10s\| \|-\|-\|-\|-\| \|shoujo_c - more juvenile girl\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_c90.png)\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_c00.png)\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_c10.png)\| \|shoujo_r - more romantic girl\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_r90.png)\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_r00.png)\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_r10.png)\| \|shoujo_n - more fantastic girl\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_n90.png)\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_n00.png)\|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_n10.png)\|
JoBuettner/rl_course_vizdoom_health_gathering_supreme	JoBuettner	2023-05-23T21:44:18Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T21:20:00Z	--- 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: 12.73 +/- 5.75 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 JoBuettner/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.
damapika/roberta-base_mod_quoref	damapika	2023-05-23T21:20:20Z	113	0	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "dataset:quoref", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-05-23T19:19:39Z	--- license: mit tags: - generated_from_trainer datasets: - quoref model-index: - name: roberta-base_mod_quoref results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base_mod_quoref This model is a fine-tuned version of [damapika/roberta-base_mod_squad](https://huggingface.co/damapika/roberta-base_mod_squad) on the quoref dataset. It achieves the following results on the evaluation set: - Loss: 1.5566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1263 \| 1.0 \| 1213 \| 1.2665 \| \| 0.7404 \| 2.0 \| 2426 \| 1.3567 \| \| 0.5172 \| 3.0 \| 3639 \| 1.5566 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
manish1993hf/sms_class_test3	manish1993hf	2023-05-23T21:04:56Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T21:00:11Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: sms_class_test3 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. --> # sms_class_test3 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: 0.0078 - Accuracy: 0.9990 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 272 \| 0.0093 \| 0.9979 \| \| 0.0472 \| 2.0 \| 544 \| 0.0080 \| 0.9990 \| \| 0.0472 \| 3.0 \| 816 \| 0.0074 \| 0.9990 \| \| 0.0005 \| 4.0 \| 1088 \| 0.0078 \| 0.9990 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
azetaaa/ppo-ML-Agents-Pyramids	azetaaa	2023-05-23T20:50:53Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-05-23T20:50:47Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: azetaaa/ppo-ML-Agents-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
raulc0399/bloomz_3b_marketmail	raulc0399	2023-05-23T20:49:57Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-05-23T20:46:28Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard 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 ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DarioLopes/marian-finetuned-kde4-en-to-fr	DarioLopes	2023-05-23T20:49:23Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-05-21T22:21:16Z	--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.752028933869816 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8559 - Bleu: 52.7520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
zib16/alpaca-lora	zib16	2023-05-23T20:42:28Z	0	0	null	[ "region:us" ]	null	2023-05-23T15:50:44Z	An Alpaca LoRA model fine-tuned as described by Sam Witteveen in https://www.youtube.com/watch?v=LSoqyynKU9E. \ The base model is the llama-7b and the data from Stanford Alpaca have been used for the fine-tuning. \ These data can be found in https://github.com/tloen/alpaca-lora. Date: April 2023
emmanuel17/a2c-PandaReachDense-v2	emmanuel17	2023-05-23T20:34:27Z	2	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T20:31:39Z	--- 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: -1.99 +/- 0.54 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 ... ```
manish1993hf/sms_class_test1	manish1993hf	2023-05-23T20:30:38Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T20:25:47Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: sms_class_test1 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. --> # sms_class_test1 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: - eval_loss: 0.0314 - eval_accuracy: 0.9964 - eval_runtime: 0.5458 - eval_samples_per_second: 511.138 - eval_steps_per_second: 16.488 - epoch: 5.0 - step: 830 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
YakovElm/Qt10Classic_with_cleaning	YakovElm	2023-05-23T20:30:37Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T20:29:00Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt10Classic_with_cleaning 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. --> # Qt10Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2156 - Train Accuracy: 0.9208 - Validation Loss: 0.2238 - Validation Accuracy: 0.9416 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.2830 \| 0.9159 \| 0.2121 \| 0.9416 \| 0 \| \| 0.2515 \| 0.9210 \| 0.2015 \| 0.9416 \| 1 \| \| 0.2156 \| 0.9208 \| 0.2238 \| 0.9416 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
whorseman/author	whorseman	2023-05-23T20:13:17Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T18:55:00Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: author 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. --> # author 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: 3.0925 - Accuracy: 0.1111 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 182 \| 3.1296 \| 0.0915 \| \| No log \| 2.0 \| 364 \| 3.0925 \| 0.1111 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Santonu001/results	Santonu001	2023-05-23T20:13:12Z	174	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "endpoints_compatible", "region:us" ]	question-answering	2023-05-23T19:26:03Z	--- tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [mrm8488/spanbert-finetuned-squadv2](https://huggingface.co/mrm8488/spanbert-finetuned-squadv2) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 0.01 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.01 \| 82 \| 3.4944 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
azetaaa/ppo-ML-Agents-SnowballTarget	azetaaa	2023-05-23T20:11:04Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-05-23T20:09:21Z	--- 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: azetaaa/ppo-ML-Agents-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Govindaramani/food_classifier	Govindaramani	2023-05-23T19:45:19Z	64	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-20T23:01:18Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Govindaramani/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. --> # Govindaramani/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: 0.3746 - Validation Loss: 0.3760 - Train Accuracy: 0.899 - 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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.7487 \| 1.6239 \| 0.828 \| 0 \| \| 1.2189 \| 0.8179 \| 0.889 \| 1 \| \| 0.6889 \| 0.5344 \| 0.906 \| 2 \| \| 0.4833 \| 0.5014 \| 0.878 \| 3 \| \| 0.3746 \| 0.3760 \| 0.899 \| 4 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
YakovElm/Apache10SetFitModel_clean_data	YakovElm	2023-05-23T19:38:28Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-05-23T19:37:54Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/Apache10SetFitModel_clean_data 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("YakovElm/Apache10SetFitModel_clean_data") # 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} } ```
kribby/cats-mobilenet-imagenet-v3	kribby	2023-05-23T19:35:12Z	4	0	tf-keras	[ "tf-keras", "mobilenet", "image-classification", "region:us" ]	image-classification	2023-05-23T19:33:34Z	--- pipeline_tag: image-classification ---
oransom48/pretrained_bert_fordiseaseclassif_1	oransom48	2023-05-23T19:34:04Z	62	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T19:12:22Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: pretrained_bert_fordiseaseclassif_1 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. --> # pretrained_bert_fordiseaseclassif_1 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Tokenizers 0.13.3
henryscheible/t5-small_stereoset_finetuned_HBRPOI	henryscheible	2023-05-23T19:30:01Z	107	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:stereoset", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-23T19:13:07Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - stereoset metrics: - accuracy model-index: - name: t5-small_stereoset_finetuned_HBRPOI results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: stereoset type: stereoset config: intersentence split: validation args: intersentence metrics: - name: Accuracy type: accuracy value: 0.6028257456828885 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small_stereoset_finetuned_HBRPOI This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the stereoset dataset. It achieves the following results on the evaluation set: - Loss: 0.4383 - Accuracy: 0.6028 - Tp: 0.4890 - Tn: 0.1138 - Fp: 0.3854 - Fn: 0.0118 ## Model description More information needed ## Intended uses & 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: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Tp \| Tn \| Fp \| Fn \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\| \| 0.4447 \| 0.43 \| 20 \| 0.3978 \| 0.5008 \| 0.5008 \| 0.0 \| 0.4992 \| 0.0 \| \| 0.3776 \| 0.85 \| 40 \| 0.3448 \| 0.6232 \| 0.5008 \| 0.1224 \| 0.3768 \| 0.0 \| \| 0.3649 \| 1.28 \| 60 \| 0.3269 \| 0.5612 \| 0.5 \| 0.0612 \| 0.4380 \| 0.0008 \| \| 0.3275 \| 1.7 \| 80 \| 0.3218 \| 0.5330 \| 0.4992 \| 0.0338 \| 0.4655 \| 0.0016 \| \| 0.2969 \| 2.13 \| 100 \| 0.3104 \| 0.6256 \| 0.4961 \| 0.1295 \| 0.3697 \| 0.0047 \| \| 0.3283 \| 2.55 \| 120 \| 0.3111 \| 0.5730 \| 0.4992 \| 0.0738 \| 0.4254 \| 0.0016 \| \| 0.3046 \| 2.98 \| 140 \| 0.3040 \| 0.5416 \| 0.4992 \| 0.0424 \| 0.4568 \| 0.0016 \| \| 0.2603 \| 3.4 \| 160 \| 0.3057 \| 0.5447 \| 0.4992 \| 0.0455 \| 0.4537 \| 0.0016 \| \| 0.2828 \| 3.83 \| 180 \| 0.3186 \| 0.5479 \| 0.4984 \| 0.0495 \| 0.4498 \| 0.0024 \| \| 0.2326 \| 4.26 \| 200 \| 0.3036 \| 0.6193 \| 0.4937 \| 0.1256 \| 0.3736 \| 0.0071 \| \| 0.2289 \| 4.68 \| 220 \| 0.3328 \| 0.5479 \| 0.4976 \| 0.0502 \| 0.4490 \| 0.0031 \| \| 0.2234 \| 5.11 \| 240 \| 0.3140 \| 0.5777 \| 0.4976 \| 0.0801 \| 0.4192 \| 0.0031 \| \| 0.2225 \| 5.53 \| 260 \| 0.3245 \| 0.5691 \| 0.4976 \| 0.0714 \| 0.4278 \| 0.0031 \| \| 0.187 \| 5.96 \| 280 \| 0.3300 \| 0.5785 \| 0.4961 \| 0.0824 \| 0.4168 \| 0.0047 \| \| 0.179 \| 6.38 \| 300 \| 0.3344 \| 0.5848 \| 0.4961 \| 0.0887 \| 0.4105 \| 0.0047 \| \| 0.1523 \| 6.81 \| 320 \| 0.3528 \| 0.5895 \| 0.4969 \| 0.0926 \| 0.4066 \| 0.0039 \| \| 0.1499 \| 7.23 \| 340 \| 0.3788 \| 0.6232 \| 0.4906 \| 0.1327 \| 0.3666 \| 0.0102 \| \| 0.1292 \| 7.66 \| 360 \| 0.3889 \| 0.5942 \| 0.4914 \| 0.1028 \| 0.3964 \| 0.0094 \| \| 0.13 \| 8.09 \| 380 \| 0.3959 \| 0.5903 \| 0.4937 \| 0.0965 \| 0.4027 \| 0.0071 \| \| 0.1216 \| 8.51 \| 400 \| 0.4169 \| 0.5856 \| 0.4922 \| 0.0934 \| 0.4058 \| 0.0086 \| \| 0.1306 \| 8.94 \| 420 \| 0.4227 \| 0.6005 \| 0.4898 \| 0.1107 \| 0.3885 \| 0.0110 \| \| 0.0968 \| 9.36 \| 440 \| 0.4334 \| 0.5965 \| 0.4914 \| 0.1052 \| 0.3940 \| 0.0094 \| \| 0.1044 \| 9.79 \| 460 \| 0.4383 \| 0.6028 \| 0.4890 \| 0.1138 \| 0.3854 \| 0.0118 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2
YakovElm/Qt20Classic	YakovElm	2023-05-23T19:29:42Z	62	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T19:29:07Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt20Classic 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. --> # Qt20Classic This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1836 - Train Accuracy: 0.9462 - Validation Loss: 0.1813 - Validation Accuracy: 0.9594 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.2163 \| 0.9454 \| 0.1596 \| 0.9586 \| 0 \| \| 0.2044 \| 0.9462 \| 0.1554 \| 0.9586 \| 1 \| \| 0.1836 \| 0.9462 \| 0.1813 \| 0.9594 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
YakovElm/Apache5SetFitModel_clean_data	YakovElm	2023-05-23T19:25:18Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-05-23T19:24:40Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/Apache5SetFitModel_clean_data 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("YakovElm/Apache5SetFitModel_clean_data") # 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} } ```
henryscheible/t5-small_winobias_finetuned_HBRPOI	henryscheible	2023-05-23T19:24:19Z	108	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-23T19:20:12Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-small_winobias_finetuned_HBRPOI results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small_winobias_finetuned_HBRPOI This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3333 - Accuracy: 0.5 - Tp: 0.5 - Tn: 0.0 - Fp: 0.5 - Fn: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - 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: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Tp \| Tn \| Fp \| Fn \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---:\|:------:\|:------:\|:---:\| \| 0.437 \| 0.8 \| 20 \| 0.3545 \| 0.5 \| 0.5 \| 0.0 \| 0.5 \| 0.0 \| \| 0.3996 \| 1.6 \| 40 \| 0.3565 \| 0.5025 \| 0.5 \| 0.0025 \| 0.4975 \| 0.0 \| \| 0.3844 \| 2.4 \| 60 \| 0.3498 \| 0.5 \| 0.5 \| 0.0 \| 0.5 \| 0.0 \| \| 0.3728 \| 3.2 \| 80 \| 0.3529 \| 0.5013 \| 0.5 \| 0.0013 \| 0.4987 \| 0.0 \| \| 0.3732 \| 4.0 \| 100 \| 0.3482 \| 0.5006 \| 0.5 \| 0.0006 \| 0.4994 \| 0.0 \| \| 0.3798 \| 4.8 \| 120 \| 0.3484 \| 0.5 \| 0.5 \| 0.0 \| 0.5 \| 0.0 \| \| 0.3607 \| 5.6 \| 140 \| 0.3475 \| 0.5006 \| 0.5 \| 0.0006 \| 0.4994 \| 0.0 \| \| 0.3688 \| 6.4 \| 160 \| 0.3456 \| 0.5 \| 0.5 \| 0.0 \| 0.5 \| 0.0 \| \| 0.3597 \| 7.2 \| 180 \| 0.3445 \| 0.5006 \| 0.5 \| 0.0006 \| 0.4994 \| 0.0 \| \| 0.3658 \| 8.0 \| 200 \| 0.3402 \| 0.5 \| 0.5 \| 0.0 \| 0.5 \| 0.0 \| \| 0.3629 \| 8.8 \| 220 \| 0.3362 \| 0.5 \| 0.5 \| 0.0 \| 0.5 \| 0.0 \| \| 0.3393 \| 9.6 \| 240 \| 0.3333 \| 0.5 \| 0.5 \| 0.0 \| 0.5 \| 0.0 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2
YakovElm/Apache20Classic_with_cleaning	YakovElm	2023-05-23T19:11:07Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T19:10:31Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache20Classic_with_cleaning 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. --> # Apache20Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1300 - Train Accuracy: 0.9622 - Validation Loss: 0.4258 - Validation Accuracy: 0.9055 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.1764 \| 0.9548 \| 0.3066 \| 0.9055 \| 0 \| \| 0.1518 \| 0.9624 \| 0.3933 \| 0.9055 \| 1 \| \| 0.1300 \| 0.9622 \| 0.4258 \| 0.9055 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
xzuyn/Pythia-Deduped-70M-GGML	xzuyn	2023-05-23T18:57:00Z	0	1	null	[ "gpt_neox", "region:us" ]	null	2023-05-23T06:14:02Z	--- tags: - gpt_neox --- # For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp) Original Model: https://huggingface.co/EleutherAI/pythia-70m-deduped
Heilgeirr/heilgeirr2	Heilgeirr	2023-05-23T18:47:54Z	31	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-05-23T18:44:31Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### heilgeirr2 Dreambooth model trained by Heilgeirr 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:
clulab/roberta-base-motivational-interviewing	clulab	2023-05-23T18:47:06Z	105	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "motivational-interviewing", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T17:14:58Z	--- language: - en license: apache-2.0 library_name: transformers pipeline_tag: text-classification tags: - motivational-interviewing metrics: - f1 widget: - text: >- I'm planning on having tuna, ground tuna, chopped celery, and chopped black pepper, and half a apple. example_title: change_talk_goal_talk_and_opportunities --- # Model Card for roberta-base-motivational-interviewing ⚠ WARNING: This is a preliminary model that is still actively under development. ⚠ This is a [roBERTa-base](https://huggingface.co/roberta-base) model fine-tuned on a small dataset of conversations between health coaches and cancer survivors. # How to Get Started with the Model You can use this model directly with a pipeline for text classification: ```python >>> import transformers >>> model_name = "clulab/roberta-base-motivational-interviewing" >>> classifier = transformers.TextClassificationPipeline( ... tokenizer=transformers.AutoTokenizer.from_pretrained(model_name), ... model=transformers.AutoModelForSequenceClassification.from_pretrained(model_name)) >>> classifier("I'm planning on having tuna, ground tuna, chopped celery, and chopped black pepper, and half a apple.") [{'label': 'change_talk_goal_talk_and_opportunities', 'score': 0.9995419979095459}] ``` # Model Details - Developed by: [Steven Bethard](https://bethard.github.io/) - Parent Model: [roBERTa-base](https://huggingface.co/roberta-base) - GitHub Repo: [LIvES repo](https://github.com/clulab/lives) # Uses The model is intended to be used for text classification, taking as input conversational utterances and predicting as output different categories of motivational interviewing behaviors. It is intended for use by health coaches to assist when reviewing their past calls with participants. Its predictions should not be used without manual review. # Training Details The model was trained on data annotated under the grant [Using Natural Language Processing to Determine Predictors of Healthy Diet and Physical Activity Behavior Change in Ovarian Cancer Survivors (NIH NCI R21CA256680)](https://reporter.nih.gov/project-details/10510666). A [roberta-base](https://huggingface.co/roberta-base) model was fine-tuned on that dataset, with texts tokenized using the standard [roberta-base](https://huggingface.co/roberta-base) tokenizer. # Evaluation On the test partition of the R21CA256680 dataset, the model achieves 0.60 precision and 0.46 recall.
YakovElm/Qt15Classic	YakovElm	2023-05-23T18:39:04Z	62	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T18:38:29Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt15Classic 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. --> # Qt15Classic This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2046 - Train Accuracy: 0.9367 - Validation Loss: 0.2038 - Validation Accuracy: 0.9505 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.2400 \| 0.9354 \| 0.1896 \| 0.9505 \| 0 \| \| 0.2235 \| 0.9367 \| 0.1826 \| 0.9505 \| 1 \| \| 0.2046 \| 0.9367 \| 0.2038 \| 0.9505 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
VinsmokeMir/Method2_E13B_SC_BS4_LR3e5	VinsmokeMir	2023-05-23T18:18:39Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T16:27:50Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Method2_E13B_SC_BS4_LR3e5 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. --> # Method2_E13B_SC_BS4_LR3e5 This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5641 - Accuracy: 0.6803 - F1 Macro: 0.6446 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:--------:\| \| No log \| 0.16 \| 500 \| 1.0767 \| 0.3976 \| 0.1896 \| \| 1.075 \| 0.32 \| 1000 \| 1.0769 \| 0.3976 \| 0.1896 \| \| 1.075 \| 0.48 \| 1500 \| 1.0183 \| 0.5539 \| 0.4151 \| \| 1.0246 \| 0.64 \| 2000 \| 0.8956 \| 0.5916 \| 0.4745 \| \| 1.0246 \| 0.8 \| 2500 \| 0.8743 \| 0.6082 \| 0.5120 \| \| 0.8948 \| 0.95 \| 3000 \| 0.8365 \| 0.6216 \| 0.5546 \| \| 0.8948 \| 1.11 \| 3500 \| 0.8635 \| 0.6311 \| 0.5752 \| \| 0.8069 \| 1.27 \| 4000 \| 0.9060 \| 0.6158 \| 0.5398 \| \| 0.8069 \| 1.43 \| 4500 \| 0.8231 \| 0.6388 \| 0.5924 \| \| 0.7969 \| 1.59 \| 5000 \| 0.8368 \| 0.6331 \| 0.5935 \| \| 0.7969 \| 1.75 \| 5500 \| 0.8262 \| 0.6477 \| 0.5981 \| \| 0.7804 \| 1.91 \| 6000 \| 0.8299 \| 0.6579 \| 0.6208 \| \| 0.7804 \| 2.07 \| 6500 \| 0.8197 \| 0.6579 \| 0.6364 \| \| 0.715 \| 2.23 \| 7000 \| 0.8498 \| 0.6624 \| 0.5955 \| \| 0.715 \| 2.39 \| 7500 \| 0.8357 \| 0.6669 \| 0.6218 \| \| 0.6953 \| 2.54 \| 8000 \| 0.8438 \| 0.6560 \| 0.6269 \| \| 0.6953 \| 2.7 \| 8500 \| 0.8528 \| 0.6669 \| 0.6022 \| \| 0.7074 \| 2.86 \| 9000 \| 0.8009 \| 0.6745 \| 0.6457 \| \| 0.7074 \| 3.02 \| 9500 \| 0.8222 \| 0.6720 \| 0.6402 \| \| 0.6598 \| 3.18 \| 10000 \| 0.9347 \| 0.6650 \| 0.6062 \| \| 0.6598 \| 3.34 \| 10500 \| 0.9053 \| 0.6803 \| 0.6510 \| \| 0.6634 \| 3.5 \| 11000 \| 0.8902 \| 0.6720 \| 0.6434 \| \| 0.6634 \| 3.66 \| 11500 \| 0.9370 \| 0.6733 \| 0.6415 \| \| 0.6182 \| 3.82 \| 12000 \| 0.8914 \| 0.6745 \| 0.6519 \| \| 0.6182 \| 3.98 \| 12500 \| 0.8938 \| 0.6752 \| 0.6389 \| \| 0.6043 \| 4.13 \| 13000 \| 1.0143 \| 0.6745 \| 0.6413 \| \| 0.6043 \| 4.29 \| 13500 \| 1.0768 \| 0.6765 \| 0.6543 \| \| 0.587 \| 4.45 \| 14000 \| 1.1154 \| 0.6790 \| 0.6421 \| \| 0.587 \| 4.61 \| 14500 \| 1.1295 \| 0.6828 \| 0.6525 \| \| 0.6345 \| 4.77 \| 15000 \| 1.1210 \| 0.6822 \| 0.6390 \| \| 0.6345 \| 4.93 \| 15500 \| 1.0062 \| 0.6726 \| 0.6380 \| \| 0.6 \| 5.09 \| 16000 \| 1.1504 \| 0.6739 \| 0.6369 \| \| 0.6 \| 5.25 \| 16500 \| 1.3298 \| 0.6733 \| 0.6280 \| \| 0.5667 \| 5.41 \| 17000 \| 1.2751 \| 0.6662 \| 0.6308 \| \| 0.5667 \| 5.57 \| 17500 \| 1.4070 \| 0.6567 \| 0.6069 \| \| 0.614 \| 5.73 \| 18000 \| 1.2956 \| 0.6694 \| 0.6284 \| \| 0.614 \| 5.88 \| 18500 \| 1.2795 \| 0.6822 \| 0.6382 \| \| 0.5651 \| 6.04 \| 19000 \| 1.3021 \| 0.6739 \| 0.6478 \| \| 0.5651 \| 6.2 \| 19500 \| 1.4076 \| 0.6682 \| 0.6333 \| \| 0.5347 \| 6.36 \| 20000 \| 1.3917 \| 0.6733 \| 0.6344 \| \| 0.5347 \| 6.52 \| 20500 \| 1.4203 \| 0.6790 \| 0.6285 \| \| 0.5278 \| 6.68 \| 21000 \| 1.3340 \| 0.6860 \| 0.6628 \| \| 0.5278 \| 6.84 \| 21500 \| 1.3521 \| 0.6873 \| 0.6489 \| \| 0.5796 \| 7.0 \| 22000 \| 1.2835 \| 0.6847 \| 0.6567 \| \| 0.5796 \| 7.16 \| 22500 \| 1.4437 \| 0.6879 \| 0.6563 \| \| 0.4627 \| 7.32 \| 23000 \| 1.5052 \| 0.6835 \| 0.6435 \| \| 0.4627 \| 7.47 \| 23500 \| 1.4991 \| 0.6707 \| 0.6434 \| \| 0.518 \| 7.63 \| 24000 \| 1.5436 \| 0.6656 \| 0.6403 \| \| 0.518 \| 7.79 \| 24500 \| 1.5247 \| 0.6784 \| 0.6433 \| \| 0.5373 \| 7.95 \| 25000 \| 1.4743 \| 0.6835 \| 0.6537 \| \| 0.5373 \| 8.11 \| 25500 \| 1.5379 \| 0.6777 \| 0.6385 \| \| 0.4539 \| 8.27 \| 26000 \| 1.5548 \| 0.6739 \| 0.6393 \| \| 0.4539 \| 8.43 \| 26500 \| 1.6174 \| 0.6669 \| 0.6378 \| \| 0.4519 \| 8.59 \| 27000 \| 1.5949 \| 0.6816 \| 0.6504 \| \| 0.4519 \| 8.75 \| 27500 \| 1.5558 \| 0.6816 \| 0.6357 \| \| 0.4813 \| 8.91 \| 28000 \| 1.5826 \| 0.6739 \| 0.6553 \| \| 0.4813 \| 9.06 \| 28500 \| 1.5929 \| 0.6867 \| 0.6540 \| \| 0.4121 \| 9.22 \| 29000 \| 1.6260 \| 0.6886 \| 0.6545 \| \| 0.4121 \| 9.38 \| 29500 \| 1.5950 \| 0.6841 \| 0.6500 \| \| 0.4451 \| 9.54 \| 30000 \| 1.6146 \| 0.6854 \| 0.6481 \| \| 0.4451 \| 9.7 \| 30500 \| 1.6587 \| 0.6796 \| 0.6493 \| \| 0.4039 \| 9.86 \| 31000 \| 1.6173 \| 0.6758 \| 0.6400 \| \| 0.4039 \| 10.02 \| 31500 \| 1.5952 \| 0.6803 \| 0.6517 \| \| 0.3921 \| 10.18 \| 32000 \| 1.7298 \| 0.6694 \| 0.6413 \| \| 0.3921 \| 10.34 \| 32500 \| 1.7106 \| 0.6796 \| 0.6467 \| \| 0.3799 \| 10.5 \| 33000 \| 1.6695 \| 0.6867 \| 0.6505 \| \| 0.3799 \| 10.66 \| 33500 \| 1.6907 \| 0.6803 \| 0.6550 \| \| 0.4003 \| 10.81 \| 34000 \| 1.6811 \| 0.6809 \| 0.6413 \| \| 0.4003 \| 10.97 \| 34500 \| 1.6644 \| 0.6771 \| 0.6352 \| \| 0.3812 \| 11.13 \| 35000 \| 1.7371 \| 0.6822 \| 0.6386 \| \| 0.3812 \| 11.29 \| 35500 \| 1.7405 \| 0.6841 \| 0.6516 \| \| 0.3399 \| 11.45 \| 36000 \| 1.6981 \| 0.6822 \| 0.6503 \| \| 0.3399 \| 11.61 \| 36500 \| 1.6536 \| 0.6847 \| 0.6483 \| \| 0.3653 \| 11.77 \| 37000 \| 1.7461 \| 0.6790 \| 0.6475 \| \| 0.3653 \| 11.93 \| 37500 \| 1.7247 \| 0.6790 \| 0.6485 \| \| 0.338 \| 12.09 \| 38000 \| 1.7433 \| 0.6905 \| 0.6532 \| \| 0.338 \| 12.25 \| 38500 \| 1.7331 \| 0.6765 \| 0.6558 \| \| 0.3302 \| 12.4 \| 39000 \| 1.7603 \| 0.6796 \| 0.6456 \| \| 0.3302 \| 12.56 \| 39500 \| 1.7784 \| 0.6726 \| 0.6505 \| \| 0.3195 \| 12.72 \| 40000 \| 1.8032 \| 0.6784 \| 0.6469 \| \| 0.3195 \| 12.88 \| 40500 \| 1.7869 \| 0.6822 \| 0.6553 \| \| 0.3508 \| 13.04 \| 41000 \| 1.7761 \| 0.6752 \| 0.6506 \| \| 0.3508 \| 13.2 \| 41500 \| 1.7806 \| 0.6847 \| 0.6454 \| \| 0.2915 \| 13.36 \| 42000 \| 1.8542 \| 0.6707 \| 0.6528 \| \| 0.2915 \| 13.52 \| 42500 \| 1.8365 \| 0.6796 \| 0.6520 \| \| 0.3023 \| 13.68 \| 43000 \| 1.8563 \| 0.6828 \| 0.6524 \| \| 0.3023 \| 13.84 \| 43500 \| 1.7947 \| 0.6752 \| 0.6495 \| \| 0.3213 \| 13.99 \| 44000 \| 1.8130 \| 0.6796 \| 0.6546 \| \| 0.3213 \| 14.15 \| 44500 \| 1.8288 \| 0.6841 \| 0.6502 \| \| 0.2644 \| 14.31 \| 45000 \| 1.8140 \| 0.6726 \| 0.6453 \| \| 0.2644 \| 14.47 \| 45500 \| 1.8711 \| 0.6809 \| 0.6552 \| \| 0.2739 \| 14.63 \| 46000 \| 1.8439 \| 0.6873 \| 0.6534 \| \| 0.2739 \| 14.79 \| 46500 \| 1.8302 \| 0.6828 \| 0.6460 \| \| 0.3012 \| 14.95 \| 47000 \| 1.8708 \| 0.6752 \| 0.6454 \| \| 0.3012 \| 15.11 \| 47500 \| 1.8498 \| 0.6822 \| 0.6487 \| \| 0.2805 \| 15.27 \| 48000 \| 1.8908 \| 0.6803 \| 0.6453 \| \| 0.2805 \| 15.43 \| 48500 \| 1.9480 \| 0.6790 \| 0.6406 \| \| 0.2895 \| 15.59 \| 49000 \| 1.8994 \| 0.6675 \| 0.6392 \| \| 0.2895 \| 15.74 \| 49500 \| 1.9135 \| 0.6790 \| 0.6461 \| \| 0.2444 \| 15.9 \| 50000 \| 1.9387 \| 0.6841 \| 0.6480 \| \| 0.2444 \| 16.06 \| 50500 \| 1.9175 \| 0.6745 \| 0.6463 \| \| 0.2569 \| 16.22 \| 51000 \| 1.9332 \| 0.6745 \| 0.6472 \| \| 0.2569 \| 16.38 \| 51500 \| 1.9400 \| 0.6771 \| 0.6445 \| \| 0.2251 \| 16.54 \| 52000 \| 1.9596 \| 0.6745 \| 0.6441 \| \| 0.2251 \| 16.7 \| 52500 \| 1.9959 \| 0.6835 \| 0.6464 \| \| 0.2686 \| 16.86 \| 53000 \| 1.9879 \| 0.6777 \| 0.6456 \| \| 0.2686 \| 17.02 \| 53500 \| 1.9882 \| 0.6828 \| 0.6471 \| \| 0.2168 \| 17.18 \| 54000 \| 2.0254 \| 0.6886 \| 0.6520 \| \| 0.2168 \| 17.33 \| 54500 \| 2.0432 \| 0.6777 \| 0.6442 \| \| 0.2735 \| 17.49 \| 55000 \| 1.9843 \| 0.6745 \| 0.6443 \| \| 0.2735 \| 17.65 \| 55500 \| 2.0330 \| 0.6828 \| 0.6451 \| \| 0.2159 \| 17.81 \| 56000 \| 2.0698 \| 0.6682 \| 0.6423 \| \| 0.2159 \| 17.97 \| 56500 \| 1.9797 \| 0.6771 \| 0.6426 \| \| 0.245 \| 18.13 \| 57000 \| 2.0008 \| 0.6726 \| 0.6383 \| \| 0.245 \| 18.29 \| 57500 \| 2.0425 \| 0.6816 \| 0.6473 \| \| 0.2036 \| 18.45 \| 58000 \| 2.0482 \| 0.6720 \| 0.6356 \| \| 0.2036 \| 18.61 \| 58500 \| 2.0950 \| 0.6675 \| 0.6384 \| \| 0.2336 \| 18.77 \| 59000 \| 2.0167 \| 0.6854 \| 0.6458 \| \| 0.2336 \| 18.92 \| 59500 \| 1.9984 \| 0.6809 \| 0.6406 \| \| 0.2332 \| 19.08 \| 60000 \| 2.0552 \| 0.6739 \| 0.6441 \| \| 0.2332 \| 19.24 \| 60500 \| 2.0450 \| 0.6784 \| 0.6459 \| \| 0.1984 \| 19.4 \| 61000 \| 2.0599 \| 0.6752 \| 0.6434 \| \| 0.1984 \| 19.56 \| 61500 \| 2.0704 \| 0.6784 \| 0.6417 \| \| 0.1945 \| 19.72 \| 62000 \| 2.0755 \| 0.6758 \| 0.6445 \| \| 0.1945 \| 19.88 \| 62500 \| 2.0660 \| 0.6809 \| 0.6428 \| \| 0.2143 \| 20.04 \| 63000 \| 2.0670 \| 0.6739 \| 0.6448 \| \| 0.2143 \| 20.2 \| 63500 \| 2.0581 \| 0.6873 \| 0.6509 \| \| 0.1878 \| 20.36 \| 64000 \| 2.1272 \| 0.6752 \| 0.6452 \| \| 0.1878 \| 20.52 \| 64500 \| 2.1002 \| 0.6803 \| 0.6511 \| \| 0.2144 \| 20.67 \| 65000 \| 2.1383 \| 0.6713 \| 0.6438 \| \| 0.2144 \| 20.83 \| 65500 \| 2.1070 \| 0.6809 \| 0.6419 \| \| 0.2121 \| 20.99 \| 66000 \| 2.1273 \| 0.6726 \| 0.6412 \| \| 0.2121 \| 21.15 \| 66500 \| 2.1605 \| 0.6707 \| 0.6395 \| \| 0.1835 \| 21.31 \| 67000 \| 2.2891 \| 0.6567 \| 0.6331 \| \| 0.1835 \| 21.47 \| 67500 \| 2.2472 \| 0.6765 \| 0.6402 \| \| 0.1991 \| 21.63 \| 68000 \| 2.2238 \| 0.6752 \| 0.6412 \| \| 0.1991 \| 21.79 \| 68500 \| 2.1965 \| 0.6669 \| 0.6372 \| \| 0.2018 \| 21.95 \| 69000 \| 2.2050 \| 0.6669 \| 0.6395 \| \| 0.2018 \| 22.11 \| 69500 \| 2.1795 \| 0.6803 \| 0.6467 \| \| 0.151 \| 22.26 \| 70000 \| 2.2214 \| 0.6777 \| 0.6430 \| \| 0.151 \| 22.42 \| 70500 \| 2.1754 \| 0.6867 \| 0.6513 \| \| 0.2078 \| 22.58 \| 71000 \| 2.1959 \| 0.6822 \| 0.6488 \| \| 0.2078 \| 22.74 \| 71500 \| 2.1933 \| 0.6860 \| 0.6481 \| \| 0.2004 \| 22.9 \| 72000 \| 2.2001 \| 0.6816 \| 0.6500 \| \| 0.2004 \| 23.06 \| 72500 \| 2.2159 \| 0.6784 \| 0.6490 \| \| 0.1773 \| 23.22 \| 73000 \| 2.2603 \| 0.6790 \| 0.6462 \| \| 0.1773 \| 23.38 \| 73500 \| 2.2331 \| 0.6777 \| 0.6470 \| \| 0.174 \| 23.54 \| 74000 \| 2.2554 \| 0.6765 \| 0.6471 \| \| 0.174 \| 23.7 \| 74500 \| 2.2000 \| 0.6854 \| 0.6517 \| \| 0.2071 \| 23.85 \| 75000 \| 2.1896 \| 0.6790 \| 0.6500 \| \| 0.2071 \| 24.01 \| 75500 \| 2.2270 \| 0.6828 \| 0.6479 \| \| 0.1419 \| 24.17 \| 76000 \| 2.2776 \| 0.6765 \| 0.6426 \| \| 0.1419 \| 24.33 \| 76500 \| 2.2895 \| 0.6809 \| 0.6437 \| \| 0.1564 \| 24.49 \| 77000 \| 2.2746 \| 0.6828 \| 0.6515 \| \| 0.1564 \| 24.65 \| 77500 \| 2.3156 \| 0.6765 \| 0.6356 \| \| 0.1802 \| 24.81 \| 78000 \| 2.2891 \| 0.6726 \| 0.6426 \| \| 0.1802 \| 24.97 \| 78500 \| 2.2610 \| 0.6835 \| 0.6502 \| \| 0.1795 \| 25.13 \| 79000 \| 2.2856 \| 0.6777 \| 0.6478 \| \| 0.1795 \| 25.29 \| 79500 \| 2.2410 \| 0.6828 \| 0.6478 \| \| 0.1753 \| 25.45 \| 80000 \| 2.2738 \| 0.6701 \| 0.6451 \| \| 0.1753 \| 25.6 \| 80500 \| 2.2679 \| 0.6847 \| 0.6440 \| \| 0.1517 \| 25.76 \| 81000 \| 2.2667 \| 0.6796 \| 0.6525 \| \| 0.1517 \| 25.92 \| 81500 \| 2.3471 \| 0.6682 \| 0.6455 \| \| 0.1593 \| 26.08 \| 82000 \| 2.2945 \| 0.6816 \| 0.6504 \| \| 0.1593 \| 26.24 \| 82500 \| 2.3202 \| 0.6841 \| 0.6456 \| \| 0.1332 \| 26.4 \| 83000 \| 2.3667 \| 0.6733 \| 0.6405 \| \| 0.1332 \| 26.56 \| 83500 \| 2.3295 \| 0.6771 \| 0.6377 \| \| 0.1765 \| 26.72 \| 84000 \| 2.3680 \| 0.6720 \| 0.6394 \| \| 0.1765 \| 26.88 \| 84500 \| 2.3246 \| 0.6828 \| 0.6456 \| \| 0.1578 \| 27.04 \| 85000 \| 2.3192 \| 0.6745 \| 0.6453 \| \| 0.1578 \| 27.19 \| 85500 \| 2.3216 \| 0.6822 \| 0.6471 \| \| 0.1355 \| 27.35 \| 86000 \| 2.3730 \| 0.6796 \| 0.6490 \| \| 0.1355 \| 27.51 \| 86500 \| 2.3650 \| 0.6758 \| 0.6415 \| \| 0.1308 \| 27.67 \| 87000 \| 2.4015 \| 0.6784 \| 0.6471 \| \| 0.1308 \| 27.83 \| 87500 \| 2.3700 \| 0.6809 \| 0.6403 \| \| 0.1446 \| 27.99 \| 88000 \| 2.3748 \| 0.6796 \| 0.6483 \| \| 0.1446 \| 28.15 \| 88500 \| 2.3575 \| 0.6809 \| 0.6497 \| \| 0.1135 \| 28.31 \| 89000 \| 2.3663 \| 0.6835 \| 0.6438 \| \| 0.1135 \| 28.47 \| 89500 \| 2.3817 \| 0.6809 \| 0.6490 \| \| 0.1354 \| 28.63 \| 90000 \| 2.4026 \| 0.6739 \| 0.6436 \| \| 0.1354 \| 28.78 \| 90500 \| 2.3825 \| 0.6745 \| 0.6392 \| \| 0.1661 \| 28.94 \| 91000 \| 2.3461 \| 0.6771 \| 0.6482 \| \| 0.1661 \| 29.1 \| 91500 \| 2.3496 \| 0.6771 \| 0.6422 \| \| 0.1188 \| 29.26 \| 92000 \| 2.3568 \| 0.6790 \| 0.6488 \| \| 0.1188 \| 29.42 \| 92500 \| 2.3496 \| 0.6828 \| 0.6430 \| \| 0.1433 \| 29.58 \| 93000 \| 2.4252 \| 0.6707 \| 0.6378 \| \| 0.1433 \| 29.74 \| 93500 \| 2.3805 \| 0.6847 \| 0.6459 \| \| 0.1328 \| 29.9 \| 94000 \| 2.3918 \| 0.6860 \| 0.6495 \| \| 0.1328 \| 30.06 \| 94500 \| 2.4026 \| 0.6828 \| 0.6495 \| \| 0.1317 \| 30.22 \| 95000 \| 2.4319 \| 0.6841 \| 0.6483 \| \| 0.1317 \| 30.38 \| 95500 \| 2.4375 \| 0.6828 \| 0.6492 \| \| 0.122 \| 30.53 \| 96000 \| 2.4401 \| 0.6822 \| 0.6475 \| \| 0.122 \| 30.69 \| 96500 \| 2.4397 \| 0.6860 \| 0.6473 \| \| 0.1266 \| 30.85 \| 97000 \| 2.4572 \| 0.6847 \| 0.6504 \| \| 0.1266 \| 31.01 \| 97500 \| 2.4506 \| 0.6847 \| 0.6513 \| \| 0.1437 \| 31.17 \| 98000 \| 2.4251 \| 0.6822 \| 0.6496 \| \| 0.1437 \| 31.33 \| 98500 \| 2.4420 \| 0.6822 \| 0.6521 \| \| 0.1205 \| 31.49 \| 99000 \| 2.4446 \| 0.6816 \| 0.6464 \| \| 0.1205 \| 31.65 \| 99500 \| 2.4408 \| 0.6790 \| 0.6450 \| \| 0.1188 \| 31.81 \| 100000 \| 2.4522 \| 0.6765 \| 0.6487 \| \| 0.1188 \| 31.97 \| 100500 \| 2.4313 \| 0.6828 \| 0.6495 \| \| 0.1326 \| 32.12 \| 101000 \| 2.4577 \| 0.6784 \| 0.6466 \| \| 0.1326 \| 32.28 \| 101500 \| 2.4524 \| 0.6822 \| 0.6479 \| \| 0.1103 \| 32.44 \| 102000 \| 2.4665 \| 0.6765 \| 0.6426 \| \| 0.1103 \| 32.6 \| 102500 \| 2.4642 \| 0.6777 \| 0.6431 \| \| 0.118 \| 32.76 \| 103000 \| 2.4628 \| 0.6771 \| 0.6451 \| \| 0.118 \| 32.92 \| 103500 \| 2.4671 \| 0.6835 \| 0.6474 \| \| 0.1214 \| 33.08 \| 104000 \| 2.4613 \| 0.6771 \| 0.6503 \| \| 0.1214 \| 33.24 \| 104500 \| 2.4833 \| 0.6771 \| 0.6475 \| \| 0.0965 \| 33.4 \| 105000 \| 2.4888 \| 0.6803 \| 0.6450 \| \| 0.0965 \| 33.56 \| 105500 \| 2.4910 \| 0.6816 \| 0.6476 \| \| 0.1207 \| 33.72 \| 106000 \| 2.4806 \| 0.6860 \| 0.6482 \| \| 0.1207 \| 33.87 \| 106500 \| 2.4741 \| 0.6771 \| 0.6445 \| \| 0.1277 \| 34.03 \| 107000 \| 2.5050 \| 0.6790 \| 0.6409 \| \| 0.1277 \| 34.19 \| 107500 \| 2.4809 \| 0.6777 \| 0.6402 \| \| 0.1164 \| 34.35 \| 108000 \| 2.5006 \| 0.6777 \| 0.6428 \| \| 0.1164 \| 34.51 \| 108500 \| 2.4889 \| 0.6822 \| 0.6474 \| \| 0.1103 \| 34.67 \| 109000 \| 2.4852 \| 0.6822 \| 0.6457 \| \| 0.1103 \| 34.83 \| 109500 \| 2.4923 \| 0.6771 \| 0.6418 \| \| 0.1013 \| 34.99 \| 110000 \| 2.4662 \| 0.6784 \| 0.6437 \| \| 0.1013 \| 35.15 \| 110500 \| 2.4755 \| 0.6822 \| 0.6483 \| \| 0.0922 \| 35.31 \| 111000 \| 2.4908 \| 0.6816 \| 0.6465 \| \| 0.0922 \| 35.46 \| 111500 \| 2.4922 \| 0.6809 \| 0.6502 \| \| 0.0856 \| 35.62 \| 112000 \| 2.5096 \| 0.6828 \| 0.6422 \| \| 0.0856 \| 35.78 \| 112500 \| 2.5035 \| 0.6828 \| 0.6463 \| \| 0.1005 \| 35.94 \| 113000 \| 2.5231 \| 0.6828 \| 0.6452 \| \| 0.1005 \| 36.1 \| 113500 \| 2.5196 \| 0.6796 \| 0.6469 \| \| 0.0884 \| 36.26 \| 114000 \| 2.5187 \| 0.6796 \| 0.6444 \| \| 0.0884 \| 36.42 \| 114500 \| 2.5180 \| 0.6790 \| 0.6454 \| \| 0.0891 \| 36.58 \| 115000 \| 2.5407 \| 0.6771 \| 0.6442 \| \| 0.0891 \| 36.74 \| 115500 \| 2.5349 \| 0.6765 \| 0.6417 \| \| 0.1082 \| 36.9 \| 116000 \| 2.5451 \| 0.6777 \| 0.6427 \| \| 0.1082 \| 37.05 \| 116500 \| 2.5349 \| 0.6803 \| 0.6469 \| \| 0.1072 \| 37.21 \| 117000 \| 2.5507 \| 0.6816 \| 0.6457 \| \| 0.1072 \| 37.37 \| 117500 \| 2.5485 \| 0.6790 \| 0.6459 \| \| 0.0882 \| 37.53 \| 118000 \| 2.5477 \| 0.6809 \| 0.6448 \| \| 0.0882 \| 37.69 \| 118500 \| 2.5620 \| 0.6790 \| 0.6401 \| \| 0.0852 \| 37.85 \| 119000 \| 2.5597 \| 0.6790 \| 0.6447 \| \| 0.0852 \| 38.01 \| 119500 \| 2.5545 \| 0.6796 \| 0.6436 \| \| 0.1029 \| 38.17 \| 120000 \| 2.5519 \| 0.6796 \| 0.6436 \| \| 0.1029 \| 38.33 \| 120500 \| 2.5539 \| 0.6822 \| 0.6463 \| \| 0.0903 \| 38.49 \| 121000 \| 2.5590 \| 0.6822 \| 0.6490 \| \| 0.0903 \| 38.65 \| 121500 \| 2.5658 \| 0.6803 \| 0.6457 \| \| 0.092 \| 38.8 \| 122000 \| 2.5590 \| 0.6803 \| 0.6433 \| \| 0.092 \| 38.96 \| 122500 \| 2.5620 \| 0.6803 \| 0.6449 \| \| 0.094 \| 39.12 \| 123000 \| 2.5634 \| 0.6796 \| 0.6436 \| \| 0.094 \| 39.28 \| 123500 \| 2.5677 \| 0.6790 \| 0.6435 \| \| 0.0801 \| 39.44 \| 124000 \| 2.5662 \| 0.6803 \| 0.6445 \| \| 0.0801 \| 39.6 \| 124500 \| 2.5648 \| 0.6796 \| 0.6440 \| \| 0.103 \| 39.76 \| 125000 \| 2.5641 \| 0.6809 \| 0.6451 \| \| 0.103 \| 39.92 \| 125500 \| 2.5641 \| 0.6803 \| 0.6446 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
bahmanreza/keras-dummy-sequential-demo	bahmanreza	2023-05-23T18:17:12Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2023-05-23T18:17:09Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| True \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
YakovElm/Apache15Classic_with_cleaning	YakovElm	2023-05-23T18:05:44Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T18:04:54Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache15Classic_with_cleaning 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. --> # Apache15Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1583 - Train Accuracy: 0.9535 - Validation Loss: 0.3355 - Validation Accuracy: 0.8924 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.1921 \| 0.9542 \| 0.3429 \| 0.8924 \| 0 \| \| 0.1792 \| 0.9542 \| 0.3336 \| 0.8924 \| 1 \| \| 0.1583 \| 0.9535 \| 0.3355 \| 0.8924 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
k22/1	k22	2023-05-23T18:04:36Z	0	0	null	[ "region:us" ]	null	2023-05-23T18:03:06Z	How to choose an edible tomato?
omegaodin/gpt2	omegaodin	2023-05-23T18:01:11Z	0	0	adapter-transformers	[ "adapter-transformers", "code", "es", "dataset:fka/awesome-chatgpt-prompts", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:OpenAssistant/oasst1", "dataset:bigcode/the-stack", "dataset:bigcode/the-stack-dedup", "dataset:databricks/databricks-dolly-15k", "license:apache-2.0", "region:us" ]	null	2023-05-23T17:57:04Z	--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts - togethercomputer/RedPajama-Data-1T - OpenAssistant/oasst1 - bigcode/the-stack - bigcode/the-stack-dedup - databricks/databricks-dolly-15k language: - es metrics: - accuracy - code_eval - character library_name: adapter-transformers tags: - code ---
ArinaRomashova/summarisation-pegasus-pubmed	ArinaRomashova	2023-05-23T17:39:39Z	101	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-23T14:36:58Z	## Validation Metrics - Loss: 2.243 - Rouge1: 37.779 - Rouge2: 14.441 - RougeL: 24.108 - RougeLsum: 33.163 - Gen Len: 125.550 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ArinaRomashova/autotrain-summarisation-pegasus-pubmed-61004134636 ```
jjhonny/qtable-taxi-v3	jjhonny	2023-05-23T17:38:13Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T17:38:11Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: qtable-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.74 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="jjhonny/qtable-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"]) ```
takuma104/lora_unetonly_rank128	takuma104	2023-05-23T17:35:35Z	3	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	2023-05-23T17:27:33Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - takuma104/lora_unetonly_rank128 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
takuma104/lora_unetonly_rank4	takuma104	2023-05-23T17:25:06Z	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	2023-05-23T17:19:48Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - takuma104/lora_unetonly_rank4 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
kudailang/kosasih	kudailang	2023-05-23T17:22:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-23T17:20:36Z	--- license: creativeml-openrail-m ---
nemuwn/bert-base-multilingual-cased-mongolian-ner	nemuwn	2023-05-23T17:04:43Z	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "mn", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-23T15:39:30Z	--- language: - mn license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-multilingual-cased-mongolian-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-multilingual-cased-mongolian-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1423 - Precision: 0.9057 - Recall: 0.9188 - F1: 0.9122 - Accuracy: 0.9753 ## Model description More information needed ## Intended uses & 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1726 \| 1.0 \| 477 \| 0.1052 \| 0.8531 \| 0.8851 \| 0.8688 \| 0.9664 \| \| 0.0827 \| 2.0 \| 954 \| 0.0975 \| 0.8722 \| 0.8987 \| 0.8852 \| 0.9699 \| \| 0.0571 \| 3.0 \| 1431 \| 0.0926 \| 0.8847 \| 0.9054 \| 0.8950 \| 0.9719 \| \| 0.0376 \| 4.0 \| 1908 \| 0.1052 \| 0.8980 \| 0.9119 \| 0.9049 \| 0.9727 \| \| 0.0271 \| 5.0 \| 2385 \| 0.1137 \| 0.9021 \| 0.9158 \| 0.9089 \| 0.9746 \| \| 0.0182 \| 6.0 \| 2862 \| 0.1304 \| 0.8839 \| 0.9106 \| 0.8970 \| 0.9712 \| \| 0.0145 \| 7.0 \| 3339 \| 0.1274 \| 0.9042 \| 0.9187 \| 0.9114 \| 0.9748 \| \| 0.0097 \| 8.0 \| 3816 \| 0.1375 \| 0.9009 \| 0.9169 \| 0.9088 \| 0.9739 \| \| 0.0063 \| 9.0 \| 4293 \| 0.1421 \| 0.9017 \| 0.9171 \| 0.9093 \| 0.9748 \| \| 0.0049 \| 10.0 \| 4770 \| 0.1423 \| 0.9057 \| 0.9188 \| 0.9122 \| 0.9753 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Hinova/q-FrozenLake-v1-4x4-noSlippery	Hinova	2023-05-23T17:00:49Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T17:00:44Z	--- 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="Hinova/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"]) ```
YakovElm/Apache10Classic_with_cleaning	YakovElm	2023-05-23T17:00:42Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T17:00:05Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache10Classic_with_cleaning 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. --> # Apache10Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1824 - Train Accuracy: 0.9385 - Validation Loss: 0.5452 - Validation Accuracy: 0.8644 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.2431 \| 0.9340 \| 0.4461 \| 0.8644 \| 0 \| \| 0.2183 \| 0.9383 \| 0.4053 \| 0.8644 \| 1 \| \| 0.1824 \| 0.9385 \| 0.5452 \| 0.8644 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
Artur01/mit-b0-finetuned-sidewalks	Artur01	2023-05-23T16:53:01Z	31	0	transformers	[ "transformers", "tf", "segformer", "generated_from_keras_callback", "license:other", "endpoints_compatible", "region:us" ]	null	2023-05-22T20:23:44Z	--- license: other tags: - generated_from_keras_callback model-index: - name: Artur01/mit-b0-finetuned-sidewalks 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. --> # Artur01/mit-b0-finetuned-sidewalks This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3943 - Validation Loss: 0.8293 - Validation Mean Iou: 0.2097 - Validation Mean Accuracy: 0.2617 - Validation Overall Accuracy: 0.7688 - Validation Accuracy Unlabeled: 0.0 - Validation Accuracy Flat-road: 0.6688 - Validation Accuracy Flat-sidewalk: 0.9258 - Validation Accuracy Flat-crosswalk: 0.4607 - Validation Accuracy Flat-cyclinglane: 0.7716 - Validation Accuracy Flat-parkingdriveway: 0.2329 - Validation Accuracy Flat-railtrack: nan - Validation Accuracy Flat-curb: 0.1001 - Validation Accuracy Human-person: 0.0011 - Validation Accuracy Human-rider: 0.0 - Validation Accuracy Vehicle-car: 0.8806 - Validation Accuracy Vehicle-truck: 0.0 - Validation Accuracy Vehicle-bus: 0.0 - Validation Accuracy Vehicle-tramtrain: nan - Validation Accuracy Vehicle-motorcycle: 0.0 - Validation Accuracy Vehicle-bicycle: 0.2446 - Validation Accuracy Vehicle-caravan: 0.0 - Validation Accuracy Vehicle-cartrailer: nan - Validation Accuracy Construction-building: 0.8260 - Validation Accuracy Construction-door: 0.0 - Validation Accuracy Construction-wall: 0.2769 - Validation Accuracy Construction-fenceguardrail: 0.0618 - Validation Accuracy Construction-bridge: 0.0 - Validation Accuracy Construction-tunnel: nan - Validation Accuracy Construction-stairs: 0.0 - Validation Accuracy Object-pole: 0.0127 - Validation Accuracy Object-trafficsign: 0.0 - Validation Accuracy Object-trafficlight: 0.0 - Validation Accuracy Nature-vegetation: 0.9125 - Validation Accuracy Nature-terrain: 0.7600 - Validation Accuracy Sky: 0.9223 - Validation Accuracy Void-ground: 0.0 - Validation Accuracy Void-dynamic: 0.0122 - Validation Accuracy Void-static: 0.0431 - Validation Accuracy Void-unclear: 0.0 - Validation Iou Unlabeled: 0.0 - Validation Iou Flat-road: 0.5491 - Validation Iou Flat-sidewalk: 0.7881 - Validation Iou Flat-crosswalk: 0.4034 - Validation Iou Flat-cyclinglane: 0.4981 - Validation Iou Flat-parkingdriveway: 0.1731 - Validation Iou Flat-railtrack: nan - Validation Iou Flat-curb: 0.0869 - Validation Iou Human-person: 0.0011 - Validation Iou Human-rider: 0.0 - Validation Iou Vehicle-car: 0.6311 - Validation Iou Vehicle-truck: 0.0 - Validation Iou Vehicle-bus: 0.0 - Validation Iou Vehicle-tramtrain: nan - Validation Iou Vehicle-motorcycle: 0.0 - Validation Iou Vehicle-bicycle: 0.2027 - Validation Iou Vehicle-caravan: 0.0 - Validation Iou Vehicle-cartrailer: nan - Validation Iou Construction-building: 0.5921 - Validation Iou Construction-door: 0.0 - Validation Iou Construction-wall: 0.2356 - Validation Iou Construction-fenceguardrail: 0.0570 - Validation Iou Construction-bridge: 0.0 - Validation Iou Construction-tunnel: nan - Validation Iou Construction-stairs: 0.0 - Validation Iou Object-pole: 0.0125 - Validation Iou Object-trafficsign: 0.0 - Validation Iou Object-trafficlight: 0.0 - Validation Iou Nature-vegetation: 0.7724 - Validation Iou Nature-terrain: 0.5932 - Validation Iou Sky: 0.8602 - Validation Iou Void-ground: 0.0 - Validation Iou Void-dynamic: 0.0120 - Validation Iou Void-static: 0.0323 - Validation Iou Void-unclear: 0.0 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Validation Mean Iou \| Validation Mean Accuracy \| Validation Overall Accuracy \| Validation Accuracy Unlabeled \| Validation Accuracy Flat-road \| Validation Accuracy Flat-sidewalk \| Validation Accuracy Flat-crosswalk \| Validation Accuracy Flat-cyclinglane \| Validation Accuracy Flat-parkingdriveway \| Validation Accuracy Flat-railtrack \| Validation Accuracy Flat-curb \| Validation Accuracy Human-person \| Validation Accuracy Human-rider \| Validation Accuracy Vehicle-car \| Validation Accuracy Vehicle-truck \| Validation Accuracy Vehicle-bus \| Validation Accuracy Vehicle-tramtrain \| Validation Accuracy Vehicle-motorcycle \| Validation Accuracy Vehicle-bicycle \| Validation Accuracy Vehicle-caravan \| Validation Accuracy Vehicle-cartrailer \| Validation Accuracy Construction-building \| Validation Accuracy Construction-door \| Validation Accuracy Construction-wall \| Validation Accuracy Construction-fenceguardrail \| Validation Accuracy Construction-bridge \| Validation Accuracy Construction-tunnel \| Validation Accuracy Construction-stairs \| Validation Accuracy Object-pole \| Validation Accuracy Object-trafficsign \| Validation Accuracy Object-trafficlight \| Validation Accuracy Nature-vegetation \| Validation Accuracy Nature-terrain \| Validation Accuracy Sky \| Validation Accuracy Void-ground \| Validation Accuracy Void-dynamic \| Validation Accuracy Void-static \| Validation Accuracy Void-unclear \| Validation Iou Unlabeled \| Validation Iou Flat-road \| Validation Iou Flat-sidewalk \| Validation Iou Flat-crosswalk \| Validation Iou Flat-cyclinglane \| Validation Iou Flat-parkingdriveway \| Validation Iou Flat-railtrack \| Validation Iou Flat-curb \| Validation Iou Human-person \| Validation Iou Human-rider \| Validation Iou Vehicle-car \| Validation Iou Vehicle-truck \| Validation Iou Vehicle-bus \| Validation Iou Vehicle-tramtrain \| Validation Iou Vehicle-motorcycle \| Validation Iou Vehicle-bicycle \| Validation Iou Vehicle-caravan \| Validation Iou Vehicle-cartrailer \| Validation Iou Construction-building \| Validation Iou Construction-door \| Validation Iou Construction-wall \| Validation Iou Construction-fenceguardrail \| Validation Iou Construction-bridge \| Validation Iou Construction-tunnel \| Validation Iou Construction-stairs \| Validation Iou Object-pole \| Validation Iou Object-trafficsign \| Validation Iou Object-trafficlight \| Validation Iou Nature-vegetation \| Validation Iou Nature-terrain \| Validation Iou Sky \| Validation Iou Void-ground \| Validation Iou Void-dynamic \| Validation Iou Void-static \| Validation Iou Void-unclear \| Epoch \| \|:----------:\|:---------------:\|:-------------------:\|:------------------------:\|:---------------------------:\|:-----------------------------:\|:-----------------------------:\|:---------------------------------:\|:----------------------------------:\|:------------------------------------:\|:----------------------------------------:\|:----------------------------------:\|:-----------------------------:\|:--------------------------------:\|:-------------------------------:\|:-------------------------------:\|:---------------------------------:\|:-------------------------------:\|:-------------------------------------:\|:--------------------------------------:\|:-----------------------------------:\|:-----------------------------------:\|:--------------------------------------:\|:-----------------------------------------:\|:-------------------------------------:\|:-------------------------------------:\|:-----------------------------------------------:\|:---------------------------------------:\|:---------------------------------------:\|:---------------------------------------:\|:-------------------------------:\|:--------------------------------------:\|:---------------------------------------:\|:-------------------------------------:\|:----------------------------------:\|:-----------------------:\|:-------------------------------:\|:--------------------------------:\|:-------------------------------:\|:--------------------------------:\|:------------------------:\|:------------------------:\|:----------------------------:\|:-----------------------------:\|:-------------------------------:\|:-----------------------------------:\|:-----------------------------:\|:------------------------:\|:---------------------------:\|:--------------------------:\|:--------------------------:\|:----------------------------:\|:--------------------------:\|:--------------------------------:\|:---------------------------------:\|:------------------------------:\|:------------------------------:\|:---------------------------------:\|:------------------------------------:\|:--------------------------------:\|:--------------------------------:\|:------------------------------------------:\|:----------------------------------:\|:----------------------------------:\|:----------------------------------:\|:--------------------------:\|:---------------------------------:\|:----------------------------------:\|:--------------------------------:\|:-----------------------------:\|:------------------:\|:--------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:-----:\| \| 1.3943 \| 0.8293 \| 0.2097 \| 0.2617 \| 0.7688 \| 0.0 \| 0.6688 \| 0.9258 \| 0.4607 \| 0.7716 \| 0.2329 \| nan \| 0.1001 \| 0.0011 \| 0.0 \| 0.8806 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2446 \| 0.0 \| nan \| 0.8260 \| 0.0 \| 0.2769 \| 0.0618 \| 0.0 \| nan \| 0.0 \| 0.0127 \| 0.0 \| 0.0 \| 0.9125 \| 0.7600 \| 0.9223 \| 0.0 \| 0.0122 \| 0.0431 \| 0.0 \| 0.0 \| 0.5491 \| 0.7881 \| 0.4034 \| 0.4981 \| 0.1731 \| nan \| 0.0869 \| 0.0011 \| 0.0 \| 0.6311 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.2027 \| 0.0 \| nan \| 0.5921 \| 0.0 \| 0.2356 \| 0.0570 \| 0.0 \| nan \| 0.0 \| 0.0125 \| 0.0 \| 0.0 \| 0.7724 \| 0.5932 \| 0.8602 \| 0.0 \| 0.0120 \| 0.0323 \| 0.0 \| 0 \| ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
Nebyx/ppo_LunarLander-v2	Nebyx	2023-05-23T16:46:42Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T16:46:16Z	--- 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: 245.42 +/- 18.58 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 ... ```
perfectino/rinahhh	perfectino	2023-05-23T16:36:40Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-23T16:35:13Z	--- license: creativeml-openrail-m ---
Iulian277/ro-bart-512	Iulian277	2023-05-23T16:15:03Z	309	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "summarization", "ro", "autotrain_compatible", "region:us" ]	summarization	2023-04-13T10:00:11Z	--- tags: - summarization - bart language: - ro inference: false --- This is a pretrained-from-scratch BART base model (140M parameters). Training was performed on a clean 50GB Romanian text corpus for 3M steps with these [scripts](https://github.com/cosmoquester/transformers-bart-pretrain). The model was trained with a maximum sequence length of 512. !! IMPORTANT !! This model was pretrained on the text corruption task, meaning this model is not usable in any downstream task without finetuning first!
mnavas/roberta-finetuned-qa-reqzarv0	mnavas	2023-05-23T16:03:55Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-05-23T15:37:43Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: roberta-finetuned-qa-reqzarv0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-qa-reqzarv0 This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
rakgesh/image-classifier-one-piece-v03_01	rakgesh	2023-05-23T16:02:00Z	3	0	tf-keras	[ "tf-keras", "mobilenet", "image-classification", "region:us" ]	image-classification	2023-05-23T15:59:09Z	--- pipeline_tag: image-classification ---
YakovElm/Apache5Classic_with_cleaning	YakovElm	2023-05-23T15:56:00Z	62	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T15:55:23Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache5Classic_with_cleaning 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. --> # Apache5Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2193 - Train Accuracy: 0.9235 - Validation Loss: 0.6107 - Validation Accuracy: 0.8194 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.3142 \| 0.9001 \| 0.4816 \| 0.8233 \| 0 \| \| 0.2820 \| 0.9099 \| 0.4622 \| 0.8233 \| 1 \| \| 0.2193 \| 0.9235 \| 0.6107 \| 0.8194 \| 2 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
khosro111/test1	khosro111	2023-05-23T15:53:10Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-05-23T15:52:34Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard 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 ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
xzuyn/LLaMa-1-MedicWizard-7B-GGML	xzuyn	2023-05-23T15:53:05Z	0	3	null	[ "llama", "alpaca", "region:us" ]	null	2023-05-21T16:18:21Z	--- tags: - llama - alpaca --- # For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp) Original Model: https://huggingface.co/xzuyn/MedicWizard-7B
davanstrien/imdb_bertopic_ten_topics	davanstrien	2023-05-23T15:52:38Z	5	0	bertopic	[ "bertopic", "region:us" ]	null	2023-05-23T15:52:31Z	--- tags: - bertopic library_name: bertopic --- # imdb_bertopic_ten_topics This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("davanstrien/imdb_bertopic_ten_topics") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 10 * Number of training documents: 103062 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| -1 \| film - movie - movies - character - characters \| 44 \| -1_film_movie_movies_character \| \| 0 \| film - movie - films - movies - too \| 25087 \| 0_film_movie_films_movies \| \| 1 \| episodes - shows - watching - tv - episode \| 20955 \| 1_episodes_shows_watching_tv \| \| 2 \| films - film - movies - godzilla - movie \| 2037 \| 2_films_film_movies_godzilla \| \| 3 \| cinderella - disney - cartoon - animation - cartoons \| 895 \| 3_cinderella_disney_cartoon_animation \| \| 4 \| gameplay - games - game - adventure - starcraft \| 465 \| 4_gameplay_games_game_adventure \| \| 5 \| holmes - sherlock - watson - doyle - conan \| 228 \| 5_holmes_sherlock_watson_doyle \| \| 6 \| panther - film - films - clouseau - movies \| 184 \| 6_panther_film_films_clouseau \| \| 7 \| metallica - metal - genres - genre - headbanger \| 55 \| 7_metallica_metal_genres_genre \| \| 8 \| che - ernesto - castro - biopic - film \| 50 \| 8_che_ernesto_castro_biopic \| </details> ## Training hyperparameters * calculate_probabilities: False * language: None * low_memory: False * min_topic_size: 40 * n_gram_range: (1, 1) * nr_topics: 10 * seed_topic_list: None * top_n_words: 10 * verbose: False ## Framework versions * Numpy: 1.22.4 * HDBSCAN: 0.8.29 * UMAP: 0.5.3 * Pandas: 1.5.3 * Scikit-Learn: 1.2.2 * Sentence-transformers: 2.2.2 * Transformers: 4.29.2 * Numba: 0.56.4 * Plotly: 5.13.1 * Python: 3.10.11
dawoz/a2c-PandaReachDense-v2	dawoz	2023-05-23T15:49:16Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T15:46:27Z	--- 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: -0.36 +/- 0.17 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 ... ```
VinsmokeMir/Fine_Tuning_SC_Method_2_Epoch_13B	VinsmokeMir	2023-05-23T15:44:19Z	184	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T15:28:29Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Fine_Tuning_SC_Method_2_Epoch_13B results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Fine_Tuning_SC_Method_2_Epoch_13B This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4244 - Accuracy: 0.6873 - F1 Macro: 0.6544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| No log \| 1.27 \| 500 \| 1.0673 \| 0.3976 \| 0.1896 \| \| 1.0138 \| 2.54 \| 1000 \| 0.8217 \| 0.6331 \| 0.5569 \| \| 1.0138 \| 3.82 \| 1500 \| 0.7889 \| 0.6662 \| 0.6049 \| \| 0.7305 \| 5.09 \| 2000 \| 0.7821 \| 0.6765 \| 0.6382 \| \| 0.7305 \| 6.36 \| 2500 \| 0.7867 \| 0.6918 \| 0.6457 \| \| 0.5856 \| 7.63 \| 3000 \| 0.8236 \| 0.6892 \| 0.6623 \| \| 0.5856 \| 8.91 \| 3500 \| 0.8490 \| 0.6835 \| 0.6551 \| \| 0.4723 \| 10.18 \| 4000 \| 0.9057 \| 0.6854 \| 0.6533 \| \| 0.4723 \| 11.45 \| 4500 \| 0.9237 \| 0.6796 \| 0.6455 \| \| 0.3896 \| 12.72 \| 5000 \| 0.9814 \| 0.6879 \| 0.6499 \| \| 0.3896 \| 13.99 \| 5500 \| 0.9984 \| 0.6745 \| 0.6487 \| \| 0.3299 \| 15.27 \| 6000 \| 1.0226 \| 0.6822 \| 0.6545 \| \| 0.3299 \| 16.54 \| 6500 \| 1.0579 \| 0.6758 \| 0.6485 \| \| 0.2783 \| 17.81 \| 7000 \| 1.0932 \| 0.6796 \| 0.6487 \| \| 0.2783 \| 19.08 \| 7500 \| 1.1047 \| 0.6950 \| 0.6609 \| \| 0.2455 \| 20.36 \| 8000 \| 1.1643 \| 0.6860 \| 0.6559 \| \| 0.2455 \| 21.63 \| 8500 \| 1.1953 \| 0.6841 \| 0.6548 \| \| 0.2181 \| 22.9 \| 9000 \| 1.2043 \| 0.6835 \| 0.6516 \| \| 0.2181 \| 24.17 \| 9500 \| 1.2603 \| 0.6867 \| 0.6502 \| \| 0.1894 \| 25.45 \| 10000 \| 1.2652 \| 0.6860 \| 0.6552 \| \| 0.1894 \| 26.72 \| 10500 \| 1.2860 \| 0.6790 \| 0.6474 \| \| 0.1757 \| 27.99 \| 11000 \| 1.2892 \| 0.6854 \| 0.6541 \| \| 0.1757 \| 29.26 \| 11500 \| 1.3400 \| 0.6803 \| 0.6496 \| \| 0.1599 \| 30.53 \| 12000 \| 1.3630 \| 0.6828 \| 0.6493 \| \| 0.1599 \| 31.81 \| 12500 \| 1.3688 \| 0.6854 \| 0.6538 \| \| 0.1531 \| 33.08 \| 13000 \| 1.3962 \| 0.6854 \| 0.6534 \| \| 0.1531 \| 34.35 \| 13500 \| 1.4021 \| 0.6841 \| 0.6523 \| \| 0.1452 \| 35.62 \| 14000 \| 1.4029 \| 0.6847 \| 0.6524 \| \| 0.1452 \| 36.9 \| 14500 \| 1.4130 \| 0.6886 \| 0.6562 \| \| 0.1391 \| 38.17 \| 15000 \| 1.4203 \| 0.6879 \| 0.6553 \| \| 0.1391 \| 39.44 \| 15500 \| 1.4244 \| 0.6873 \| 0.6544 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
DaniloTrotta/Kandinsky_2.1	DaniloTrotta	2023-05-23T15:43:07Z	1	0	open_clip	[ "open_clip", "Kandinsky", "text-image", "text-to-image", "license:apache-2.0", "region:us" ]	text-to-image	2023-05-23T12:00:50Z	--- license: apache-2.0 tags: - Kandinsky - text-image inference: true pipeline_tag: text-to-image library_name: open_clip --- # Kandinsky 2.1 [Open In Colab](https://colab.research.google.com/drive/1xSbu-b-EwYd6GdaFPRVgvXBX_mciZ41e?usp=sharing) [GitHub repository](https://github.com/ai-forever/Kandinsky-2) [Habr post](https://habr.com/ru/company/sberbank/blog/725282/) [Demo](https://rudalle.ru/) ## Architecture Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. For diffusion mapping of latent spaces we use transformer with num_layers=20, num_heads=32 and hidden_size=2048. ![](https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png) Other architecture parts: + Text encoder (XLM-Roberta-Large-Vit-L-14) - 560M + Diffusion Image Prior — 1B + CLIP image encoder (ViT-L/14) - 427M + Latent Diffusion U-Net - 1.22B + MoVQ encoder/decoder - 67M ![](https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/einstein.png) # Authors + Arseniy Shakhmatov: [Github](https://github.com/cene555), [Blog](https://t.me/gradientdip) + Anton Razzhigaev: [Github](https://github.com/razzant), [Blog](https://t.me/abstractDL) + Aleksandr Nikolich: [Github](https://github.com/AlexWortega), [Blog](https://t.me/lovedeathtransformers) + Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse) + Igor Pavlov: [Github](https://github.com/boomb0om) + Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey) + Denis Dimitrov: [Github](https://github.com/denndimitrov)
openllmplayground/pandagpt_7b_max_len_512	openllmplayground	2023-05-23T15:42:41Z	0	1	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-05-17T18:13:29Z	--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-7B model with maximum sequence length of 512. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).
openllmplayground/pandagpt_13b_max_len_256	openllmplayground	2023-05-23T15:42:15Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-05-18T16:33:04Z	--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-13B model with maximum sequence length of 256. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).
openllmplayground/pandagpt_13b_max_len_400	openllmplayground	2023-05-23T15:41:35Z	0	6	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-05-21T05:50:07Z	--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-13B model with maximum sequence length of 400. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).
openllmplayground/pandagpt_7b_max_len_1024	openllmplayground	2023-05-23T15:41:04Z	0	8	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-05-22T07:37:53Z	--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-7B model with maximum sequence length of 1024. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).
gerardoalemanm/roberta-large-peft-p-tunning	gerardoalemanm	2023-05-23T15:37:56Z	0	0	null	[ "license:c-uda", "region:us" ]	null	2023-05-23T15:29:47Z	--- license: c-uda --- Model fine tunned with peft and Roberta-large model Uses the dataset glue
YakovElm/Qt15SetFitModel	YakovElm	2023-05-23T15:36:58Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-05-23T15:36:20Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/Qt15SetFitModel 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("YakovElm/Qt15SetFitModel") # 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} } ```
skyadmin/cog-webui-sd	skyadmin	2023-05-23T15:36:11Z	67	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	2023-05-10T13:36:02Z	# Chill Watcher consider deploy on: - huggingface inference point - replicate api - lightning.ai # platform comparison > all support autoscaling \|platform\|prediction speed\|charges\|deploy handiness\| \|-\|-\|-\|-\| \|huggingface\|fast:20s\|high:$0.6/hr (without autoscaling)\|easy:git push\| \|replicate\|fast if used frequently: 30s, slow if needs initialization: 5min\|low: $0.02 per generation\|difficult: build image and upload\| \|lightning.ai\|fast with app running: 20s, slow if idle: XXs\|low: free $30 per month, $0.18 per init, $0.02 per run\|easy: one command\| # platform deploy options ## huggingface > [docs](https://huggingface.co/docs/inference-endpoints/guides/custom_handler) - requirements: use pip packages in `requirements.txt` - `init()` and `predict()` function: use `handler.py`, implement the `EndpointHandler` class - more: modify `handler.py` for requests and inference and explore more highly-customized features - deploy: git (lfs) push to huggingface repository(the whole directory including models and weights, etc.), and use inference endpoints to deploy. Click and deploy automaticly, very simple. - call api: use the url provide by inference endpoints after endpoint is ready(build, initialize and in a "running" state), make a post request to the url using request schema definied in the `handler.py` ## replicate > [docs](https://replicate.com/docs/guides/push-a-model) - requirements: specify all requirements(pip packages, system packages, python version, cuda, etc.) in `cog.yaml` - `init()` and `predict()` function: use `predict.py`, implement the `Predictor` class - more: modify `predict.py` - deploy: 1. get a linux GPU machine with 60GB disk space; 2. install [cog](https://replicate.com/docs/guides/push-a-model) and [docker](https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository) 3. `git pull` the current repository from huggingface, including large model files 4. after `predict.py` and `cog.yaml` is correctly coded, run `cog login`, `cog push`, then cog will build a docker image locally and push the image to replicate. As the image could take 30GB or so disk space, it would cost a lot network bandwidth. - call api: if everything runs successfully and the docker image is pushed to replicate, you will see a web-ui and an API example directly in your replicate repository ## lightning.ai > docs: [code](https://lightning.ai/docs/app/stable/levels/basic/real_lightning_component_implementations.html), [deploy](https://lightning.ai/docs/app/stable/workflows/run_app_on_cloud/) - requirements: - pip packages are listed in `requirements_lightning.txt`, because some requirements are different from those in huggingface. Rename it to `requirements.txt` - other pip packages, system packages and some big model weight files download commands, can be listed using a custom build config. Checkout `class CustomBuildConfig(BuildConfig)` in `app.py`. In a custom build config you can use many linux commands such as `wget` and `sudo apt-get update`. The custom build config will be executed on the `__init__()` of the `PythonServer` class - `init()` and `predict()` function: use `app.py`, implement the `PythonServer` class. Note: - some packages haven't been installed when the file is called(these packages may be installed when `__init__()` is called), so some import code should be in the function, not at the top of the file, or you may get import errors. - you can't save your own value to `PythonServer.self` unless it's predifined in the variables, so don't assign any self-defined variables to `self` - if you use the custom build config, you should implement `PythonServer`'s `__init()__` yourself, so don't forget to use the correct function signature - more: ... - deploy: - `pip install lightning` - prepare the directory on your local computer(no need to have a GPU) - list big files in the `.lightningignore` file to avoid big file upload and save deploy time cost - run `lightning run app app.py --cloud` in the local terminal, and it will upload the files in the directory to lightning cloud, and start deploying on the cloud - check error logs on the web-ui, use `all logs` - call api: only if the app starts successfully, you can see a valid url in the `settings` page of the web-ui. Open that url, and you can see the api ### some stackoverflow: install docker: - https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository install git-lfs: - https://github.com/git-lfs/git-lfs/blob/main/INSTALLING.md linux: ``` curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh \| sudo bash sudo apt-get install git-lfs ``` --- license: apache-2.0 ---
kitrakrev/ppo-LunarLander-v2	kitrakrev	2023-05-23T15:29:50Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T15:29:28Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.88 +/- 16.00 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 ... ```
Bailefan/dqn-SpaceInvadersNoFrameskip-v4	Bailefan	2023-05-23T15:20:16Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-23T15:19:37Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 687.00 +/- 135.15 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bailefan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bailefan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Bailefan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
da2el/slskmz	da2el	2023-05-23T15:14:14Z	0	3	null	[ "stable-diffusion", "lora", "school uniform", "school swimsuit", "ja", "license:creativeml-openrail-m", "region:us" ]	null	2023-05-23T14:06:11Z	--- license: creativeml-openrail-m language: - ja tags: - stable-diffusion - lora - school uniform - school swimsuit --- # Sailor School Swimsuit セーラー服＋スク水は人類の宝です。 Sailor school uniform + school swimsuits are treasures of humanity. 水手服+校服泳装是人类的宝藏。 ## Features スクール水着の上からセーラー服を着用した姿が生成されます。 ※このLoRAを使わなくてもセーラー服とスク水の組み合わせは生成できます。ちょっと確率を上げる程度だと思ってください The generated image depicts a person wearing a sailor uniform over a school swimsuit. ※Even without using LoRA, it is possible to generate a combination of sailor uniforms and school swimsuits. Please consider it as just slightly increasing the probability. 生成的图像描绘了一个穿着水手服的人在校服泳装上面。 ※即使不使用 LoRA，也可以生成帆船制服和学校泳装的组合。请将其视为稍微增加了概率 ## How to Use - Clip skip: 2 - LoRA weight: 0.3 - 0.5 - prompt: - Short sleeves: slskmz short - Sleeveless: slskmz sleeveless ``` <lora:slskmz_v2:0.3>, slskmz short, sailor school uniform, school swimsuit, white shirt ``` `sailor school uniform, school swimsuit, white shirt` も入力したほうが出現率が上がります If you also input `sailor school uniform, school swimsuit, white shirt`, the probability of them appearing will increase 如果您还输入了 `sailor school uniform, school swimsuit, white shirt` ，它们出现的概率将会增加。 ![](img/sample.webp) ## Author daniel, Japan
braintML123/Tania	braintML123	2023-05-23T15:03:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T03:07:26Z	--- license: creativeml-openrail-m ---
Jakehova/SimpleClassifierWithLLMs	Jakehova	2023-05-23T14:56:28Z	0	0	null	[ "region:us" ]	null	2023-05-23T14:21:48Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID This is a simple classifier using the 20 Newgroups dataset ## Model Details Uses sklearn.datasets to pull 20 Newsgroups data. It runs through a variety of transformers (I'm not sure if this is the right terminology) to classify the data provided. ### Model Description The focus of this is to fine tune a model with Text Classification. - Developed by: FourthBrain - Model type: Simple Classifier - Language(s) (NLP): distilbert-base-uncased - License: MIT (?) ## Uses This is for a class so shouldnt be used for anything more than learning. ### Direct Use Learning ## Training Details ### Training Data [20 Newgroups](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) [More Information Needed]
VinsmokeMir/FineTuning_Method_2_SC	VinsmokeMir	2023-05-23T14:49:02Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-23T13:55:32Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: FineTuning_Method_2_SC results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # FineTuning_Method_2_SC This model is a fine-tuned version of [rafsankabir/Pretrained_E13_Method2](https://huggingface.co/rafsankabir/Pretrained_E13_Method2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3223 - Accuracy: 0.6790 - F1 Macro: 0.6487 ## Model description More information needed ## Intended uses & 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_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| No log \| 0.32 \| 500 \| 1.0745 \| 0.3976 \| 0.1896 \| \| 1.0543 \| 0.64 \| 1000 \| 0.9059 \| 0.5967 \| 0.4614 \| \| 1.0543 \| 0.95 \| 1500 \| 0.8259 \| 0.6414 \| 0.5633 \| \| 0.8389 \| 1.27 \| 2000 \| 0.8177 \| 0.6394 \| 0.5715 \| \| 0.8389 \| 1.59 \| 2500 \| 0.8269 \| 0.6356 \| 0.5724 \| \| 0.7713 \| 1.91 \| 3000 \| 0.7916 \| 0.6631 \| 0.6238 \| \| 0.7713 \| 2.23 \| 3500 \| 0.7996 \| 0.6745 \| 0.6155 \| \| 0.6734 \| 2.54 \| 4000 \| 0.7921 \| 0.6624 \| 0.6307 \| \| 0.6734 \| 2.86 \| 4500 \| 0.7743 \| 0.6726 \| 0.6459 \| \| 0.6309 \| 3.18 \| 5000 \| 0.8343 \| 0.6803 \| 0.6382 \| \| 0.6309 \| 3.5 \| 5500 \| 0.8233 \| 0.6784 \| 0.6390 \| \| 0.5582 \| 3.82 \| 6000 \| 0.8678 \| 0.6631 \| 0.6273 \| \| 0.5582 \| 4.13 \| 6500 \| 0.8621 \| 0.6758 \| 0.6368 \| \| 0.4988 \| 4.45 \| 7000 \| 0.9389 \| 0.6720 \| 0.6386 \| \| 0.4988 \| 4.77 \| 7500 \| 0.9067 \| 0.6918 \| 0.6505 \| \| 0.4885 \| 5.09 \| 8000 \| 0.9116 \| 0.6937 \| 0.6583 \| \| 0.4885 \| 5.41 \| 8500 \| 1.0357 \| 0.6822 \| 0.6459 \| \| 0.427 \| 5.73 \| 9000 \| 0.9428 \| 0.6847 \| 0.6479 \| \| 0.427 \| 6.04 \| 9500 \| 1.0233 \| 0.6752 \| 0.6531 \| \| 0.4034 \| 6.36 \| 10000 \| 1.1578 \| 0.6835 \| 0.6515 \| \| 0.4034 \| 6.68 \| 10500 \| 1.1870 \| 0.6790 \| 0.6545 \| \| 0.4053 \| 7.0 \| 11000 \| 1.0370 \| 0.7007 \| 0.6651 \| \| 0.4053 \| 7.32 \| 11500 \| 1.2087 \| 0.6822 \| 0.6497 \| \| 0.3545 \| 7.63 \| 12000 \| 1.2255 \| 0.6847 \| 0.6605 \| \| 0.3545 \| 7.95 \| 12500 \| 1.2710 \| 0.6905 \| 0.6609 \| \| 0.3437 \| 8.27 \| 13000 \| 1.3646 \| 0.6918 \| 0.6618 \| \| 0.3437 \| 8.59 \| 13500 \| 1.3767 \| 0.6879 \| 0.6563 \| \| 0.3407 \| 8.91 \| 14000 \| 1.2705 \| 0.6796 \| 0.6506 \| \| 0.3407 \| 9.22 \| 14500 \| 1.4605 \| 0.6803 \| 0.6496 \| \| 0.2876 \| 9.54 \| 15000 \| 1.4202 \| 0.6860 \| 0.6555 \| \| 0.2876 \| 9.86 \| 15500 \| 1.4151 \| 0.6847 \| 0.6517 \| \| 0.3035 \| 10.18 \| 16000 \| 1.4536 \| 0.6713 \| 0.6514 \| \| 0.3035 \| 10.5 \| 16500 \| 1.4806 \| 0.6828 \| 0.6469 \| \| 0.2733 \| 10.81 \| 17000 \| 1.4596 \| 0.6899 \| 0.6552 \| \| 0.2733 \| 11.13 \| 17500 \| 1.6183 \| 0.6886 \| 0.6557 \| \| 0.2562 \| 11.45 \| 18000 \| 1.6054 \| 0.6771 \| 0.6591 \| \| 0.2562 \| 11.77 \| 18500 \| 1.5966 \| 0.6701 \| 0.6503 \| \| 0.2582 \| 12.09 \| 19000 \| 1.5659 \| 0.6822 \| 0.6531 \| \| 0.2582 \| 12.4 \| 19500 \| 1.6146 \| 0.6867 \| 0.6575 \| \| 0.2368 \| 12.72 \| 20000 \| 1.6207 \| 0.6899 \| 0.6629 \| \| 0.2368 \| 13.04 \| 20500 \| 1.5220 \| 0.6918 \| 0.6640 \| \| 0.245 \| 13.36 \| 21000 \| 1.6572 \| 0.6720 \| 0.6489 \| \| 0.245 \| 13.68 \| 21500 \| 1.6443 \| 0.6860 \| 0.6590 \| \| 0.2226 \| 13.99 \| 22000 \| 1.6238 \| 0.6847 \| 0.6589 \| \| 0.2226 \| 14.31 \| 22500 \| 1.7241 \| 0.6777 \| 0.6521 \| \| 0.2117 \| 14.63 \| 23000 \| 1.6134 \| 0.6867 \| 0.6580 \| \| 0.2117 \| 14.95 \| 23500 \| 1.6723 \| 0.6911 \| 0.6618 \| \| 0.2056 \| 15.27 \| 24000 \| 1.6257 \| 0.6892 \| 0.6529 \| \| 0.2056 \| 15.59 \| 24500 \| 1.7072 \| 0.6796 \| 0.6531 \| \| 0.1859 \| 15.9 \| 25000 \| 1.7174 \| 0.6771 \| 0.6554 \| \| 0.1859 \| 16.22 \| 25500 \| 1.6951 \| 0.6879 \| 0.6555 \| \| 0.1725 \| 16.54 \| 26000 \| 1.7240 \| 0.6905 \| 0.6632 \| \| 0.1725 \| 16.86 \| 26500 \| 1.7126 \| 0.6879 \| 0.6608 \| \| 0.1817 \| 17.18 \| 27000 \| 1.7949 \| 0.6847 \| 0.6520 \| \| 0.1817 \| 17.49 \| 27500 \| 1.7694 \| 0.6911 \| 0.6622 \| \| 0.1617 \| 17.81 \| 28000 \| 1.7891 \| 0.6828 \| 0.6527 \| \| 0.1617 \| 18.13 \| 28500 \| 1.7860 \| 0.6790 \| 0.6526 \| \| 0.1628 \| 18.45 \| 29000 \| 1.8127 \| 0.6867 \| 0.6605 \| \| 0.1628 \| 18.77 \| 29500 \| 1.7317 \| 0.6892 \| 0.6610 \| \| 0.1736 \| 19.08 \| 30000 \| 1.7273 \| 0.6899 \| 0.6569 \| \| 0.1736 \| 19.4 \| 30500 \| 1.7853 \| 0.6854 \| 0.6584 \| \| 0.1441 \| 19.72 \| 31000 \| 1.7866 \| 0.6918 \| 0.6624 \| \| 0.1441 \| 20.04 \| 31500 \| 1.7842 \| 0.6873 \| 0.6580 \| \| 0.1392 \| 20.36 \| 32000 \| 1.8669 \| 0.6860 \| 0.6597 \| \| 0.1392 \| 20.67 \| 32500 \| 1.8392 \| 0.6899 \| 0.6639 \| \| 0.159 \| 20.99 \| 33000 \| 1.8412 \| 0.6784 \| 0.6552 \| \| 0.159 \| 21.31 \| 33500 \| 1.8673 \| 0.6854 \| 0.6584 \| \| 0.1275 \| 21.63 \| 34000 \| 1.8622 \| 0.6854 \| 0.6571 \| \| 0.1275 \| 21.95 \| 34500 \| 1.8622 \| 0.6796 \| 0.6583 \| \| 0.1216 \| 22.26 \| 35000 \| 1.9509 \| 0.6854 \| 0.6604 \| \| 0.1216 \| 22.58 \| 35500 \| 1.9425 \| 0.6809 \| 0.6550 \| \| 0.1351 \| 22.9 \| 36000 \| 1.9496 \| 0.6784 \| 0.6559 \| \| 0.1351 \| 23.22 \| 36500 \| 1.9685 \| 0.6847 \| 0.6582 \| \| 0.1221 \| 23.54 \| 37000 \| 1.9112 \| 0.6911 \| 0.6642 \| \| 0.1221 \| 23.85 \| 37500 \| 1.9341 \| 0.6726 \| 0.6526 \| \| 0.1155 \| 24.17 \| 38000 \| 1.9573 \| 0.6899 \| 0.6614 \| \| 0.1155 \| 24.49 \| 38500 \| 1.9853 \| 0.6873 \| 0.6580 \| \| 0.1139 \| 24.81 \| 39000 \| 1.9915 \| 0.6790 \| 0.6533 \| \| 0.1139 \| 25.13 \| 39500 \| 1.9997 \| 0.6796 \| 0.6539 \| \| 0.1166 \| 25.45 \| 40000 \| 1.9994 \| 0.6847 \| 0.6592 \| \| 0.1166 \| 25.76 \| 40500 \| 1.9848 \| 0.6745 \| 0.6513 \| \| 0.1128 \| 26.08 \| 41000 \| 2.0095 \| 0.6867 \| 0.6578 \| \| 0.1128 \| 26.4 \| 41500 \| 2.0585 \| 0.6822 \| 0.6547 \| \| 0.1048 \| 26.72 \| 42000 \| 2.0293 \| 0.6777 \| 0.6510 \| \| 0.1048 \| 27.04 \| 42500 \| 2.0797 \| 0.6758 \| 0.6512 \| \| 0.1 \| 27.35 \| 43000 \| 2.1162 \| 0.6822 \| 0.6544 \| \| 0.1 \| 27.67 \| 43500 \| 2.0569 \| 0.6835 \| 0.6538 \| \| 0.1106 \| 27.99 \| 44000 \| 2.0991 \| 0.6828 \| 0.6565 \| \| 0.1106 \| 28.31 \| 44500 \| 2.0976 \| 0.6841 \| 0.6563 \| \| 0.0886 \| 28.63 \| 45000 \| 2.1305 \| 0.6854 \| 0.6532 \| \| 0.0886 \| 28.94 \| 45500 \| 2.1015 \| 0.6867 \| 0.6564 \| \| 0.1027 \| 29.26 \| 46000 \| 2.1105 \| 0.6867 \| 0.6559 \| \| 0.1027 \| 29.58 \| 46500 \| 2.1396 \| 0.6765 \| 0.6499 \| \| 0.1057 \| 29.9 \| 47000 \| 2.1237 \| 0.6790 \| 0.6501 \| \| 0.1057 \| 30.22 \| 47500 \| 2.1849 \| 0.6790 \| 0.6518 \| \| 0.0876 \| 30.53 \| 48000 \| 2.1346 \| 0.6841 \| 0.6533 \| \| 0.0876 \| 30.85 \| 48500 \| 2.1441 \| 0.6828 \| 0.6540 \| \| 0.0856 \| 31.17 \| 49000 \| 2.1528 \| 0.6911 \| 0.6600 \| \| 0.0856 \| 31.49 \| 49500 \| 2.1725 \| 0.6847 \| 0.6509 \| \| 0.0869 \| 31.81 \| 50000 \| 2.2085 \| 0.6771 \| 0.6503 \| \| 0.0869 \| 32.12 \| 50500 \| 2.2606 \| 0.6688 \| 0.6434 \| \| 0.0848 \| 32.44 \| 51000 \| 2.2510 \| 0.6745 \| 0.6451 \| \| 0.0848 \| 32.76 \| 51500 \| 2.2528 \| 0.6739 \| 0.6496 \| \| 0.0816 \| 33.08 \| 52000 \| 2.2532 \| 0.6758 \| 0.6503 \| \| 0.0816 \| 33.4 \| 52500 \| 2.2356 \| 0.6803 \| 0.6500 \| \| 0.0793 \| 33.72 \| 53000 \| 2.2579 \| 0.6745 \| 0.6483 \| \| 0.0793 \| 34.03 \| 53500 \| 2.2126 \| 0.6816 \| 0.6520 \| \| 0.0767 \| 34.35 \| 54000 \| 2.2504 \| 0.6803 \| 0.6497 \| \| 0.0767 \| 34.67 \| 54500 \| 2.2601 \| 0.6803 \| 0.6524 \| \| 0.0844 \| 34.99 \| 55000 \| 2.2785 \| 0.6733 \| 0.6470 \| \| 0.0844 \| 35.31 \| 55500 \| 2.2756 \| 0.6784 \| 0.6520 \| \| 0.0755 \| 35.62 \| 56000 \| 2.2813 \| 0.6816 \| 0.6542 \| \| 0.0755 \| 35.94 \| 56500 \| 2.2752 \| 0.6803 \| 0.6518 \| \| 0.077 \| 36.26 \| 57000 \| 2.2815 \| 0.6796 \| 0.6518 \| \| 0.077 \| 36.58 \| 57500 \| 2.2861 \| 0.6803 \| 0.6514 \| \| 0.0752 \| 36.9 \| 58000 \| 2.2929 \| 0.6771 \| 0.6505 \| \| 0.0752 \| 37.21 \| 58500 \| 2.2859 \| 0.6816 \| 0.6537 \| \| 0.0698 \| 37.53 \| 59000 \| 2.3117 \| 0.6796 \| 0.6525 \| \| 0.0698 \| 37.85 \| 59500 \| 2.3038 \| 0.6816 \| 0.6511 \| \| 0.0613 \| 38.17 \| 60000 \| 2.3176 \| 0.6765 \| 0.6477 \| \| 0.0613 \| 38.49 \| 60500 \| 2.3131 \| 0.6796 \| 0.6493 \| \| 0.0706 \| 38.8 \| 61000 \| 2.3161 \| 0.6777 \| 0.6477 \| \| 0.0706 \| 39.12 \| 61500 \| 2.3127 \| 0.6784 \| 0.6484 \| \| 0.0678 \| 39.44 \| 62000 \| 2.3174 \| 0.6765 \| 0.6467 \| \| 0.0678 \| 39.76 \| 62500 \| 2.3223 \| 0.6790 \| 0.6487 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
thomasavare/distilbert-ft-test3	thomasavare	2023-05-23T14:41:21Z	66	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-07T10:08:10Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert-ft-test3 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. --> # distilbert-ft-test3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [thomasavare/waste-classification-v2](https://huggingface.co/datasets/thomasavare/waste-classification-v2). It is part of my master thesis at Politecnico di Torino in partenership with ReLearn. It achieves the following results on the test set: accuracy \| precision \| recall \| f1 \| ---------\|-----------\|--------\|--------\| 0.974 \| 0.9805 \| 0.9732 \| 0.9725 \| ## Model description DistilBERT finetuned for waste classification on 50 different classes as part of my master thesis at Politecnico di Torino. ## Intended uses & limitations Use for waste classification on 50 different waste classes (see [dataset](https://huggingface.co/datasets/thomasavare/waste-classification-v2)) ## Training and evaluation data [waste-classification-v2 dataset](https://huggingface.co/datasets/thomasavare/waste-classification-v2) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
AlexPerkin/distilbert-base-uncased-finetuned-squad	AlexPerkin	2023-05-23T14:35:13Z	115	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-05-23T12:23:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1517 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2334 \| 1.0 \| 5533 \| 1.1622 \| \| 0.9541 \| 2.0 \| 11066 \| 1.1228 \| \| 0.7519 \| 3.0 \| 16599 \| 1.1517 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
devraj4522/sentiment-analys	devraj4522	2023-05-23T14:32:10Z	0	0	null	[ "region:us" ]	null	2023-05-23T13:08:17Z	# Social Media Sentiment analysis @app.route('/predict-str', methods=['POST']) def predict_message(): data = request.json message = data.get('message', '') start_time = time.time() prediction = predictor.predict(message) response = { 'message': message, 'prediction': prediction, 'elapsed_time': time.time() - start_time } return jsonify(response) @app.route('/predict-list', methods=['POST']) def predict_list(): data = request.json messages = data.get('messages', []) start_time = time.time() predictions = predictor.predict(messages) response = { 'messages': messages, 'predictions': predictions, 'elapsed_time': time.time() - start_time } return jsonify(response) if __name__ == '__main__': app.run() ## API Endpoints - predict-str: Predicts the sentiment of a single message - parameters: message - json response: {message, prediction, elapsed_time} - predict-list: Predicts the sentiment of a list of messages - parameters: messages (list) - json response: {messages, predictions, elapsed_time}
HanNayeoniee/my_awesome_eli5_clm-model	HanNayeoniee	2023-05-23T14:31:42Z	211	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-23T13:45:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7255 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8888 \| 1.0 \| 565 \| 3.7433 \| \| 3.8141 \| 2.0 \| 1130 \| 3.7292 \| \| 3.7701 \| 3.0 \| 1695 \| 3.7255 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.7.1 - Datasets 2.12.0 - Tokenizers 0.13.3
aravind-selvam/x_rotated	aravind-selvam	2023-05-23T14:20:23Z	45	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-05-23T13:07:14Z	--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: x_rotated 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. --> # x_rotated This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1574 - Cer: 0.0377 ## Model description More information needed ## Intended uses & 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2067 \| 1.0 \| 500 \| 0.2103 \| 0.0538 \| \| 0.1043 \| 2.0 \| 1000 \| 0.1667 \| 0.0445 \| \| 0.0667 \| 3.0 \| 1500 \| 0.1571 \| 0.0388 \| \| 0.0489 \| 4.0 \| 2000 \| 0.1574 \| 0.0377 \| ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

EnterNameBros/DialoGPT-large-Senko-san-ver-2

EnterNameBros

2023-05-24T03:42:34Z

143

1

transformers

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

text-generation

2023-05-24T03:17:01Z

--- pipeline_tag: conversational language: - en metrics: - character - accuracy ---

AdonaiHS/unit_8_LunarLander-v2

AdonaiHS

2023-05-24T03:38:36Z

0

null

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

reinforcement-learning

2023-05-24T03:38:30Z

--- 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: -268.70 +/- 134.46 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': 'AdonaiHS/unit_8_LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```

YakovElm/Jira5Classic_with_cleaning

YakovElm

2023-05-24T03:37:03Z

61

0

transformers

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

text-classification

2023-05-24T03:36:17Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Jira5Classic_with_cleaning results: [] ---  # Jira5Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2646 - Train Accuracy: 0.8919 - Validation Loss: 1.1625 - Validation Accuracy: 0.5584 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.5257 | 0.7639 | 0.6646 | 0.5931 | 0 | | 0.4200 | 0.7901 | 1.2433 | 0.4890 | 1 | | 0.2646 | 0.8919 | 1.1625 | 0.5584 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

mio/chtholly

mio

2023-05-24T03:21:37Z

3

9

espnet

[ "espnet", "audio", "text-to-speech", "jp", "dataset:chtholly", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]

text-to-speech

2023-05-22T13:06:29Z

--- tags: - espnet - audio - text-to-speech language: jp datasets: - chtholly license: cc-by-4.0 widget: - text: "こんにちは、クトリ・ノタ・セニオリスです。終末なにしてますか？忙しいですか？救ってもらっていいですか？" --- ## ESPnet2 TTS model ![](./珂朵莉.jpg) ### `mio/chtholly` This model was trained by mio using chtholly recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 0232f540a98ece921477b961db8ae019211da9af pip install -e . cd egs2/chtholly/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model mio/chtholly ``` ## TTS config <details><summary>expand</summary> ``` config: conf/tuning/finetune_vits.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_chtholly_vits_finetune_from_jsut ngpu: 1 seed: 777 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 50705 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - total_count - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: -1 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 50 use_matplotlib: true use_tensorboard: false create_graph_in_tensorboard: false use_wandb: true wandb_project: chtholly wandb_id: null wandb_entity: null wandb_name: vits_finetune_chtholly_from_jsut wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: - downloads/f3698edf589206588f58f5ec837fa516/exp/tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause/train.total_count.ave_10best.pth:tts:tts ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 1000 batch_size: 20 valid_batch_size: null batch_bins: 5000000 valid_batch_bins: null train_shape_file: - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/text_shape.phn - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/speech_shape valid_shape_file: - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/text_shape.phn - exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/22k/raw/train/text - text - text - - dump/22k/raw/train/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/22k/raw/dev/text - text - text - - dump/22k/raw/dev/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adamw optim_conf: lr: 0.0001 betas: - 0.8 - 0.99 eps: 1.0e-09 weight_decay: 0.0 scheduler: exponentiallr scheduler_conf: gamma: 0.999875 optim2: adamw optim2_conf: lr: 0.0001 betas: - 0.8 - 0.99 eps: 1.0e-09 weight_decay: 0.0 scheduler2: exponentiallr scheduler2_conf: gamma: 0.999875 generator_first: false token_list: - <blank> - <unk> - '1' - '2' - '0' - '3' - '4' - '-1' - '5' - a - o - '-2' - i - '-3' - u - e - k - n - t - '6' - r - '-4' - s - N - m - pau - '7' - sh - d - g - w - '8' - U - '-5' - I - cl - h - y - b - '9' - j - ts - ch - '-6' - z - p - '-7' - f - ky - ry - '-8' - gy - '-9' - hy - ny - '-10' - by - my - '-11' - '-12' - '-13' - py - '-14' - '-15' - v - '10' - '-16' - '-17' - '11' - '-21' - '-20' - '12' - '-19' - '13' - '-18' - '14' - dy - '15' - ty - '-22' - '16' - '18' - '19' - '17' - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: jaconv g2p: pyopenjtalk_accent_with_pause feats_extract: linear_spectrogram feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null normalize: null normalize_conf: {} tts: vits tts_conf: generator_type: vits_generator generator_params: hidden_channels: 192 spks: -1 global_channels: -1 segment_size: 32 text_encoder_attention_heads: 2 text_encoder_ffn_expand: 4 text_encoder_blocks: 6 text_encoder_positionwise_layer_type: conv1d text_encoder_positionwise_conv_kernel_size: 3 text_encoder_positional_encoding_layer_type: rel_pos text_encoder_self_attention_layer_type: rel_selfattn text_encoder_activation_type: swish text_encoder_normalize_before: true text_encoder_dropout_rate: 0.1 text_encoder_positional_dropout_rate: 0.0 text_encoder_attention_dropout_rate: 0.1 use_macaron_style_in_text_encoder: true use_conformer_conv_in_text_encoder: false text_encoder_conformer_kernel_size: -1 decoder_kernel_size: 7 decoder_channels: 512 decoder_upsample_scales: - 8 - 8 - 2 - 2 decoder_upsample_kernel_sizes: - 16 - 16 - 4 - 4 decoder_resblock_kernel_sizes: - 3 - 7 - 11 decoder_resblock_dilations: - - 1 - 3 - 5 - - 1 - 3 - 5 - - 1 - 3 - 5 use_weight_norm_in_decoder: true posterior_encoder_kernel_size: 5 posterior_encoder_layers: 16 posterior_encoder_stacks: 1 posterior_encoder_base_dilation: 1 posterior_encoder_dropout_rate: 0.0 use_weight_norm_in_posterior_encoder: true flow_flows: 4 flow_kernel_size: 5 flow_base_dilation: 1 flow_layers: 4 flow_dropout_rate: 0.0 use_weight_norm_in_flow: true use_only_mean_in_flow: true stochastic_duration_predictor_kernel_size: 3 stochastic_duration_predictor_dropout_rate: 0.5 stochastic_duration_predictor_flows: 4 stochastic_duration_predictor_dds_conv_layers: 3 vocabs: 85 aux_channels: 513 discriminator_type: hifigan_multi_scale_multi_period_discriminator discriminator_params: scales: 1 scale_downsample_pooling: AvgPool1d scale_downsample_pooling_params: kernel_size: 4 stride: 2 padding: 2 scale_discriminator_params: in_channels: 1 out_channels: 1 kernel_sizes: - 15 - 41 - 5 - 3 channels: 128 max_downsample_channels: 1024 max_groups: 16 bias: true downsample_scales: - 2 - 2 - 4 - 4 - 1 nonlinear_activation: LeakyReLU nonlinear_activation_params: negative_slope: 0.1 use_weight_norm: true use_spectral_norm: false follow_official_norm: false periods: - 2 - 3 - 5 - 7 - 11 period_discriminator_params: in_channels: 1 out_channels: 1 kernel_sizes: - 5 - 3 channels: 32 downsample_scales: - 3 - 3 - 3 - 3 - 1 max_downsample_channels: 1024 bias: true nonlinear_activation: LeakyReLU nonlinear_activation_params: negative_slope: 0.1 use_weight_norm: true use_spectral_norm: false generator_adv_loss_params: average_by_discriminators: false loss_type: mse discriminator_adv_loss_params: average_by_discriminators: false loss_type: mse feat_match_loss_params: average_by_discriminators: false average_by_layers: false include_final_outputs: true mel_loss_params: fs: 22050 n_fft: 1024 hop_length: 256 win_length: null window: hann n_mels: 80 fmin: 0 fmax: null log_base: null lambda_adv: 1.0 lambda_mel: 45.0 lambda_feat_match: 2.0 lambda_dur: 1.0 lambda_kl: 1.0 sampling_rate: 22050 cache_generator_outputs: true pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: '202207' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

YakovElm/IntelDAOS20SetFitModel_clean_data

YakovElm

2023-05-24T03:20:40Z

3

0

sentence-transformers

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

text-classification

2023-05-24T03:20:00Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/IntelDAOS20SetFitModel_clean_data 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("YakovElm/IntelDAOS20SetFitModel_clean_data") # 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} } ```

sarahpuspdew/DeepRLCourse_Unit4-Reinforce-CartPole-v1

sarahpuspdew

2023-05-24T03:20:03Z

0

null

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

reinforcement-learning

2023-05-23T01:51:05Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: DeepRLCourse_Unit4-Reinforce-CartPole-v1 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

YakovElm/IntelDAOS15SetFitModel_clean_data

YakovElm

2023-05-24T03:06:54Z

3

0

sentence-transformers

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

text-classification

2023-05-24T03:06:18Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/IntelDAOS15SetFitModel_clean_data 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("YakovElm/IntelDAOS15SetFitModel_clean_data") # 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} } ```

arjunpatel/bloom-speed-check-small

arjunpatel

2023-05-24T02:54:32Z

0

transformers

[ "transformers", "text-generation", "en", "endpoints_compatible", "region:us" ]

text-generation

2023-05-15T03:27:00Z

--- language: - en pipeline_tag: text-generation library_name: transformers ---

Erdenebold/test-distilbert-base-multilingual-cased

Erdenebold

2023-05-24T02:53:05Z

103

0

transformers

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

token-classification

2023-05-24T02:11:40Z

--- language: - mn license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: test-distilbert-base-multilingual-cased results: [] ---  # test-distilbert-base-multilingual-cased This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1533 - Precision: 0.8783 - Recall: 0.9010 - F1: 0.8895 - Accuracy: 0.9721 ## Model description More information needed ## Intended uses & 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2124 | 1.0 | 477 | 0.1286 | 0.8065 | 0.8469 | 0.8262 | 0.9586 | | 0.103 | 2.0 | 954 | 0.1113 | 0.8374 | 0.8772 | 0.8568 | 0.9663 | | 0.0673 | 3.0 | 1431 | 0.1124 | 0.8480 | 0.8810 | 0.8641 | 0.9668 | | 0.0474 | 4.0 | 1908 | 0.1165 | 0.8658 | 0.8922 | 0.8788 | 0.9710 | | 0.0338 | 5.0 | 2385 | 0.1254 | 0.8664 | 0.8909 | 0.8785 | 0.9692 | | 0.0236 | 6.0 | 2862 | 0.1349 | 0.8686 | 0.8954 | 0.8818 | 0.9707 | | 0.018 | 7.0 | 3339 | 0.1428 | 0.8772 | 0.8991 | 0.8880 | 0.9715 | | 0.0133 | 8.0 | 3816 | 0.1505 | 0.8739 | 0.8961 | 0.8849 | 0.9712 | | 0.0106 | 9.0 | 4293 | 0.1529 | 0.8812 | 0.9012 | 0.8911 | 0.9720 | | 0.0082 | 10.0 | 4770 | 0.1533 | 0.8783 | 0.9010 | 0.8895 | 0.9721 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

bbbdbbb/sd-class-butterflies-32_test

bbbdbbb

2023-05-24T02:43:12Z

30

0

diffusers

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

unconditional-image-generation

2023-05-24T02:40:57Z

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

dclab/task-test

dclab

2023-05-24T02:37:37Z

0

null

[ "text-generation", "license:other", "region:us" ]

text-generation

2023-05-23T14:37:47Z

--- pipeline_tag: text-generation license: other ---

YakovElm/IntelDAOS20Classic_with_cleaning

YakovElm

2023-05-24T02:36:25Z

61

0

transformers

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

text-classification

2023-05-24T02:35:50Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: IntelDAOS20Classic_with_cleaning results: [] ---  # IntelDAOS20Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1286 - Train Accuracy: 0.9610 - Validation Loss: 0.3677 - Validation Accuracy: 0.9099 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2178 | 0.9600 | 0.3604 | 0.9099 | 0 | | 0.1502 | 0.9610 | 0.3197 | 0.9099 | 1 | | 0.1286 | 0.9610 | 0.3677 | 0.9099 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

cebernalc/ppo-LunarLander-v2

cebernalc

2023-05-24T02:28:13Z

5

0

stable-baselines3

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

reinforcement-learning

2023-05-24T02:27:53Z

--- 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: 248.03 +/- 23.78 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

YakovElm/IntelDAOS10Classic_with_cleaning

YakovElm

2023-05-24T02:08:53Z

61

0

transformers

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

text-classification

2023-05-24T02:08:18Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: IntelDAOS10Classic_with_cleaning results: [] ---  # IntelDAOS10Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2283 - Train Accuracy: 0.9210 - Validation Loss: 0.4310 - Validation Accuracy: 0.8739 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3141 | 0.9200 | 0.3738 | 0.8739 | 0 | | 0.2612 | 0.9200 | 0.4105 | 0.8739 | 1 | | 0.2283 | 0.9210 | 0.4310 | 0.8739 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

dev2bit/es2bash-mt5

dev2bit

2023-05-24T01:57:45Z

9

1

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "code", "bash", "es", "dataset:dev2bit/es2bash", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-23T23:23:17Z

--- license: apache-2.0 datasets: - dev2bit/es2bash language: - es pipeline_tag: text2text-generation tags: - code - bash widget: - text: Muestra el contenido de file.py que se encuentra en ~/project/ example_title: cat - text: Lista los 3 primeros archivos en /bin example_title: ls - text: Por favor, cambia al directorio /home/user/project/ example_title: cd - text: Lista todos los átomos del universo example_title: noCommand - text: ls -lh example_title: literal - text: file.txt example_title: simple --- # es2bash-mt5: Spanish to Bash Model <p align="center"> <img width="460" height="300" src="https://dev2bit.com/wp-content/themes/lovecraft_child/assets/icons/dev2bit_monitor2.svg"> </p> Developed by dev2bit, es2bash-mt5 is a language transformer model that is capable of predicting the optimal Bash command given a natural language request in Spanish. This model represents a major advancement in human-computer interaction, providing a natural language interface for Unix operating system commands. ## About the Model es2bash-mt5 is a fine-tuning model based on mt5-small. It has been trained on the dev2bit/es2bash dataset, which specializes in translating natural language in Spanish into Bash commands. This model is optimized for processing requests related to the commands: * `cat` * `ls` * `cd` ## Usage Below is an example of how to use es2bash-mt5 with the Hugging Face Transformers library: ```python from transformers import pipeline translator = pipeline('translation', model='dev2bit/es2bash-mt5') request = "listar los archivos en el directorio actual" translated = translator(request, max_length=512) print(translated[0]['translation_text']) ``` This will print the Bash command corresponding to the given Spanish request. ## Contributions We appreciate your contributions! You can help improve es2bash-mt5 in various ways, including: * Testing the model and reporting any issues or suggestions in the Issues section. * Improving the documentation. * Providing usage examples. --- # es2bash-mt5: Modelo de español a Bash Desarrollado por dev2bit, `es2bash-mt5` es un modelo transformador de lenguaje que tiene la capacidad de predecir el comando Bash óptimo dada una solicitud en lenguaje natural en español. Este modelo representa un gran avance en la interacción humano-computadora, proporcionando una interfaz de lenguaje natural para los comandos del sistema operativo Unix. ## Sobre el modelo `es2bash-mt5` es un modelo de ajuste fino basado en `mt5-small`. Ha sido entrenado en el conjunto de datos `dev2bit/es2bash`, especializado en la traducción de lenguaje natural en español a comandos Bash. Este modelo está optimizado para procesar solicitudes relacionadas con los comandos: * `cat` * `ls` * `cd` ## Uso A continuación, se muestra un ejemplo de cómo usar `es2bash-mt5` con la biblioteca Hugging Face Transformers: ```python from transformers import pipeline translator = pipeline('translation', model='dev2bit/es2bash-mt5') request = "listar los archivos en el directorio actual" translated = translator(request, max_length=512) print(translated[0]['translation_text']) ``` Esto imprimirá el comando Bash correspondiente a la solicitud dada en español. ## Contribuciones Agradecemos sus contribuciones! Puede ayudar a mejorar es2bash-mt5 de varias formas, incluyendo: * Probar el modelo y reportar cualquier problema o sugerencia en la sección de Issues. * Mejorando la documentación. * Proporcionando ejemplos de uso. --- This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the es2bash dataset. It achieves the following results on the evaluation set: - Loss: 0.0919 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 28 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 21.394 | 1.0 | 672 | 1.7470 | | 2.5294 | 2.0 | 1344 | 0.6350 | | 0.5873 | 3.0 | 2016 | 0.2996 | | 0.3802 | 4.0 | 2688 | 0.2142 | | 0.2951 | 5.0 | 3360 | 0.1806 | | 0.225 | 6.0 | 4032 | 0.1565 | | 0.2065 | 7.0 | 4704 | 0.1461 | | 0.1944 | 8.0 | 5376 | 0.1343 | | 0.174 | 9.0 | 6048 | 0.1281 | | 0.1647 | 10.0 | 6720 | 0.1207 | | 0.1566 | 11.0 | 7392 | 0.1140 | | 0.1498 | 12.0 | 8064 | 0.1106 | | 0.1382 | 13.0 | 8736 | 0.1076 | | 0.1393 | 14.0 | 9408 | 0.1042 | | 0.1351 | 15.0 | 10080 | 0.1019 | | 0.13 | 16.0 | 10752 | 0.0998 | | 0.1292 | 17.0 | 11424 | 0.0983 | | 0.1265 | 18.0 | 12096 | 0.0973 | | 0.1255 | 19.0 | 12768 | 0.0969 | | 0.1216 | 20.0 | 13440 | 0.0956 | | 0.1216 | 21.0 | 14112 | 0.0946 | | 0.123 | 22.0 | 14784 | 0.0938 | | 0.113 | 23.0 | 15456 | 0.0931 | | 0.1185 | 24.0 | 16128 | 0.0929 | | 0.1125 | 25.0 | 16800 | 0.0927 | | 0.1213 | 26.0 | 17472 | 0.0925 | | 0.1153 | 27.0 | 18144 | 0.0921 | | 0.1134 | 28.0 | 18816 | 0.0919 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

HippyHoppity/ppo-LunarLander-v2

HippyHoppity

2023-05-24T01:53:38Z

0

stable-baselines3

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

reinforcement-learning

2023-05-24T01:53:07Z

--- 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: 246.66 +/- 21.99 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 ... ```

ausboss/llama-30b-SuperHOT-4bit

ausboss

2023-05-24T01:35:50Z

7

5

transformers

[ "transformers", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-05-23T18:23:06Z

Merge of [SuperHOT-LoRA-prototype](https://huggingface.co/kaiokendev/SuperHOT-LoRA-prototype) and [llama-30b](https://huggingface.co/huggyllama/llama-30b) Llama30B-SuperHOT-4bit-128g.safetensors Quantization: ``` CUDA_VISIBLE_DEVICES=0 python llama.py ausboss/Llama30B-SuperHOT c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors Llama30B-SuperHOT-4bit-128g.safetensors ``` Llama30B-SuperHOT-4bit.safetensors Quantization: ``` CUDA_VISIBLE_DEVICES=0 python llama.py ausboss/Llama30B-SuperHOT c4 --wbits 4 --true-sequential --save_safetensors Llama30B-SuperHOT-4bit.safetensors ``` # From the SuperHot Page: ## Prototypes for SuperHOT No guarantees for output quality, simply uploading what I have so others can play around with it. Not even sure if the rank in cutoff-8192 is correct (think it should be 10 maybe.. can't remember) All prototypes are extremely early epochs (sub 0.5) ## Model/Training All trained with Flash Attention with conversation sequence lengths ranging from 8K to 16K tokens (No Alibi unless otherwise mentioned) All trained on LLaMa 13B 4-bit (no groupsize) (*Personally, I like the 8K cutoff version better, so I would say start with that one*) ## Data A combination of various datasets and cleaned logs converted into datasets including but not limited to: - Bluemoon Fanbased - Roleplaying Guild - Community-sourced outputs - [Dan's PocketDoc/RUCAIBox-Story-Generation-Alpaca](https://huggingface.co/datasets/PocketDoc/RUCAIBox-Story-Generation-Alpaca) - [IlyaGusev/gpt_roleplay_realm](https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm) - others ## Bias SuperHOT is a fiction-focused model. No alignment has been performed on the training data. Be mindful that this model may output harmful, violent, or otherwise problematic content ## Format Any format should work with such early checkpoints. However the training data is entirely in the following format: ``` --- mode: chat characters: <char1 name>: <descriptive tags for char1> <char2 name>: <descriptive tags for char2> summary: <summary of the story thus far or the purpose of the chat> (optional) <any other miscellaneous data> --- <chat history> ``` By "any other miscellaneous data", it means you should be able to put any additional metadata for the story or characters. I.e., ``` ... locations: location1: <tags for location1> inventory: item1: <tags for item1> ``` Again, format does not hold such a large weight on these early checkpoints. I have found success with the following setup for an RPG-like experience. Just play around with the format and see what works: ``` --- mode: rpg characters: You: a new player system: The system controls the RPG, handles character creation, world narration, and quest management. Also controls any NPCs and inventory tracking. Their first message provides a lengthy introduction for the player into the RPG world they are about to play in. After completing the character creation, the system will give a lengthy introduction into the world of ___. The first quest will follow right after rpg setting: The world of ___ rpg rules: Any rules typical of RPG games, including typical items, battle stats, etc --- ```

mauhcs/distilbert-base-uncased-finetuned-emotion

mauhcs

2023-05-24T01:35:18Z

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

2023-05-23T01:56:39Z

--- 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: 0.925 - name: F1 type: f1 value: 0.9249666408719047 ---  # 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: 0.2147 - Accuracy: 0.925 - F1: 0.9250 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8493 | 1.0 | 250 | 0.3120 | 0.9115 | 0.9084 | | 0.2513 | 2.0 | 500 | 0.2147 | 0.925 | 0.9250 | ### Framework versions - Transformers 4.29.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

edhrdh/cathx

edhrdh

2023-05-24T00:49:11Z

0

null

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

null

2023-05-24T00:47:02Z

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

Erdenebold/testing_mongolian-roberta_base

Erdenebold

2023-05-24T00:33:57Z

101

0

transformers

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

token-classification

2023-05-23T23:33:51Z

--- language: - mn tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: testing_mongolian-roberta_base results: [] ---  # testing_mongolian-roberta_base This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1244 - Precision: 0.9311 - Recall: 0.9399 - F1: 0.9355 - Accuracy: 0.9821 ## Model description More information needed ## Intended uses & 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1683 | 1.0 | 477 | 0.0805 | 0.8377 | 0.8921 | 0.8640 | 0.9730 | | 0.0545 | 2.0 | 954 | 0.0739 | 0.9205 | 0.9334 | 0.9269 | 0.9806 | | 0.0292 | 3.0 | 1431 | 0.0778 | 0.9270 | 0.9354 | 0.9312 | 0.9817 | | 0.0164 | 4.0 | 1908 | 0.0884 | 0.9290 | 0.9360 | 0.9325 | 0.9820 | | 0.008 | 5.0 | 2385 | 0.1025 | 0.9247 | 0.9365 | 0.9306 | 0.9811 | | 0.0057 | 6.0 | 2862 | 0.1093 | 0.9294 | 0.9369 | 0.9331 | 0.9815 | | 0.0037 | 7.0 | 3339 | 0.1173 | 0.9336 | 0.9412 | 0.9374 | 0.9822 | | 0.0026 | 8.0 | 3816 | 0.1217 | 0.9281 | 0.9374 | 0.9327 | 0.9817 | | 0.0016 | 9.0 | 4293 | 0.1225 | 0.9334 | 0.9399 | 0.9366 | 0.9821 | | 0.0012 | 10.0 | 4770 | 0.1244 | 0.9311 | 0.9399 | 0.9355 | 0.9821 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

sd-concepts-library/clothes

sd-concepts-library

2023-05-24T00:05:08Z

0

1

null

[ "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]

null

2023-05-24T00:05:05Z

--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### Clothes on Stable Diffusion This is the `<cat-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<cat-toy> 0](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_3.jpg) ![<cat-toy> 1](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_2.jpg) ![<cat-toy> 2](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_1.jpg) ![<cat-toy> 3](https://huggingface.co/sd-concepts-library/clothes/resolve/main/concept_images/OUINT4_4.jpg)

D-Roberts/tf-efficientformer-l3-300-dev1

D-Roberts

2023-05-23T23:43:54Z

62

0

transformers

[ "transformers", "tf", "efficientformer", "image-feature-extraction", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-feature-extraction

2023-05-17T13:48:42Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tf-efficientformer-l3-300-dev1 results: [] ---  # tf-efficientformer-l3-300-dev1 dev only ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - TensorFlow 2.11.1 - Datasets 2.12.0 - Tokenizers 0.13.3

D-Roberts/tf-efficientformer-l1-300-dev1

D-Roberts

2023-05-23T23:43:15Z

61

0

transformers

[ "transformers", "tf", "efficientformer", "image-feature-extraction", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-feature-extraction

2023-05-17T13:47:22Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tf-efficientformer-l1-300-dev1 results: [] ---  # tf-efficientformer-l1-300-dev1 dev-only ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - TensorFlow 2.11.1 - Datasets 2.12.0 - Tokenizers 0.13.3

wiorz/legal_bert_small_summarized_defined

wiorz

2023-05-23T23:21:46Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-22T23:57:17Z

--- license: cc-by-sa-4.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: legal_bert_small_summarized_defined results: [] ---  # legal_bert_small_summarized_defined This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8897 - Accuracy: 0.835 - Precision: 0.5 - Recall: 0.1515 - F1: 0.2326 - D-index: 1.5181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1600 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| | No log | 1.0 | 200 | 0.4467 | 0.835 | 0.0 | 0.0 | 0.0 | 1.4607 | | No log | 2.0 | 400 | 0.4909 | 0.835 | 0.0 | 0.0 | 0.0 | 1.4607 | | 0.5409 | 3.0 | 600 | 0.4941 | 0.83 | 0.4545 | 0.1515 | 0.2273 | 1.5113 | | 0.5409 | 4.0 | 800 | 0.5612 | 0.84 | 0.6 | 0.0909 | 0.1579 | 1.5021 | | 0.4849 | 5.0 | 1000 | 0.6301 | 0.84 | 0.5714 | 0.1212 | 0.2 | 1.5135 | | 0.4849 | 6.0 | 1200 | 0.8969 | 0.84 | 0.6 | 0.0909 | 0.1579 | 1.5021 | | 0.4849 | 7.0 | 1400 | 1.3171 | 0.82 | 0.3636 | 0.1212 | 0.1818 | 1.4865 | | 0.2104 | 8.0 | 1600 | 1.6653 | 0.775 | 0.2692 | 0.2121 | 0.2373 | 1.4593 | | 0.2104 | 9.0 | 1800 | 1.7041 | 0.795 | 0.3182 | 0.2121 | 0.2545 | 1.4866 | | 0.0314 | 10.0 | 2000 | 1.7495 | 0.815 | 0.3571 | 0.1515 | 0.2128 | 1.4911 | | 0.0314 | 11.0 | 2200 | 1.7627 | 0.815 | 0.3571 | 0.1515 | 0.2128 | 1.4911 | | 0.0314 | 12.0 | 2400 | 1.7892 | 0.825 | 0.375 | 0.0909 | 0.1463 | 1.4819 | | 0.0067 | 13.0 | 2600 | 1.8211 | 0.83 | 0.4444 | 0.1212 | 0.1905 | 1.5000 | | 0.0067 | 14.0 | 2800 | 1.8567 | 0.83 | 0.4444 | 0.1212 | 0.1905 | 1.5000 | | 0.0 | 15.0 | 3000 | 1.8817 | 0.83 | 0.4444 | 0.1212 | 0.1905 | 1.5000 | | 0.0 | 16.0 | 3200 | 1.8590 | 0.825 | 0.4167 | 0.1515 | 0.2222 | 1.5046 | | 0.0 | 17.0 | 3400 | 1.8619 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 | | 0.0014 | 18.0 | 3600 | 1.8744 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 | | 0.0014 | 19.0 | 3800 | 1.8849 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 | | 0.0 | 20.0 | 4000 | 1.8897 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

asenella/reproducing_mvae_mmnist_seed_0

asenella

2023-05-23T23:01:10Z

0

null

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

null

2023-05-23T23:01:00Z

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

kaminomisan/PhenixB7b

kaminomisan

2023-05-23T22:54:11Z

5

0

transformers

[ "transformers", "pytorch", "mpt", "text-generation", "Composer", "MosaicML", "llm-foundry", "StreamingDatasets", "custom_code", "dataset:mc4", "dataset:c4", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:bigcode/the-stack", "dataset:allenai/s2orc", "arxiv:2108.12409", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2010.04245", "arxiv:1909.08053", "arxiv:2302.06675", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-05-23T22:54:11Z

--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false duplicated_from: mosaicml/mpt-7b --- # MPT-7B MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B is * **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models). * **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B: The following models are finetuned on MPT-7B: * [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths. Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3). At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b). * License: Apache 2.0 * [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following. Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. * License: _CC-By-SA-3.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct) * [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets. * License: _CC-By-NC-SA-4.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-chat) ## Model Date May 5, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-1btms90mc-GipE2ufuPkKY0QBrmF3LSA)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`: ```python config = transformers.AutoConfig.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) config.attn_config['attn_impl'] = 'triton' model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', config=config, torch_dtype=torch.bfloat16, trust_remote_code=True ) model.to(device='cuda:0') ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python config = transformers.AutoConfig.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) config.update({"max_seq_len": 4096}) model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 6.7B | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 50432 | | sequence length | 2048 | ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix: | Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs | |-------------|----------------------------|------------|----------------------------|--------| | mC4 3.1.0 - English | 417.99 B | 0.33 | 330 B | 0.14 | | C4 - English - SemDedup 80% | 100.42 B | 0.299 | 299 B | 2.98 | | RedPajama - CommonCrawl | 878.45 B | 0.1 | 100 B | 0.11 | | The Stack - Selected Languages | 463.78 B | 0.1 | 100 B | 0.22 | | RedPajama - Wikipedia - En | 4.87 B | 0.04 | 40 B | 8.21 | | The Stack - Markdown | 107.07 B | 0.035 | 35 B | 0.33 | | S2ORC | 48.85 B | 0.033 | 33 B | 0.68 | | RedPajama - Books | 26.02 B | 0.03 | 30B | 1.15 | | RedPajama - arXiv | 28.10 B | 0.019 | 19 B | 0.68 | | RedPajama - StackExchange | 20.54 B | 0.014 | 14 B |0.68 | Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B (Base) is **not** intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B 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. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## 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. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, ly Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-03-28}, % change this date urldate = {2023-03-28} % change this date } ```

YakovElm/Hyperledger15Classic_with_cleaning

YakovElm

2023-05-23T22:44:21Z

61

0

transformers

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

text-classification

2023-05-23T22:43:21Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Hyperledger15Classic_with_cleaning results: [] ---  # Hyperledger15Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2347 - Train Accuracy: 0.9045 - Validation Loss: 0.3515 - Validation Accuracy: 0.8651 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3126 | 0.9031 | 0.3318 | 0.8807 | 0 | | 0.2844 | 0.9028 | 0.3275 | 0.8807 | 1 | | 0.2347 | 0.9045 | 0.3515 | 0.8651 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

dgalik/distilbert-finetuning-hate-speech-score-all-samples-3splits-seedv2-dropout005-epochs-10

dgalik

2023-05-23T22:31:48Z

31

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2023-05-23T21:28:22Z

--- tags: - generated_from_trainer model-index: - name: distilbert-finetuning-hate-speech-score-all-samples-3splits-seedv2-dropout005-epochs-10 results: [] ---  # distilbert-finetuning-hate-speech-score-all-samples-3splits-seedv2-dropout005-epochs-10 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3501 - Mse: 0.3501 - Rmse: 0.5917 - Mae: 0.2542 - R2: 0.9374 ## Model description More information needed ## Intended uses & 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: 10 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

YakovElm/Qt20Classic_with_cleaning

YakovElm

2023-05-23T22:11:51Z

61

0

transformers

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

text-classification

2023-05-23T22:10:56Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt20Classic_with_cleaning results: [] ---  # Qt20Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1619 - Train Accuracy: 0.9500 - Validation Loss: 0.1838 - Validation Accuracy: 0.9554 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2142 | 0.9462 | 0.1640 | 0.9586 | 0 | | 0.1934 | 0.9462 | 0.1576 | 0.9586 | 1 | | 0.1619 | 0.9500 | 0.1838 | 0.9554 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

SenY/shoujo

SenY

2023-05-23T21:51:54Z

0

58

null

[ "art", "region:us" ]

null

2023-03-12T02:08:45Z

--- tags: - art --- # shoujo.safetensors Concepts: Shoujo Manga ![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo.png) ```prompt example <lora:shoujo:1> 1girl ``` ```prompt example <lora:shoujo:1.4> 1girl ``` |<div style="width:16rem">name</div>|90s|00s|10s| |-|-|-|-| |shoujo_c - more juvenile girl|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_c90.png)|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_c00.png)|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_c10.png)| |shoujo_r - more romantic girl|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_r90.png)|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_r00.png)|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_r10.png)| |shoujo_n - more fantastic girl|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_n90.png)|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_n00.png)|![](https://huggingface.co/SenY/shoujo/resolve/main/shoujo_n10.png)|

JoBuettner/rl_course_vizdoom_health_gathering_supreme

JoBuettner

2023-05-23T21:44:18Z

0

sample-factory

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

reinforcement-learning

2023-05-23T21:20:00Z

--- 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: 12.73 +/- 5.75 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 JoBuettner/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.

damapika/roberta-base_mod_quoref

damapika

2023-05-23T21:20:20Z

113

0

transformers

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

question-answering

2023-05-23T19:19:39Z

--- license: mit tags: - generated_from_trainer datasets: - quoref model-index: - name: roberta-base_mod_quoref results: [] ---  # roberta-base_mod_quoref This model is a fine-tuned version of [damapika/roberta-base_mod_squad](https://huggingface.co/damapika/roberta-base_mod_squad) on the quoref dataset. It achieves the following results on the evaluation set: - Loss: 1.5566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.1263 | 1.0 | 1213 | 1.2665 | | 0.7404 | 2.0 | 2426 | 1.3567 | | 0.5172 | 3.0 | 3639 | 1.5566 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

manish1993hf/sms_class_test3

manish1993hf

2023-05-23T21:04:56Z

108

0

transformers

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

text-classification

2023-05-23T21:00:11Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: sms_class_test3 results: [] ---  # sms_class_test3 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: 0.0078 - Accuracy: 0.9990 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 272 | 0.0093 | 0.9979 | | 0.0472 | 2.0 | 544 | 0.0080 | 0.9990 | | 0.0472 | 3.0 | 816 | 0.0074 | 0.9990 | | 0.0005 | 4.0 | 1088 | 0.0078 | 0.9990 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

azetaaa/ppo-ML-Agents-Pyramids

azetaaa

2023-05-23T20:50:53Z

3

0

ml-agents

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

reinforcement-learning

2023-05-23T20:50:47Z

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

raulc0399/bloomz_3b_marketmail

raulc0399

2023-05-23T20:49:57Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2023-05-23T20:46:28Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard 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 ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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

DarioLopes

2023-05-23T20:49:23Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-05-21T22:21:16Z

--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.752028933869816 ---  # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8559 - Bleu: 52.7520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

zib16/alpaca-lora

zib16

2023-05-23T20:42:28Z

0

null

[ "region:us" ]

null

2023-05-23T15:50:44Z

An Alpaca LoRA model fine-tuned as described by Sam Witteveen in https://www.youtube.com/watch?v=LSoqyynKU9E. \ The base model is the llama-7b and the data from Stanford Alpaca have been used for the fine-tuning. \ These data can be found in https://github.com/tloen/alpaca-lora. Date: April 2023

emmanuel17/a2c-PandaReachDense-v2

emmanuel17

2023-05-23T20:34:27Z

2

0

stable-baselines3

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

reinforcement-learning

2023-05-23T20:31:39Z

--- 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: -1.99 +/- 0.54 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 ... ```

manish1993hf/sms_class_test1

manish1993hf

2023-05-23T20:30:38Z

103

0

transformers

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

text-classification

2023-05-23T20:25:47Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: sms_class_test1 results: [] ---  # sms_class_test1 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: - eval_loss: 0.0314 - eval_accuracy: 0.9964 - eval_runtime: 0.5458 - eval_samples_per_second: 511.138 - eval_steps_per_second: 16.488 - epoch: 5.0 - step: 830 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

YakovElm/Qt10Classic_with_cleaning

YakovElm

2023-05-23T20:30:37Z

61

0

transformers

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

text-classification

2023-05-23T20:29:00Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt10Classic_with_cleaning results: [] ---  # Qt10Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2156 - Train Accuracy: 0.9208 - Validation Loss: 0.2238 - Validation Accuracy: 0.9416 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2830 | 0.9159 | 0.2121 | 0.9416 | 0 | | 0.2515 | 0.9210 | 0.2015 | 0.9416 | 1 | | 0.2156 | 0.9208 | 0.2238 | 0.9416 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

whorseman/author

whorseman

2023-05-23T20:13:17Z

107

0

transformers

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

text-classification

2023-05-23T18:55:00Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: author results: [] ---  # author 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: 3.0925 - Accuracy: 0.1111 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 182 | 3.1296 | 0.0915 | | No log | 2.0 | 364 | 3.0925 | 0.1111 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Santonu001/results

Santonu001

2023-05-23T20:13:12Z

174

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "endpoints_compatible", "region:us" ]

question-answering

2023-05-23T19:26:03Z

--- tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [mrm8488/spanbert-finetuned-squadv2](https://huggingface.co/mrm8488/spanbert-finetuned-squadv2) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 0.01 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.01 | 82 | 3.4944 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

azetaaa/ppo-ML-Agents-SnowballTarget

azetaaa

2023-05-23T20:11:04Z

9

0

ml-agents

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

reinforcement-learning

2023-05-23T20:09:21Z

--- 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: azetaaa/ppo-ML-Agents-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Govindaramani/food_classifier

Govindaramani

2023-05-23T19:45:19Z

64

0

transformers

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

image-classification

2023-05-20T23:01:18Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Govindaramani/food_classifier results: [] ---  # Govindaramani/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: 0.3746 - Validation Loss: 0.3760 - Train Accuracy: 0.899 - 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 | |:----------:|:---------------:|:--------------:|:-----:| | 2.7487 | 1.6239 | 0.828 | 0 | | 1.2189 | 0.8179 | 0.889 | 1 | | 0.6889 | 0.5344 | 0.906 | 2 | | 0.4833 | 0.5014 | 0.878 | 3 | | 0.3746 | 0.3760 | 0.899 | 4 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

YakovElm/Apache10SetFitModel_clean_data

YakovElm

2023-05-23T19:38:28Z

3

0

sentence-transformers

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

text-classification

2023-05-23T19:37:54Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/Apache10SetFitModel_clean_data 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("YakovElm/Apache10SetFitModel_clean_data") # 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} } ```

kribby/cats-mobilenet-imagenet-v3

kribby

2023-05-23T19:35:12Z

4

0

tf-keras

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

image-classification

2023-05-23T19:33:34Z

--- pipeline_tag: image-classification ---

oransom48/pretrained_bert_fordiseaseclassif_1

oransom48

2023-05-23T19:34:04Z

62

0

transformers

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

text-classification

2023-05-23T19:12:22Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: pretrained_bert_fordiseaseclassif_1 results: [] ---  # pretrained_bert_fordiseaseclassif_1 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Tokenizers 0.13.3

henryscheible/t5-small_stereoset_finetuned_HBRPOI

henryscheible

2023-05-23T19:30:01Z

107

0

transformers

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

text2text-generation

2023-05-23T19:13:07Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - stereoset metrics: - accuracy model-index: - name: t5-small_stereoset_finetuned_HBRPOI results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: stereoset type: stereoset config: intersentence split: validation args: intersentence metrics: - name: Accuracy type: accuracy value: 0.6028257456828885 ---  # t5-small_stereoset_finetuned_HBRPOI This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the stereoset dataset. It achieves the following results on the evaluation set: - Loss: 0.4383 - Accuracy: 0.6028 - Tp: 0.4890 - Tn: 0.1138 - Fp: 0.3854 - Fn: 0.0118 ## Model description More information needed ## Intended uses & 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: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:| | 0.4447 | 0.43 | 20 | 0.3978 | 0.5008 | 0.5008 | 0.0 | 0.4992 | 0.0 | | 0.3776 | 0.85 | 40 | 0.3448 | 0.6232 | 0.5008 | 0.1224 | 0.3768 | 0.0 | | 0.3649 | 1.28 | 60 | 0.3269 | 0.5612 | 0.5 | 0.0612 | 0.4380 | 0.0008 | | 0.3275 | 1.7 | 80 | 0.3218 | 0.5330 | 0.4992 | 0.0338 | 0.4655 | 0.0016 | | 0.2969 | 2.13 | 100 | 0.3104 | 0.6256 | 0.4961 | 0.1295 | 0.3697 | 0.0047 | | 0.3283 | 2.55 | 120 | 0.3111 | 0.5730 | 0.4992 | 0.0738 | 0.4254 | 0.0016 | | 0.3046 | 2.98 | 140 | 0.3040 | 0.5416 | 0.4992 | 0.0424 | 0.4568 | 0.0016 | | 0.2603 | 3.4 | 160 | 0.3057 | 0.5447 | 0.4992 | 0.0455 | 0.4537 | 0.0016 | | 0.2828 | 3.83 | 180 | 0.3186 | 0.5479 | 0.4984 | 0.0495 | 0.4498 | 0.0024 | | 0.2326 | 4.26 | 200 | 0.3036 | 0.6193 | 0.4937 | 0.1256 | 0.3736 | 0.0071 | | 0.2289 | 4.68 | 220 | 0.3328 | 0.5479 | 0.4976 | 0.0502 | 0.4490 | 0.0031 | | 0.2234 | 5.11 | 240 | 0.3140 | 0.5777 | 0.4976 | 0.0801 | 0.4192 | 0.0031 | | 0.2225 | 5.53 | 260 | 0.3245 | 0.5691 | 0.4976 | 0.0714 | 0.4278 | 0.0031 | | 0.187 | 5.96 | 280 | 0.3300 | 0.5785 | 0.4961 | 0.0824 | 0.4168 | 0.0047 | | 0.179 | 6.38 | 300 | 0.3344 | 0.5848 | 0.4961 | 0.0887 | 0.4105 | 0.0047 | | 0.1523 | 6.81 | 320 | 0.3528 | 0.5895 | 0.4969 | 0.0926 | 0.4066 | 0.0039 | | 0.1499 | 7.23 | 340 | 0.3788 | 0.6232 | 0.4906 | 0.1327 | 0.3666 | 0.0102 | | 0.1292 | 7.66 | 360 | 0.3889 | 0.5942 | 0.4914 | 0.1028 | 0.3964 | 0.0094 | | 0.13 | 8.09 | 380 | 0.3959 | 0.5903 | 0.4937 | 0.0965 | 0.4027 | 0.0071 | | 0.1216 | 8.51 | 400 | 0.4169 | 0.5856 | 0.4922 | 0.0934 | 0.4058 | 0.0086 | | 0.1306 | 8.94 | 420 | 0.4227 | 0.6005 | 0.4898 | 0.1107 | 0.3885 | 0.0110 | | 0.0968 | 9.36 | 440 | 0.4334 | 0.5965 | 0.4914 | 0.1052 | 0.3940 | 0.0094 | | 0.1044 | 9.79 | 460 | 0.4383 | 0.6028 | 0.4890 | 0.1138 | 0.3854 | 0.0118 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2

YakovElm/Qt20Classic

YakovElm

2023-05-23T19:29:42Z

62

0

transformers

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

text-classification

2023-05-23T19:29:07Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt20Classic results: [] ---  # Qt20Classic This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1836 - Train Accuracy: 0.9462 - Validation Loss: 0.1813 - Validation Accuracy: 0.9594 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2163 | 0.9454 | 0.1596 | 0.9586 | 0 | | 0.2044 | 0.9462 | 0.1554 | 0.9586 | 1 | | 0.1836 | 0.9462 | 0.1813 | 0.9594 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

YakovElm/Apache5SetFitModel_clean_data

YakovElm

2023-05-23T19:25:18Z

3

0

sentence-transformers

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

text-classification

2023-05-23T19:24:40Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/Apache5SetFitModel_clean_data 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("YakovElm/Apache5SetFitModel_clean_data") # 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} } ```

henryscheible/t5-small_winobias_finetuned_HBRPOI

henryscheible

2023-05-23T19:24:19Z

108

0

transformers

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

text2text-generation

2023-05-23T19:20:12Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-small_winobias_finetuned_HBRPOI results: [] ---  # t5-small_winobias_finetuned_HBRPOI This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3333 - Accuracy: 0.5 - Tp: 0.5 - Tn: 0.0 - Fp: 0.5 - Fn: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - 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: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:------:|:------:|:---:| | 0.437 | 0.8 | 20 | 0.3545 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | | 0.3996 | 1.6 | 40 | 0.3565 | 0.5025 | 0.5 | 0.0025 | 0.4975 | 0.0 | | 0.3844 | 2.4 | 60 | 0.3498 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | | 0.3728 | 3.2 | 80 | 0.3529 | 0.5013 | 0.5 | 0.0013 | 0.4987 | 0.0 | | 0.3732 | 4.0 | 100 | 0.3482 | 0.5006 | 0.5 | 0.0006 | 0.4994 | 0.0 | | 0.3798 | 4.8 | 120 | 0.3484 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | | 0.3607 | 5.6 | 140 | 0.3475 | 0.5006 | 0.5 | 0.0006 | 0.4994 | 0.0 | | 0.3688 | 6.4 | 160 | 0.3456 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | | 0.3597 | 7.2 | 180 | 0.3445 | 0.5006 | 0.5 | 0.0006 | 0.4994 | 0.0 | | 0.3658 | 8.0 | 200 | 0.3402 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | | 0.3629 | 8.8 | 220 | 0.3362 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | | 0.3393 | 9.6 | 240 | 0.3333 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2

YakovElm/Apache20Classic_with_cleaning

YakovElm

2023-05-23T19:11:07Z

61

0

transformers

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

text-classification

2023-05-23T19:10:31Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache20Classic_with_cleaning results: [] ---  # Apache20Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1300 - Train Accuracy: 0.9622 - Validation Loss: 0.4258 - Validation Accuracy: 0.9055 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.1764 | 0.9548 | 0.3066 | 0.9055 | 0 | | 0.1518 | 0.9624 | 0.3933 | 0.9055 | 1 | | 0.1300 | 0.9622 | 0.4258 | 0.9055 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

xzuyn/Pythia-Deduped-70M-GGML

xzuyn

2023-05-23T18:57:00Z

0

1

null

[ "gpt_neox", "region:us" ]

null

2023-05-23T06:14:02Z

--- tags: - gpt_neox --- # For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp) Original Model: https://huggingface.co/EleutherAI/pythia-70m-deduped

Heilgeirr/heilgeirr2

Heilgeirr

2023-05-23T18:47:54Z

31

0

diffusers

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

text-to-image

2023-05-23T18:44:31Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### heilgeirr2 Dreambooth model trained by Heilgeirr 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:

clulab/roberta-base-motivational-interviewing

clulab

2023-05-23T18:47:06Z

105

1

transformers

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

text-classification

2023-05-23T17:14:58Z

--- language: - en license: apache-2.0 library_name: transformers pipeline_tag: text-classification tags: - motivational-interviewing metrics: - f1 widget: - text: >- I'm planning on having tuna, ground tuna, chopped celery, and chopped black pepper, and half a apple. example_title: change_talk_goal_talk_and_opportunities --- # Model Card for roberta-base-motivational-interviewing ⚠ WARNING: This is a preliminary model that is still actively under development. ⚠ This is a [roBERTa-base](https://huggingface.co/roberta-base) model fine-tuned on a small dataset of conversations between health coaches and cancer survivors. # How to Get Started with the Model You can use this model directly with a pipeline for text classification: ```python >>> import transformers >>> model_name = "clulab/roberta-base-motivational-interviewing" >>> classifier = transformers.TextClassificationPipeline( ... tokenizer=transformers.AutoTokenizer.from_pretrained(model_name), ... model=transformers.AutoModelForSequenceClassification.from_pretrained(model_name)) >>> classifier("I'm planning on having tuna, ground tuna, chopped celery, and chopped black pepper, and half a apple.") [{'label': 'change_talk_goal_talk_and_opportunities', 'score': 0.9995419979095459}] ``` # Model Details - **Developed by:** [Steven Bethard](https://bethard.github.io/) - **Parent Model:** [roBERTa-base](https://huggingface.co/roberta-base) - **GitHub Repo:** [LIvES repo](https://github.com/clulab/lives) # Uses The model is intended to be used for text classification, taking as input conversational utterances and predicting as output different categories of motivational interviewing behaviors. It is intended for use by health coaches to assist when reviewing their past calls with participants. Its predictions should not be used without manual review. # Training Details The model was trained on data annotated under the grant [Using Natural Language Processing to Determine Predictors of Healthy Diet and Physical Activity Behavior Change in Ovarian Cancer Survivors (NIH NCI R21CA256680)](https://reporter.nih.gov/project-details/10510666). A [roberta-base](https://huggingface.co/roberta-base) model was fine-tuned on that dataset, with texts tokenized using the standard [roberta-base](https://huggingface.co/roberta-base) tokenizer. # Evaluation On the test partition of the R21CA256680 dataset, the model achieves 0.60 precision and 0.46 recall.

YakovElm/Qt15Classic

YakovElm

2023-05-23T18:39:04Z

62

0

transformers

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

text-classification

2023-05-23T18:38:29Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Qt15Classic results: [] ---  # Qt15Classic This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2046 - Train Accuracy: 0.9367 - Validation Loss: 0.2038 - Validation Accuracy: 0.9505 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2400 | 0.9354 | 0.1896 | 0.9505 | 0 | | 0.2235 | 0.9367 | 0.1826 | 0.9505 | 1 | | 0.2046 | 0.9367 | 0.2038 | 0.9505 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

VinsmokeMir/Method2_E13B_SC_BS4_LR3e5

VinsmokeMir

2023-05-23T18:18:39Z

3

0

transformers

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

text-classification

2023-05-23T16:27:50Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Method2_E13B_SC_BS4_LR3e5 results: [] ---  # Method2_E13B_SC_BS4_LR3e5 This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5641 - Accuracy: 0.6803 - F1 Macro: 0.6446 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | |:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:| | No log | 0.16 | 500 | 1.0767 | 0.3976 | 0.1896 | | 1.075 | 0.32 | 1000 | 1.0769 | 0.3976 | 0.1896 | | 1.075 | 0.48 | 1500 | 1.0183 | 0.5539 | 0.4151 | | 1.0246 | 0.64 | 2000 | 0.8956 | 0.5916 | 0.4745 | | 1.0246 | 0.8 | 2500 | 0.8743 | 0.6082 | 0.5120 | | 0.8948 | 0.95 | 3000 | 0.8365 | 0.6216 | 0.5546 | | 0.8948 | 1.11 | 3500 | 0.8635 | 0.6311 | 0.5752 | | 0.8069 | 1.27 | 4000 | 0.9060 | 0.6158 | 0.5398 | | 0.8069 | 1.43 | 4500 | 0.8231 | 0.6388 | 0.5924 | | 0.7969 | 1.59 | 5000 | 0.8368 | 0.6331 | 0.5935 | | 0.7969 | 1.75 | 5500 | 0.8262 | 0.6477 | 0.5981 | | 0.7804 | 1.91 | 6000 | 0.8299 | 0.6579 | 0.6208 | | 0.7804 | 2.07 | 6500 | 0.8197 | 0.6579 | 0.6364 | | 0.715 | 2.23 | 7000 | 0.8498 | 0.6624 | 0.5955 | | 0.715 | 2.39 | 7500 | 0.8357 | 0.6669 | 0.6218 | | 0.6953 | 2.54 | 8000 | 0.8438 | 0.6560 | 0.6269 | | 0.6953 | 2.7 | 8500 | 0.8528 | 0.6669 | 0.6022 | | 0.7074 | 2.86 | 9000 | 0.8009 | 0.6745 | 0.6457 | | 0.7074 | 3.02 | 9500 | 0.8222 | 0.6720 | 0.6402 | | 0.6598 | 3.18 | 10000 | 0.9347 | 0.6650 | 0.6062 | | 0.6598 | 3.34 | 10500 | 0.9053 | 0.6803 | 0.6510 | | 0.6634 | 3.5 | 11000 | 0.8902 | 0.6720 | 0.6434 | | 0.6634 | 3.66 | 11500 | 0.9370 | 0.6733 | 0.6415 | | 0.6182 | 3.82 | 12000 | 0.8914 | 0.6745 | 0.6519 | | 0.6182 | 3.98 | 12500 | 0.8938 | 0.6752 | 0.6389 | | 0.6043 | 4.13 | 13000 | 1.0143 | 0.6745 | 0.6413 | | 0.6043 | 4.29 | 13500 | 1.0768 | 0.6765 | 0.6543 | | 0.587 | 4.45 | 14000 | 1.1154 | 0.6790 | 0.6421 | | 0.587 | 4.61 | 14500 | 1.1295 | 0.6828 | 0.6525 | | 0.6345 | 4.77 | 15000 | 1.1210 | 0.6822 | 0.6390 | | 0.6345 | 4.93 | 15500 | 1.0062 | 0.6726 | 0.6380 | | 0.6 | 5.09 | 16000 | 1.1504 | 0.6739 | 0.6369 | | 0.6 | 5.25 | 16500 | 1.3298 | 0.6733 | 0.6280 | | 0.5667 | 5.41 | 17000 | 1.2751 | 0.6662 | 0.6308 | | 0.5667 | 5.57 | 17500 | 1.4070 | 0.6567 | 0.6069 | | 0.614 | 5.73 | 18000 | 1.2956 | 0.6694 | 0.6284 | | 0.614 | 5.88 | 18500 | 1.2795 | 0.6822 | 0.6382 | | 0.5651 | 6.04 | 19000 | 1.3021 | 0.6739 | 0.6478 | | 0.5651 | 6.2 | 19500 | 1.4076 | 0.6682 | 0.6333 | | 0.5347 | 6.36 | 20000 | 1.3917 | 0.6733 | 0.6344 | | 0.5347 | 6.52 | 20500 | 1.4203 | 0.6790 | 0.6285 | | 0.5278 | 6.68 | 21000 | 1.3340 | 0.6860 | 0.6628 | | 0.5278 | 6.84 | 21500 | 1.3521 | 0.6873 | 0.6489 | | 0.5796 | 7.0 | 22000 | 1.2835 | 0.6847 | 0.6567 | | 0.5796 | 7.16 | 22500 | 1.4437 | 0.6879 | 0.6563 | | 0.4627 | 7.32 | 23000 | 1.5052 | 0.6835 | 0.6435 | | 0.4627 | 7.47 | 23500 | 1.4991 | 0.6707 | 0.6434 | | 0.518 | 7.63 | 24000 | 1.5436 | 0.6656 | 0.6403 | | 0.518 | 7.79 | 24500 | 1.5247 | 0.6784 | 0.6433 | | 0.5373 | 7.95 | 25000 | 1.4743 | 0.6835 | 0.6537 | | 0.5373 | 8.11 | 25500 | 1.5379 | 0.6777 | 0.6385 | | 0.4539 | 8.27 | 26000 | 1.5548 | 0.6739 | 0.6393 | | 0.4539 | 8.43 | 26500 | 1.6174 | 0.6669 | 0.6378 | | 0.4519 | 8.59 | 27000 | 1.5949 | 0.6816 | 0.6504 | | 0.4519 | 8.75 | 27500 | 1.5558 | 0.6816 | 0.6357 | | 0.4813 | 8.91 | 28000 | 1.5826 | 0.6739 | 0.6553 | | 0.4813 | 9.06 | 28500 | 1.5929 | 0.6867 | 0.6540 | | 0.4121 | 9.22 | 29000 | 1.6260 | 0.6886 | 0.6545 | | 0.4121 | 9.38 | 29500 | 1.5950 | 0.6841 | 0.6500 | | 0.4451 | 9.54 | 30000 | 1.6146 | 0.6854 | 0.6481 | | 0.4451 | 9.7 | 30500 | 1.6587 | 0.6796 | 0.6493 | | 0.4039 | 9.86 | 31000 | 1.6173 | 0.6758 | 0.6400 | | 0.4039 | 10.02 | 31500 | 1.5952 | 0.6803 | 0.6517 | | 0.3921 | 10.18 | 32000 | 1.7298 | 0.6694 | 0.6413 | | 0.3921 | 10.34 | 32500 | 1.7106 | 0.6796 | 0.6467 | | 0.3799 | 10.5 | 33000 | 1.6695 | 0.6867 | 0.6505 | | 0.3799 | 10.66 | 33500 | 1.6907 | 0.6803 | 0.6550 | | 0.4003 | 10.81 | 34000 | 1.6811 | 0.6809 | 0.6413 | | 0.4003 | 10.97 | 34500 | 1.6644 | 0.6771 | 0.6352 | | 0.3812 | 11.13 | 35000 | 1.7371 | 0.6822 | 0.6386 | | 0.3812 | 11.29 | 35500 | 1.7405 | 0.6841 | 0.6516 | | 0.3399 | 11.45 | 36000 | 1.6981 | 0.6822 | 0.6503 | | 0.3399 | 11.61 | 36500 | 1.6536 | 0.6847 | 0.6483 | | 0.3653 | 11.77 | 37000 | 1.7461 | 0.6790 | 0.6475 | | 0.3653 | 11.93 | 37500 | 1.7247 | 0.6790 | 0.6485 | | 0.338 | 12.09 | 38000 | 1.7433 | 0.6905 | 0.6532 | | 0.338 | 12.25 | 38500 | 1.7331 | 0.6765 | 0.6558 | | 0.3302 | 12.4 | 39000 | 1.7603 | 0.6796 | 0.6456 | | 0.3302 | 12.56 | 39500 | 1.7784 | 0.6726 | 0.6505 | | 0.3195 | 12.72 | 40000 | 1.8032 | 0.6784 | 0.6469 | | 0.3195 | 12.88 | 40500 | 1.7869 | 0.6822 | 0.6553 | | 0.3508 | 13.04 | 41000 | 1.7761 | 0.6752 | 0.6506 | | 0.3508 | 13.2 | 41500 | 1.7806 | 0.6847 | 0.6454 | | 0.2915 | 13.36 | 42000 | 1.8542 | 0.6707 | 0.6528 | | 0.2915 | 13.52 | 42500 | 1.8365 | 0.6796 | 0.6520 | | 0.3023 | 13.68 | 43000 | 1.8563 | 0.6828 | 0.6524 | | 0.3023 | 13.84 | 43500 | 1.7947 | 0.6752 | 0.6495 | | 0.3213 | 13.99 | 44000 | 1.8130 | 0.6796 | 0.6546 | | 0.3213 | 14.15 | 44500 | 1.8288 | 0.6841 | 0.6502 | | 0.2644 | 14.31 | 45000 | 1.8140 | 0.6726 | 0.6453 | | 0.2644 | 14.47 | 45500 | 1.8711 | 0.6809 | 0.6552 | | 0.2739 | 14.63 | 46000 | 1.8439 | 0.6873 | 0.6534 | | 0.2739 | 14.79 | 46500 | 1.8302 | 0.6828 | 0.6460 | | 0.3012 | 14.95 | 47000 | 1.8708 | 0.6752 | 0.6454 | | 0.3012 | 15.11 | 47500 | 1.8498 | 0.6822 | 0.6487 | | 0.2805 | 15.27 | 48000 | 1.8908 | 0.6803 | 0.6453 | | 0.2805 | 15.43 | 48500 | 1.9480 | 0.6790 | 0.6406 | | 0.2895 | 15.59 | 49000 | 1.8994 | 0.6675 | 0.6392 | | 0.2895 | 15.74 | 49500 | 1.9135 | 0.6790 | 0.6461 | | 0.2444 | 15.9 | 50000 | 1.9387 | 0.6841 | 0.6480 | | 0.2444 | 16.06 | 50500 | 1.9175 | 0.6745 | 0.6463 | | 0.2569 | 16.22 | 51000 | 1.9332 | 0.6745 | 0.6472 | | 0.2569 | 16.38 | 51500 | 1.9400 | 0.6771 | 0.6445 | | 0.2251 | 16.54 | 52000 | 1.9596 | 0.6745 | 0.6441 | | 0.2251 | 16.7 | 52500 | 1.9959 | 0.6835 | 0.6464 | | 0.2686 | 16.86 | 53000 | 1.9879 | 0.6777 | 0.6456 | | 0.2686 | 17.02 | 53500 | 1.9882 | 0.6828 | 0.6471 | | 0.2168 | 17.18 | 54000 | 2.0254 | 0.6886 | 0.6520 | | 0.2168 | 17.33 | 54500 | 2.0432 | 0.6777 | 0.6442 | | 0.2735 | 17.49 | 55000 | 1.9843 | 0.6745 | 0.6443 | | 0.2735 | 17.65 | 55500 | 2.0330 | 0.6828 | 0.6451 | | 0.2159 | 17.81 | 56000 | 2.0698 | 0.6682 | 0.6423 | | 0.2159 | 17.97 | 56500 | 1.9797 | 0.6771 | 0.6426 | | 0.245 | 18.13 | 57000 | 2.0008 | 0.6726 | 0.6383 | | 0.245 | 18.29 | 57500 | 2.0425 | 0.6816 | 0.6473 | | 0.2036 | 18.45 | 58000 | 2.0482 | 0.6720 | 0.6356 | | 0.2036 | 18.61 | 58500 | 2.0950 | 0.6675 | 0.6384 | | 0.2336 | 18.77 | 59000 | 2.0167 | 0.6854 | 0.6458 | | 0.2336 | 18.92 | 59500 | 1.9984 | 0.6809 | 0.6406 | | 0.2332 | 19.08 | 60000 | 2.0552 | 0.6739 | 0.6441 | | 0.2332 | 19.24 | 60500 | 2.0450 | 0.6784 | 0.6459 | | 0.1984 | 19.4 | 61000 | 2.0599 | 0.6752 | 0.6434 | | 0.1984 | 19.56 | 61500 | 2.0704 | 0.6784 | 0.6417 | | 0.1945 | 19.72 | 62000 | 2.0755 | 0.6758 | 0.6445 | | 0.1945 | 19.88 | 62500 | 2.0660 | 0.6809 | 0.6428 | | 0.2143 | 20.04 | 63000 | 2.0670 | 0.6739 | 0.6448 | | 0.2143 | 20.2 | 63500 | 2.0581 | 0.6873 | 0.6509 | | 0.1878 | 20.36 | 64000 | 2.1272 | 0.6752 | 0.6452 | | 0.1878 | 20.52 | 64500 | 2.1002 | 0.6803 | 0.6511 | | 0.2144 | 20.67 | 65000 | 2.1383 | 0.6713 | 0.6438 | | 0.2144 | 20.83 | 65500 | 2.1070 | 0.6809 | 0.6419 | | 0.2121 | 20.99 | 66000 | 2.1273 | 0.6726 | 0.6412 | | 0.2121 | 21.15 | 66500 | 2.1605 | 0.6707 | 0.6395 | | 0.1835 | 21.31 | 67000 | 2.2891 | 0.6567 | 0.6331 | | 0.1835 | 21.47 | 67500 | 2.2472 | 0.6765 | 0.6402 | | 0.1991 | 21.63 | 68000 | 2.2238 | 0.6752 | 0.6412 | | 0.1991 | 21.79 | 68500 | 2.1965 | 0.6669 | 0.6372 | | 0.2018 | 21.95 | 69000 | 2.2050 | 0.6669 | 0.6395 | | 0.2018 | 22.11 | 69500 | 2.1795 | 0.6803 | 0.6467 | | 0.151 | 22.26 | 70000 | 2.2214 | 0.6777 | 0.6430 | | 0.151 | 22.42 | 70500 | 2.1754 | 0.6867 | 0.6513 | | 0.2078 | 22.58 | 71000 | 2.1959 | 0.6822 | 0.6488 | | 0.2078 | 22.74 | 71500 | 2.1933 | 0.6860 | 0.6481 | | 0.2004 | 22.9 | 72000 | 2.2001 | 0.6816 | 0.6500 | | 0.2004 | 23.06 | 72500 | 2.2159 | 0.6784 | 0.6490 | | 0.1773 | 23.22 | 73000 | 2.2603 | 0.6790 | 0.6462 | | 0.1773 | 23.38 | 73500 | 2.2331 | 0.6777 | 0.6470 | | 0.174 | 23.54 | 74000 | 2.2554 | 0.6765 | 0.6471 | | 0.174 | 23.7 | 74500 | 2.2000 | 0.6854 | 0.6517 | | 0.2071 | 23.85 | 75000 | 2.1896 | 0.6790 | 0.6500 | | 0.2071 | 24.01 | 75500 | 2.2270 | 0.6828 | 0.6479 | | 0.1419 | 24.17 | 76000 | 2.2776 | 0.6765 | 0.6426 | | 0.1419 | 24.33 | 76500 | 2.2895 | 0.6809 | 0.6437 | | 0.1564 | 24.49 | 77000 | 2.2746 | 0.6828 | 0.6515 | | 0.1564 | 24.65 | 77500 | 2.3156 | 0.6765 | 0.6356 | | 0.1802 | 24.81 | 78000 | 2.2891 | 0.6726 | 0.6426 | | 0.1802 | 24.97 | 78500 | 2.2610 | 0.6835 | 0.6502 | | 0.1795 | 25.13 | 79000 | 2.2856 | 0.6777 | 0.6478 | | 0.1795 | 25.29 | 79500 | 2.2410 | 0.6828 | 0.6478 | | 0.1753 | 25.45 | 80000 | 2.2738 | 0.6701 | 0.6451 | | 0.1753 | 25.6 | 80500 | 2.2679 | 0.6847 | 0.6440 | | 0.1517 | 25.76 | 81000 | 2.2667 | 0.6796 | 0.6525 | | 0.1517 | 25.92 | 81500 | 2.3471 | 0.6682 | 0.6455 | | 0.1593 | 26.08 | 82000 | 2.2945 | 0.6816 | 0.6504 | | 0.1593 | 26.24 | 82500 | 2.3202 | 0.6841 | 0.6456 | | 0.1332 | 26.4 | 83000 | 2.3667 | 0.6733 | 0.6405 | | 0.1332 | 26.56 | 83500 | 2.3295 | 0.6771 | 0.6377 | | 0.1765 | 26.72 | 84000 | 2.3680 | 0.6720 | 0.6394 | | 0.1765 | 26.88 | 84500 | 2.3246 | 0.6828 | 0.6456 | | 0.1578 | 27.04 | 85000 | 2.3192 | 0.6745 | 0.6453 | | 0.1578 | 27.19 | 85500 | 2.3216 | 0.6822 | 0.6471 | | 0.1355 | 27.35 | 86000 | 2.3730 | 0.6796 | 0.6490 | | 0.1355 | 27.51 | 86500 | 2.3650 | 0.6758 | 0.6415 | | 0.1308 | 27.67 | 87000 | 2.4015 | 0.6784 | 0.6471 | | 0.1308 | 27.83 | 87500 | 2.3700 | 0.6809 | 0.6403 | | 0.1446 | 27.99 | 88000 | 2.3748 | 0.6796 | 0.6483 | | 0.1446 | 28.15 | 88500 | 2.3575 | 0.6809 | 0.6497 | | 0.1135 | 28.31 | 89000 | 2.3663 | 0.6835 | 0.6438 | | 0.1135 | 28.47 | 89500 | 2.3817 | 0.6809 | 0.6490 | | 0.1354 | 28.63 | 90000 | 2.4026 | 0.6739 | 0.6436 | | 0.1354 | 28.78 | 90500 | 2.3825 | 0.6745 | 0.6392 | | 0.1661 | 28.94 | 91000 | 2.3461 | 0.6771 | 0.6482 | | 0.1661 | 29.1 | 91500 | 2.3496 | 0.6771 | 0.6422 | | 0.1188 | 29.26 | 92000 | 2.3568 | 0.6790 | 0.6488 | | 0.1188 | 29.42 | 92500 | 2.3496 | 0.6828 | 0.6430 | | 0.1433 | 29.58 | 93000 | 2.4252 | 0.6707 | 0.6378 | | 0.1433 | 29.74 | 93500 | 2.3805 | 0.6847 | 0.6459 | | 0.1328 | 29.9 | 94000 | 2.3918 | 0.6860 | 0.6495 | | 0.1328 | 30.06 | 94500 | 2.4026 | 0.6828 | 0.6495 | | 0.1317 | 30.22 | 95000 | 2.4319 | 0.6841 | 0.6483 | | 0.1317 | 30.38 | 95500 | 2.4375 | 0.6828 | 0.6492 | | 0.122 | 30.53 | 96000 | 2.4401 | 0.6822 | 0.6475 | | 0.122 | 30.69 | 96500 | 2.4397 | 0.6860 | 0.6473 | | 0.1266 | 30.85 | 97000 | 2.4572 | 0.6847 | 0.6504 | | 0.1266 | 31.01 | 97500 | 2.4506 | 0.6847 | 0.6513 | | 0.1437 | 31.17 | 98000 | 2.4251 | 0.6822 | 0.6496 | | 0.1437 | 31.33 | 98500 | 2.4420 | 0.6822 | 0.6521 | | 0.1205 | 31.49 | 99000 | 2.4446 | 0.6816 | 0.6464 | | 0.1205 | 31.65 | 99500 | 2.4408 | 0.6790 | 0.6450 | | 0.1188 | 31.81 | 100000 | 2.4522 | 0.6765 | 0.6487 | | 0.1188 | 31.97 | 100500 | 2.4313 | 0.6828 | 0.6495 | | 0.1326 | 32.12 | 101000 | 2.4577 | 0.6784 | 0.6466 | | 0.1326 | 32.28 | 101500 | 2.4524 | 0.6822 | 0.6479 | | 0.1103 | 32.44 | 102000 | 2.4665 | 0.6765 | 0.6426 | | 0.1103 | 32.6 | 102500 | 2.4642 | 0.6777 | 0.6431 | | 0.118 | 32.76 | 103000 | 2.4628 | 0.6771 | 0.6451 | | 0.118 | 32.92 | 103500 | 2.4671 | 0.6835 | 0.6474 | | 0.1214 | 33.08 | 104000 | 2.4613 | 0.6771 | 0.6503 | | 0.1214 | 33.24 | 104500 | 2.4833 | 0.6771 | 0.6475 | | 0.0965 | 33.4 | 105000 | 2.4888 | 0.6803 | 0.6450 | | 0.0965 | 33.56 | 105500 | 2.4910 | 0.6816 | 0.6476 | | 0.1207 | 33.72 | 106000 | 2.4806 | 0.6860 | 0.6482 | | 0.1207 | 33.87 | 106500 | 2.4741 | 0.6771 | 0.6445 | | 0.1277 | 34.03 | 107000 | 2.5050 | 0.6790 | 0.6409 | | 0.1277 | 34.19 | 107500 | 2.4809 | 0.6777 | 0.6402 | | 0.1164 | 34.35 | 108000 | 2.5006 | 0.6777 | 0.6428 | | 0.1164 | 34.51 | 108500 | 2.4889 | 0.6822 | 0.6474 | | 0.1103 | 34.67 | 109000 | 2.4852 | 0.6822 | 0.6457 | | 0.1103 | 34.83 | 109500 | 2.4923 | 0.6771 | 0.6418 | | 0.1013 | 34.99 | 110000 | 2.4662 | 0.6784 | 0.6437 | | 0.1013 | 35.15 | 110500 | 2.4755 | 0.6822 | 0.6483 | | 0.0922 | 35.31 | 111000 | 2.4908 | 0.6816 | 0.6465 | | 0.0922 | 35.46 | 111500 | 2.4922 | 0.6809 | 0.6502 | | 0.0856 | 35.62 | 112000 | 2.5096 | 0.6828 | 0.6422 | | 0.0856 | 35.78 | 112500 | 2.5035 | 0.6828 | 0.6463 | | 0.1005 | 35.94 | 113000 | 2.5231 | 0.6828 | 0.6452 | | 0.1005 | 36.1 | 113500 | 2.5196 | 0.6796 | 0.6469 | | 0.0884 | 36.26 | 114000 | 2.5187 | 0.6796 | 0.6444 | | 0.0884 | 36.42 | 114500 | 2.5180 | 0.6790 | 0.6454 | | 0.0891 | 36.58 | 115000 | 2.5407 | 0.6771 | 0.6442 | | 0.0891 | 36.74 | 115500 | 2.5349 | 0.6765 | 0.6417 | | 0.1082 | 36.9 | 116000 | 2.5451 | 0.6777 | 0.6427 | | 0.1082 | 37.05 | 116500 | 2.5349 | 0.6803 | 0.6469 | | 0.1072 | 37.21 | 117000 | 2.5507 | 0.6816 | 0.6457 | | 0.1072 | 37.37 | 117500 | 2.5485 | 0.6790 | 0.6459 | | 0.0882 | 37.53 | 118000 | 2.5477 | 0.6809 | 0.6448 | | 0.0882 | 37.69 | 118500 | 2.5620 | 0.6790 | 0.6401 | | 0.0852 | 37.85 | 119000 | 2.5597 | 0.6790 | 0.6447 | | 0.0852 | 38.01 | 119500 | 2.5545 | 0.6796 | 0.6436 | | 0.1029 | 38.17 | 120000 | 2.5519 | 0.6796 | 0.6436 | | 0.1029 | 38.33 | 120500 | 2.5539 | 0.6822 | 0.6463 | | 0.0903 | 38.49 | 121000 | 2.5590 | 0.6822 | 0.6490 | | 0.0903 | 38.65 | 121500 | 2.5658 | 0.6803 | 0.6457 | | 0.092 | 38.8 | 122000 | 2.5590 | 0.6803 | 0.6433 | | 0.092 | 38.96 | 122500 | 2.5620 | 0.6803 | 0.6449 | | 0.094 | 39.12 | 123000 | 2.5634 | 0.6796 | 0.6436 | | 0.094 | 39.28 | 123500 | 2.5677 | 0.6790 | 0.6435 | | 0.0801 | 39.44 | 124000 | 2.5662 | 0.6803 | 0.6445 | | 0.0801 | 39.6 | 124500 | 2.5648 | 0.6796 | 0.6440 | | 0.103 | 39.76 | 125000 | 2.5641 | 0.6809 | 0.6451 | | 0.103 | 39.92 | 125500 | 2.5641 | 0.6803 | 0.6446 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

bahmanreza/keras-dummy-sequential-demo

bahmanreza

2023-05-23T18:17:12Z

0

keras

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

null

2023-05-23T18:17:09Z

YakovElm/Apache15Classic_with_cleaning

YakovElm

2023-05-23T18:05:44Z

61

0

transformers

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

text-classification

2023-05-23T18:04:54Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache15Classic_with_cleaning results: [] ---  # Apache15Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1583 - Train Accuracy: 0.9535 - Validation Loss: 0.3355 - Validation Accuracy: 0.8924 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.1921 | 0.9542 | 0.3429 | 0.8924 | 0 | | 0.1792 | 0.9542 | 0.3336 | 0.8924 | 1 | | 0.1583 | 0.9535 | 0.3355 | 0.8924 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

k22/1

k22

2023-05-23T18:04:36Z

0

null

[ "region:us" ]

null

2023-05-23T18:03:06Z

How to choose an edible tomato?

omegaodin/gpt2

omegaodin

2023-05-23T18:01:11Z

0

adapter-transformers

[ "adapter-transformers", "code", "es", "dataset:fka/awesome-chatgpt-prompts", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:OpenAssistant/oasst1", "dataset:bigcode/the-stack", "dataset:bigcode/the-stack-dedup", "dataset:databricks/databricks-dolly-15k", "license:apache-2.0", "region:us" ]

null

2023-05-23T17:57:04Z

--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts - togethercomputer/RedPajama-Data-1T - OpenAssistant/oasst1 - bigcode/the-stack - bigcode/the-stack-dedup - databricks/databricks-dolly-15k language: - es metrics: - accuracy - code_eval - character library_name: adapter-transformers tags: - code ---

ArinaRomashova/summarisation-pegasus-pubmed

ArinaRomashova

2023-05-23T17:39:39Z

101

0

transformers

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

text2text-generation

2023-05-23T14:36:58Z

## Validation Metrics - Loss: 2.243 - Rouge1: 37.779 - Rouge2: 14.441 - RougeL: 24.108 - RougeLsum: 33.163 - Gen Len: 125.550 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ArinaRomashova/autotrain-summarisation-pegasus-pubmed-61004134636 ```

jjhonny/qtable-taxi-v3

jjhonny

2023-05-23T17:38:13Z

0

null

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

reinforcement-learning

2023-05-23T17:38:11Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: qtable-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.74 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="jjhonny/qtable-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"]) ```

takuma104/lora_unetonly_rank128

takuma104

2023-05-23T17:35:35Z

3

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

2023-05-23T17:27:33Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - takuma104/lora_unetonly_rank128 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.

takuma104/lora_unetonly_rank4

takuma104

2023-05-23T17:25:06Z

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

2023-05-23T17:19:48Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - takuma104/lora_unetonly_rank4 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.

kudailang/kosasih

kudailang

2023-05-23T17:22:19Z

0

null

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

null

2023-05-23T17:20:36Z

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

nemuwn/bert-base-multilingual-cased-mongolian-ner

nemuwn

2023-05-23T17:04:43Z

117

0

transformers

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

token-classification

2023-05-23T15:39:30Z

--- language: - mn license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-multilingual-cased-mongolian-ner results: [] ---  # bert-base-multilingual-cased-mongolian-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1423 - Precision: 0.9057 - Recall: 0.9188 - F1: 0.9122 - Accuracy: 0.9753 ## Model description More information needed ## Intended uses & 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1726 | 1.0 | 477 | 0.1052 | 0.8531 | 0.8851 | 0.8688 | 0.9664 | | 0.0827 | 2.0 | 954 | 0.0975 | 0.8722 | 0.8987 | 0.8852 | 0.9699 | | 0.0571 | 3.0 | 1431 | 0.0926 | 0.8847 | 0.9054 | 0.8950 | 0.9719 | | 0.0376 | 4.0 | 1908 | 0.1052 | 0.8980 | 0.9119 | 0.9049 | 0.9727 | | 0.0271 | 5.0 | 2385 | 0.1137 | 0.9021 | 0.9158 | 0.9089 | 0.9746 | | 0.0182 | 6.0 | 2862 | 0.1304 | 0.8839 | 0.9106 | 0.8970 | 0.9712 | | 0.0145 | 7.0 | 3339 | 0.1274 | 0.9042 | 0.9187 | 0.9114 | 0.9748 | | 0.0097 | 8.0 | 3816 | 0.1375 | 0.9009 | 0.9169 | 0.9088 | 0.9739 | | 0.0063 | 9.0 | 4293 | 0.1421 | 0.9017 | 0.9171 | 0.9093 | 0.9748 | | 0.0049 | 10.0 | 4770 | 0.1423 | 0.9057 | 0.9188 | 0.9122 | 0.9753 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

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

Hinova

2023-05-23T17:00:49Z

0

null

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

reinforcement-learning

2023-05-23T17:00:44Z

--- 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="Hinova/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"]) ```

YakovElm/Apache10Classic_with_cleaning

YakovElm

2023-05-23T17:00:42Z

61

0

transformers

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

text-classification

2023-05-23T17:00:05Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache10Classic_with_cleaning results: [] ---  # Apache10Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1824 - Train Accuracy: 0.9385 - Validation Loss: 0.5452 - Validation Accuracy: 0.8644 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2431 | 0.9340 | 0.4461 | 0.8644 | 0 | | 0.2183 | 0.9383 | 0.4053 | 0.8644 | 1 | | 0.1824 | 0.9385 | 0.5452 | 0.8644 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

Artur01/mit-b0-finetuned-sidewalks

Artur01

2023-05-23T16:53:01Z

31

0

transformers

[ "transformers", "tf", "segformer", "generated_from_keras_callback", "license:other", "endpoints_compatible", "region:us" ]

null

2023-05-22T20:23:44Z

--- license: other tags: - generated_from_keras_callback model-index: - name: Artur01/mit-b0-finetuned-sidewalks results: [] ---  # Artur01/mit-b0-finetuned-sidewalks This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3943 - Validation Loss: 0.8293 - Validation Mean Iou: 0.2097 - Validation Mean Accuracy: 0.2617 - Validation Overall Accuracy: 0.7688 - Validation Accuracy Unlabeled: 0.0 - Validation Accuracy Flat-road: 0.6688 - Validation Accuracy Flat-sidewalk: 0.9258 - Validation Accuracy Flat-crosswalk: 0.4607 - Validation Accuracy Flat-cyclinglane: 0.7716 - Validation Accuracy Flat-parkingdriveway: 0.2329 - Validation Accuracy Flat-railtrack: nan - Validation Accuracy Flat-curb: 0.1001 - Validation Accuracy Human-person: 0.0011 - Validation Accuracy Human-rider: 0.0 - Validation Accuracy Vehicle-car: 0.8806 - Validation Accuracy Vehicle-truck: 0.0 - Validation Accuracy Vehicle-bus: 0.0 - Validation Accuracy Vehicle-tramtrain: nan - Validation Accuracy Vehicle-motorcycle: 0.0 - Validation Accuracy Vehicle-bicycle: 0.2446 - Validation Accuracy Vehicle-caravan: 0.0 - Validation Accuracy Vehicle-cartrailer: nan - Validation Accuracy Construction-building: 0.8260 - Validation Accuracy Construction-door: 0.0 - Validation Accuracy Construction-wall: 0.2769 - Validation Accuracy Construction-fenceguardrail: 0.0618 - Validation Accuracy Construction-bridge: 0.0 - Validation Accuracy Construction-tunnel: nan - Validation Accuracy Construction-stairs: 0.0 - Validation Accuracy Object-pole: 0.0127 - Validation Accuracy Object-trafficsign: 0.0 - Validation Accuracy Object-trafficlight: 0.0 - Validation Accuracy Nature-vegetation: 0.9125 - Validation Accuracy Nature-terrain: 0.7600 - Validation Accuracy Sky: 0.9223 - Validation Accuracy Void-ground: 0.0 - Validation Accuracy Void-dynamic: 0.0122 - Validation Accuracy Void-static: 0.0431 - Validation Accuracy Void-unclear: 0.0 - Validation Iou Unlabeled: 0.0 - Validation Iou Flat-road: 0.5491 - Validation Iou Flat-sidewalk: 0.7881 - Validation Iou Flat-crosswalk: 0.4034 - Validation Iou Flat-cyclinglane: 0.4981 - Validation Iou Flat-parkingdriveway: 0.1731 - Validation Iou Flat-railtrack: nan - Validation Iou Flat-curb: 0.0869 - Validation Iou Human-person: 0.0011 - Validation Iou Human-rider: 0.0 - Validation Iou Vehicle-car: 0.6311 - Validation Iou Vehicle-truck: 0.0 - Validation Iou Vehicle-bus: 0.0 - Validation Iou Vehicle-tramtrain: nan - Validation Iou Vehicle-motorcycle: 0.0 - Validation Iou Vehicle-bicycle: 0.2027 - Validation Iou Vehicle-caravan: 0.0 - Validation Iou Vehicle-cartrailer: nan - Validation Iou Construction-building: 0.5921 - Validation Iou Construction-door: 0.0 - Validation Iou Construction-wall: 0.2356 - Validation Iou Construction-fenceguardrail: 0.0570 - Validation Iou Construction-bridge: 0.0 - Validation Iou Construction-tunnel: nan - Validation Iou Construction-stairs: 0.0 - Validation Iou Object-pole: 0.0125 - Validation Iou Object-trafficsign: 0.0 - Validation Iou Object-trafficlight: 0.0 - Validation Iou Nature-vegetation: 0.7724 - Validation Iou Nature-terrain: 0.5932 - Validation Iou Sky: 0.8602 - Validation Iou Void-ground: 0.0 - Validation Iou Void-dynamic: 0.0120 - Validation Iou Void-static: 0.0323 - Validation Iou Void-unclear: 0.0 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Unlabeled | Validation Accuracy Flat-road | Validation Accuracy Flat-sidewalk | Validation Accuracy Flat-crosswalk | Validation Accuracy Flat-cyclinglane | Validation Accuracy Flat-parkingdriveway | Validation Accuracy Flat-railtrack | Validation Accuracy Flat-curb | Validation Accuracy Human-person | Validation Accuracy Human-rider | Validation Accuracy Vehicle-car | Validation Accuracy Vehicle-truck | Validation Accuracy Vehicle-bus | Validation Accuracy Vehicle-tramtrain | Validation Accuracy Vehicle-motorcycle | Validation Accuracy Vehicle-bicycle | Validation Accuracy Vehicle-caravan | Validation Accuracy Vehicle-cartrailer | Validation Accuracy Construction-building | Validation Accuracy Construction-door | Validation Accuracy Construction-wall | Validation Accuracy Construction-fenceguardrail | Validation Accuracy Construction-bridge | Validation Accuracy Construction-tunnel | Validation Accuracy Construction-stairs | Validation Accuracy Object-pole | Validation Accuracy Object-trafficsign | Validation Accuracy Object-trafficlight | Validation Accuracy Nature-vegetation | Validation Accuracy Nature-terrain | Validation Accuracy Sky | Validation Accuracy Void-ground | Validation Accuracy Void-dynamic | Validation Accuracy Void-static | Validation Accuracy Void-unclear | Validation Iou Unlabeled | Validation Iou Flat-road | Validation Iou Flat-sidewalk | Validation Iou Flat-crosswalk | Validation Iou Flat-cyclinglane | Validation Iou Flat-parkingdriveway | Validation Iou Flat-railtrack | Validation Iou Flat-curb | Validation Iou Human-person | Validation Iou Human-rider | Validation Iou Vehicle-car | Validation Iou Vehicle-truck | Validation Iou Vehicle-bus | Validation Iou Vehicle-tramtrain | Validation Iou Vehicle-motorcycle | Validation Iou Vehicle-bicycle | Validation Iou Vehicle-caravan | Validation Iou Vehicle-cartrailer | Validation Iou Construction-building | Validation Iou Construction-door | Validation Iou Construction-wall | Validation Iou Construction-fenceguardrail | Validation Iou Construction-bridge | Validation Iou Construction-tunnel | Validation Iou Construction-stairs | Validation Iou Object-pole | Validation Iou Object-trafficsign | Validation Iou Object-trafficlight | Validation Iou Nature-vegetation | Validation Iou Nature-terrain | Validation Iou Sky | Validation Iou Void-ground | Validation Iou Void-dynamic | Validation Iou Void-static | Validation Iou Void-unclear | Epoch | |:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:-----------------------------:|:-----------------------------:|:---------------------------------:|:----------------------------------:|:------------------------------------:|:----------------------------------------:|:----------------------------------:|:-----------------------------:|:--------------------------------:|:-------------------------------:|:-------------------------------:|:---------------------------------:|:-------------------------------:|:-------------------------------------:|:--------------------------------------:|:-----------------------------------:|:-----------------------------------:|:--------------------------------------:|:-----------------------------------------:|:-------------------------------------:|:-------------------------------------:|:-----------------------------------------------:|:---------------------------------------:|:---------------------------------------:|:---------------------------------------:|:-------------------------------:|:--------------------------------------:|:---------------------------------------:|:-------------------------------------:|:----------------------------------:|:-----------------------:|:-------------------------------:|:--------------------------------:|:-------------------------------:|:--------------------------------:|:------------------------:|:------------------------:|:----------------------------:|:-----------------------------:|:-------------------------------:|:-----------------------------------:|:-----------------------------:|:------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:----------------------------:|:--------------------------:|:--------------------------------:|:---------------------------------:|:------------------------------:|:------------------------------:|:---------------------------------:|:------------------------------------:|:--------------------------------:|:--------------------------------:|:------------------------------------------:|:----------------------------------:|:----------------------------------:|:----------------------------------:|:--------------------------:|:---------------------------------:|:----------------------------------:|:--------------------------------:|:-----------------------------:|:------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:-----:| | 1.3943 | 0.8293 | 0.2097 | 0.2617 | 0.7688 | 0.0 | 0.6688 | 0.9258 | 0.4607 | 0.7716 | 0.2329 | nan | 0.1001 | 0.0011 | 0.0 | 0.8806 | 0.0 | 0.0 | nan | 0.0 | 0.2446 | 0.0 | nan | 0.8260 | 0.0 | 0.2769 | 0.0618 | 0.0 | nan | 0.0 | 0.0127 | 0.0 | 0.0 | 0.9125 | 0.7600 | 0.9223 | 0.0 | 0.0122 | 0.0431 | 0.0 | 0.0 | 0.5491 | 0.7881 | 0.4034 | 0.4981 | 0.1731 | nan | 0.0869 | 0.0011 | 0.0 | 0.6311 | 0.0 | 0.0 | nan | 0.0 | 0.2027 | 0.0 | nan | 0.5921 | 0.0 | 0.2356 | 0.0570 | 0.0 | nan | 0.0 | 0.0125 | 0.0 | 0.0 | 0.7724 | 0.5932 | 0.8602 | 0.0 | 0.0120 | 0.0323 | 0.0 | 0 | ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

Nebyx/ppo_LunarLander-v2

Nebyx

2023-05-23T16:46:42Z

0

stable-baselines3

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

reinforcement-learning

2023-05-23T16:46:16Z

--- 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: 245.42 +/- 18.58 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 ... ```

perfectino/rinahhh

perfectino

2023-05-23T16:36:40Z

0

null

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

null

2023-05-23T16:35:13Z

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

Iulian277/ro-bart-512

Iulian277

2023-05-23T16:15:03Z

309

0

transformers

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

summarization

2023-04-13T10:00:11Z

--- tags: - summarization - bart language: - ro inference: false --- This is a pretrained-from-scratch **BART base** model (**140M** parameters). Training was performed on a clean **50GB Romanian** text corpus for 3M steps with these [scripts](https://github.com/cosmoquester/transformers-bart-pretrain). The model was trained with a maximum sequence length of **512**. **!! IMPORTANT !!** This model was pretrained on the text corruption task, meaning this model is **not usable** in any downstream task **without finetuning** first!

mnavas/roberta-finetuned-qa-reqzarv0

mnavas

2023-05-23T16:03:55Z

102

0

transformers

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

question-answering

2023-05-23T15:37:43Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: roberta-finetuned-qa-reqzarv0 results: [] ---  # roberta-finetuned-qa-reqzarv0 This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

rakgesh/image-classifier-one-piece-v03_01

rakgesh

2023-05-23T16:02:00Z

3

0

tf-keras

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

image-classification

2023-05-23T15:59:09Z

--- pipeline_tag: image-classification ---

YakovElm/Apache5Classic_with_cleaning

YakovElm

2023-05-23T15:56:00Z

62

0

transformers

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

text-classification

2023-05-23T15:55:23Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Apache5Classic_with_cleaning results: [] ---  # Apache5Classic_with_cleaning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2193 - Train Accuracy: 0.9235 - Validation Loss: 0.6107 - Validation Accuracy: 0.8194 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3142 | 0.9001 | 0.4816 | 0.8233 | 0 | | 0.2820 | 0.9099 | 0.4622 | 0.8233 | 1 | | 0.2193 | 0.9235 | 0.6107 | 0.8194 | 2 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

khosro111/test1

khosro111

2023-05-23T15:53:10Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2023-05-23T15:52:34Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard 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 ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

xzuyn/LLaMa-1-MedicWizard-7B-GGML

xzuyn

2023-05-23T15:53:05Z

0

3

null

[ "llama", "alpaca", "region:us" ]

null

2023-05-21T16:18:21Z

--- tags: - llama - alpaca --- # For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp) Original Model: https://huggingface.co/xzuyn/MedicWizard-7B

davanstrien/imdb_bertopic_ten_topics

davanstrien

2023-05-23T15:52:38Z

5

0

bertopic

[ "bertopic", "region:us" ]

null

2023-05-23T15:52:31Z

--- tags: - bertopic library_name: bertopic --- # imdb_bertopic_ten_topics This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("davanstrien/imdb_bertopic_ten_topics") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 10 * Number of training documents: 103062 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | -1 | film - movie - movies - character - characters | 44 | -1_film_movie_movies_character | | 0 | film - movie - films - movies - too | 25087 | 0_film_movie_films_movies | | 1 | episodes - shows - watching - tv - episode | 20955 | 1_episodes_shows_watching_tv | | 2 | films - film - movies - godzilla - movie | 2037 | 2_films_film_movies_godzilla | | 3 | cinderella - disney - cartoon - animation - cartoons | 895 | 3_cinderella_disney_cartoon_animation | | 4 | gameplay - games - game - adventure - starcraft | 465 | 4_gameplay_games_game_adventure | | 5 | holmes - sherlock - watson - doyle - conan | 228 | 5_holmes_sherlock_watson_doyle | | 6 | panther - film - films - clouseau - movies | 184 | 6_panther_film_films_clouseau | | 7 | metallica - metal - genres - genre - headbanger | 55 | 7_metallica_metal_genres_genre | | 8 | che - ernesto - castro - biopic - film | 50 | 8_che_ernesto_castro_biopic | </details> ## Training hyperparameters * calculate_probabilities: False * language: None * low_memory: False * min_topic_size: 40 * n_gram_range: (1, 1) * nr_topics: 10 * seed_topic_list: None * top_n_words: 10 * verbose: False ## Framework versions * Numpy: 1.22.4 * HDBSCAN: 0.8.29 * UMAP: 0.5.3 * Pandas: 1.5.3 * Scikit-Learn: 1.2.2 * Sentence-transformers: 2.2.2 * Transformers: 4.29.2 * Numba: 0.56.4 * Plotly: 5.13.1 * Python: 3.10.11

dawoz/a2c-PandaReachDense-v2

dawoz

2023-05-23T15:49:16Z

1

0

stable-baselines3

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

reinforcement-learning

2023-05-23T15:46:27Z

--- 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: -0.36 +/- 0.17 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 ... ```

VinsmokeMir/Fine_Tuning_SC_Method_2_Epoch_13B

VinsmokeMir

2023-05-23T15:44:19Z

184

0

transformers

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

text-classification

2023-05-23T15:28:29Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Fine_Tuning_SC_Method_2_Epoch_13B results: [] ---  # Fine_Tuning_SC_Method_2_Epoch_13B This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4244 - Accuracy: 0.6873 - F1 Macro: 0.6544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | No log | 1.27 | 500 | 1.0673 | 0.3976 | 0.1896 | | 1.0138 | 2.54 | 1000 | 0.8217 | 0.6331 | 0.5569 | | 1.0138 | 3.82 | 1500 | 0.7889 | 0.6662 | 0.6049 | | 0.7305 | 5.09 | 2000 | 0.7821 | 0.6765 | 0.6382 | | 0.7305 | 6.36 | 2500 | 0.7867 | 0.6918 | 0.6457 | | 0.5856 | 7.63 | 3000 | 0.8236 | 0.6892 | 0.6623 | | 0.5856 | 8.91 | 3500 | 0.8490 | 0.6835 | 0.6551 | | 0.4723 | 10.18 | 4000 | 0.9057 | 0.6854 | 0.6533 | | 0.4723 | 11.45 | 4500 | 0.9237 | 0.6796 | 0.6455 | | 0.3896 | 12.72 | 5000 | 0.9814 | 0.6879 | 0.6499 | | 0.3896 | 13.99 | 5500 | 0.9984 | 0.6745 | 0.6487 | | 0.3299 | 15.27 | 6000 | 1.0226 | 0.6822 | 0.6545 | | 0.3299 | 16.54 | 6500 | 1.0579 | 0.6758 | 0.6485 | | 0.2783 | 17.81 | 7000 | 1.0932 | 0.6796 | 0.6487 | | 0.2783 | 19.08 | 7500 | 1.1047 | 0.6950 | 0.6609 | | 0.2455 | 20.36 | 8000 | 1.1643 | 0.6860 | 0.6559 | | 0.2455 | 21.63 | 8500 | 1.1953 | 0.6841 | 0.6548 | | 0.2181 | 22.9 | 9000 | 1.2043 | 0.6835 | 0.6516 | | 0.2181 | 24.17 | 9500 | 1.2603 | 0.6867 | 0.6502 | | 0.1894 | 25.45 | 10000 | 1.2652 | 0.6860 | 0.6552 | | 0.1894 | 26.72 | 10500 | 1.2860 | 0.6790 | 0.6474 | | 0.1757 | 27.99 | 11000 | 1.2892 | 0.6854 | 0.6541 | | 0.1757 | 29.26 | 11500 | 1.3400 | 0.6803 | 0.6496 | | 0.1599 | 30.53 | 12000 | 1.3630 | 0.6828 | 0.6493 | | 0.1599 | 31.81 | 12500 | 1.3688 | 0.6854 | 0.6538 | | 0.1531 | 33.08 | 13000 | 1.3962 | 0.6854 | 0.6534 | | 0.1531 | 34.35 | 13500 | 1.4021 | 0.6841 | 0.6523 | | 0.1452 | 35.62 | 14000 | 1.4029 | 0.6847 | 0.6524 | | 0.1452 | 36.9 | 14500 | 1.4130 | 0.6886 | 0.6562 | | 0.1391 | 38.17 | 15000 | 1.4203 | 0.6879 | 0.6553 | | 0.1391 | 39.44 | 15500 | 1.4244 | 0.6873 | 0.6544 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

DaniloTrotta/Kandinsky_2.1

DaniloTrotta

2023-05-23T15:43:07Z

1

0

open_clip

[ "open_clip", "Kandinsky", "text-image", "text-to-image", "license:apache-2.0", "region:us" ]

text-to-image

2023-05-23T12:00:50Z

--- license: apache-2.0 tags: - Kandinsky - text-image inference: true pipeline_tag: text-to-image library_name: open_clip --- # Kandinsky 2.1 [Open In Colab](https://colab.research.google.com/drive/1xSbu-b-EwYd6GdaFPRVgvXBX_mciZ41e?usp=sharing) [GitHub repository](https://github.com/ai-forever/Kandinsky-2) [Habr post](https://habr.com/ru/company/sberbank/blog/725282/) [Demo](https://rudalle.ru/) ## Architecture Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. For diffusion mapping of latent spaces we use transformer with num_layers=20, num_heads=32 and hidden_size=2048. ![](https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png) Other architecture parts: + Text encoder (XLM-Roberta-Large-Vit-L-14) - 560M + Diffusion Image Prior — 1B + CLIP image encoder (ViT-L/14) - 427M + Latent Diffusion U-Net - 1.22B + MoVQ encoder/decoder - 67M ![](https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/einstein.png) # Authors + Arseniy Shakhmatov: [Github](https://github.com/cene555), [Blog](https://t.me/gradientdip) + Anton Razzhigaev: [Github](https://github.com/razzant), [Blog](https://t.me/abstractDL) + Aleksandr Nikolich: [Github](https://github.com/AlexWortega), [Blog](https://t.me/lovedeathtransformers) + Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse) + Igor Pavlov: [Github](https://github.com/boomb0om) + Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey) + Denis Dimitrov: [Github](https://github.com/denndimitrov)

openllmplayground/pandagpt_7b_max_len_512

openllmplayground

2023-05-23T15:42:41Z

0

1

null

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

null

2023-05-17T18:13:29Z

--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-7B model with maximum sequence length of 512. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).

openllmplayground/pandagpt_13b_max_len_256

openllmplayground

2023-05-23T15:42:15Z

0

null

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

null

2023-05-18T16:33:04Z

--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-13B model with maximum sequence length of 256. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).

openllmplayground/pandagpt_13b_max_len_400

openllmplayground

2023-05-23T15:41:35Z

0

6

null

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

null

2023-05-21T05:50:07Z

--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-13B model with maximum sequence length of 400. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).

openllmplayground/pandagpt_7b_max_len_1024

openllmplayground

2023-05-23T15:41:04Z

0

8

null

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

null

2023-05-22T07:37:53Z

--- license: cc-by-nc-sa-4.0 --- This model contains the delta weights of PandaGPT built upon the version-0 of Vicuna-7B model with maximum sequence length of 1024. For more details on the model usage, please refer to our [main project repository](https://github.com/yxuansu/PandaGPT).

gerardoalemanm/roberta-large-peft-p-tunning

gerardoalemanm

2023-05-23T15:37:56Z

0

null

[ "license:c-uda", "region:us" ]

null

2023-05-23T15:29:47Z

--- license: c-uda --- Model fine tunned with peft and Roberta-large model Uses the dataset glue

YakovElm/Qt15SetFitModel

YakovElm

2023-05-23T15:36:58Z

3

0

sentence-transformers

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

text-classification

2023-05-23T15:36:20Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YakovElm/Qt15SetFitModel 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("YakovElm/Qt15SetFitModel") # 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} } ```

skyadmin/cog-webui-sd

skyadmin

2023-05-23T15:36:11Z

67

0

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

2023-05-10T13:36:02Z

# Chill Watcher consider deploy on: - huggingface inference point - replicate api - lightning.ai # platform comparison > all support autoscaling |platform|prediction speed|charges|deploy handiness| |-|-|-|-| |huggingface|fast:20s|high:$0.6/hr (without autoscaling)|easy:git push| |replicate|fast if used frequently: 30s, slow if needs initialization: 5min|low: $0.02 per generation|difficult: build image and upload| |lightning.ai|fast with app running: 20s, slow if idle: XXs|low: free $30 per month, $0.18 per init, $0.02 per run|easy: one command| # platform deploy options ## huggingface > [docs](https://huggingface.co/docs/inference-endpoints/guides/custom_handler) - requirements: use pip packages in `requirements.txt` - `init()` and `predict()` function: use `handler.py`, implement the `EndpointHandler` class - more: modify `handler.py` for requests and inference and explore more highly-customized features - deploy: git (lfs) push to huggingface repository(the whole directory including models and weights, etc.), and use inference endpoints to deploy. Click and deploy automaticly, very simple. - call api: use the url provide by inference endpoints after endpoint is ready(build, initialize and in a "running" state), make a post request to the url using request schema definied in the `handler.py` ## replicate > [docs](https://replicate.com/docs/guides/push-a-model) - requirements: specify all requirements(pip packages, system packages, python version, cuda, etc.) in `cog.yaml` - `init()` and `predict()` function: use `predict.py`, implement the `Predictor` class - more: modify `predict.py` - deploy: 1. get a linux GPU machine with 60GB disk space; 2. install [cog](https://replicate.com/docs/guides/push-a-model) and [docker](https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository) 3. `git pull` the current repository from huggingface, including large model files 4. after `predict.py` and `cog.yaml` is correctly coded, run `cog login`, `cog push`, then cog will build a docker image locally and push the image to replicate. As the image could take 30GB or so disk space, it would cost a lot network bandwidth. - call api: if everything runs successfully and the docker image is pushed to replicate, you will see a web-ui and an API example directly in your replicate repository ## lightning.ai > docs: [code](https://lightning.ai/docs/app/stable/levels/basic/real_lightning_component_implementations.html), [deploy](https://lightning.ai/docs/app/stable/workflows/run_app_on_cloud/) - requirements: - pip packages are listed in `requirements_lightning.txt`, because some requirements are different from those in huggingface. Rename it to `requirements.txt` - other pip packages, system packages and some big model weight files download commands, can be listed using a custom build config. Checkout `class CustomBuildConfig(BuildConfig)` in `app.py`. In a custom build config you can use many linux commands such as `wget` and `sudo apt-get update`. The custom build config will be executed on the `__init__()` of the `PythonServer` class - `init()` and `predict()` function: use `app.py`, implement the `PythonServer` class. Note: - some packages haven't been installed when the file is called(these packages may be installed when `__init__()` is called), so some import code should be in the function, not at the top of the file, or you may get import errors. - you can't save your own value to `PythonServer.self` unless it's predifined in the variables, so don't assign any self-defined variables to `self` - if you use the custom build config, you should implement `PythonServer`'s `__init()__` yourself, so don't forget to use the correct function signature - more: ... - deploy: - `pip install lightning` - prepare the directory on your local computer(no need to have a GPU) - list big files in the `.lightningignore` file to avoid big file upload and save deploy time cost - run `lightning run app app.py --cloud` in the local terminal, and it will upload the files in the directory to lightning cloud, and start deploying on the cloud - check error logs on the web-ui, use `all logs` - call api: only if the app starts successfully, you can see a valid url in the `settings` page of the web-ui. Open that url, and you can see the api ### some stackoverflow: install docker: - https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository install git-lfs: - https://github.com/git-lfs/git-lfs/blob/main/INSTALLING.md linux: ``` curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash sudo apt-get install git-lfs ``` --- license: apache-2.0 ---

kitrakrev/ppo-LunarLander-v2

kitrakrev

2023-05-23T15:29:50Z

0

stable-baselines3

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

reinforcement-learning

2023-05-23T15:29:28Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.88 +/- 16.00 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 ... ```

Bailefan/dqn-SpaceInvadersNoFrameskip-v4

Bailefan

2023-05-23T15:20:16Z

0

stable-baselines3

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

reinforcement-learning

2023-05-23T15:19:37Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 687.00 +/- 135.15 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bailefan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bailefan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Bailefan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

da2el/slskmz

da2el

2023-05-23T15:14:14Z

0

3

null

[ "stable-diffusion", "lora", "school uniform", "school swimsuit", "ja", "license:creativeml-openrail-m", "region:us" ]

null

2023-05-23T14:06:11Z

--- license: creativeml-openrail-m language: - ja tags: - stable-diffusion - lora - school uniform - school swimsuit --- # Sailor School Swimsuit セーラー服＋スク水は人類の宝です。 Sailor school uniform + school swimsuits are treasures of humanity. 水手服+校服泳装是人类的宝藏。 ## Features スクール水着の上からセーラー服を着用した姿が生成されます。 ※このLoRAを使わなくてもセーラー服とスク水の組み合わせは生成できます。ちょっと確率を上げる程度だと思ってください The generated image depicts a person wearing a sailor uniform over a school swimsuit. ※Even without using LoRA, it is possible to generate a combination of sailor uniforms and school swimsuits. Please consider it as just slightly increasing the probability. 生成的图像描绘了一个穿着水手服的人在校服泳装上面。 ※即使不使用 LoRA，也可以生成帆船制服和学校泳装的组合。请将其视为稍微增加了概率 ## How to Use - Clip skip: 2 - LoRA weight: 0.3 - 0.5 - prompt: - Short sleeves: slskmz short - Sleeveless: slskmz sleeveless ``` <lora:slskmz_v2:0.3>, slskmz short, sailor school uniform, school swimsuit, white shirt ``` `sailor school uniform, school swimsuit, white shirt` も入力したほうが出現率が上がります If you also input `sailor school uniform, school swimsuit, white shirt`, the probability of them appearing will increase 如果您还输入了 `sailor school uniform, school swimsuit, white shirt` ，它们出现的概率将会增加。 ![](img/sample.webp) ## Author daniel, Japan

braintML123/Tania

braintML123

2023-05-23T15:03:23Z

0

null

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

null

2023-04-26T03:07:26Z

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

Jakehova/SimpleClassifierWithLLMs

Jakehova

2023-05-23T14:56:28Z

0

null

[ "region:us" ]

null

2023-05-23T14:21:48Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID This is a simple classifier using the 20 Newgroups dataset ## Model Details Uses sklearn.datasets to pull 20 Newsgroups data. It runs through a variety of transformers (I'm not sure if this is the right terminology) to classify the data provided. ### Model Description The focus of this is to fine tune a model with Text Classification. - **Developed by:** FourthBrain - **Model type:** Simple Classifier - **Language(s) (NLP):** distilbert-base-uncased - **License:** MIT (?) ## Uses This is for a class so shouldnt be used for anything more than learning. ### Direct Use Learning ## Training Details ### Training Data [20 Newgroups](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) [More Information Needed]

VinsmokeMir/FineTuning_Method_2_SC

VinsmokeMir

2023-05-23T14:49:02Z

3

0

transformers

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

text-classification

2023-05-23T13:55:32Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: FineTuning_Method_2_SC results: [] ---  # FineTuning_Method_2_SC This model is a fine-tuned version of [rafsankabir/Pretrained_E13_Method2](https://huggingface.co/rafsankabir/Pretrained_E13_Method2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3223 - Accuracy: 0.6790 - F1 Macro: 0.6487 ## Model description More information needed ## Intended uses & 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_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | No log | 0.32 | 500 | 1.0745 | 0.3976 | 0.1896 | | 1.0543 | 0.64 | 1000 | 0.9059 | 0.5967 | 0.4614 | | 1.0543 | 0.95 | 1500 | 0.8259 | 0.6414 | 0.5633 | | 0.8389 | 1.27 | 2000 | 0.8177 | 0.6394 | 0.5715 | | 0.8389 | 1.59 | 2500 | 0.8269 | 0.6356 | 0.5724 | | 0.7713 | 1.91 | 3000 | 0.7916 | 0.6631 | 0.6238 | | 0.7713 | 2.23 | 3500 | 0.7996 | 0.6745 | 0.6155 | | 0.6734 | 2.54 | 4000 | 0.7921 | 0.6624 | 0.6307 | | 0.6734 | 2.86 | 4500 | 0.7743 | 0.6726 | 0.6459 | | 0.6309 | 3.18 | 5000 | 0.8343 | 0.6803 | 0.6382 | | 0.6309 | 3.5 | 5500 | 0.8233 | 0.6784 | 0.6390 | | 0.5582 | 3.82 | 6000 | 0.8678 | 0.6631 | 0.6273 | | 0.5582 | 4.13 | 6500 | 0.8621 | 0.6758 | 0.6368 | | 0.4988 | 4.45 | 7000 | 0.9389 | 0.6720 | 0.6386 | | 0.4988 | 4.77 | 7500 | 0.9067 | 0.6918 | 0.6505 | | 0.4885 | 5.09 | 8000 | 0.9116 | 0.6937 | 0.6583 | | 0.4885 | 5.41 | 8500 | 1.0357 | 0.6822 | 0.6459 | | 0.427 | 5.73 | 9000 | 0.9428 | 0.6847 | 0.6479 | | 0.427 | 6.04 | 9500 | 1.0233 | 0.6752 | 0.6531 | | 0.4034 | 6.36 | 10000 | 1.1578 | 0.6835 | 0.6515 | | 0.4034 | 6.68 | 10500 | 1.1870 | 0.6790 | 0.6545 | | 0.4053 | 7.0 | 11000 | 1.0370 | 0.7007 | 0.6651 | | 0.4053 | 7.32 | 11500 | 1.2087 | 0.6822 | 0.6497 | | 0.3545 | 7.63 | 12000 | 1.2255 | 0.6847 | 0.6605 | | 0.3545 | 7.95 | 12500 | 1.2710 | 0.6905 | 0.6609 | | 0.3437 | 8.27 | 13000 | 1.3646 | 0.6918 | 0.6618 | | 0.3437 | 8.59 | 13500 | 1.3767 | 0.6879 | 0.6563 | | 0.3407 | 8.91 | 14000 | 1.2705 | 0.6796 | 0.6506 | | 0.3407 | 9.22 | 14500 | 1.4605 | 0.6803 | 0.6496 | | 0.2876 | 9.54 | 15000 | 1.4202 | 0.6860 | 0.6555 | | 0.2876 | 9.86 | 15500 | 1.4151 | 0.6847 | 0.6517 | | 0.3035 | 10.18 | 16000 | 1.4536 | 0.6713 | 0.6514 | | 0.3035 | 10.5 | 16500 | 1.4806 | 0.6828 | 0.6469 | | 0.2733 | 10.81 | 17000 | 1.4596 | 0.6899 | 0.6552 | | 0.2733 | 11.13 | 17500 | 1.6183 | 0.6886 | 0.6557 | | 0.2562 | 11.45 | 18000 | 1.6054 | 0.6771 | 0.6591 | | 0.2562 | 11.77 | 18500 | 1.5966 | 0.6701 | 0.6503 | | 0.2582 | 12.09 | 19000 | 1.5659 | 0.6822 | 0.6531 | | 0.2582 | 12.4 | 19500 | 1.6146 | 0.6867 | 0.6575 | | 0.2368 | 12.72 | 20000 | 1.6207 | 0.6899 | 0.6629 | | 0.2368 | 13.04 | 20500 | 1.5220 | 0.6918 | 0.6640 | | 0.245 | 13.36 | 21000 | 1.6572 | 0.6720 | 0.6489 | | 0.245 | 13.68 | 21500 | 1.6443 | 0.6860 | 0.6590 | | 0.2226 | 13.99 | 22000 | 1.6238 | 0.6847 | 0.6589 | | 0.2226 | 14.31 | 22500 | 1.7241 | 0.6777 | 0.6521 | | 0.2117 | 14.63 | 23000 | 1.6134 | 0.6867 | 0.6580 | | 0.2117 | 14.95 | 23500 | 1.6723 | 0.6911 | 0.6618 | | 0.2056 | 15.27 | 24000 | 1.6257 | 0.6892 | 0.6529 | | 0.2056 | 15.59 | 24500 | 1.7072 | 0.6796 | 0.6531 | | 0.1859 | 15.9 | 25000 | 1.7174 | 0.6771 | 0.6554 | | 0.1859 | 16.22 | 25500 | 1.6951 | 0.6879 | 0.6555 | | 0.1725 | 16.54 | 26000 | 1.7240 | 0.6905 | 0.6632 | | 0.1725 | 16.86 | 26500 | 1.7126 | 0.6879 | 0.6608 | | 0.1817 | 17.18 | 27000 | 1.7949 | 0.6847 | 0.6520 | | 0.1817 | 17.49 | 27500 | 1.7694 | 0.6911 | 0.6622 | | 0.1617 | 17.81 | 28000 | 1.7891 | 0.6828 | 0.6527 | | 0.1617 | 18.13 | 28500 | 1.7860 | 0.6790 | 0.6526 | | 0.1628 | 18.45 | 29000 | 1.8127 | 0.6867 | 0.6605 | | 0.1628 | 18.77 | 29500 | 1.7317 | 0.6892 | 0.6610 | | 0.1736 | 19.08 | 30000 | 1.7273 | 0.6899 | 0.6569 | | 0.1736 | 19.4 | 30500 | 1.7853 | 0.6854 | 0.6584 | | 0.1441 | 19.72 | 31000 | 1.7866 | 0.6918 | 0.6624 | | 0.1441 | 20.04 | 31500 | 1.7842 | 0.6873 | 0.6580 | | 0.1392 | 20.36 | 32000 | 1.8669 | 0.6860 | 0.6597 | | 0.1392 | 20.67 | 32500 | 1.8392 | 0.6899 | 0.6639 | | 0.159 | 20.99 | 33000 | 1.8412 | 0.6784 | 0.6552 | | 0.159 | 21.31 | 33500 | 1.8673 | 0.6854 | 0.6584 | | 0.1275 | 21.63 | 34000 | 1.8622 | 0.6854 | 0.6571 | | 0.1275 | 21.95 | 34500 | 1.8622 | 0.6796 | 0.6583 | | 0.1216 | 22.26 | 35000 | 1.9509 | 0.6854 | 0.6604 | | 0.1216 | 22.58 | 35500 | 1.9425 | 0.6809 | 0.6550 | | 0.1351 | 22.9 | 36000 | 1.9496 | 0.6784 | 0.6559 | | 0.1351 | 23.22 | 36500 | 1.9685 | 0.6847 | 0.6582 | | 0.1221 | 23.54 | 37000 | 1.9112 | 0.6911 | 0.6642 | | 0.1221 | 23.85 | 37500 | 1.9341 | 0.6726 | 0.6526 | | 0.1155 | 24.17 | 38000 | 1.9573 | 0.6899 | 0.6614 | | 0.1155 | 24.49 | 38500 | 1.9853 | 0.6873 | 0.6580 | | 0.1139 | 24.81 | 39000 | 1.9915 | 0.6790 | 0.6533 | | 0.1139 | 25.13 | 39500 | 1.9997 | 0.6796 | 0.6539 | | 0.1166 | 25.45 | 40000 | 1.9994 | 0.6847 | 0.6592 | | 0.1166 | 25.76 | 40500 | 1.9848 | 0.6745 | 0.6513 | | 0.1128 | 26.08 | 41000 | 2.0095 | 0.6867 | 0.6578 | | 0.1128 | 26.4 | 41500 | 2.0585 | 0.6822 | 0.6547 | | 0.1048 | 26.72 | 42000 | 2.0293 | 0.6777 | 0.6510 | | 0.1048 | 27.04 | 42500 | 2.0797 | 0.6758 | 0.6512 | | 0.1 | 27.35 | 43000 | 2.1162 | 0.6822 | 0.6544 | | 0.1 | 27.67 | 43500 | 2.0569 | 0.6835 | 0.6538 | | 0.1106 | 27.99 | 44000 | 2.0991 | 0.6828 | 0.6565 | | 0.1106 | 28.31 | 44500 | 2.0976 | 0.6841 | 0.6563 | | 0.0886 | 28.63 | 45000 | 2.1305 | 0.6854 | 0.6532 | | 0.0886 | 28.94 | 45500 | 2.1015 | 0.6867 | 0.6564 | | 0.1027 | 29.26 | 46000 | 2.1105 | 0.6867 | 0.6559 | | 0.1027 | 29.58 | 46500 | 2.1396 | 0.6765 | 0.6499 | | 0.1057 | 29.9 | 47000 | 2.1237 | 0.6790 | 0.6501 | | 0.1057 | 30.22 | 47500 | 2.1849 | 0.6790 | 0.6518 | | 0.0876 | 30.53 | 48000 | 2.1346 | 0.6841 | 0.6533 | | 0.0876 | 30.85 | 48500 | 2.1441 | 0.6828 | 0.6540 | | 0.0856 | 31.17 | 49000 | 2.1528 | 0.6911 | 0.6600 | | 0.0856 | 31.49 | 49500 | 2.1725 | 0.6847 | 0.6509 | | 0.0869 | 31.81 | 50000 | 2.2085 | 0.6771 | 0.6503 | | 0.0869 | 32.12 | 50500 | 2.2606 | 0.6688 | 0.6434 | | 0.0848 | 32.44 | 51000 | 2.2510 | 0.6745 | 0.6451 | | 0.0848 | 32.76 | 51500 | 2.2528 | 0.6739 | 0.6496 | | 0.0816 | 33.08 | 52000 | 2.2532 | 0.6758 | 0.6503 | | 0.0816 | 33.4 | 52500 | 2.2356 | 0.6803 | 0.6500 | | 0.0793 | 33.72 | 53000 | 2.2579 | 0.6745 | 0.6483 | | 0.0793 | 34.03 | 53500 | 2.2126 | 0.6816 | 0.6520 | | 0.0767 | 34.35 | 54000 | 2.2504 | 0.6803 | 0.6497 | | 0.0767 | 34.67 | 54500 | 2.2601 | 0.6803 | 0.6524 | | 0.0844 | 34.99 | 55000 | 2.2785 | 0.6733 | 0.6470 | | 0.0844 | 35.31 | 55500 | 2.2756 | 0.6784 | 0.6520 | | 0.0755 | 35.62 | 56000 | 2.2813 | 0.6816 | 0.6542 | | 0.0755 | 35.94 | 56500 | 2.2752 | 0.6803 | 0.6518 | | 0.077 | 36.26 | 57000 | 2.2815 | 0.6796 | 0.6518 | | 0.077 | 36.58 | 57500 | 2.2861 | 0.6803 | 0.6514 | | 0.0752 | 36.9 | 58000 | 2.2929 | 0.6771 | 0.6505 | | 0.0752 | 37.21 | 58500 | 2.2859 | 0.6816 | 0.6537 | | 0.0698 | 37.53 | 59000 | 2.3117 | 0.6796 | 0.6525 | | 0.0698 | 37.85 | 59500 | 2.3038 | 0.6816 | 0.6511 | | 0.0613 | 38.17 | 60000 | 2.3176 | 0.6765 | 0.6477 | | 0.0613 | 38.49 | 60500 | 2.3131 | 0.6796 | 0.6493 | | 0.0706 | 38.8 | 61000 | 2.3161 | 0.6777 | 0.6477 | | 0.0706 | 39.12 | 61500 | 2.3127 | 0.6784 | 0.6484 | | 0.0678 | 39.44 | 62000 | 2.3174 | 0.6765 | 0.6467 | | 0.0678 | 39.76 | 62500 | 2.3223 | 0.6790 | 0.6487 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

thomasavare/distilbert-ft-test3

thomasavare

2023-05-23T14:41:21Z

66

0

transformers

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

text-classification

2023-05-07T10:08:10Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert-ft-test3 results: [] ---  # distilbert-ft-test3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [thomasavare/waste-classification-v2](https://huggingface.co/datasets/thomasavare/waste-classification-v2). It is part of my master thesis at Politecnico di Torino in partenership with ReLearn. It achieves the following results on the test set: accuracy | precision | recall | f1 | ---------|-----------|--------|--------| 0.974 | 0.9805 | 0.9732 | 0.9725 | ## Model description DistilBERT finetuned for waste classification on 50 different classes as part of my master thesis at Politecnico di Torino. ## Intended uses & limitations Use for waste classification on 50 different waste classes (see [dataset](https://huggingface.co/datasets/thomasavare/waste-classification-v2)) ## Training and evaluation data [waste-classification-v2 dataset](https://huggingface.co/datasets/thomasavare/waste-classification-v2) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

AlexPerkin/distilbert-base-uncased-finetuned-squad

AlexPerkin

2023-05-23T14:35:13Z

115

0

transformers

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

question-answering

2023-05-23T12:23:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1517 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.2334 | 1.0 | 5533 | 1.1622 | | 0.9541 | 2.0 | 11066 | 1.1228 | | 0.7519 | 3.0 | 16599 | 1.1517 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

devraj4522/sentiment-analys

devraj4522

2023-05-23T14:32:10Z

0

null

[ "region:us" ]

null

2023-05-23T13:08:17Z

# Social Media Sentiment analysis @app.route('/predict-str', methods=['POST']) def predict_message(): data = request.json message = data.get('message', '') start_time = time.time() prediction = predictor.predict(message) response = { 'message': message, 'prediction': prediction, 'elapsed_time': time.time() - start_time } return jsonify(response) @app.route('/predict-list', methods=['POST']) def predict_list(): data = request.json messages = data.get('messages', []) start_time = time.time() predictions = predictor.predict(messages) response = { 'messages': messages, 'predictions': predictions, 'elapsed_time': time.time() - start_time } return jsonify(response) if __name__ == '__main__': app.run() ## API Endpoints - predict-str: Predicts the sentiment of a single message - parameters: message - json response: {message, prediction, elapsed_time} - predict-list: Predicts the sentiment of a list of messages - parameters: messages (list) - json response: {messages, predictions, elapsed_time}

HanNayeoniee/my_awesome_eli5_clm-model

HanNayeoniee

2023-05-23T14:31:42Z

211

0

transformers

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

text-generation

2023-05-23T13:45:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7255 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.8888 | 1.0 | 565 | 3.7433 | | 3.8141 | 2.0 | 1130 | 3.7292 | | 3.7701 | 3.0 | 1695 | 3.7255 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.7.1 - Datasets 2.12.0 - Tokenizers 0.13.3

aravind-selvam/x_rotated

aravind-selvam

2023-05-23T14:20:23Z

45

0

transformers

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

image-text-to-text

2023-05-23T13:07:14Z

--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: x_rotated results: [] ---  # x_rotated This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1574 - Cer: 0.0377 ## Model description More information needed ## Intended uses & 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2067 | 1.0 | 500 | 0.2103 | 0.0538 | | 0.1043 | 2.0 | 1000 | 0.1667 | 0.0445 | | 0.0667 | 3.0 | 1500 | 0.1571 | 0.0388 | | 0.0489 | 4.0 | 2000 | 0.1574 | 0.0377 | ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3