librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-02 12:32:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 534 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-02 12:31:20	card stringlengths 11 1.01M
asenella/mmnist_MMVAEPlusconfig_adapted_resnets_seed_0_ratio_0_c	asenella	2023-07-11T03:00:48Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-11T03:00:34Z	--- 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") ```
Azizslanguagesmodels/turkishReviews-ds-mini	Azizslanguagesmodels	2023-07-11T02:48:42Z	61	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-11T02:43:12Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: Azizslanguagesmodels/turkishReviews-ds-mini results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Azizslanguagesmodels/turkishReviews-ds-mini This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.1781 - Validation Loss: 9.2629 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -896, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 10.2821 \| 9.9897 \| 0 \| \| 9.6595 \| 9.6377 \| 1 \| \| 9.1781 \| 9.2629 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
alex2awesome/source-role-model	alex2awesome	2023-07-11T02:46:00Z	1	0	transformers	[ "transformers", "pytorch", "roberta", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	null	2023-07-11T02:14:08Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: source-role-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. --> # source-role-model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5543 - F1: 0.5814 ## Model description More information needed ## Intended uses & 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.12 \| 100 \| 1.0000 \| 0.3391 \| \| No log \| 0.25 \| 200 \| 0.8371 \| 0.5055 \| \| No log \| 0.37 \| 300 \| 0.8684 \| 0.5019 \| \| No log \| 0.49 \| 400 \| 0.8668 \| 0.5208 \| \| 0.9644 \| 0.62 \| 500 \| 0.8473 \| 0.5422 \| \| 0.9644 \| 0.74 \| 600 \| 0.8852 \| 0.4956 \| \| 0.9644 \| 0.86 \| 700 \| 0.8368 \| 0.5124 \| \| 0.9644 \| 0.99 \| 800 \| 0.7913 \| 0.5848 \| \| 0.9644 \| 1.11 \| 900 \| 1.0570 \| 0.4950 \| \| 0.8375 \| 1.23 \| 1000 \| 0.9402 \| 0.5280 \| \| 0.8375 \| 1.35 \| 1100 \| 0.8023 \| 0.5084 \| \| 0.8375 \| 1.48 \| 1200 \| 0.9299 \| 0.4807 \| \| 0.8375 \| 1.6 \| 1300 \| 0.9661 \| 0.5194 \| \| 0.8375 \| 1.72 \| 1400 \| 0.8014 \| 0.6016 \| \| 0.8149 \| 1.85 \| 1500 \| 0.8608 \| 0.6105 \| \| 0.8149 \| 1.97 \| 1600 \| 0.9195 \| 0.5741 \| \| 0.8149 \| 2.09 \| 1700 \| 1.2378 \| 0.5964 \| \| 0.8149 \| 2.22 \| 1800 \| 1.0415 \| 0.5902 \| \| 0.8149 \| 2.34 \| 1900 \| 1.0499 \| 0.5526 \| \| 0.6932 \| 2.46 \| 2000 \| 1.0600 \| 0.5832 \| \| 0.6932 \| 2.59 \| 2100 \| 0.9368 \| 0.6074 \| \| 0.6932 \| 2.71 \| 2200 \| 1.0872 \| 0.6270 \| \| 0.6932 \| 2.83 \| 2300 \| 1.0912 \| 0.5707 \| \| 0.6932 \| 2.96 \| 2400 \| 0.8815 \| 0.5602 \| \| 0.6214 \| 3.08 \| 2500 \| 1.1650 \| 0.5993 \| \| 0.6214 \| 3.2 \| 2600 \| 1.4485 \| 0.5821 \| \| 0.6214 \| 3.33 \| 2700 \| 1.5382 \| 0.5775 \| \| 0.6214 \| 3.45 \| 2800 \| 1.3999 \| 0.5696 \| \| 0.6214 \| 3.57 \| 2900 \| 1.3702 \| 0.6114 \| \| 0.5686 \| 3.69 \| 3000 \| 1.3840 \| 0.5635 \| \| 0.5686 \| 3.82 \| 3100 \| 1.3547 \| 0.5403 \| \| 0.5686 \| 3.94 \| 3200 \| 1.0283 \| 0.5723 \| \| 0.5686 \| 4.06 \| 3300 \| 1.3593 \| 0.6242 \| \| 0.5686 \| 4.19 \| 3400 \| 1.5985 \| 0.6004 \| \| 0.4807 \| 4.31 \| 3500 \| 1.5351 \| 0.6177 \| \| 0.4807 \| 4.43 \| 3600 \| 1.4109 \| 0.5779 \| \| 0.4807 \| 4.56 \| 3700 \| 1.6972 \| 0.5637 \| \| 0.4807 \| 4.68 \| 3800 \| 1.5336 \| 0.6047 \| \| 0.4807 \| 4.8 \| 3900 \| 1.7811 \| 0.5909 \| \| 0.4387 \| 4.93 \| 4000 \| 1.5862 \| 0.5869 \| \| 0.4387 \| 5.05 \| 4100 \| 1.7106 \| 0.5637 \| \| 0.4387 \| 5.17 \| 4200 \| 1.5251 \| 0.5624 \| \| 0.4387 \| 5.3 \| 4300 \| 1.5519 \| 0.5944 \| \| 0.4387 \| 5.42 \| 4400 \| 1.7315 \| 0.5908 \| \| 0.3219 \| 5.54 \| 4500 \| 1.7588 \| 0.6015 \| \| 0.3219 \| 5.67 \| 4600 \| 1.9277 \| 0.5635 \| \| 0.3219 \| 5.79 \| 4700 \| 1.7663 \| 0.5891 \| \| 0.3219 \| 5.91 \| 4800 \| 1.8401 \| 0.5917 \| \| 0.3219 \| 6.03 \| 4900 \| 2.0516 \| 0.5845 \| \| 0.2311 \| 6.16 \| 5000 \| 2.0510 \| 0.6166 \| \| 0.2311 \| 6.28 \| 5100 \| 2.1673 \| 0.5732 \| \| 0.2311 \| 6.4 \| 5200 \| 2.0931 \| 0.5819 \| \| 0.2311 \| 6.53 \| 5300 \| 2.2803 \| 0.5961 \| \| 0.2311 \| 6.65 \| 5400 \| 1.9985 \| 0.6010 \| \| 0.1669 \| 6.77 \| 5500 \| 2.1742 \| 0.5664 \| \| 0.1669 \| 6.9 \| 5600 \| 2.1021 \| 0.5732 \| \| 0.1669 \| 7.02 \| 5700 \| 2.2043 \| 0.5641 \| \| 0.1669 \| 7.14 \| 5800 \| 2.2018 \| 0.5837 \| \| 0.1669 \| 7.27 \| 5900 \| 2.3575 \| 0.5721 \| \| 0.1698 \| 7.39 \| 6000 \| 2.4663 \| 0.5662 \| \| 0.1698 \| 7.51 \| 6100 \| 2.2658 \| 0.5851 \| \| 0.1698 \| 7.64 \| 6200 \| 2.1585 \| 0.5676 \| \| 0.1698 \| 7.76 \| 6300 \| 2.1755 \| 0.5774 \| \| 0.1698 \| 7.88 \| 6400 \| 2.2680 \| 0.5696 \| \| 0.1378 \| 8.0 \| 6500 \| 2.3505 \| 0.5615 \| \| 0.1378 \| 8.13 \| 6600 \| 2.2773 \| 0.5705 \| \| 0.1378 \| 8.25 \| 6700 \| 2.3112 \| 0.5662 \| \| 0.1378 \| 8.37 \| 6800 \| 2.4572 \| 0.5679 \| \| 0.1378 \| 8.5 \| 6900 \| 2.4642 \| 0.5766 \| \| 0.0756 \| 8.62 \| 7000 \| 2.4643 \| 0.5885 \| \| 0.0756 \| 8.74 \| 7100 \| 2.5096 \| 0.5779 \| \| 0.0756 \| 8.87 \| 7200 \| 2.4261 \| 0.5789 \| \| 0.0756 \| 8.99 \| 7300 \| 2.3973 \| 0.5757 \| \| 0.0756 \| 9.11 \| 7400 \| 2.4137 \| 0.5906 \| \| 0.0842 \| 9.24 \| 7500 \| 2.4577 \| 0.5844 \| \| 0.0842 \| 9.36 \| 7600 \| 2.5034 \| 0.5840 \| \| 0.0842 \| 9.48 \| 7700 \| 2.5176 \| 0.5810 \| \| 0.0842 \| 9.61 \| 7800 \| 2.5240 \| 0.5852 \| \| 0.0842 \| 9.73 \| 7900 \| 2.5141 \| 0.5824 \| \| 0.0634 \| 9.85 \| 8000 \| 2.5482 \| 0.5814 \| \| 0.0634 \| 9.98 \| 8100 \| 2.5543 \| 0.5814 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
vimonteglione/ppo-Huggy	vimonteglione	2023-07-11T02:42:10Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-11T02:42:00Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vimonteglione/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
NasimB/gpt2-concat-cbt-mod-formatting-iorder-rarity-all-4k	NasimB	2023-07-11T02:39:48Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-11T00:45:49Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-cbt-mod-formatting-iorder-rarity-all-4k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-cbt-mod-formatting-iorder-rarity-all-4k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6962 \| 0.29 \| 500 \| 5.6482 \| \| 5.3352 \| 0.59 \| 1000 \| 5.2168 \| \| 4.9963 \| 0.88 \| 1500 \| 4.9671 \| \| 4.7147 \| 1.17 \| 2000 \| 4.8164 \| \| 4.5508 \| 1.46 \| 2500 \| 4.6852 \| \| 4.4503 \| 1.76 \| 3000 \| 4.5766 \| \| 4.3233 \| 2.05 \| 3500 \| 4.4995 \| \| 4.1239 \| 2.34 \| 4000 \| 4.4513 \| \| 4.0934 \| 2.63 \| 4500 \| 4.3905 \| \| 4.0645 \| 2.93 \| 5000 \| 4.3376 \| \| 3.8538 \| 3.22 \| 5500 \| 4.3338 \| \| 3.7937 \| 3.51 \| 6000 \| 4.3034 \| \| 3.781 \| 3.8 \| 6500 \| 4.2718 \| \| 3.6821 \| 4.1 \| 7000 \| 4.2702 \| \| 3.5082 \| 4.39 \| 7500 \| 4.2633 \| \| 3.5078 \| 4.68 \| 8000 \| 4.2471 \| \| 3.4936 \| 4.97 \| 8500 \| 4.2346 \| \| 3.34 \| 5.27 \| 9000 \| 4.2492 \| \| 3.3145 \| 5.56 \| 9500 \| 4.2471 \| \| 3.315 \| 5.85 \| 10000 \| 4.2463 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
alex2awesome/source-affiliation-model	alex2awesome	2023-07-11T02:37:57Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	null	2023-07-10T23:11:23Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: source-affiliation-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. --> # source-affiliation-model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3321 - F1: 0.5348 ## Model description More information needed ## Intended uses & 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.12 \| 100 \| 1.4535 \| 0.2435 \| \| No log \| 0.25 \| 200 \| 1.3128 \| 0.3899 \| \| No log \| 0.37 \| 300 \| 1.2888 \| 0.4413 \| \| No log \| 0.49 \| 400 \| 1.1560 \| 0.4614 \| \| 1.4848 \| 0.62 \| 500 \| 1.0988 \| 0.4477 \| \| 1.4848 \| 0.74 \| 600 \| 1.1211 \| 0.4583 \| \| 1.4848 \| 0.86 \| 700 \| 1.1152 \| 0.4693 \| \| 1.4848 \| 0.99 \| 800 \| 1.0176 \| 0.5018 \| \| 1.4848 \| 1.11 \| 900 \| 1.0942 \| 0.4774 \| \| 1.1019 \| 1.23 \| 1000 \| 1.1785 \| 0.5119 \| \| 1.1019 \| 1.35 \| 1100 \| 1.0751 \| 0.4797 \| \| 1.1019 \| 1.48 \| 1200 \| 1.0759 \| 0.5206 \| \| 1.1019 \| 1.6 \| 1300 \| 1.0756 \| 0.5231 \| \| 1.1019 \| 1.72 \| 1400 \| 1.1329 \| 0.4547 \| \| 0.9431 \| 1.85 \| 1500 \| 1.0617 \| 0.4852 \| \| 0.9431 \| 1.97 \| 1600 \| 1.1046 \| 0.5254 \| \| 0.9431 \| 2.09 \| 1700 \| 1.2489 \| 0.5069 \| \| 0.9431 \| 2.22 \| 1800 \| 1.2113 \| 0.5363 \| \| 0.9431 \| 2.34 \| 1900 \| 1.1782 \| 0.5546 \| \| 0.7589 \| 2.46 \| 2000 \| 1.0453 \| 0.5862 \| \| 0.7589 \| 2.59 \| 2100 \| 1.0810 \| 0.5223 \| \| 0.7589 \| 2.71 \| 2200 \| 1.1470 \| 0.5872 \| \| 0.7589 \| 2.83 \| 2300 \| 1.1522 \| 0.5553 \| \| 0.7589 \| 2.96 \| 2400 \| 1.0712 \| 0.6273 \| \| 0.6875 \| 3.08 \| 2500 \| 1.3458 \| 0.5768 \| \| 0.6875 \| 3.2 \| 2600 \| 1.7052 \| 0.5491 \| \| 0.6875 \| 3.33 \| 2700 \| 1.5080 \| 0.6582 \| \| 0.6875 \| 3.45 \| 2800 \| 1.5851 \| 0.5965 \| \| 0.6875 \| 3.57 \| 2900 \| 1.4771 \| 0.5691 \| \| 0.5391 \| 3.69 \| 3000 \| 1.6717 \| 0.5350 \| \| 0.5391 \| 3.82 \| 3100 \| 1.5607 \| 0.5448 \| \| 0.5391 \| 3.94 \| 3200 \| 1.5464 \| 0.6062 \| \| 0.5391 \| 4.06 \| 3300 \| 1.7645 \| 0.5755 \| \| 0.5391 \| 4.19 \| 3400 \| 1.6715 \| 0.5504 \| \| 0.4928 \| 4.31 \| 3500 \| 1.7604 \| 0.5626 \| \| 0.4928 \| 4.43 \| 3600 \| 1.8984 \| 0.5142 \| \| 0.4928 \| 4.56 \| 3700 \| 1.8012 \| 0.5763 \| \| 0.4928 \| 4.68 \| 3800 \| 1.7107 \| 0.5671 \| \| 0.4928 \| 4.8 \| 3900 \| 1.7697 \| 0.5598 \| \| 0.4233 \| 4.93 \| 4000 \| 1.6296 \| 0.6084 \| \| 0.4233 \| 5.05 \| 4100 \| 2.0418 \| 0.5343 \| \| 0.4233 \| 5.17 \| 4200 \| 1.8203 \| 0.5526 \| \| 0.4233 \| 5.3 \| 4300 \| 1.9760 \| 0.5292 \| \| 0.4233 \| 5.42 \| 4400 \| 2.0136 \| 0.5153 \| \| 0.2518 \| 5.54 \| 4500 \| 2.0137 \| 0.5121 \| \| 0.2518 \| 5.67 \| 4600 \| 2.0053 \| 0.5257 \| \| 0.2518 \| 5.79 \| 4700 \| 1.9539 \| 0.5423 \| \| 0.2518 \| 5.91 \| 4800 \| 2.0159 \| 0.5686 \| \| 0.2518 \| 6.03 \| 4900 \| 2.0411 \| 0.5817 \| \| 0.2234 \| 6.16 \| 5000 \| 2.0025 \| 0.5780 \| \| 0.2234 \| 6.28 \| 5100 \| 2.1189 \| 0.5413 \| \| 0.2234 \| 6.4 \| 5200 \| 2.1936 \| 0.5628 \| \| 0.2234 \| 6.53 \| 5300 \| 2.1825 \| 0.5210 \| \| 0.2234 \| 6.65 \| 5400 \| 2.0767 \| 0.5471 \| \| 0.1829 \| 6.77 \| 5500 \| 1.9747 \| 0.5587 \| \| 0.1829 \| 6.9 \| 5600 \| 2.1182 \| 0.5847 \| \| 0.1829 \| 7.02 \| 5700 \| 2.1597 \| 0.5437 \| \| 0.1829 \| 7.14 \| 5800 \| 2.0307 \| 0.5629 \| \| 0.1829 \| 7.27 \| 5900 \| 2.0912 \| 0.5450 \| \| 0.1226 \| 7.39 \| 6000 \| 2.2383 \| 0.5379 \| \| 0.1226 \| 7.51 \| 6100 \| 2.2311 \| 0.5834 \| \| 0.1226 \| 7.64 \| 6200 \| 2.2456 \| 0.5438 \| \| 0.1226 \| 7.76 \| 6300 \| 2.2423 \| 0.5860 \| \| 0.1226 \| 7.88 \| 6400 \| 2.2922 \| 0.5245 \| \| 0.0883 \| 8.0 \| 6500 \| 2.3304 \| 0.5650 \| \| 0.0883 \| 8.13 \| 6600 \| 2.3929 \| 0.5288 \| \| 0.0883 \| 8.25 \| 6700 \| 2.3928 \| 0.5344 \| \| 0.0883 \| 8.37 \| 6800 \| 2.3854 \| 0.5266 \| \| 0.0883 \| 8.5 \| 6900 \| 2.4275 \| 0.5339 \| \| 0.044 \| 8.62 \| 7000 \| 2.3929 \| 0.5380 \| \| 0.044 \| 8.74 \| 7100 \| 2.3587 \| 0.5339 \| \| 0.044 \| 8.87 \| 7200 \| 2.3372 \| 0.5423 \| \| 0.044 \| 8.99 \| 7300 \| 2.3488 \| 0.5424 \| \| 0.044 \| 9.11 \| 7400 \| 2.3543 \| 0.5818 \| \| 0.0558 \| 9.24 \| 7500 \| 2.3397 \| 0.5554 \| \| 0.0558 \| 9.36 \| 7600 \| 2.3255 \| 0.5394 \| \| 0.0558 \| 9.48 \| 7700 \| 2.3184 \| 0.5557 \| \| 0.0558 \| 9.61 \| 7800 \| 2.3293 \| 0.5669 \| \| 0.0558 \| 9.73 \| 7900 \| 2.3358 \| 0.5666 \| \| 0.0323 \| 9.85 \| 8000 \| 2.3307 \| 0.5344 \| \| 0.0323 \| 9.98 \| 8100 \| 2.3321 \| 0.5348 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
IS4XD/Ris3	IS4XD	2023-07-11T02:36:03Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-07-11T02:36:03Z	--- license: bigscience-openrail-m ---
RavenFangsk/chronoborous-33B-GPTQ	RavenFangsk	2023-07-11T02:28:20Z	5	0	transformers	[ "transformers", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T03:26:46Z	Auto-GPTQ'd version of https://huggingface.co/Henk717/chronoboros-33B
sl8425/troubleshooting_steps_classification_model	sl8425	2023-07-11T02:20:13Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T19:07:39Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: sl8425/troubleshooting_steps_classification_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # sl8425/troubleshooting_steps_classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6664 - Validation Loss: 0.7197 - Train Accuracy: 0.7923 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 921, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.5468 \| 0.9262 \| 0.7317 \| 0 \| \| 0.8223 \| 0.7546 \| 0.7830 \| 1 \| \| 0.6664 \| 0.7197 \| 0.7923 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9	jordyvl	2023-07-11T02:14:34Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-11T01:01:03Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 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. --> # vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2366 - Accuracy: 0.63 - Brier Loss: 0.5035 - Nll: 2.8588 - F1 Micro: 0.63 - F1 Macro: 0.6311 - Ece: 0.1649 - Aurc: 0.1472 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 2.8887 \| 0.1225 \| 0.9306 \| 15.9457 \| 0.1225 \| 0.1226 \| 0.1434 \| 0.8620 \| \| No log \| 2.0 \| 50 \| 2.2120 \| 0.3775 \| 0.7577 \| 9.7500 \| 0.3775 \| 0.3483 \| 0.1992 \| 0.3776 \| \| No log \| 3.0 \| 75 \| 1.7681 \| 0.495 \| 0.6387 \| 5.6935 \| 0.495 \| 0.4838 \| 0.1885 \| 0.2491 \| \| No log \| 4.0 \| 100 \| 1.6420 \| 0.5225 \| 0.6038 \| 5.2427 \| 0.5225 \| 0.5242 \| 0.1757 \| 0.2301 \| \| No log \| 5.0 \| 125 \| 1.5877 \| 0.545 \| 0.5986 \| 4.6187 \| 0.545 \| 0.5282 \| 0.1808 \| 0.2248 \| \| No log \| 6.0 \| 150 \| 1.6460 \| 0.5125 \| 0.6162 \| 3.9942 \| 0.5125 \| 0.5060 \| 0.1962 \| 0.2295 \| \| No log \| 7.0 \| 175 \| 1.8436 \| 0.5125 \| 0.6538 \| 4.1740 \| 0.5125 \| 0.4932 \| 0.2299 \| 0.2451 \| \| No log \| 8.0 \| 200 \| 1.8205 \| 0.545 \| 0.6453 \| 5.0752 \| 0.545 \| 0.5234 \| 0.2057 \| 0.2432 \| \| No log \| 9.0 \| 225 \| 1.7399 \| 0.55 \| 0.6260 \| 4.5896 \| 0.55 \| 0.5460 \| 0.2057 \| 0.2258 \| \| No log \| 10.0 \| 250 \| 1.8559 \| 0.55 \| 0.6521 \| 5.0532 \| 0.55 \| 0.5368 \| 0.2209 \| 0.2560 \| \| No log \| 11.0 \| 275 \| 1.8636 \| 0.5625 \| 0.6488 \| 4.6642 \| 0.5625 \| 0.5544 \| 0.2335 \| 0.2187 \| \| No log \| 12.0 \| 300 \| 1.7461 \| 0.55 \| 0.6356 \| 4.1298 \| 0.55 \| 0.5638 \| 0.2047 \| 0.2313 \| \| No log \| 13.0 \| 325 \| 1.7468 \| 0.5625 \| 0.6281 \| 4.5451 \| 0.5625 \| 0.5570 \| 0.2224 \| 0.2214 \| \| No log \| 14.0 \| 350 \| 1.9616 \| 0.545 \| 0.6884 \| 3.7999 \| 0.545 \| 0.5484 \| 0.2691 \| 0.2624 \| \| No log \| 15.0 \| 375 \| 2.0977 \| 0.5175 \| 0.7138 \| 4.3792 \| 0.5175 \| 0.5055 \| 0.2658 \| 0.2917 \| \| No log \| 16.0 \| 400 \| 2.0238 \| 0.5275 \| 0.6896 \| 4.5299 \| 0.5275 \| 0.5177 \| 0.2664 \| 0.2603 \| \| No log \| 17.0 \| 425 \| 1.8687 \| 0.535 \| 0.6534 \| 3.7356 \| 0.535 \| 0.5388 \| 0.2490 \| 0.2448 \| \| No log \| 18.0 \| 450 \| 1.8210 \| 0.5575 \| 0.6492 \| 4.3823 \| 0.5575 \| 0.5537 \| 0.2533 \| 0.2268 \| \| No log \| 19.0 \| 475 \| 1.7610 \| 0.555 \| 0.6325 \| 3.9697 \| 0.555 \| 0.5503 \| 0.2292 \| 0.2161 \| \| 0.5398 \| 20.0 \| 500 \| 1.7125 \| 0.5825 \| 0.6125 \| 3.4176 \| 0.5825 \| 0.5731 \| 0.2140 \| 0.1859 \| \| 0.5398 \| 21.0 \| 525 \| 1.6296 \| 0.5775 \| 0.6163 \| 3.6014 \| 0.5775 \| 0.5871 \| 0.2236 \| 0.2051 \| \| 0.5398 \| 22.0 \| 550 \| 1.5965 \| 0.57 \| 0.5908 \| 3.7668 \| 0.57 \| 0.5712 \| 0.2058 \| 0.1883 \| \| 0.5398 \| 23.0 \| 575 \| 1.4828 \| 0.5875 \| 0.5646 \| 3.7028 \| 0.5875 \| 0.5854 \| 0.1944 \| 0.1714 \| \| 0.5398 \| 24.0 \| 600 \| 1.3983 \| 0.6075 \| 0.5481 \| 3.3608 \| 0.6075 \| 0.6107 \| 0.1966 \| 0.1628 \| \| 0.5398 \| 25.0 \| 625 \| 1.5241 \| 0.5925 \| 0.5866 \| 3.3669 \| 0.5925 \| 0.6019 \| 0.2069 \| 0.1886 \| \| 0.5398 \| 26.0 \| 650 \| 1.5540 \| 0.58 \| 0.5780 \| 3.5184 \| 0.58 \| 0.5710 \| 0.2131 \| 0.1857 \| \| 0.5398 \| 27.0 \| 675 \| 1.4653 \| 0.6 \| 0.5768 \| 2.9877 \| 0.6 \| 0.6043 \| 0.2166 \| 0.1781 \| \| 0.5398 \| 28.0 \| 700 \| 1.4883 \| 0.5925 \| 0.5646 \| 3.7789 \| 0.5925 \| 0.5910 \| 0.2096 \| 0.1746 \| \| 0.5398 \| 29.0 \| 725 \| 1.5738 \| 0.59 \| 0.5914 \| 4.0558 \| 0.59 \| 0.5879 \| 0.2150 \| 0.1957 \| \| 0.5398 \| 30.0 \| 750 \| 1.4017 \| 0.6025 \| 0.5583 \| 3.4791 \| 0.6025 \| 0.6023 \| 0.2150 \| 0.1752 \| \| 0.5398 \| 31.0 \| 775 \| 1.3500 \| 0.61 \| 0.5365 \| 3.2560 \| 0.61 \| 0.6157 \| 0.1988 \| 0.1579 \| \| 0.5398 \| 32.0 \| 800 \| 1.2977 \| 0.6375 \| 0.5140 \| 3.0503 \| 0.6375 \| 0.6395 \| 0.1847 \| 0.1534 \| \| 0.5398 \| 33.0 \| 825 \| 1.3471 \| 0.6175 \| 0.5406 \| 3.1888 \| 0.6175 \| 0.6104 \| 0.2077 \| 0.1689 \| \| 0.5398 \| 34.0 \| 850 \| 1.2992 \| 0.615 \| 0.5219 \| 2.8944 \| 0.615 \| 0.6191 \| 0.1826 \| 0.1574 \| \| 0.5398 \| 35.0 \| 875 \| 1.2733 \| 0.6225 \| 0.5124 \| 2.9352 \| 0.6225 \| 0.6238 \| 0.1588 \| 0.1505 \| \| 0.5398 \| 36.0 \| 900 \| 1.2821 \| 0.6175 \| 0.5231 \| 3.0142 \| 0.6175 \| 0.6169 \| 0.1672 \| 0.1553 \| \| 0.5398 \| 37.0 \| 925 \| 1.2819 \| 0.61 \| 0.5200 \| 2.6874 \| 0.61 \| 0.6116 \| 0.1847 \| 0.1540 \| \| 0.5398 \| 38.0 \| 950 \| 1.2664 \| 0.615 \| 0.5145 \| 2.9287 \| 0.615 \| 0.6159 \| 0.1961 \| 0.1528 \| \| 0.5398 \| 39.0 \| 975 \| 1.2584 \| 0.6225 \| 0.5134 \| 3.0058 \| 0.6225 \| 0.6230 \| 0.1747 \| 0.1508 \| \| 0.0507 \| 40.0 \| 1000 \| 1.2562 \| 0.615 \| 0.5114 \| 2.9269 \| 0.615 \| 0.6169 \| 0.1815 \| 0.1504 \| \| 0.0507 \| 41.0 \| 1025 \| 1.2525 \| 0.6225 \| 0.5101 \| 2.9199 \| 0.6225 \| 0.6239 \| 0.1770 \| 0.1496 \| \| 0.0507 \| 42.0 \| 1050 \| 1.2573 \| 0.62 \| 0.5133 \| 2.9195 \| 0.62 \| 0.6221 \| 0.1824 \| 0.1511 \| \| 0.0507 \| 43.0 \| 1075 \| 1.2536 \| 0.6125 \| 0.5131 \| 2.9026 \| 0.6125 \| 0.6121 \| 0.1820 \| 0.1511 \| \| 0.0507 \| 44.0 \| 1100 \| 1.2543 \| 0.6225 \| 0.5109 \| 3.0693 \| 0.6225 \| 0.6235 \| 0.1647 \| 0.1500 \| \| 0.0507 \| 45.0 \| 1125 \| 1.2526 \| 0.6125 \| 0.5117 \| 2.9018 \| 0.6125 \| 0.6141 \| 0.1788 \| 0.1500 \| \| 0.0507 \| 46.0 \| 1150 \| 1.2432 \| 0.615 \| 0.5068 \| 2.9042 \| 0.615 \| 0.6167 \| 0.1762 \| 0.1484 \| \| 0.0507 \| 47.0 \| 1175 \| 1.2485 \| 0.6275 \| 0.5098 \| 2.8927 \| 0.6275 \| 0.6251 \| 0.1590 \| 0.1496 \| \| 0.0507 \| 48.0 \| 1200 \| 1.2576 \| 0.6125 \| 0.5140 \| 2.8956 \| 0.6125 \| 0.6137 \| 0.1824 \| 0.1524 \| \| 0.0507 \| 49.0 \| 1225 \| 1.2468 \| 0.62 \| 0.5094 \| 2.8918 \| 0.62 \| 0.6204 \| 0.1832 \| 0.1496 \| \| 0.0507 \| 50.0 \| 1250 \| 1.2479 \| 0.6175 \| 0.5102 \| 2.8921 \| 0.6175 \| 0.6178 \| 0.1706 \| 0.1491 \| \| 0.0507 \| 51.0 \| 1275 \| 1.2393 \| 0.6225 \| 0.5057 \| 2.8813 \| 0.6225 \| 0.6229 \| 0.1784 \| 0.1486 \| \| 0.0507 \| 52.0 \| 1300 \| 1.2463 \| 0.6175 \| 0.5085 \| 2.8959 \| 0.6175 \| 0.6184 \| 0.1669 \| 0.1495 \| \| 0.0507 \| 53.0 \| 1325 \| 1.2391 \| 0.62 \| 0.5061 \| 2.8828 \| 0.62 \| 0.6215 \| 0.1803 \| 0.1471 \| \| 0.0507 \| 54.0 \| 1350 \| 1.2538 \| 0.6175 \| 0.5121 \| 2.8795 \| 0.6175 \| 0.6167 \| 0.1680 \| 0.1512 \| \| 0.0507 \| 55.0 \| 1375 \| 1.2407 \| 0.625 \| 0.5064 \| 2.8830 \| 0.625 \| 0.6259 \| 0.1842 \| 0.1482 \| \| 0.0507 \| 56.0 \| 1400 \| 1.2488 \| 0.62 \| 0.5099 \| 2.8769 \| 0.62 \| 0.6198 \| 0.1568 \| 0.1499 \| \| 0.0507 \| 57.0 \| 1425 \| 1.2402 \| 0.625 \| 0.5052 \| 2.8778 \| 0.625 \| 0.6260 \| 0.1616 \| 0.1481 \| \| 0.0507 \| 58.0 \| 1450 \| 1.2457 \| 0.625 \| 0.5077 \| 2.8786 \| 0.625 \| 0.6260 \| 0.1759 \| 0.1474 \| \| 0.0507 \| 59.0 \| 1475 \| 1.2430 \| 0.6275 \| 0.5073 \| 2.8744 \| 0.6275 \| 0.6266 \| 0.1652 \| 0.1486 \| \| 0.0319 \| 60.0 \| 1500 \| 1.2399 \| 0.625 \| 0.5056 \| 2.8767 \| 0.625 \| 0.6256 \| 0.1701 \| 0.1474 \| \| 0.0319 \| 61.0 \| 1525 \| 1.2460 \| 0.63 \| 0.5087 \| 2.8758 \| 0.63 \| 0.6329 \| 0.1865 \| 0.1491 \| \| 0.0319 \| 62.0 \| 1550 \| 1.2410 \| 0.6225 \| 0.5058 \| 2.8719 \| 0.6225 \| 0.6229 \| 0.1752 \| 0.1477 \| \| 0.0319 \| 63.0 \| 1575 \| 1.2418 \| 0.63 \| 0.5060 \| 2.8746 \| 0.63 \| 0.6319 \| 0.1692 \| 0.1484 \| \| 0.0319 \| 64.0 \| 1600 \| 1.2424 \| 0.6275 \| 0.5069 \| 2.8672 \| 0.6275 \| 0.6279 \| 0.1903 \| 0.1475 \| \| 0.0319 \| 65.0 \| 1625 \| 1.2413 \| 0.63 \| 0.5061 \| 2.8747 \| 0.63 \| 0.6304 \| 0.1737 \| 0.1471 \| \| 0.0319 \| 66.0 \| 1650 \| 1.2385 \| 0.6325 \| 0.5039 \| 2.8726 \| 0.6325 \| 0.6358 \| 0.1792 \| 0.1473 \| \| 0.0319 \| 67.0 \| 1675 \| 1.2368 \| 0.625 \| 0.5047 \| 2.8661 \| 0.625 \| 0.6261 \| 0.1843 \| 0.1467 \| \| 0.0319 \| 68.0 \| 1700 \| 1.2370 \| 0.6275 \| 0.5039 \| 2.8691 \| 0.6275 \| 0.6294 \| 0.1724 \| 0.1471 \| \| 0.0319 \| 69.0 \| 1725 \| 1.2382 \| 0.63 \| 0.5050 \| 2.8659 \| 0.63 \| 0.6317 \| 0.1698 \| 0.1472 \| \| 0.0319 \| 70.0 \| 1750 \| 1.2396 \| 0.6275 \| 0.5051 \| 2.8670 \| 0.6275 \| 0.6290 \| 0.1790 \| 0.1474 \| \| 0.0319 \| 71.0 \| 1775 \| 1.2378 \| 0.625 \| 0.5045 \| 2.8637 \| 0.625 \| 0.6268 \| 0.1742 \| 0.1476 \| \| 0.0319 \| 72.0 \| 1800 \| 1.2360 \| 0.625 \| 0.5037 \| 2.8669 \| 0.625 \| 0.6269 \| 0.1778 \| 0.1468 \| \| 0.0319 \| 73.0 \| 1825 \| 1.2390 \| 0.63 \| 0.5049 \| 2.8638 \| 0.63 \| 0.6310 \| 0.1711 \| 0.1474 \| \| 0.0319 \| 74.0 \| 1850 \| 1.2372 \| 0.625 \| 0.5045 \| 2.8640 \| 0.625 \| 0.6269 \| 0.1817 \| 0.1475 \| \| 0.0319 \| 75.0 \| 1875 \| 1.2375 \| 0.63 \| 0.5044 \| 2.8640 \| 0.63 \| 0.6313 \| 0.1703 \| 0.1472 \| \| 0.0319 \| 76.0 \| 1900 \| 1.2372 \| 0.6275 \| 0.5041 \| 2.8621 \| 0.6275 \| 0.6290 \| 0.1794 \| 0.1473 \| \| 0.0319 \| 77.0 \| 1925 \| 1.2374 \| 0.63 \| 0.5041 \| 2.8629 \| 0.63 \| 0.6313 \| 0.1722 \| 0.1472 \| \| 0.0319 \| 78.0 \| 1950 \| 1.2367 \| 0.6275 \| 0.5039 \| 2.8620 \| 0.6275 \| 0.6294 \| 0.1704 \| 0.1474 \| \| 0.0319 \| 79.0 \| 1975 \| 1.2371 \| 0.6275 \| 0.5039 \| 2.8619 \| 0.6275 \| 0.6294 \| 0.1639 \| 0.1474 \| \| 0.0314 \| 80.0 \| 2000 \| 1.2372 \| 0.63 \| 0.5041 \| 2.8612 \| 0.63 \| 0.6310 \| 0.1750 \| 0.1474 \| \| 0.0314 \| 81.0 \| 2025 \| 1.2368 \| 0.63 \| 0.5038 \| 2.8613 \| 0.63 \| 0.6309 \| 0.1648 \| 0.1473 \| \| 0.0314 \| 82.0 \| 2050 \| 1.2370 \| 0.63 \| 0.5038 \| 2.8607 \| 0.63 \| 0.6305 \| 0.1782 \| 0.1473 \| \| 0.0314 \| 83.0 \| 2075 \| 1.2368 \| 0.63 \| 0.5038 \| 2.8609 \| 0.63 \| 0.6307 \| 0.1686 \| 0.1472 \| \| 0.0314 \| 84.0 \| 2100 \| 1.2368 \| 0.63 \| 0.5037 \| 2.8603 \| 0.63 \| 0.6305 \| 0.1667 \| 0.1472 \| \| 0.0314 \| 85.0 \| 2125 \| 1.2366 \| 0.63 \| 0.5036 \| 2.8601 \| 0.63 \| 0.6309 \| 0.1686 \| 0.1473 \| \| 0.0314 \| 86.0 \| 2150 \| 1.2367 \| 0.6325 \| 0.5037 \| 2.8600 \| 0.6325 \| 0.6335 \| 0.1751 \| 0.1471 \| \| 0.0314 \| 87.0 \| 2175 \| 1.2369 \| 0.63 \| 0.5037 \| 2.8598 \| 0.63 \| 0.6307 \| 0.1730 \| 0.1473 \| \| 0.0314 \| 88.0 \| 2200 \| 1.2367 \| 0.63 \| 0.5036 \| 2.8595 \| 0.63 \| 0.6307 \| 0.1657 \| 0.1472 \| \| 0.0314 \| 89.0 \| 2225 \| 1.2366 \| 0.63 \| 0.5036 \| 2.8597 \| 0.63 \| 0.6307 \| 0.1680 \| 0.1472 \| \| 0.0314 \| 90.0 \| 2250 \| 1.2366 \| 0.63 \| 0.5036 \| 2.8594 \| 0.63 \| 0.6307 \| 0.1580 \| 0.1472 \| \| 0.0314 \| 91.0 \| 2275 \| 1.2366 \| 0.63 \| 0.5035 \| 2.8593 \| 0.63 \| 0.6307 \| 0.1677 \| 0.1472 \| \| 0.0314 \| 92.0 \| 2300 \| 1.2367 \| 0.63 \| 0.5035 \| 2.8593 \| 0.63 \| 0.6307 \| 0.1616 \| 0.1472 \| \| 0.0314 \| 93.0 \| 2325 \| 1.2366 \| 0.63 \| 0.5035 \| 2.8590 \| 0.63 \| 0.6307 \| 0.1625 \| 0.1472 \| \| 0.0314 \| 94.0 \| 2350 \| 1.2366 \| 0.6325 \| 0.5035 \| 2.8590 \| 0.6325 \| 0.6333 \| 0.1586 \| 0.1470 \| \| 0.0314 \| 95.0 \| 2375 \| 1.2366 \| 0.63 \| 0.5035 \| 2.8591 \| 0.63 \| 0.6307 \| 0.1580 \| 0.1472 \| \| 0.0314 \| 96.0 \| 2400 \| 1.2366 \| 0.63 \| 0.5035 \| 2.8589 \| 0.63 \| 0.6307 \| 0.1695 \| 0.1471 \| \| 0.0314 \| 97.0 \| 2425 \| 1.2366 \| 0.63 \| 0.5035 \| 2.8589 \| 0.63 \| 0.6311 \| 0.1648 \| 0.1472 \| \| 0.0314 \| 98.0 \| 2450 \| 1.2366 \| 0.63 \| 0.5035 \| 2.8588 \| 0.63 \| 0.6311 \| 0.1695 \| 0.1471 \| \| 0.0314 \| 99.0 \| 2475 \| 1.2366 \| 0.6325 \| 0.5035 \| 2.8589 \| 0.6325 \| 0.6337 \| 0.1724 \| 0.1470 \| \| 0.0312 \| 100.0 \| 2500 \| 1.2366 \| 0.63 \| 0.5035 \| 2.8588 \| 0.63 \| 0.6311 \| 0.1649 \| 0.1472 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented	hafidikhsan	2023-07-11T02:12:58Z	103	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-11T02:10:43Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0403 - Accuracy: 0.744 - F1: 0.7432 - Precision: 0.7436 - Recall: 0.744 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.8567 \| 1.0 \| 313 \| 0.9539 \| 0.5388 \| 0.5159 \| 0.5387 \| 0.5388 \| \| 0.665 \| 2.0 \| 626 \| 0.7520 \| 0.6512 \| 0.6545 \| 0.6625 \| 0.6512 \| \| 0.629 \| 3.0 \| 939 \| 0.7775 \| 0.7008 \| 0.6980 \| 0.6978 \| 0.7008 \| \| 0.4793 \| 4.0 \| 1252 \| 0.8696 \| 0.7268 \| 0.7295 \| 0.7365 \| 0.7268 \| \| 0.2273 \| 5.0 \| 1565 \| 1.0403 \| 0.744 \| 0.7432 \| 0.7436 \| 0.744 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
zwtharry/PPO-rocket	zwtharry	2023-07-11T02:09:34Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T02:09:13Z	--- 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: 234.64 +/- 40.86 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 ... ```
manhtt-079/vipubmed-deberta-base	manhtt-079	2023-07-11T01:59:35Z	7	0	transformers	[ "transformers", "pytorch", "tf", "deberta-v2", "transformer", "vietnamese", "nlp", "bert", "deberta", "fill-mask", "vi", "dataset:VietAI/vi_pubmed", "license:mit", "endpoints_compatible", "region:us" ]	fill-mask	2023-05-06T10:35:36Z	--- language: - vi metrics: - f1 pipeline_tag: fill-mask license: mit datasets: - VietAI/vi_pubmed tags: - transformer - vietnamese - nlp - bert - deberta - deberta-v2 --- # ViPubMedDeBERTa: A Vietnamese pretrained biomedical language representation model ## Model description ## Model variations - `vipubmed-deberta-xsmall`: 22M backbone parameters - `vipubmed-deberta-base`: 86M backbone parameters ## How to use You can use this model directly with a pipeline for masked language modeling:<br> _NOTE:_ The input text should be already word-segmented, you can use [Pyvi](https://github.com/trungtv/pyvi) (Python Vietnamese Core NLP Toolkit) to segment word before passing to the model. ```python >>> from transformers import pipeline >>> model = pipeline('fill-mask', model='manhtt-079/vipubmed-deberta-base') >>> text_with_mask = """Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ) . FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm . Phẫu_thuật được coi là phương_thức điều_trị tốt nhất , tiếp_theo là hóa_trị . Trong trường_hợp của chúng_tôi , [MASK] cắt bỏ không_thể thực_hiện được , do đó bệnh_nhân được hóa_trị hai dòng , sau đó là cấy_ghép tủy xương , sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên""" >>> model(text_with_mask) [{'score': 0.8480948805809021, 'token': 1621, 'token_str': 'phẫu_thuật', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, phẫu_thuật cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.1136574074625969, 'token': 83, 'token_str': 'việc', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, việc cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.014141257852315903, 'token': 589, 'token_str': 'phương_pháp', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, phương_pháp cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.0024715897161513567, 'token': 454, 'token_str': 'điều_trị', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, điều_trị cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.002370780799537897, 'token': 485, 'token_str': 'quá_trình', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, quá_trình cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}] ``` #### Get features: - With PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('manhtt-079/vipubmed-deberta-base') model = AutoModel.from_pretrained("manhtt-079/vipubmed-deberta-base") text = "Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS )." model_inputs = tokenizer(text, return_tensors='pt') outputs = model(model_inputs) ``` - With TensorFlow ```python from transformers import AutoTokenizer, TFAutoModel tokenizer = AutoTokenizer.from_pretrained('manhtt-079/vipubmed-deberta-base') model = TFAutoModel.from_pretrained("manhtt-079/vipubmed-deberta-base") text = "Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS )." model_inputs = tokenizer(text, return_tensors='tf') outputs = model(model_inputs) ``` ## Pre-training data The ViPubMedDeBERTa model was pre-trained on [ViPubmed](https://github.com/vietai/ViPubmed), a dataset consisting of 20M Vietnamese Biomedical abstracts generated by large scale translation. ## Training procedure ### Data deduplication A fuzzy deduplication, targeting documents with high overlap, was conducted at the document level to enhance quality and address overfitting. Employing Locality Sensitive Hashing (LSH) with a threshold of 0.9 ensured the removal of documents with overlap exceeding 90%. This process resulted in an average reduction of the dataset's size by 3%. ### Pretraining We employ our model based on the [ViDeBERTa](https://github.com/HySonLab/ViDeBERTa) architecture and leverage its pre-trained checkpoint to continue pre-training. Our model was trained on a single A100 GPU (40GB) for 350 thousand steps, with a batch size of 16 and gradient accumulation steps set to 4 (resulting in a total of 64). The sequence length was limited to 512 tokens and the model peak learning rate of 1e-4. ## Evaluation results
casque/TemplarAssassinv0.2	casque	2023-07-11T01:29:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T01:26:51Z	--- license: creativeml-openrail-m ---
liyingjian/Reinforce-policy-gradient	liyingjian	2023-07-11T01:28:57Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T01:28:48Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-policy-gradient results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 403.00 +/- 194.00 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
AndreNasci/distilbert-base-uncased-finetuned-cola	AndreNasci	2023-07-11T01:24:44Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T23:58:13Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: AndreNasci/distilbert-base-uncased-finetuned-cola 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. --> # AndreNasci/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1932 - Validation Loss: 0.5147 - Train Matthews Correlation: 0.5469 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5120 \| 0.4538 \| 0.4858 \| 0 \| \| 0.3206 \| 0.4722 \| 0.5116 \| 1 \| \| 0.1932 \| 0.5147 \| 0.5469 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
MDelan/distilbert-base-uncased-finetuned-cola	MDelan	2023-07-11T01:19:40Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T01:14:40Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MDelan/distilbert-base-uncased-finetuned-cola 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. --> # MDelan/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1879 - Validation Loss: 0.5580 - Train Matthews Correlation: 0.5127 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5181 \| 0.4661 \| 0.4379 \| 0 \| \| 0.3140 \| 0.4981 \| 0.4774 \| 1 \| \| 0.1879 \| 0.5580 \| 0.5127 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
beomi/kollama-7b	beomi	2023-07-11T01:18:13Z	71	10	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "KoLLAMA", "KoreanGPT", "ko", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-04-10T15:43:35Z	--- license: mit language: - ko - en metrics: - perplexity - accuracy pipeline_tag: text-generation tags: - llama - KoLLAMA - KoreanGPT --- > 🚧 Note: this repo is under construction 🚧 ## Todo ✅ - finish ⏳ - currently working on it - ✅ Train new BBPE Tokenizer - ✅ Test train code on TPUv4 Pods (with model parallel) - ✅ Converting test (jax to PyTorch) - ✅ LM train validation on minimal dataset (1 sentence 1000 step) - ⏳ Build Data Shuffler (curriculum learning) - ⏳ Train 7B Model - Train 13B Model - Train 33B Model - Train 65B Model # KoLLaMA Model Card KoLLaMA (7B) trained on Korean/English/Code dataset with LLaMA Architecture via JAX, with the warm support from [Google TPU Research Cloud program](https://sites.research.google/trc/about/) for providing part of the computation resources. ## Model details Researcher developing the model Junbum Lee (aka Beomi) Model date KoLLaMA was trained between 2022.04~ Model version This is alpha version of the model. Model type LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters. (This repo contains 7B model!) Paper or resources for more information More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/. More info for KoAlpaca: [TBD] Citations details KoLLAMA: [TBD] LLAMA: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/ License MIT Where to send questions or comments about the model Questions and comments about KoLLaMA can be sent via the [GitHub repository](https://github.com/beomi/KoLLAMA) of the project , by opening an issue. ## Intended use Primary intended uses The primary use of KoLLaMA is research on Korean Opensource large language models Primary intended users The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence. Out-of-scope use cases LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers. ## Factors Relevant factors One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model. ## Evaluation datasets [TBD] ## Training dataset [TBD] ## Ethical considerations Data The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data. Human life The model is not intended to inform decisions about matters central to human life, and should not be used in such a way. Risks and harms Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard. Use cases LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
lucs1265/distilbert-base-uncased-finetuned-cola	lucs1265	2023-07-11T01:11:57Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T01:06:54Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: lucs1265/distilbert-base-uncased-finetuned-cola 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. --> # lucs1265/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1898 - Validation Loss: 0.5233 - Train Matthews Correlation: 0.5286 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5194 \| 0.4536 \| 0.4725 \| 0 \| \| 0.3249 \| 0.4763 \| 0.4867 \| 1 \| \| 0.1898 \| 0.5233 \| 0.5286 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
mrovejaxd/ABL_b	mrovejaxd	2023-07-11T01:07:35Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-11T00:07:08Z	--- tags: - generated_from_trainer model-index: - name: ABL_b 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. --> # ABL_b This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.2
casque/Windrunnerv0.2	casque	2023-07-11T01:03:37Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T01:00:46Z	--- license: creativeml-openrail-m ---
hopkins/strict-small-4	hopkins	2023-07-11T00:43:51Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-13T21:25:31Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: strict-small-4 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. --> # strict-small-4 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.8588 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 9 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.9925 \| 1.83 \| 1000 \| 4.2033 \| \| 3.7647 \| 3.67 \| 2000 \| 3.9152 \| \| 3.3569 \| 5.5 \| 3000 \| 3.8495 \| \| 3.0079 \| 7.34 \| 4000 \| 3.8588 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
casque/CrystalMaidenv0.2	casque	2023-07-11T00:42:48Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T00:39:34Z	--- license: creativeml-openrail-m ---
ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH	ALM-AHME	2023-07-11T00:40:15Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T02:43:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Augmented-Final split: train args: Augmented-Final metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0929 \| 1.0 \| 281 \| 0.0919 \| 0.9657 \| \| 0.0908 \| 2.0 \| 562 \| 0.0127 \| 0.9967 \| \| 0.0525 \| 3.0 \| 843 \| 0.0133 \| 0.9947 \| \| 0.1301 \| 4.0 \| 1125 \| 0.0270 \| 0.9927 \| \| 0.0624 \| 5.0 \| 1406 \| 0.0064 \| 0.9973 \| \| 0.0506 \| 6.0 \| 1687 \| 0.0025 \| 0.999 \| \| 0.0001 \| 6.99 \| 1967 \| 0.0002 \| 1.0 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Corran/all_mini_lm_paraphrase_L3_v2_12tr_5t	Corran	2023-07-11T00:37:54Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-11T00:37:49Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # Corran/all_mini_lm_paraphrase_L3_v2_12tr_5t 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("Corran/all_mini_lm_paraphrase_L3_v2_12tr_5t") # 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} } ```
jpherrerap/ner-bert-base-spanish-wwm-uncased	jpherrerap	2023-07-11T00:35:25Z	125	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:jpherrerap/competencia2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T23:53:37Z	--- language: - es tags: - generated_from_trainer datasets: - jpherrerap/competencia2 model-index: - name: ner-bert-base-spanish-wwm-uncased 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. --> # ner-bert-base-spanish-wwm-uncased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the jpherrerap/competencia2 dataset. It achieves the following results on the evaluation set: - Loss: 1.5112 - Body Part Precision: 0.0 - Body Part Recall: 0.0 - Body Part F1: 0.0 - Body Part Number: 0 - Disease Precision: 0.0 - Disease Recall: 0.0 - Disease F1: 0.0 - Disease Number: 0 - Family Member Precision: 0.0 - Family Member Recall: 0.0 - Family Member F1: 0.0 - Family Member Number: 0 - Medication Precision: 0.0 - Medication Recall: 0.0 - Medication F1: 0.0 - Medication Number: 0 - Procedure Precision: 0.0 - Procedure Recall: 0.0 - Procedure F1: 0.0 - Procedure Number: 0 - Overall Precision: 0.0 - Overall Recall: 0.0 - Overall F1: 0.0 - Overall Accuracy: 0.6713 ## Model description More information needed ## Intended uses & 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: 13 - 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 \| Body Part Precision \| Body Part Recall \| Body Part F1 \| Body Part Number \| Disease Precision \| Disease Recall \| Disease F1 \| Disease Number \| Family Member Precision \| Family Member Recall \| Family Member F1 \| Family Member Number \| Medication Precision \| Medication Recall \| Medication F1 \| Medication Number \| Procedure Precision \| Procedure Recall \| Procedure F1 \| Procedure Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:--------------:\|:-----------------------:\|:--------------------:\|:----------------:\|:--------------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-----------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.3372 \| 1.0 \| 1004 \| 1.5112 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0.6713 \| \| 0.1611 \| 2.0 \| 2008 \| 1.7235 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0.6705 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Unmand/procare_business_unit	Unmand	2023-07-11T00:33:12Z	4	0	spacy	[ "spacy", "text-classification", "en", "region:us" ]	text-classification	2023-07-11T00:31:49Z	--- tags: - spacy - text-classification language: - en model-index: - name: en_procare_business_unit results: [] --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_procare_business_unit` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.5.4,<3.6.0` \| \| Default Pipeline \| `textcat_multilabel` \| \| Components \| `textcat_multilabel` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (30 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `textcat_multilabel` \| `Health and Wellbeing`, `NT Consulting`, `SA Workers Comp`, `VIC Workers Comp`, `ACT Consultancy`, `NSW Consulting`, `Staff Cards`, `NT MAC`, `NSW CTP`, `NSW Workers Comp`, `SA Consulting`, `ACT Workers Comp`, `Life and A&H`, `QLD Workers Comp`, `NT Workers Comp`, `Treatment`, `State Authorities Superannuation Scheme`, `ACT CTP`, `NULL`, `National Consulting`, `WA Workers Comp`, `QLD CTP`, `VIC TAC`, `WA Consulting`, `TAS Consulting`, `QLD Consulting`, `VIC Consulting`, `Comcare`, `TAS Workers Comp`, `SA CTP` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `CATS_SCORE` \| 81.21 \| \| `CATS_MICRO_P` \| 89.37 \| \| `CATS_MICRO_R` \| 59.54 \| \| `CATS_MICRO_F` \| 71.47 \| \| `CATS_MACRO_P` \| 67.87 \| \| `CATS_MACRO_R` \| 33.52 \| \| `CATS_MACRO_F` \| 42.52 \| \| `CATS_MACRO_AUC` \| 81.21 \| \| `TEXTCAT_MULTILABEL_LOSS` \| 73.72 \|
layoric/openllama-7b-qlora-orca	layoric	2023-07-11T00:31:19Z	4	0	peft	[ "peft", "region:us" ]	null	2023-07-09T23:58:03Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
bobobert4/poca-SoccerTwos	bobobert4	2023-07-11T00:18:04Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-07-11T00:16:06Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: bobobert4/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
bastianchinchon/nominal-groups-recognition-beto-clinical-wl-es	bastianchinchon	2023-07-10T23:58:42Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:bastianchinchon/spanish_nominal_groups_conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T22:32:12Z	--- language: - es tags: - generated_from_trainer datasets: - bastianchinchon/spanish_nominal_groups_conll2003 model-index: - name: nominal-groups-recognition-beto-clinical-wl-es results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nominal-groups-recognition-beto-clinical-wl-es This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the bastianchinchon/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2338 - Body Part Precision: 0.7894 - Body Part Recall: 0.8257 - Body Part F1: 0.8071 - Body Part Number: 413 - Disease Precision: 0.7790 - Disease Recall: 0.8133 - Disease F1: 0.7958 - Disease Number: 975 - Family Member Precision: 0.8286 - Family Member Recall: 0.9667 - Family Member F1: 0.8923 - Family Member Number: 30 - Medication Precision: 0.8913 - Medication Recall: 0.8817 - Medication F1: 0.8865 - Medication Number: 93 - Procedure Precision: 0.7130 - Procedure Recall: 0.7910 - Procedure F1: 0.75 - Procedure Number: 311 - Overall Precision: 0.7758 - Overall Recall: 0.8183 - Overall F1: 0.7965 - Overall Accuracy: 0.9382 ## Model description More information needed ## Intended uses & 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: 13 - 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 \| Body Part Precision \| Body Part Recall \| Body Part F1 \| Body Part Number \| Disease Precision \| Disease Recall \| Disease F1 \| Disease Number \| Family Member Precision \| Family Member Recall \| Family Member F1 \| Family Member Number \| Medication Precision \| Medication Recall \| Medication F1 \| Medication Number \| Procedure Precision \| Procedure Recall \| Procedure F1 \| Procedure Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:--------------:\|:-----------------------:\|:--------------------:\|:----------------:\|:--------------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-----------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.2998 \| 1.0 \| 1004 \| 0.2127 \| 0.7460 \| 0.7893 \| 0.7671 \| 413 \| 0.7612 \| 0.7815 \| 0.7713 \| 975 \| 0.9062 \| 0.9667 \| 0.9355 \| 30 \| 0.8462 \| 0.8280 \| 0.8370 \| 93 \| 0.6583 \| 0.7556 \| 0.7036 \| 311 \| 0.7450 \| 0.7843 \| 0.7642 \| 0.9331 \| \| 0.1566 \| 2.0 \| 2008 \| 0.2278 \| 0.7780 \| 0.8232 \| 0.8 \| 413 \| 0.7847 \| 0.8 \| 0.7923 \| 975 \| 0.8529 \| 0.9667 \| 0.9062 \| 30 \| 0.8710 \| 0.8710 \| 0.8710 \| 93 \| 0.7346 \| 0.7653 \| 0.7496 \| 311 \| 0.7800 \| 0.8057 \| 0.7927 \| 0.9367 \| \| 0.1089 \| 3.0 \| 3012 \| 0.2338 \| 0.7894 \| 0.8257 \| 0.8071 \| 413 \| 0.7790 \| 0.8133 \| 0.7958 \| 975 \| 0.8286 \| 0.9667 \| 0.8923 \| 30 \| 0.8913 \| 0.8817 \| 0.8865 \| 93 \| 0.7130 \| 0.7910 \| 0.75 \| 311 \| 0.7758 \| 0.8183 \| 0.7965 \| 0.9382 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jz0214/sd-class-butterflies-64	jz0214	2023-07-10T23:52:24Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-10T23:50:42Z	--- 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('jz0214/sd-class-butterflies-64') image = pipeline().images[0] image ```
aliceBG/distilbert-base-uncased-finetuned-cola	aliceBG	2023-07-10T23:38:28Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-09T23:52:39Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: aliceBG/distilbert-base-uncased-finetuned-cola 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. --> # aliceBG/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1834 - Validation Loss: 0.5540 - Train Matthews Correlation: 0.5495 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5170 \| 0.4723 \| 0.4122 \| 0 \| \| 0.3177 \| 0.4714 \| 0.5232 \| 1 \| \| 0.1834 \| 0.5540 \| 0.5495 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
digiplay/Juggernaut_final	digiplay	2023-07-10T23:21:23Z	1,591	15	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T22:56:03Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Models info : https://civitai.com/models/46422?modelVersionId=114770 Sample image I made thru huggingface's API: ![1396153a-5288-40f0-a52a-1e3b8cd9ad29.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/xIDqdaAoABeyKlucmgtXN.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/92fd888b-9f8f-4230-ac30-3ad36fb4800b/01H50CX4KX66D605VM2QMK1EP2-0.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5b10f2a0-f162-497a-80b1-ea5875d99ad8/01H50KNH2P89XCA1EV4EC35GGC-0.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7a4ee832-3d5d-4c69-85a8-235206e07b18/01H50K2G4JSN79V68A4B3RCTQ9-0.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/64b7297e-208f-476d-bbc3-f3420b159ee0/01H50MNJPGKZ7M889HWVK8KKK6-0.jpeg)
jz0214/sd-class-butterflies-32	jz0214	2023-07-10T23:09:47Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-10T23:08:46Z	--- 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('jz0214/sd-class-butterflies-32') image = pipeline().images[0] image ```
PistonPalacios/Piston	PistonPalacios	2023-07-10T23:04:10Z	0	0	diffusers	[ "diffusers", "legal", "es", "dataset:fka/awesome-chatgpt-prompts", "doi:10.57967/hf/0876", "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T22:50:18Z	--- license: creativeml-openrail-m datasets: - fka/awesome-chatgpt-prompts language: - es library_name: diffusers tags: - legal ---
trevorj/dqn-SpaceInvadersNoFrameskip-v4	trevorj	2023-07-10T22:41:49Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T22:41:13Z	--- 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: 523.00 +/- 142.73 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 trevorj -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 trevorj -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 trevorj ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.9	jordyvl	2023-07-10T22:40:13Z	161	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T22:00:19Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 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. --> # vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5373 - Accuracy: 0.85 - Brier Loss: 0.2432 - Nll: 1.1157 - F1 Micro: 0.85 - F1 Macro: 0.8450 - Ece: 0.1621 - Aurc: 0.0427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.1036 \| 0.215 \| 0.8753 \| 5.3195 \| 0.2150 \| 0.1264 \| 0.2571 \| 0.6923 \| \| No log \| 2.0 \| 14 \| 1.6952 \| 0.405 \| 0.7407 \| 3.4929 \| 0.405 \| 0.2416 \| 0.2907 \| 0.4040 \| \| No log \| 3.0 \| 21 \| 1.1843 \| 0.62 \| 0.5633 \| 2.0113 \| 0.62 \| 0.5725 \| 0.2740 \| 0.2014 \| \| No log \| 4.0 \| 28 \| 0.8797 \| 0.71 \| 0.4080 \| 1.7043 \| 0.7100 \| 0.6683 \| 0.2024 \| 0.1125 \| \| No log \| 5.0 \| 35 \| 0.8570 \| 0.715 \| 0.3837 \| 1.6476 \| 0.715 \| 0.7280 \| 0.2189 \| 0.1079 \| \| No log \| 6.0 \| 42 \| 0.7484 \| 0.775 \| 0.3285 \| 1.5962 \| 0.775 \| 0.7668 \| 0.1873 \| 0.0816 \| \| No log \| 7.0 \| 49 \| 0.7337 \| 0.79 \| 0.3131 \| 1.5377 \| 0.79 \| 0.7779 \| 0.1904 \| 0.0771 \| \| No log \| 8.0 \| 56 \| 0.6709 \| 0.795 \| 0.3012 \| 1.2156 \| 0.795 \| 0.7776 \| 0.1939 \| 0.0761 \| \| No log \| 9.0 \| 63 \| 0.6901 \| 0.795 \| 0.3069 \| 1.4725 \| 0.795 \| 0.7916 \| 0.1882 \| 0.0769 \| \| No log \| 10.0 \| 70 \| 0.7960 \| 0.75 \| 0.3586 \| 1.4426 \| 0.75 \| 0.7406 \| 0.1868 \| 0.0976 \| \| No log \| 11.0 \| 77 \| 0.7489 \| 0.77 \| 0.3296 \| 1.6202 \| 0.7700 \| 0.7794 \| 0.2020 \| 0.0878 \| \| No log \| 12.0 \| 84 \| 0.7068 \| 0.785 \| 0.3270 \| 1.4127 \| 0.785 \| 0.7812 \| 0.1922 \| 0.0759 \| \| No log \| 13.0 \| 91 \| 0.6687 \| 0.79 \| 0.3050 \| 1.3820 \| 0.79 \| 0.7945 \| 0.1818 \| 0.0625 \| \| No log \| 14.0 \| 98 \| 0.6052 \| 0.79 \| 0.2854 \| 1.0602 \| 0.79 \| 0.7716 \| 0.1702 \| 0.0590 \| \| No log \| 15.0 \| 105 \| 0.6369 \| 0.795 \| 0.2959 \| 1.0580 \| 0.795 \| 0.7953 \| 0.1709 \| 0.0603 \| \| No log \| 16.0 \| 112 \| 0.6204 \| 0.81 \| 0.2816 \| 1.1886 \| 0.81 \| 0.8050 \| 0.1657 \| 0.0702 \| \| No log \| 17.0 \| 119 \| 0.5648 \| 0.83 \| 0.2475 \| 1.2506 \| 0.83 \| 0.8241 \| 0.1347 \| 0.0612 \| \| No log \| 18.0 \| 126 \| 0.5849 \| 0.83 \| 0.2672 \| 1.2245 \| 0.83 \| 0.8155 \| 0.1646 \| 0.0601 \| \| No log \| 19.0 \| 133 \| 0.5536 \| 0.835 \| 0.2475 \| 1.0514 \| 0.835 \| 0.8254 \| 0.1683 \| 0.0531 \| \| No log \| 20.0 \| 140 \| 0.5689 \| 0.835 \| 0.2513 \| 1.2369 \| 0.835 \| 0.8437 \| 0.1722 \| 0.0489 \| \| No log \| 21.0 \| 147 \| 0.5540 \| 0.83 \| 0.2485 \| 1.2139 \| 0.83 \| 0.8165 \| 0.1641 \| 0.0608 \| \| No log \| 22.0 \| 154 \| 0.5352 \| 0.835 \| 0.2402 \| 1.0108 \| 0.835 \| 0.8295 \| 0.1408 \| 0.0430 \| \| No log \| 23.0 \| 161 \| 0.5380 \| 0.84 \| 0.2403 \| 1.2280 \| 0.8400 \| 0.8347 \| 0.1405 \| 0.0436 \| \| No log \| 24.0 \| 168 \| 0.5422 \| 0.835 \| 0.2471 \| 1.0204 \| 0.835 \| 0.8324 \| 0.1606 \| 0.0445 \| \| No log \| 25.0 \| 175 \| 0.5342 \| 0.85 \| 0.2404 \| 1.0767 \| 0.85 \| 0.8487 \| 0.1469 \| 0.0432 \| \| No log \| 26.0 \| 182 \| 0.5374 \| 0.84 \| 0.2429 \| 1.0774 \| 0.8400 \| 0.8334 \| 0.1420 \| 0.0462 \| \| No log \| 27.0 \| 189 \| 0.5311 \| 0.85 \| 0.2395 \| 1.0748 \| 0.85 \| 0.8487 \| 0.1439 \| 0.0446 \| \| No log \| 28.0 \| 196 \| 0.5298 \| 0.85 \| 0.2384 \| 1.1337 \| 0.85 \| 0.8487 \| 0.1570 \| 0.0437 \| \| No log \| 29.0 \| 203 \| 0.5387 \| 0.845 \| 0.2435 \| 1.1319 \| 0.845 \| 0.8424 \| 0.1539 \| 0.0458 \| \| No log \| 30.0 \| 210 \| 0.5361 \| 0.85 \| 0.2430 \| 1.0648 \| 0.85 \| 0.8450 \| 0.1679 \| 0.0431 \| \| No log \| 31.0 \| 217 \| 0.5339 \| 0.85 \| 0.2413 \| 1.0676 \| 0.85 \| 0.8487 \| 0.1646 \| 0.0428 \| \| No log \| 32.0 \| 224 \| 0.5345 \| 0.85 \| 0.2421 \| 1.0709 \| 0.85 \| 0.8487 \| 0.1476 \| 0.0440 \| \| No log \| 33.0 \| 231 \| 0.5343 \| 0.85 \| 0.2421 \| 1.1236 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0431 \| \| No log \| 34.0 \| 238 \| 0.5353 \| 0.845 \| 0.2426 \| 1.1244 \| 0.845 \| 0.8424 \| 0.1710 \| 0.0428 \| \| No log \| 35.0 \| 245 \| 0.5346 \| 0.85 \| 0.2423 \| 1.0649 \| 0.85 \| 0.8487 \| 0.1520 \| 0.0440 \| \| No log \| 36.0 \| 252 \| 0.5356 \| 0.855 \| 0.2422 \| 1.1241 \| 0.855 \| 0.8517 \| 0.1814 \| 0.0429 \| \| No log \| 37.0 \| 259 \| 0.5357 \| 0.85 \| 0.2426 \| 1.1237 \| 0.85 \| 0.8450 \| 0.1670 \| 0.0425 \| \| No log \| 38.0 \| 266 \| 0.5356 \| 0.845 \| 0.2426 \| 1.1226 \| 0.845 \| 0.8419 \| 0.1607 \| 0.0435 \| \| No log \| 39.0 \| 273 \| 0.5347 \| 0.855 \| 0.2420 \| 1.0739 \| 0.855 \| 0.8517 \| 0.1597 \| 0.0427 \| \| No log \| 40.0 \| 280 \| 0.5356 \| 0.855 \| 0.2423 \| 1.1203 \| 0.855 \| 0.8517 \| 0.1676 \| 0.0435 \| \| No log \| 41.0 \| 287 \| 0.5365 \| 0.85 \| 0.2431 \| 1.1199 \| 0.85 \| 0.8450 \| 0.1780 \| 0.0429 \| \| No log \| 42.0 \| 294 \| 0.5356 \| 0.85 \| 0.2426 \| 1.1173 \| 0.85 \| 0.8450 \| 0.1653 \| 0.0430 \| \| No log \| 43.0 \| 301 \| 0.5363 \| 0.85 \| 0.2428 \| 1.1189 \| 0.85 \| 0.8450 \| 0.1550 \| 0.0435 \| \| No log \| 44.0 \| 308 \| 0.5345 \| 0.85 \| 0.2418 \| 1.1193 \| 0.85 \| 0.8450 \| 0.1590 \| 0.0428 \| \| No log \| 45.0 \| 315 \| 0.5374 \| 0.85 \| 0.2435 \| 1.1202 \| 0.85 \| 0.8450 \| 0.1633 \| 0.0435 \| \| No log \| 46.0 \| 322 \| 0.5355 \| 0.85 \| 0.2423 \| 1.1183 \| 0.85 \| 0.8450 \| 0.1564 \| 0.0428 \| \| No log \| 47.0 \| 329 \| 0.5354 \| 0.85 \| 0.2425 \| 1.1176 \| 0.85 \| 0.8450 \| 0.1509 \| 0.0429 \| \| No log \| 48.0 \| 336 \| 0.5369 \| 0.85 \| 0.2433 \| 1.1177 \| 0.85 \| 0.8450 \| 0.1517 \| 0.0432 \| \| No log \| 49.0 \| 343 \| 0.5361 \| 0.85 \| 0.2428 \| 1.1182 \| 0.85 \| 0.8450 \| 0.1490 \| 0.0428 \| \| No log \| 50.0 \| 350 \| 0.5364 \| 0.85 \| 0.2431 \| 1.1179 \| 0.85 \| 0.8450 \| 0.1654 \| 0.0430 \| \| No log \| 51.0 \| 357 \| 0.5365 \| 0.85 \| 0.2428 \| 1.1185 \| 0.85 \| 0.8450 \| 0.1729 \| 0.0432 \| \| No log \| 52.0 \| 364 \| 0.5364 \| 0.85 \| 0.2430 \| 1.1165 \| 0.85 \| 0.8450 \| 0.1614 \| 0.0429 \| \| No log \| 53.0 \| 371 \| 0.5362 \| 0.85 \| 0.2429 \| 1.1167 \| 0.85 \| 0.8450 \| 0.1694 \| 0.0430 \| \| No log \| 54.0 \| 378 \| 0.5369 \| 0.85 \| 0.2432 \| 1.1170 \| 0.85 \| 0.8450 \| 0.1597 \| 0.0432 \| \| No log \| 55.0 \| 385 \| 0.5368 \| 0.85 \| 0.2430 \| 1.1168 \| 0.85 \| 0.8450 \| 0.1670 \| 0.0429 \| \| No log \| 56.0 \| 392 \| 0.5367 \| 0.85 \| 0.2430 \| 1.1180 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0430 \| \| No log \| 57.0 \| 399 \| 0.5364 \| 0.85 \| 0.2429 \| 1.1163 \| 0.85 \| 0.8450 \| 0.1649 \| 0.0429 \| \| No log \| 58.0 \| 406 \| 0.5364 \| 0.85 \| 0.2430 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1611 \| 0.0429 \| \| No log \| 59.0 \| 413 \| 0.5365 \| 0.85 \| 0.2428 \| 1.1163 \| 0.85 \| 0.8450 \| 0.1591 \| 0.0429 \| \| No log \| 60.0 \| 420 \| 0.5364 \| 0.85 \| 0.2429 \| 1.1155 \| 0.85 \| 0.8450 \| 0.1588 \| 0.0429 \| \| No log \| 61.0 \| 427 \| 0.5370 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1772 \| 0.0432 \| \| No log \| 62.0 \| 434 \| 0.5367 \| 0.85 \| 0.2429 \| 1.1167 \| 0.85 \| 0.8450 \| 0.1622 \| 0.0429 \| \| No log \| 63.0 \| 441 \| 0.5362 \| 0.85 \| 0.2428 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1503 \| 0.0428 \| \| No log \| 64.0 \| 448 \| 0.5372 \| 0.85 \| 0.2433 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1616 \| 0.0432 \| \| No log \| 65.0 \| 455 \| 0.5371 \| 0.85 \| 0.2431 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1499 \| 0.0429 \| \| No log \| 66.0 \| 462 \| 0.5367 \| 0.85 \| 0.2430 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1591 \| 0.0427 \| \| No log \| 67.0 \| 469 \| 0.5367 \| 0.85 \| 0.2430 \| 1.1164 \| 0.85 \| 0.8450 \| 0.1562 \| 0.0428 \| \| No log \| 68.0 \| 476 \| 0.5368 \| 0.85 \| 0.2430 \| 1.1168 \| 0.85 \| 0.8450 \| 0.1556 \| 0.0427 \| \| No log \| 69.0 \| 483 \| 0.5368 \| 0.85 \| 0.2431 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1593 \| 0.0428 \| \| No log \| 70.0 \| 490 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1628 \| 0.0428 \| \| No log \| 71.0 \| 497 \| 0.5371 \| 0.85 \| 0.2432 \| 1.1163 \| 0.85 \| 0.8450 \| 0.1599 \| 0.0429 \| \| 0.1708 \| 72.0 \| 504 \| 0.5370 \| 0.85 \| 0.2430 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1559 \| 0.0430 \| \| 0.1708 \| 73.0 \| 511 \| 0.5372 \| 0.85 \| 0.2433 \| 1.1154 \| 0.85 \| 0.8450 \| 0.1556 \| 0.0428 \| \| 0.1708 \| 74.0 \| 518 \| 0.5370 \| 0.85 \| 0.2429 \| 1.1165 \| 0.85 \| 0.8450 \| 0.1540 \| 0.0428 \| \| 0.1708 \| 75.0 \| 525 \| 0.5371 \| 0.85 \| 0.2431 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1616 \| 0.0427 \| \| 0.1708 \| 76.0 \| 532 \| 0.5369 \| 0.85 \| 0.2431 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0427 \| \| 0.1708 \| 77.0 \| 539 \| 0.5369 \| 0.85 \| 0.2430 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1623 \| 0.0429 \| \| 0.1708 \| 78.0 \| 546 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0427 \| \| 0.1708 \| 79.0 \| 553 \| 0.5375 \| 0.85 \| 0.2433 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1688 \| 0.0429 \| \| 0.1708 \| 80.0 \| 560 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1623 \| 0.0429 \| \| 0.1708 \| 81.0 \| 567 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0428 \| \| 0.1708 \| 82.0 \| 574 \| 0.5374 \| 0.85 \| 0.2433 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1622 \| 0.0428 \| \| 0.1708 \| 83.0 \| 581 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1159 \| 0.85 \| 0.8450 \| 0.1622 \| 0.0428 \| \| 0.1708 \| 84.0 \| 588 \| 0.5371 \| 0.85 \| 0.2431 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 85.0 \| 595 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1687 \| 0.0426 \| \| 0.1708 \| 86.0 \| 602 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0426 \| \| 0.1708 \| 87.0 \| 609 \| 0.5374 \| 0.85 \| 0.2432 \| 1.1159 \| 0.85 \| 0.8450 \| 0.1687 \| 0.0428 \| \| 0.1708 \| 88.0 \| 616 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 89.0 \| 623 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 90.0 \| 630 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 91.0 \| 637 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 92.0 \| 644 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 93.0 \| 651 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 94.0 \| 658 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 95.0 \| 665 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 96.0 \| 672 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 97.0 \| 679 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 98.0 \| 686 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 99.0 \| 693 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 100.0 \| 700 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
Raizel123/SNoonzlora	Raizel123	2023-07-10T22:35:30Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T22:32:26Z	--- license: creativeml-openrail-m ---
Raizel123/Mbyonglora	Raizel123	2023-07-10T22:31:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T22:27:47Z	--- license: creativeml-openrail-m ---
jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_MSE	jordyvl	2023-07-10T22:30:03Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T21:13:38Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_MSE 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. --> # vit-small_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4673 - Accuracy: 0.6425 - Brier Loss: 0.4763 - Nll: 3.0680 - F1 Micro: 0.6425 - F1 Macro: 0.6485 - Ece: 0.1946 - Aurc: 0.1381 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 4.4851 \| 0.06 \| 0.9565 \| 13.8276 \| 0.06 \| 0.0556 \| 0.1688 \| 0.9385 \| \| No log \| 2.0 \| 50 \| 3.5619 \| 0.3775 \| 0.7827 \| 6.2649 \| 0.3775 \| 0.3611 \| 0.2331 \| 0.3882 \| \| No log \| 3.0 \| 75 \| 2.8990 \| 0.5025 \| 0.6453 \| 4.7376 \| 0.5025 \| 0.4858 \| 0.1689 \| 0.2658 \| \| No log \| 4.0 \| 100 \| 2.5972 \| 0.515 \| 0.5980 \| 4.4210 \| 0.515 \| 0.4895 \| 0.1605 \| 0.2249 \| \| No log \| 5.0 \| 125 \| 2.4353 \| 0.56 \| 0.5762 \| 3.4885 \| 0.56 \| 0.5566 \| 0.1548 \| 0.2100 \| \| No log \| 6.0 \| 150 \| 2.4157 \| 0.5475 \| 0.5864 \| 3.8261 \| 0.5475 \| 0.5323 \| 0.1837 \| 0.2167 \| \| No log \| 7.0 \| 175 \| 2.1786 \| 0.6075 \| 0.5203 \| 3.4565 \| 0.6075 \| 0.6103 \| 0.1403 \| 0.1670 \| \| No log \| 8.0 \| 200 \| 2.1082 \| 0.63 \| 0.5040 \| 3.3570 \| 0.63 \| 0.6246 \| 0.1580 \| 0.1530 \| \| No log \| 9.0 \| 225 \| 2.0472 \| 0.625 \| 0.5042 \| 3.8572 \| 0.625 \| 0.6184 \| 0.1552 \| 0.1530 \| \| No log \| 10.0 \| 250 \| 2.0589 \| 0.6025 \| 0.5468 \| 3.5723 \| 0.6025 \| 0.5982 \| 0.1781 \| 0.1785 \| \| No log \| 11.0 \| 275 \| 1.8965 \| 0.65 \| 0.4755 \| 3.4466 \| 0.65 \| 0.6497 \| 0.1605 \| 0.1475 \| \| No log \| 12.0 \| 300 \| 1.9014 \| 0.6325 \| 0.5066 \| 3.0881 \| 0.6325 \| 0.6359 \| 0.1658 \| 0.1591 \| \| No log \| 13.0 \| 325 \| 1.7904 \| 0.6175 \| 0.5162 \| 3.4673 \| 0.6175 \| 0.6141 \| 0.1525 \| 0.1598 \| \| No log \| 14.0 \| 350 \| 1.8624 \| 0.625 \| 0.5173 \| 3.6824 \| 0.625 \| 0.6179 \| 0.1567 \| 0.1624 \| \| No log \| 15.0 \| 375 \| 1.7083 \| 0.6625 \| 0.4817 \| 3.1296 \| 0.6625 \| 0.6686 \| 0.1651 \| 0.1405 \| \| No log \| 16.0 \| 400 \| 1.8848 \| 0.59 \| 0.5478 \| 4.3761 \| 0.59 \| 0.5913 \| 0.2083 \| 0.1696 \| \| No log \| 17.0 \| 425 \| 1.7238 \| 0.6125 \| 0.5229 \| 3.1232 \| 0.6125 \| 0.6052 \| 0.1833 \| 0.1553 \| \| No log \| 18.0 \| 450 \| 1.7126 \| 0.625 \| 0.5152 \| 2.9267 \| 0.625 \| 0.6284 \| 0.1747 \| 0.1565 \| \| No log \| 19.0 \| 475 \| 1.6459 \| 0.6275 \| 0.5024 \| 2.9078 \| 0.6275 \| 0.6219 \| 0.1766 \| 0.1527 \| \| 1.0542 \| 20.0 \| 500 \| 1.6029 \| 0.6275 \| 0.4855 \| 3.0931 \| 0.6275 \| 0.6316 \| 0.1720 \| 0.1414 \| \| 1.0542 \| 21.0 \| 525 \| 1.6566 \| 0.6525 \| 0.4847 \| 3.0998 \| 0.6525 \| 0.6479 \| 0.1558 \| 0.1438 \| \| 1.0542 \| 22.0 \| 550 \| 1.6169 \| 0.645 \| 0.4894 \| 3.0081 \| 0.645 \| 0.6471 \| 0.1687 \| 0.1400 \| \| 1.0542 \| 23.0 \| 575 \| 1.5322 \| 0.6525 \| 0.4557 \| 3.3587 \| 0.6525 \| 0.6520 \| 0.1428 \| 0.1247 \| \| 1.0542 \| 24.0 \| 600 \| 1.5991 \| 0.6475 \| 0.4787 \| 2.9349 \| 0.6475 \| 0.6444 \| 0.1580 \| 0.1450 \| \| 1.0542 \| 25.0 \| 625 \| 1.5625 \| 0.6375 \| 0.4926 \| 3.0245 \| 0.6375 \| 0.6378 \| 0.1641 \| 0.1433 \| \| 1.0542 \| 26.0 \| 650 \| 1.5366 \| 0.64 \| 0.4884 \| 3.3388 \| 0.64 \| 0.6461 \| 0.1595 \| 0.1453 \| \| 1.0542 \| 27.0 \| 675 \| 1.5686 \| 0.65 \| 0.4765 \| 3.5120 \| 0.65 \| 0.6504 \| 0.1625 \| 0.1359 \| \| 1.0542 \| 28.0 \| 700 \| 1.5562 \| 0.6475 \| 0.4817 \| 3.0348 \| 0.6475 \| 0.6488 \| 0.1459 \| 0.1388 \| \| 1.0542 \| 29.0 \| 725 \| 1.5213 \| 0.6475 \| 0.4719 \| 3.2628 \| 0.6475 \| 0.6475 \| 0.1634 \| 0.1326 \| \| 1.0542 \| 30.0 \| 750 \| 1.5492 \| 0.6675 \| 0.4730 \| 3.1693 \| 0.6675 \| 0.6679 \| 0.1469 \| 0.1415 \| \| 1.0542 \| 31.0 \| 775 \| 1.5311 \| 0.65 \| 0.4896 \| 3.0881 \| 0.65 \| 0.6504 \| 0.1815 \| 0.1380 \| \| 1.0542 \| 32.0 \| 800 \| 1.5556 \| 0.6475 \| 0.4821 \| 3.1829 \| 0.6475 \| 0.6491 \| 0.1640 \| 0.1405 \| \| 1.0542 \| 33.0 \| 825 \| 1.5471 \| 0.6375 \| 0.4846 \| 3.4190 \| 0.6375 \| 0.6407 \| 0.1628 \| 0.1415 \| \| 1.0542 \| 34.0 \| 850 \| 1.4809 \| 0.6575 \| 0.4714 \| 2.9136 \| 0.6575 \| 0.6612 \| 0.1729 \| 0.1338 \| \| 1.0542 \| 35.0 \| 875 \| 1.5256 \| 0.66 \| 0.4773 \| 3.2303 \| 0.66 \| 0.6650 \| 0.1746 \| 0.1368 \| \| 1.0542 \| 36.0 \| 900 \| 1.4929 \| 0.6675 \| 0.4671 \| 3.2360 \| 0.6675 \| 0.6698 \| 0.1698 \| 0.1309 \| \| 1.0542 \| 37.0 \| 925 \| 1.4923 \| 0.645 \| 0.4880 \| 3.0567 \| 0.645 \| 0.6564 \| 0.1764 \| 0.1395 \| \| 1.0542 \| 38.0 \| 950 \| 1.5038 \| 0.665 \| 0.4672 \| 3.2116 \| 0.665 \| 0.6661 \| 0.1588 \| 0.1343 \| \| 1.0542 \| 39.0 \| 975 \| 1.4708 \| 0.6625 \| 0.4669 \| 3.1420 \| 0.6625 \| 0.6675 \| 0.1683 \| 0.1301 \| \| 0.0522 \| 40.0 \| 1000 \| 1.5153 \| 0.6475 \| 0.4865 \| 3.1796 \| 0.6475 \| 0.6447 \| 0.1639 \| 0.1400 \| \| 0.0522 \| 41.0 \| 1025 \| 1.4705 \| 0.6575 \| 0.4642 \| 3.2196 \| 0.6575 \| 0.6626 \| 0.1440 \| 0.1308 \| \| 0.0522 \| 42.0 \| 1050 \| 1.4844 \| 0.6575 \| 0.4722 \| 3.2445 \| 0.6575 \| 0.6595 \| 0.1746 \| 0.1328 \| \| 0.0522 \| 43.0 \| 1075 \| 1.4957 \| 0.6425 \| 0.4828 \| 3.1456 \| 0.6425 \| 0.6468 \| 0.1499 \| 0.1417 \| \| 0.0522 \| 44.0 \| 1100 \| 1.5179 \| 0.645 \| 0.4910 \| 3.3921 \| 0.645 \| 0.6470 \| 0.1861 \| 0.1433 \| \| 0.0522 \| 45.0 \| 1125 \| 1.4878 \| 0.6425 \| 0.4839 \| 3.2139 \| 0.6425 \| 0.6478 \| 0.1720 \| 0.1403 \| \| 0.0522 \| 46.0 \| 1150 \| 1.4666 \| 0.655 \| 0.4741 \| 2.9333 \| 0.655 \| 0.6601 \| 0.1813 \| 0.1347 \| \| 0.0522 \| 47.0 \| 1175 \| 1.4954 \| 0.6575 \| 0.4776 \| 3.2102 \| 0.6575 \| 0.6604 \| 0.1842 \| 0.1390 \| \| 0.0522 \| 48.0 \| 1200 \| 1.4976 \| 0.645 \| 0.4856 \| 3.1539 \| 0.645 \| 0.6493 \| 0.1549 \| 0.1407 \| \| 0.0522 \| 49.0 \| 1225 \| 1.4772 \| 0.64 \| 0.4780 \| 2.9845 \| 0.64 \| 0.6445 \| 0.1826 \| 0.1388 \| \| 0.0522 \| 50.0 \| 1250 \| 1.4584 \| 0.65 \| 0.4703 \| 3.0776 \| 0.65 \| 0.6533 \| 0.1685 \| 0.1352 \| \| 0.0522 \| 51.0 \| 1275 \| 1.4828 \| 0.6325 \| 0.4844 \| 3.1425 \| 0.6325 \| 0.6377 \| 0.1641 \| 0.1409 \| \| 0.0522 \| 52.0 \| 1300 \| 1.4676 \| 0.6525 \| 0.4737 \| 3.1483 \| 0.6525 \| 0.6565 \| 0.1773 \| 0.1358 \| \| 0.0522 \| 53.0 \| 1325 \| 1.4675 \| 0.6475 \| 0.4791 \| 3.1411 \| 0.6475 \| 0.6515 \| 0.1820 \| 0.1388 \| \| 0.0522 \| 54.0 \| 1350 \| 1.4724 \| 0.645 \| 0.4764 \| 3.0744 \| 0.645 \| 0.6499 \| 0.1847 \| 0.1382 \| \| 0.0522 \| 55.0 \| 1375 \| 1.4689 \| 0.6425 \| 0.4769 \| 3.2256 \| 0.6425 \| 0.6476 \| 0.1839 \| 0.1376 \| \| 0.0522 \| 56.0 \| 1400 \| 1.4660 \| 0.6425 \| 0.4760 \| 2.9907 \| 0.6425 \| 0.6479 \| 0.1906 \| 0.1378 \| \| 0.0522 \| 57.0 \| 1425 \| 1.4663 \| 0.645 \| 0.4757 \| 3.0722 \| 0.645 \| 0.6514 \| 0.1705 \| 0.1367 \| \| 0.0522 \| 58.0 \| 1450 \| 1.4678 \| 0.65 \| 0.4770 \| 3.0710 \| 0.65 \| 0.6546 \| 0.1794 \| 0.1371 \| \| 0.0522 \| 59.0 \| 1475 \| 1.4717 \| 0.64 \| 0.4786 \| 3.0737 \| 0.64 \| 0.6455 \| 0.1889 \| 0.1392 \| \| 0.0064 \| 60.0 \| 1500 \| 1.4691 \| 0.645 \| 0.4768 \| 3.0688 \| 0.645 \| 0.6499 \| 0.1815 \| 0.1378 \| \| 0.0064 \| 61.0 \| 1525 \| 1.4689 \| 0.64 \| 0.4767 \| 3.0688 \| 0.64 \| 0.6452 \| 0.1846 \| 0.1382 \| \| 0.0064 \| 62.0 \| 1550 \| 1.4689 \| 0.64 \| 0.4770 \| 3.0674 \| 0.64 \| 0.6455 \| 0.1937 \| 0.1383 \| \| 0.0064 \| 63.0 \| 1575 \| 1.4687 \| 0.6425 \| 0.4767 \| 3.0700 \| 0.6425 \| 0.6485 \| 0.1897 \| 0.1381 \| \| 0.0064 \| 64.0 \| 1600 \| 1.4674 \| 0.6425 \| 0.4764 \| 3.0675 \| 0.6425 \| 0.6472 \| 0.1855 \| 0.1375 \| \| 0.0064 \| 65.0 \| 1625 \| 1.4681 \| 0.6425 \| 0.4766 \| 3.0694 \| 0.6425 \| 0.6485 \| 0.1917 \| 0.1381 \| \| 0.0064 \| 66.0 \| 1650 \| 1.4681 \| 0.6425 \| 0.4766 \| 3.0687 \| 0.6425 \| 0.6472 \| 0.1905 \| 0.1378 \| \| 0.0064 \| 67.0 \| 1675 \| 1.4667 \| 0.645 \| 0.4757 \| 3.0681 \| 0.645 \| 0.6505 \| 0.1899 \| 0.1375 \| \| 0.0064 \| 68.0 \| 1700 \| 1.4683 \| 0.6425 \| 0.4771 \| 3.0686 \| 0.6425 \| 0.6474 \| 0.1871 \| 0.1379 \| \| 0.0064 \| 69.0 \| 1725 \| 1.4672 \| 0.64 \| 0.4760 \| 3.0679 \| 0.64 \| 0.6455 \| 0.1932 \| 0.1380 \| \| 0.0064 \| 70.0 \| 1750 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0683 \| 0.6425 \| 0.6474 \| 0.1955 \| 0.1376 \| \| 0.0064 \| 71.0 \| 1775 \| 1.4676 \| 0.645 \| 0.4763 \| 3.0680 \| 0.645 \| 0.6505 \| 0.1921 \| 0.1376 \| \| 0.0064 \| 72.0 \| 1800 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0683 \| 0.6425 \| 0.6474 \| 0.1946 \| 0.1376 \| \| 0.0064 \| 73.0 \| 1825 \| 1.4675 \| 0.6425 \| 0.4763 \| 3.0682 \| 0.6425 \| 0.6474 \| 0.1946 \| 0.1377 \| \| 0.0064 \| 74.0 \| 1850 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0682 \| 0.6425 \| 0.6485 \| 0.1945 \| 0.1380 \| \| 0.0064 \| 75.0 \| 1875 \| 1.4674 \| 0.64 \| 0.4763 \| 3.0680 \| 0.64 \| 0.6455 \| 0.1960 \| 0.1380 \| \| 0.0064 \| 76.0 \| 1900 \| 1.4675 \| 0.64 \| 0.4764 \| 3.0682 \| 0.64 \| 0.6455 \| 0.1972 \| 0.1381 \| \| 0.0064 \| 77.0 \| 1925 \| 1.4675 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1947 \| 0.1380 \| \| 0.0064 \| 78.0 \| 1950 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1381 \| \| 0.0064 \| 79.0 \| 1975 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6474 \| 0.1935 \| 0.1376 \| \| 0.0 \| 80.0 \| 2000 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1380 \| \| 0.0 \| 81.0 \| 2025 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1380 \| \| 0.0 \| 82.0 \| 2050 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1935 \| 0.1380 \| \| 0.0 \| 83.0 \| 2075 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 84.0 \| 2100 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1381 \| \| 0.0 \| 85.0 \| 2125 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 86.0 \| 2150 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 87.0 \| 2175 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1381 \| \| 0.0 \| 88.0 \| 2200 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 89.0 \| 2225 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 90.0 \| 2250 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 91.0 \| 2275 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 92.0 \| 2300 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 93.0 \| 2325 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 94.0 \| 2350 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1909 \| 0.1381 \| \| 0.0 \| 95.0 \| 2375 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 96.0 \| 2400 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 97.0 \| 2425 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 98.0 \| 2450 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 99.0 \| 2475 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 100.0 \| 2500 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
Raizel123/Alfilora	Raizel123	2023-07-10T22:23:13Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T22:18:35Z	--- license: creativeml-openrail-m ---
NasimB/gpt2-dp-mod-datasets-txt-processing-rarity-all	NasimB	2023-07-10T22:14:46Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T19:52:59Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-mod-datasets-txt-processing-rarity-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-dp-mod-datasets-txt-processing-rarity-all This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7606 \| 0.29 \| 500 \| 5.6933 \| \| 5.4375 \| 0.59 \| 1000 \| 5.2559 \| \| 5.0937 \| 0.88 \| 1500 \| 5.0171 \| \| 4.8204 \| 1.18 \| 2000 \| 4.8701 \| \| 4.6728 \| 1.47 \| 2500 \| 4.7593 \| \| 4.574 \| 1.77 \| 3000 \| 4.6587 \| \| 4.4456 \| 2.06 \| 3500 \| 4.5885 \| \| 4.258 \| 2.36 \| 4000 \| 4.5468 \| \| 4.2423 \| 2.65 \| 4500 \| 4.4860 \| \| 4.2036 \| 2.94 \| 5000 \| 4.4302 \| \| 3.9737 \| 3.24 \| 5500 \| 4.4364 \| \| 3.9439 \| 3.53 \| 6000 \| 4.4019 \| \| 3.9271 \| 3.83 \| 6500 \| 4.3632 \| \| 3.7901 \| 4.12 \| 7000 \| 4.3689 \| \| 3.6474 \| 4.42 \| 7500 \| 4.3662 \| \| 3.6414 \| 4.71 \| 8000 \| 4.3472 \| \| 3.6338 \| 5.01 \| 8500 \| 4.3344 \| \| 3.3764 \| 5.3 \| 9000 \| 4.3618 \| \| 3.3821 \| 5.59 \| 9500 \| 4.3568 \| \| 3.3777 \| 5.89 \| 10000 \| 4.3513 \| \| 3.2752 \| 6.18 \| 10500 \| 4.3602 \| \| 3.2228 \| 6.48 \| 11000 \| 4.3652 \| \| 3.2172 \| 6.77 \| 11500 \| 4.3656 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
MnLgt/swivel_inversion	MnLgt	2023-07-10T22:11:42Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-07-10T22:11:41Z	--- license: mit --- ### swivel_inversion on Stable Diffusion This is the `<swivel-chair>` 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`: ![<swivel-chair> 0](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/942493f105b42e65e9bbb2afb8fd24ee.jpg) ![<swivel-chair> 1](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/25a60f0b7e1df4480da0096f4855d3cd.jpg) ![<swivel-chair> 2](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/099ba1cdff4a7d6d76437ec3b9d48743.jpg) ![<swivel-chair> 3](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a95a8487c048359027c5dc1f2f4231cd.jpg) ![<swivel-chair> 4](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e2258228f0c125fc4f0d2b3c27c4b5b5.jpg) ![<swivel-chair> 5](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/7692300d5457b1ad0b9b77bb4370a7b5.jpg) ![<swivel-chair> 6](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3b7fb905cd512af41d664db5b5c9c489.jpg) ![<swivel-chair> 7](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/89803df4905f81d2c1f70a1a7faf68fd.jpg) ![<swivel-chair> 8](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/570e6f83c4b0cd052893aee8e7030521.jpg) ![<swivel-chair> 9](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/465e22dc7bbfd6f42a803e8ab35c0609.jpg) ![<swivel-chair> 10](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a6490c193d2815bd520a2478fcdb543f.jpg) ![<swivel-chair> 11](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/c789ecc814b35df75187611633dbd84a.jpg) ![<swivel-chair> 12](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/11ca822a037aa86f3316564ac212ac1c.jpg) ![<swivel-chair> 13](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/5dea3e2b3148e21a4cb4dfe8dea7af08.jpg) ![<swivel-chair> 14](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/46b89058d8c2342f3c152b50039cb0c9.jpg) ![<swivel-chair> 15](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/749bf590f4dd9f4c3ef1ffd58e7db3e8.jpg) ![<swivel-chair> 16](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6e32521663ac1cd6d0999e4a09dbf5a1.jpg) ![<swivel-chair> 17](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/bd3580a999d8ec073f2e9e7584fb1479.jpg) ![<swivel-chair> 18](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/fa116ff22175ba831f641af9bc1b44c8.jpg) ![<swivel-chair> 19](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/f32510be7c6e3d2d540d53ef0c0b5536.jpg) ![<swivel-chair> 20](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3aab159b96dbcc1d403eeeea81191fb2.jpg) ![<swivel-chair> 21](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6405e9bae158bf99eab384a36468c0cc.jpg) ![<swivel-chair> 22](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e727d064c7c19b510acaacb2637c195e.jpg) ![<swivel-chair> 23](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ebf0269fca456ea8e2e307f6de6222ef.jpg) ![<swivel-chair> 24](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ac4a58c646a756d07608c485bbe7fa45.jpg)
jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.7	jordyvl	2023-07-10T21:59:33Z	161	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T21:19:56Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 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. --> # vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4918 - Accuracy: 0.85 - Brier Loss: 0.2583 - Nll: 1.0894 - F1 Micro: 0.85 - F1 Macro: 0.8374 - Ece: 0.1917 - Aurc: 0.0470 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.8329 \| 0.225 \| 0.8761 \| 5.2731 \| 0.225 \| 0.1384 \| 0.2607 \| 0.6977 \| \| No log \| 2.0 \| 14 \| 1.4785 \| 0.405 \| 0.7460 \| 3.4067 \| 0.405 \| 0.2289 \| 0.3097 \| 0.4085 \| \| No log \| 3.0 \| 21 \| 1.0406 \| 0.6 \| 0.5725 \| 1.8722 \| 0.6 \| 0.5345 \| 0.3050 \| 0.2010 \| \| No log \| 4.0 \| 28 \| 0.8087 \| 0.725 \| 0.4192 \| 1.6096 \| 0.7250 \| 0.6767 \| 0.2345 \| 0.1149 \| \| No log \| 5.0 \| 35 \| 0.7666 \| 0.735 \| 0.3731 \| 1.6189 \| 0.735 \| 0.7350 \| 0.2377 \| 0.1011 \| \| No log \| 6.0 \| 42 \| 0.6960 \| 0.78 \| 0.3413 \| 1.5230 \| 0.78 \| 0.7592 \| 0.2295 \| 0.0868 \| \| No log \| 7.0 \| 49 \| 0.6490 \| 0.805 \| 0.3110 \| 1.4861 \| 0.805 \| 0.7864 \| 0.2138 \| 0.0785 \| \| No log \| 8.0 \| 56 \| 0.6238 \| 0.795 \| 0.3069 \| 1.2098 \| 0.795 \| 0.7816 \| 0.2065 \| 0.0698 \| \| No log \| 9.0 \| 63 \| 0.5755 \| 0.83 \| 0.2866 \| 1.1943 \| 0.83 \| 0.8117 \| 0.1937 \| 0.0694 \| \| No log \| 10.0 \| 70 \| 0.6360 \| 0.77 \| 0.3164 \| 1.2608 \| 0.7700 \| 0.7550 \| 0.1785 \| 0.0677 \| \| No log \| 11.0 \| 77 \| 0.6548 \| 0.785 \| 0.3335 \| 1.4895 \| 0.785 \| 0.7707 \| 0.2281 \| 0.0885 \| \| No log \| 12.0 \| 84 \| 0.5847 \| 0.805 \| 0.3002 \| 1.4317 \| 0.805 \| 0.7807 \| 0.2264 \| 0.0756 \| \| No log \| 13.0 \| 91 \| 0.5956 \| 0.81 \| 0.3040 \| 1.2590 \| 0.81 \| 0.7928 \| 0.2241 \| 0.0556 \| \| No log \| 14.0 \| 98 \| 0.5692 \| 0.81 \| 0.3025 \| 1.2119 \| 0.81 \| 0.8043 \| 0.2235 \| 0.0665 \| \| No log \| 15.0 \| 105 \| 0.5223 \| 0.83 \| 0.2762 \| 1.1162 \| 0.83 \| 0.8221 \| 0.1798 \| 0.0552 \| \| No log \| 16.0 \| 112 \| 0.4981 \| 0.84 \| 0.2523 \| 1.0864 \| 0.8400 \| 0.8372 \| 0.1868 \| 0.0396 \| \| No log \| 17.0 \| 119 \| 0.5207 \| 0.805 \| 0.2741 \| 1.0416 \| 0.805 \| 0.7897 \| 0.1960 \| 0.0551 \| \| No log \| 18.0 \| 126 \| 0.5165 \| 0.84 \| 0.2723 \| 1.1596 \| 0.8400 \| 0.8325 \| 0.1942 \| 0.0506 \| \| No log \| 19.0 \| 133 \| 0.4979 \| 0.845 \| 0.2573 \| 1.2329 \| 0.845 \| 0.8297 \| 0.1825 \| 0.0444 \| \| No log \| 20.0 \| 140 \| 0.4953 \| 0.855 \| 0.2565 \| 1.1213 \| 0.855 \| 0.8442 \| 0.1844 \| 0.0474 \| \| No log \| 21.0 \| 147 \| 0.5296 \| 0.82 \| 0.2792 \| 1.0000 \| 0.82 \| 0.8218 \| 0.1768 \| 0.0523 \| \| No log \| 22.0 \| 154 \| 0.5027 \| 0.835 \| 0.2625 \| 0.9926 \| 0.835 \| 0.8238 \| 0.2035 \| 0.0481 \| \| No log \| 23.0 \| 161 \| 0.5027 \| 0.84 \| 0.2642 \| 1.0500 \| 0.8400 \| 0.8299 \| 0.1616 \| 0.0482 \| \| No log \| 24.0 \| 168 \| 0.5017 \| 0.84 \| 0.2616 \| 1.0560 \| 0.8400 \| 0.8314 \| 0.1819 \| 0.0497 \| \| No log \| 25.0 \| 175 \| 0.4942 \| 0.85 \| 0.2594 \| 1.1003 \| 0.85 \| 0.8407 \| 0.1793 \| 0.0483 \| \| No log \| 26.0 \| 182 \| 0.4943 \| 0.83 \| 0.2586 \| 1.0436 \| 0.83 \| 0.8140 \| 0.1869 \| 0.0518 \| \| No log \| 27.0 \| 189 \| 0.4950 \| 0.835 \| 0.2613 \| 1.0817 \| 0.835 \| 0.8224 \| 0.2039 \| 0.0504 \| \| No log \| 28.0 \| 196 \| 0.4957 \| 0.85 \| 0.2599 \| 1.1109 \| 0.85 \| 0.8309 \| 0.2058 \| 0.0485 \| \| No log \| 29.0 \| 203 \| 0.4956 \| 0.845 \| 0.2599 \| 1.0914 \| 0.845 \| 0.8304 \| 0.1916 \| 0.0492 \| \| No log \| 30.0 \| 210 \| 0.4893 \| 0.84 \| 0.2561 \| 1.0890 \| 0.8400 \| 0.8214 \| 0.2071 \| 0.0482 \| \| No log \| 31.0 \| 217 \| 0.4920 \| 0.835 \| 0.2587 \| 1.0907 \| 0.835 \| 0.8270 \| 0.2031 \| 0.0482 \| \| No log \| 32.0 \| 224 \| 0.4927 \| 0.83 \| 0.2601 \| 1.0879 \| 0.83 \| 0.8157 \| 0.2093 \| 0.0500 \| \| No log \| 33.0 \| 231 \| 0.4925 \| 0.835 \| 0.2593 \| 1.0886 \| 0.835 \| 0.8270 \| 0.1810 \| 0.0484 \| \| No log \| 34.0 \| 238 \| 0.4909 \| 0.845 \| 0.2578 \| 1.0871 \| 0.845 \| 0.8304 \| 0.1916 \| 0.0478 \| \| No log \| 35.0 \| 245 \| 0.4927 \| 0.845 \| 0.2591 \| 1.0866 \| 0.845 \| 0.8378 \| 0.1943 \| 0.0473 \| \| No log \| 36.0 \| 252 \| 0.4919 \| 0.85 \| 0.2581 \| 1.0891 \| 0.85 \| 0.8342 \| 0.2193 \| 0.0475 \| \| No log \| 37.0 \| 259 \| 0.4908 \| 0.845 \| 0.2579 \| 1.0867 \| 0.845 \| 0.8346 \| 0.2215 \| 0.0474 \| \| No log \| 38.0 \| 266 \| 0.4929 \| 0.85 \| 0.2590 \| 1.0873 \| 0.85 \| 0.8407 \| 0.1884 \| 0.0471 \| \| No log \| 39.0 \| 273 \| 0.4913 \| 0.85 \| 0.2584 \| 1.0861 \| 0.85 \| 0.8374 \| 0.1944 \| 0.0474 \| \| No log \| 40.0 \| 280 \| 0.4933 \| 0.835 \| 0.2595 \| 1.0871 \| 0.835 \| 0.8248 \| 0.1893 \| 0.0491 \| \| No log \| 41.0 \| 287 \| 0.4936 \| 0.84 \| 0.2599 \| 1.0863 \| 0.8400 \| 0.8276 \| 0.1860 \| 0.0486 \| \| No log \| 42.0 \| 294 \| 0.4911 \| 0.85 \| 0.2580 \| 1.0861 \| 0.85 \| 0.8374 \| 0.2186 \| 0.0474 \| \| No log \| 43.0 \| 301 \| 0.4915 \| 0.85 \| 0.2581 \| 1.0860 \| 0.85 \| 0.8374 \| 0.2023 \| 0.0475 \| \| No log \| 44.0 \| 308 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0874 \| 0.85 \| 0.8374 \| 0.2013 \| 0.0477 \| \| No log \| 45.0 \| 315 \| 0.4915 \| 0.85 \| 0.2583 \| 1.0862 \| 0.85 \| 0.8374 \| 0.1941 \| 0.0475 \| \| No log \| 46.0 \| 322 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0878 \| 0.85 \| 0.8374 \| 0.1852 \| 0.0473 \| \| No log \| 47.0 \| 329 \| 0.4916 \| 0.85 \| 0.2583 \| 1.0873 \| 0.85 \| 0.8374 \| 0.2089 \| 0.0473 \| \| No log \| 48.0 \| 336 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0879 \| 0.85 \| 0.8374 \| 0.2026 \| 0.0477 \| \| No log \| 49.0 \| 343 \| 0.4918 \| 0.845 \| 0.2584 \| 1.0884 \| 0.845 \| 0.8282 \| 0.1963 \| 0.0478 \| \| No log \| 50.0 \| 350 \| 0.4922 \| 0.85 \| 0.2587 \| 1.0871 \| 0.85 \| 0.8374 \| 0.2102 \| 0.0474 \| \| No log \| 51.0 \| 357 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0879 \| 0.85 \| 0.8374 \| 0.2095 \| 0.0474 \| \| No log \| 52.0 \| 364 \| 0.4926 \| 0.85 \| 0.2589 \| 1.0878 \| 0.85 \| 0.8374 \| 0.2022 \| 0.0477 \| \| No log \| 53.0 \| 371 \| 0.4920 \| 0.85 \| 0.2586 \| 1.0888 \| 0.85 \| 0.8374 \| 0.2027 \| 0.0475 \| \| No log \| 54.0 \| 378 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0886 \| 0.85 \| 0.8374 \| 0.2020 \| 0.0474 \| \| No log \| 55.0 \| 385 \| 0.4921 \| 0.85 \| 0.2587 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1929 \| 0.0471 \| \| No log \| 56.0 \| 392 \| 0.4925 \| 0.85 \| 0.2589 \| 1.0881 \| 0.85 \| 0.8374 \| 0.1946 \| 0.0473 \| \| No log \| 57.0 \| 399 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1932 \| 0.0472 \| \| No log \| 58.0 \| 406 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0877 \| 0.85 \| 0.8374 \| 0.1948 \| 0.0476 \| \| No log \| 59.0 \| 413 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0883 \| 0.85 \| 0.8374 \| 0.1931 \| 0.0472 \| \| No log \| 60.0 \| 420 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0882 \| 0.85 \| 0.8374 \| 0.1945 \| 0.0475 \| \| No log \| 61.0 \| 427 \| 0.4916 \| 0.85 \| 0.2582 \| 1.0883 \| 0.85 \| 0.8374 \| 0.1936 \| 0.0472 \| \| No log \| 62.0 \| 434 \| 0.4920 \| 0.85 \| 0.2586 \| 1.0882 \| 0.85 \| 0.8374 \| 0.1942 \| 0.0473 \| \| No log \| 63.0 \| 441 \| 0.4922 \| 0.85 \| 0.2587 \| 1.0889 \| 0.85 \| 0.8374 \| 0.1935 \| 0.0473 \| \| No log \| 64.0 \| 448 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0885 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0473 \| \| No log \| 65.0 \| 455 \| 0.4916 \| 0.85 \| 0.2582 \| 1.0887 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0474 \| \| No log \| 66.0 \| 462 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0883 \| 0.85 \| 0.8374 \| 0.1849 \| 0.0472 \| \| No log \| 67.0 \| 469 \| 0.4917 \| 0.85 \| 0.2584 \| 1.0887 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0472 \| \| No log \| 68.0 \| 476 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0888 \| 0.85 \| 0.8374 \| 0.2011 \| 0.0471 \| \| No log \| 69.0 \| 483 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0889 \| 0.85 \| 0.8374 \| 0.2007 \| 0.0471 \| \| No log \| 70.0 \| 490 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0886 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0474 \| \| No log \| 71.0 \| 497 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0888 \| 0.85 \| 0.8374 \| 0.1940 \| 0.0474 \| \| 0.1824 \| 72.0 \| 504 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0889 \| 0.85 \| 0.8374 \| 0.2011 \| 0.0471 \| \| 0.1824 \| 73.0 \| 511 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0887 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0472 \| \| 0.1824 \| 74.0 \| 518 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0472 \| \| 0.1824 \| 75.0 \| 525 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1846 \| 0.0472 \| \| 0.1824 \| 76.0 \| 532 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0889 \| 0.85 \| 0.8374 \| 0.1930 \| 0.0472 \| \| 0.1824 \| 77.0 \| 539 \| 0.4917 \| 0.85 \| 0.2582 \| 1.0891 \| 0.85 \| 0.8374 \| 0.2005 \| 0.0472 \| \| 0.1824 \| 78.0 \| 546 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1928 \| 0.0472 \| \| 0.1824 \| 79.0 \| 553 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1845 \| 0.0473 \| \| 0.1824 \| 80.0 \| 560 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1929 \| 0.0473 \| \| 0.1824 \| 81.0 \| 567 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1925 \| 0.0471 \| \| 0.1824 \| 82.0 \| 574 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 83.0 \| 581 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1916 \| 0.0471 \| \| 0.1824 \| 84.0 \| 588 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1926 \| 0.0471 \| \| 0.1824 \| 85.0 \| 595 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 86.0 \| 602 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1927 \| 0.0472 \| \| 0.1824 \| 87.0 \| 609 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 88.0 \| 616 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 89.0 \| 623 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0471 \| \| 0.1824 \| 90.0 \| 630 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1998 \| 0.0471 \| \| 0.1824 \| 91.0 \| 637 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1916 \| 0.0471 \| \| 0.1824 \| 92.0 \| 644 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 93.0 \| 651 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0471 \| \| 0.1824 \| 94.0 \| 658 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 95.0 \| 665 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 96.0 \| 672 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 97.0 \| 679 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1916 \| 0.0471 \| \| 0.1824 \| 98.0 \| 686 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 99.0 \| 693 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 100.0 \| 700 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
umanlp/babelbert-ft-xlm-r	umanlp	2023-07-10T21:57:04Z	160	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	feature-extraction	2023-07-07T21:22:09Z	This model is one of the artifacts of the paper [Massively Multilingual Lexical Specialization of Multilingual Transformers](https://aclanthology.org/2023.acl-long.426/). It was obtained by fine-tuning the representations of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the dataset [babelbert-dataset](https://huggingface.co/datasets/umanlp/babelbert-dataset).
voyzan/unit1-bonus1-Huggy-A01	voyzan	2023-07-10T21:19:37Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-10T21:19:36Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: voyzan/unit1-bonus1-Huggy-A01 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE	jordyvl	2023-07-10T21:13:05Z	164	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T20:08:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE 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. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7723 - Accuracy: 0.6025 - Brier Loss: 0.5295 - Nll: 3.6748 - F1 Micro: 0.6025 - F1 Macro: 0.6055 - Ece: 0.1688 - Aurc: 0.1708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 4.7870 \| 0.065 \| 0.9655 \| 17.0930 \| 0.065 \| 0.0550 \| 0.1747 \| 0.9357 \| \| No log \| 2.0 \| 50 \| 3.9498 \| 0.205 \| 0.8858 \| 9.5780 \| 0.205 \| 0.1863 \| 0.1692 \| 0.6618 \| \| No log \| 3.0 \| 75 \| 3.3698 \| 0.3675 \| 0.7672 \| 6.4908 \| 0.3675 \| 0.3392 \| 0.1676 \| 0.4195 \| \| No log \| 4.0 \| 100 \| 2.9935 \| 0.4075 \| 0.6958 \| 5.5595 \| 0.4075 \| 0.3820 \| 0.1828 \| 0.3327 \| \| No log \| 5.0 \| 125 \| 2.8351 \| 0.455 \| 0.6591 \| 4.8619 \| 0.455 \| 0.4351 \| 0.1561 \| 0.2833 \| \| No log \| 6.0 \| 150 \| 2.8196 \| 0.4725 \| 0.6595 \| 4.7785 \| 0.4725 \| 0.4367 \| 0.1808 \| 0.2790 \| \| No log \| 7.0 \| 175 \| 2.6352 \| 0.5075 \| 0.6234 \| 4.9881 \| 0.5075 \| 0.4886 \| 0.1563 \| 0.2493 \| \| No log \| 8.0 \| 200 \| 2.5325 \| 0.525 \| 0.6162 \| 4.3297 \| 0.525 \| 0.5026 \| 0.1724 \| 0.2365 \| \| No log \| 9.0 \| 225 \| 2.5459 \| 0.53 \| 0.6099 \| 5.1608 \| 0.53 \| 0.5148 \| 0.1944 \| 0.2350 \| \| No log \| 10.0 \| 250 \| 2.5573 \| 0.5325 \| 0.6161 \| 5.4495 \| 0.5325 \| 0.5212 \| 0.2052 \| 0.2397 \| \| No log \| 11.0 \| 275 \| 2.3199 \| 0.5675 \| 0.5828 \| 4.1247 \| 0.5675 \| 0.5626 \| 0.1849 \| 0.2071 \| \| No log \| 12.0 \| 300 \| 2.2917 \| 0.565 \| 0.5758 \| 4.1738 \| 0.565 \| 0.5694 \| 0.1992 \| 0.2023 \| \| No log \| 13.0 \| 325 \| 2.2744 \| 0.555 \| 0.5974 \| 4.2323 \| 0.555 \| 0.5544 \| 0.1982 \| 0.2203 \| \| No log \| 14.0 \| 350 \| 2.1638 \| 0.5625 \| 0.5807 \| 4.2049 \| 0.5625 \| 0.5629 \| 0.1868 \| 0.2049 \| \| No log \| 15.0 \| 375 \| 2.1934 \| 0.5575 \| 0.5903 \| 4.3813 \| 0.5575 \| 0.5614 \| 0.1868 \| 0.2022 \| \| No log \| 16.0 \| 400 \| 2.1092 \| 0.5625 \| 0.5702 \| 3.6094 \| 0.5625 \| 0.5700 \| 0.1846 \| 0.2011 \| \| No log \| 17.0 \| 425 \| 2.0379 \| 0.5875 \| 0.5642 \| 4.4351 \| 0.5875 \| 0.5822 \| 0.2036 \| 0.1959 \| \| No log \| 18.0 \| 450 \| 2.0303 \| 0.5825 \| 0.5558 \| 3.6847 \| 0.5825 \| 0.5820 \| 0.1684 \| 0.1881 \| \| No log \| 19.0 \| 475 \| 2.0506 \| 0.57 \| 0.5749 \| 4.0014 \| 0.57 \| 0.5708 \| 0.1725 \| 0.2027 \| \| 1.5026 \| 20.0 \| 500 \| 1.9932 \| 0.5875 \| 0.5524 \| 3.8003 \| 0.5875 \| 0.5914 \| 0.1843 \| 0.1831 \| \| 1.5026 \| 21.0 \| 525 \| 2.0131 \| 0.565 \| 0.5643 \| 4.0681 \| 0.565 \| 0.5635 \| 0.1776 \| 0.1957 \| \| 1.5026 \| 22.0 \| 550 \| 2.0162 \| 0.5725 \| 0.5712 \| 3.7068 \| 0.5725 \| 0.5766 \| 0.1934 \| 0.1955 \| \| 1.5026 \| 23.0 \| 575 \| 1.9093 \| 0.605 \| 0.5381 \| 3.7930 \| 0.605 \| 0.6032 \| 0.1539 \| 0.1749 \| \| 1.5026 \| 24.0 \| 600 \| 1.9607 \| 0.575 \| 0.5561 \| 4.5740 \| 0.575 \| 0.5789 \| 0.1782 \| 0.1902 \| \| 1.5026 \| 25.0 \| 625 \| 1.8971 \| 0.5825 \| 0.5408 \| 3.7290 \| 0.5825 \| 0.5754 \| 0.1836 \| 0.1751 \| \| 1.5026 \| 26.0 \| 650 \| 1.9217 \| 0.5775 \| 0.5537 \| 3.8085 \| 0.5775 \| 0.5844 \| 0.1725 \| 0.1843 \| \| 1.5026 \| 27.0 \| 675 \| 1.9493 \| 0.585 \| 0.5606 \| 3.6743 \| 0.585 \| 0.5953 \| 0.1755 \| 0.1882 \| \| 1.5026 \| 28.0 \| 700 \| 1.8884 \| 0.585 \| 0.5437 \| 3.7865 \| 0.585 \| 0.5828 \| 0.1801 \| 0.1822 \| \| 1.5026 \| 29.0 \| 725 \| 1.9242 \| 0.585 \| 0.5479 \| 3.9607 \| 0.585 \| 0.5856 \| 0.1619 \| 0.1817 \| \| 1.5026 \| 30.0 \| 750 \| 1.8767 \| 0.5975 \| 0.5470 \| 3.7995 \| 0.5975 \| 0.5966 \| 0.1599 \| 0.1790 \| \| 1.5026 \| 31.0 \| 775 \| 1.8723 \| 0.5925 \| 0.5337 \| 3.8962 \| 0.5925 \| 0.5972 \| 0.1678 \| 0.1729 \| \| 1.5026 \| 32.0 \| 800 \| 1.9093 \| 0.585 \| 0.5545 \| 3.8776 \| 0.585 \| 0.5830 \| 0.1902 \| 0.1841 \| \| 1.5026 \| 33.0 \| 825 \| 1.8667 \| 0.595 \| 0.5363 \| 3.8926 \| 0.595 \| 0.5917 \| 0.1772 \| 0.1745 \| \| 1.5026 \| 34.0 \| 850 \| 1.8403 \| 0.59 \| 0.5521 \| 3.8560 \| 0.59 \| 0.5953 \| 0.1711 \| 0.1800 \| \| 1.5026 \| 35.0 \| 875 \| 1.8464 \| 0.5925 \| 0.5380 \| 4.0376 \| 0.5925 \| 0.5970 \| 0.1719 \| 0.1756 \| \| 1.5026 \| 36.0 \| 900 \| 1.8441 \| 0.5975 \| 0.5411 \| 3.7193 \| 0.5975 \| 0.6008 \| 0.1569 \| 0.1753 \| \| 1.5026 \| 37.0 \| 925 \| 1.8599 \| 0.5875 \| 0.5402 \| 3.9139 \| 0.5875 \| 0.5908 \| 0.1779 \| 0.1789 \| \| 1.5026 \| 38.0 \| 950 \| 1.8559 \| 0.6 \| 0.5458 \| 3.8970 \| 0.6 \| 0.5991 \| 0.1583 \| 0.1804 \| \| 1.5026 \| 39.0 \| 975 \| 1.8285 \| 0.61 \| 0.5370 \| 3.6292 \| 0.61 \| 0.6155 \| 0.1623 \| 0.1722 \| \| 0.0745 \| 40.0 \| 1000 \| 1.8309 \| 0.5975 \| 0.5432 \| 3.6865 \| 0.5975 \| 0.6017 \| 0.1663 \| 0.1821 \| \| 0.0745 \| 41.0 \| 1025 \| 1.8237 \| 0.59 \| 0.5348 \| 3.6213 \| 0.59 \| 0.5921 \| 0.1695 \| 0.1738 \| \| 0.0745 \| 42.0 \| 1050 \| 1.8421 \| 0.605 \| 0.5360 \| 3.8592 \| 0.605 \| 0.6048 \| 0.1601 \| 0.1743 \| \| 0.0745 \| 43.0 \| 1075 \| 1.8158 \| 0.5975 \| 0.5300 \| 3.4537 \| 0.5975 \| 0.5953 \| 0.1696 \| 0.1707 \| \| 0.0745 \| 44.0 \| 1100 \| 1.8238 \| 0.5875 \| 0.5358 \| 3.7706 \| 0.5875 \| 0.5923 \| 0.1797 \| 0.1754 \| \| 0.0745 \| 45.0 \| 1125 \| 1.8214 \| 0.595 \| 0.5463 \| 3.4742 \| 0.595 \| 0.5981 \| 0.1800 \| 0.1770 \| \| 0.0745 \| 46.0 \| 1150 \| 1.8162 \| 0.5925 \| 0.5317 \| 3.9260 \| 0.5925 \| 0.5950 \| 0.1646 \| 0.1733 \| \| 0.0745 \| 47.0 \| 1175 \| 1.8050 \| 0.5975 \| 0.5392 \| 3.8322 \| 0.5975 \| 0.5979 \| 0.1794 \| 0.1763 \| \| 0.0745 \| 48.0 \| 1200 \| 1.8214 \| 0.5975 \| 0.5347 \| 3.7965 \| 0.5975 \| 0.6009 \| 0.1555 \| 0.1746 \| \| 0.0745 \| 49.0 \| 1225 \| 1.7813 \| 0.6 \| 0.5294 \| 3.8398 \| 0.6 \| 0.6005 \| 0.1674 \| 0.1688 \| \| 0.0745 \| 50.0 \| 1250 \| 1.8179 \| 0.6075 \| 0.5336 \| 3.4690 \| 0.6075 \| 0.6112 \| 0.1743 \| 0.1748 \| \| 0.0745 \| 51.0 \| 1275 \| 1.7953 \| 0.595 \| 0.5380 \| 3.7781 \| 0.595 \| 0.5990 \| 0.1380 \| 0.1727 \| \| 0.0745 \| 52.0 \| 1300 \| 1.7897 \| 0.6 \| 0.5323 \| 3.7412 \| 0.6 \| 0.6013 \| 0.1603 \| 0.1707 \| \| 0.0745 \| 53.0 \| 1325 \| 1.8072 \| 0.59 \| 0.5428 \| 3.5993 \| 0.59 \| 0.5947 \| 0.1571 \| 0.1773 \| \| 0.0745 \| 54.0 \| 1350 \| 1.7834 \| 0.605 \| 0.5219 \| 3.7600 \| 0.605 \| 0.6049 \| 0.1563 \| 0.1671 \| \| 0.0745 \| 55.0 \| 1375 \| 1.7920 \| 0.595 \| 0.5361 \| 3.5986 \| 0.595 \| 0.5978 \| 0.1512 \| 0.1717 \| \| 0.0745 \| 56.0 \| 1400 \| 1.8074 \| 0.5925 \| 0.5387 \| 3.5383 \| 0.5925 \| 0.5962 \| 0.1669 \| 0.1741 \| \| 0.0745 \| 57.0 \| 1425 \| 1.7893 \| 0.605 \| 0.5346 \| 3.6929 \| 0.605 \| 0.6039 \| 0.1641 \| 0.1681 \| \| 0.0745 \| 58.0 \| 1450 \| 1.7787 \| 0.6 \| 0.5317 \| 3.7652 \| 0.6 \| 0.6004 \| 0.1850 \| 0.1726 \| \| 0.0745 \| 59.0 \| 1475 \| 1.7888 \| 0.595 \| 0.5323 \| 3.4558 \| 0.595 \| 0.5975 \| 0.1797 \| 0.1732 \| \| 0.0231 \| 60.0 \| 1500 \| 1.8064 \| 0.58 \| 0.5332 \| 3.7773 \| 0.58 \| 0.5839 \| 0.1819 \| 0.1762 \| \| 0.0231 \| 61.0 \| 1525 \| 1.7795 \| 0.6075 \| 0.5298 \| 3.7998 \| 0.6075 \| 0.6086 \| 0.1678 \| 0.1704 \| \| 0.0231 \| 62.0 \| 1550 \| 1.7826 \| 0.595 \| 0.5318 \| 3.6741 \| 0.595 \| 0.5916 \| 0.1550 \| 0.1715 \| \| 0.0231 \| 63.0 \| 1575 \| 1.7704 \| 0.5925 \| 0.5325 \| 3.5942 \| 0.5925 \| 0.5941 \| 0.1619 \| 0.1712 \| \| 0.0231 \| 64.0 \| 1600 \| 1.7901 \| 0.6025 \| 0.5289 \| 3.4459 \| 0.6025 \| 0.6054 \| 0.2022 \| 0.1712 \| \| 0.0231 \| 65.0 \| 1625 \| 1.7944 \| 0.59 \| 0.5381 \| 3.7591 \| 0.59 \| 0.5910 \| 0.1599 \| 0.1756 \| \| 0.0231 \| 66.0 \| 1650 \| 1.7721 \| 0.605 \| 0.5256 \| 3.5227 \| 0.605 \| 0.6045 \| 0.1525 \| 0.1677 \| \| 0.0231 \| 67.0 \| 1675 \| 1.7779 \| 0.5975 \| 0.5306 \| 3.6792 \| 0.5975 \| 0.5994 \| 0.1667 \| 0.1714 \| \| 0.0231 \| 68.0 \| 1700 \| 1.7724 \| 0.6 \| 0.5250 \| 3.7552 \| 0.6 \| 0.6022 \| 0.1818 \| 0.1683 \| \| 0.0231 \| 69.0 \| 1725 \| 1.7765 \| 0.6025 \| 0.5283 \| 3.4264 \| 0.6025 \| 0.6019 \| 0.1671 \| 0.1700 \| \| 0.0231 \| 70.0 \| 1750 \| 1.7784 \| 0.6 \| 0.5276 \| 3.6887 \| 0.6 \| 0.6053 \| 0.1715 \| 0.1703 \| \| 0.0231 \| 71.0 \| 1775 \| 1.7659 \| 0.6 \| 0.5282 \| 3.6051 \| 0.6 \| 0.6006 \| 0.1722 \| 0.1691 \| \| 0.0231 \| 72.0 \| 1800 \| 1.7882 \| 0.5975 \| 0.5329 \| 3.5950 \| 0.5975 \| 0.6016 \| 0.1981 \| 0.1716 \| \| 0.0231 \| 73.0 \| 1825 \| 1.7678 \| 0.6 \| 0.5287 \| 3.6691 \| 0.6 \| 0.6032 \| 0.1733 \| 0.1696 \| \| 0.0231 \| 74.0 \| 1850 \| 1.7716 \| 0.6 \| 0.5286 \| 3.7576 \| 0.6 \| 0.6013 \| 0.1734 \| 0.1692 \| \| 0.0231 \| 75.0 \| 1875 \| 1.7704 \| 0.6 \| 0.5299 \| 3.5917 \| 0.6 \| 0.6016 \| 0.1645 \| 0.1709 \| \| 0.0231 \| 76.0 \| 1900 \| 1.7729 \| 0.6 \| 0.5298 \| 3.6758 \| 0.6 \| 0.6024 \| 0.1766 \| 0.1710 \| \| 0.0231 \| 77.0 \| 1925 \| 1.7749 \| 0.6 \| 0.5308 \| 3.6022 \| 0.6 \| 0.6030 \| 0.1604 \| 0.1717 \| \| 0.0231 \| 78.0 \| 1950 \| 1.7720 \| 0.6 \| 0.5294 \| 3.6759 \| 0.6 \| 0.6017 \| 0.1786 \| 0.1708 \| \| 0.0231 \| 79.0 \| 1975 \| 1.7734 \| 0.6025 \| 0.5288 \| 3.6765 \| 0.6025 \| 0.6048 \| 0.1673 \| 0.1698 \| \| 0.0059 \| 80.0 \| 2000 \| 1.7709 \| 0.6 \| 0.5286 \| 3.6755 \| 0.6 \| 0.6020 \| 0.1749 \| 0.1704 \| \| 0.0059 \| 81.0 \| 2025 \| 1.7730 \| 0.6 \| 0.5295 \| 3.6760 \| 0.6 \| 0.6020 \| 0.1677 \| 0.1708 \| \| 0.0059 \| 82.0 \| 2050 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6756 \| 0.6025 \| 0.6055 \| 0.1626 \| 0.1708 \| \| 0.0059 \| 83.0 \| 2075 \| 1.7721 \| 0.6025 \| 0.5295 \| 3.6741 \| 0.6025 \| 0.6055 \| 0.1709 \| 0.1708 \| \| 0.0059 \| 84.0 \| 2100 \| 1.7725 \| 0.6025 \| 0.5297 \| 3.6747 \| 0.6025 \| 0.6048 \| 0.1627 \| 0.1709 \| \| 0.0059 \| 85.0 \| 2125 \| 1.7724 \| 0.6025 \| 0.5295 \| 3.6751 \| 0.6025 \| 0.6055 \| 0.1639 \| 0.1707 \| \| 0.0059 \| 86.0 \| 2150 \| 1.7724 \| 0.6025 \| 0.5296 \| 3.6751 \| 0.6025 \| 0.6055 \| 0.1630 \| 0.1708 \| \| 0.0059 \| 87.0 \| 2175 \| 1.7724 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1638 \| 0.1707 \| \| 0.0059 \| 88.0 \| 2200 \| 1.7722 \| 0.6025 \| 0.5295 \| 3.6752 \| 0.6025 \| 0.6055 \| 0.1645 \| 0.1708 \| \| 0.0059 \| 89.0 \| 2225 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6747 \| 0.6025 \| 0.6055 \| 0.1639 \| 0.1708 \| \| 0.0059 \| 90.0 \| 2250 \| 1.7723 \| 0.6025 \| 0.5294 \| 3.6750 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 91.0 \| 2275 \| 1.7723 \| 0.6025 \| 0.5294 \| 3.6750 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 92.0 \| 2300 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6747 \| 0.6025 \| 0.6055 \| 0.1639 \| 0.1708 \| \| 0.0059 \| 93.0 \| 2325 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1637 \| 0.1707 \| \| 0.0059 \| 94.0 \| 2350 \| 1.7722 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| \| 0.0059 \| 95.0 \| 2375 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 96.0 \| 2400 \| 1.7723 \| 0.6025 \| 0.5294 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1707 \| \| 0.0059 \| 97.0 \| 2425 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| \| 0.0059 \| 98.0 \| 2450 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 99.0 \| 2475 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| \| 0.0 \| 100.0 \| 2500 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO	skrl	2023-07-10T21:06:55Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T19:47:47Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -8.89 +/- 10.3 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltScrew type: IsaacGymEnvs-FactoryTaskNutBoltScrew --- <!-- --- torch: -21.51 +/- 14.99 jax: -35.77 +/- 0.39 numpy: -8.89 +/- 10.3 --- --> # IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: FactoryTaskNutBoltScrew - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 128 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 32 # 128 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
skrl/IsaacGymEnvs-FactoryTaskNutBoltPick-PPO	skrl	2023-07-10T20:49:13Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T19:46:39Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -13.83 +/- 0.26 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltPick type: IsaacGymEnvs-FactoryTaskNutBoltPick --- <!-- --- torch: -14.79 +/- 2.68 jax: -13.87 +/- 0.06 numpy: -13.83 +/- 0.26 --- --> # IsaacGymEnvs-FactoryTaskNutBoltPick-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: FactoryTaskNutBoltPick - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPick-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPick-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 120 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 30 # 120 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
vk21/ppo-SnowballTarget-unit5	vk21	2023-07-10T20:34:28Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-10T20:34:22Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vk21/ppo-SnowballTarget-unit5 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
DarkAirforce/dqn-SpaceInvadersNoFrameskip-v4	DarkAirforce	2023-07-10T20:33:23Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T19:24:04Z	--- 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: 534.00 +/- 175.24 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 DarkAirforce -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 DarkAirforce -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 DarkAirforce ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
grace-pro/afriberta-large-finetuned-hausa	grace-pro	2023-07-10T20:28:21Z	127	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T19:28:18Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-large-finetuned-hausa 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. --> # afriberta-large-finetuned-hausa This model is a fine-tuned version of [castorini/afriberta_large](https://huggingface.co/castorini/afriberta_large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Precision: 0.7114 - Recall: 0.5238 - F1: 0.6034 - Accuracy: 0.9652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1373 \| 1.0 \| 2624 \| 0.1267 \| 0.6804 \| 0.4519 \| 0.5431 \| 0.9612 \| \| 0.1102 \| 2.0 \| 5248 \| 0.1186 \| 0.6927 \| 0.5020 \| 0.5821 \| 0.9635 \| \| 0.0849 \| 3.0 \| 7872 \| 0.1269 \| 0.7114 \| 0.5036 \| 0.5897 \| 0.9645 \| \| 0.0683 \| 4.0 \| 10496 \| 0.1341 \| 0.7159 \| 0.5078 \| 0.5941 \| 0.9650 \| \| 0.0567 \| 5.0 \| 13120 \| 0.1448 \| 0.7114 \| 0.5238 \| 0.6034 \| 0.9652 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO	skrl	2023-07-10T20:15:49Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T19:47:18Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -38.54 +/- 17.49 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltPlace type: IsaacGymEnvs-FactoryTaskNutBoltPlace --- <!-- --- torch: -38.54 +/- 17.49 jax: -60.9 +/- 0.84 numpy: -58.9 +/- 1.8 --- --> # IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: FactoryTaskNutBoltPlace - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 120 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 30 # 120 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
MaitreHibou/Reinforce-Pixelcopter-PLE-v0	MaitreHibou	2023-07-10T20:12:10Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T19:26:33Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.00 +/- 20.11 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
blzncz/segformer-finetuned-4ss1st3r_s3gs3m-10k-steps	blzncz	2023-07-10T20:04:14Z	188	0	transformers	[ "transformers", "pytorch", "segformer", "image-segmentation", "vision", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2023-07-10T10:49:12Z	--- license: other tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-finetuned-4ss1st3r_s3gs3m-10k-steps 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. --> # segformer-finetuned-4ss1st3r_s3gs3m-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m dataset. It achieves the following results on the evaluation set: - Loss: 0.3966 - Mean Iou: 0.5967 - Mean Accuracy: 0.8460 - Overall Accuracy: 0.9344 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9510 - Accuracy Fallo malla: 0.8524 - Accuracy Fallo adhesivo: 0.9362 - Accuracy Fallo burbuja: 0.6444 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9239 - Iou Fallo malla: 0.7125 - Iou Fallo adhesivo: 0.8335 - Iou Fallo burbuja: 0.5139 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Bg \| Accuracy Fallo cohesivo \| Accuracy Fallo malla \| Accuracy Fallo adhesivo \| Accuracy Fallo burbuja \| Iou Bg \| Iou Fallo cohesivo \| Iou Fallo malla \| Iou Fallo adhesivo \| Iou Fallo burbuja \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:-----------------------:\|:--------------------:\|:-----------------------:\|:----------------------:\|:------:\|:------------------:\|:---------------:\|:------------------:\|:-----------------:\| \| 0.4796 \| 1.0 \| 133 \| 0.4190 \| 0.4518 \| 0.6689 \| 0.9049 \| nan \| 0.9277 \| 0.8091 \| 0.9381 \| 0.0008 \| 0.0 \| 0.8866 \| 0.6536 \| 0.7179 \| 0.0008 \| \| 0.2665 \| 2.0 \| 266 \| 0.3667 \| 0.5096 \| 0.7283 \| 0.9001 \| nan \| 0.9111 \| 0.8964 \| 0.8731 \| 0.2324 \| 0.0 \| 0.8802 \| 0.6013 \| 0.8467 \| 0.2197 \| \| 0.2158 \| 3.0 \| 399 \| 0.3210 \| 0.5505 \| 0.7807 \| 0.9142 \| nan \| 0.9250 \| 0.8685 \| 0.9414 \| 0.3878 \| 0.0 \| 0.8952 \| 0.6239 \| 0.8901 \| 0.3432 \| \| 0.1737 \| 4.0 \| 532 \| 0.3572 \| 0.5370 \| 0.7851 \| 0.8905 \| nan \| 0.8905 \| 0.9102 \| 0.9121 \| 0.4277 \| 0.0 \| 0.8671 \| 0.5637 \| 0.8777 \| 0.3764 \| \| 0.1602 \| 5.0 \| 665 \| 0.6273 \| 0.4086 \| 0.7632 \| 0.7743 \| nan \| 0.7333 \| 0.9343 \| 0.9685 \| 0.4168 \| 0.0 \| 0.7198 \| 0.4460 \| 0.5324 \| 0.3449 \| \| 0.1707 \| 6.0 \| 798 \| 0.3534 \| 0.5442 \| 0.7953 \| 0.9025 \| nan \| 0.9056 \| 0.9031 \| 0.9234 \| 0.4492 \| 0.0 \| 0.8812 \| 0.5985 \| 0.8629 \| 0.3783 \| \| 0.1376 \| 7.0 \| 931 \| 0.3266 \| 0.5513 \| 0.7634 \| 0.9262 \| nan \| 0.9434 \| 0.8621 \| 0.9288 \| 0.3195 \| 0.0 \| 0.9109 \| 0.6623 \| 0.8866 \| 0.2968 \| \| 0.1346 \| 8.0 \| 1064 \| 0.4976 \| 0.4916 \| 0.7900 \| 0.8396 \| nan \| 0.8190 \| 0.9133 \| 0.9713 \| 0.4565 \| 0.0 \| 0.8041 \| 0.4662 \| 0.7906 \| 0.3970 \| \| 0.1319 \| 9.0 \| 1197 \| 0.3650 \| 0.5652 \| 0.8404 \| 0.9043 \| nan \| 0.9053 \| 0.8856 \| 0.9593 \| 0.6113 \| 0.0 \| 0.8829 \| 0.5992 \| 0.8734 \| 0.4706 \| \| 0.1229 \| 10.0 \| 1330 \| 0.3201 \| 0.5666 \| 0.7963 \| 0.9299 \| nan \| 0.9435 \| 0.8764 \| 0.9389 \| 0.4265 \| 0.0 \| 0.9171 \| 0.6896 \| 0.8499 \| 0.3763 \| \| 0.1142 \| 11.0 \| 1463 \| 0.3824 \| 0.5576 \| 0.8204 \| 0.9020 \| nan \| 0.8988 \| 0.9231 \| 0.9456 \| 0.5142 \| 0.0 \| 0.8795 \| 0.6001 \| 0.8711 \| 0.4374 \| \| 0.0983 \| 12.0 \| 1596 \| 0.3133 \| 0.5812 \| 0.8297 \| 0.9293 \| nan \| 0.9354 \| 0.9046 \| 0.9558 \| 0.5229 \| 0.0 \| 0.9136 \| 0.6969 \| 0.8618 \| 0.4335 \| \| 0.1058 \| 13.0 \| 1729 \| 0.2965 \| 0.5860 \| 0.8250 \| 0.9364 \| nan \| 0.9528 \| 0.8496 \| 0.9598 \| 0.5378 \| 0.0 \| 0.9253 \| 0.7162 \| 0.8502 \| 0.4383 \| \| 0.1052 \| 14.0 \| 1862 \| 0.2839 \| 0.6064 \| 0.8275 \| 0.9460 \| nan \| 0.9674 \| 0.8517 \| 0.9290 \| 0.5621 \| 0.0 \| 0.9355 \| 0.7492 \| 0.8930 \| 0.4540 \| \| 0.0911 \| 15.0 \| 1995 \| 0.3245 \| 0.5853 \| 0.8116 \| 0.9368 \| nan \| 0.9565 \| 0.8504 \| 0.9298 \| 0.5099 \| 0.0 \| 0.9243 \| 0.7171 \| 0.8534 \| 0.4318 \| \| 0.0889 \| 16.0 \| 2128 \| 0.3094 \| 0.5969 \| 0.8225 \| 0.9422 \| nan \| 0.9615 \| 0.8559 \| 0.9376 \| 0.5351 \| 0.0 \| 0.9313 \| 0.7353 \| 0.8726 \| 0.4451 \| \| 0.0827 \| 17.0 \| 2261 \| 0.4776 \| 0.5187 \| 0.8195 \| 0.8547 \| nan \| 0.8390 \| 0.9163 \| 0.9440 \| 0.5786 \| 0.0 \| 0.8207 \| 0.4920 \| 0.8216 \| 0.4590 \| \| 0.0939 \| 18.0 \| 2394 \| 0.3923 \| 0.5364 \| 0.8375 \| 0.8948 \| nan \| 0.8950 \| 0.8831 \| 0.9437 \| 0.6282 \| 0.0 \| 0.8746 \| 0.6268 \| 0.7090 \| 0.4717 \| \| 0.0799 \| 19.0 \| 2527 \| 0.3560 \| 0.5776 \| 0.8252 \| 0.9254 \| nan \| 0.9337 \| 0.8933 \| 0.9409 \| 0.5331 \| 0.0 \| 0.9096 \| 0.6846 \| 0.8519 \| 0.4422 \| \| 0.075 \| 20.0 \| 2660 \| 0.3803 \| 0.5796 \| 0.8338 \| 0.9194 \| nan \| 0.9249 \| 0.9078 \| 0.9238 \| 0.5788 \| 0.0 \| 0.9032 \| 0.6459 \| 0.8821 \| 0.4670 \| \| 0.0844 \| 21.0 \| 2793 \| 0.2885 \| 0.6170 \| 0.8334 \| 0.9507 \| nan \| 0.9757 \| 0.8296 \| 0.9390 \| 0.5892 \| 0.0 \| 0.9412 \| 0.7654 \| 0.8933 \| 0.4852 \| \| 0.0746 \| 22.0 \| 2926 \| 0.3222 \| 0.5831 \| 0.8160 \| 0.9331 \| nan \| 0.9481 \| 0.8685 \| 0.9370 \| 0.5105 \| 0.0 \| 0.9193 \| 0.7032 \| 0.8716 \| 0.4215 \| \| 0.072 \| 23.0 \| 3059 \| 0.3481 \| 0.5878 \| 0.8336 \| 0.9266 \| nan \| 0.9357 \| 0.8952 \| 0.9271 \| 0.5764 \| 0.0 \| 0.9123 \| 0.6824 \| 0.8720 \| 0.4725 \| \| 0.0735 \| 24.0 \| 3192 \| 0.3196 \| 0.5974 \| 0.8403 \| 0.9353 \| nan \| 0.9496 \| 0.8666 \| 0.9430 \| 0.6018 \| 0.0 \| 0.9225 \| 0.7165 \| 0.8649 \| 0.4832 \| \| 0.0674 \| 25.0 \| 3325 \| 0.3407 \| 0.5927 \| 0.8435 \| 0.9282 \| nan \| 0.9401 \| 0.8786 \| 0.9246 \| 0.6304 \| 0.0 \| 0.9141 \| 0.6844 \| 0.8696 \| 0.4953 \| \| 0.0712 \| 26.0 \| 3458 \| 0.3356 \| 0.5906 \| 0.8420 \| 0.9301 \| nan \| 0.9405 \| 0.8895 \| 0.9299 \| 0.6080 \| 0.0 \| 0.9160 \| 0.6905 \| 0.8743 \| 0.4722 \| \| 0.072 \| 27.0 \| 3591 \| 0.3491 \| 0.5833 \| 0.8372 \| 0.9286 \| nan \| 0.9415 \| 0.8636 \| 0.9425 \| 0.6012 \| 0.0 \| 0.9161 \| 0.6966 \| 0.8246 \| 0.4790 \| \| 0.0641 \| 28.0 \| 3724 \| 0.3130 \| 0.6087 \| 0.8422 \| 0.9473 \| nan \| 0.9697 \| 0.8357 \| 0.9427 \| 0.6208 \| 0.0 \| 0.9386 \| 0.7613 \| 0.8599 \| 0.4837 \| \| 0.0597 \| 29.0 \| 3857 \| 0.3828 \| 0.5666 \| 0.8394 \| 0.9107 \| nan \| 0.9141 \| 0.8934 \| 0.9411 \| 0.6092 \| 0.0 \| 0.8924 \| 0.6327 \| 0.8343 \| 0.4735 \| \| 0.0648 \| 30.0 \| 3990 \| 0.3435 \| 0.6001 \| 0.8372 \| 0.9403 \| nan \| 0.9569 \| 0.8708 \| 0.9276 \| 0.5935 \| 0.0 \| 0.9292 \| 0.7312 \| 0.8779 \| 0.4623 \| \| 0.0618 \| 31.0 \| 4123 \| 0.3531 \| 0.5963 \| 0.8521 \| 0.9303 \| nan \| 0.9450 \| 0.8621 \| 0.9240 \| 0.6773 \| 0.0 \| 0.9179 \| 0.6842 \| 0.8730 \| 0.5063 \| \| 0.0556 \| 32.0 \| 4256 \| 0.3307 \| 0.6037 \| 0.8417 \| 0.9401 \| nan \| 0.9576 \| 0.8637 \| 0.9271 \| 0.6183 \| 0.0 \| 0.9298 \| 0.7274 \| 0.8637 \| 0.4974 \| \| 0.0616 \| 33.0 \| 4389 \| 0.3510 \| 0.5911 \| 0.8347 \| 0.9298 \| nan \| 0.9424 \| 0.8714 \| 0.9388 \| 0.5863 \| 0.0 \| 0.9158 \| 0.6914 \| 0.8745 \| 0.4740 \| \| 0.0603 \| 34.0 \| 4522 \| 0.3467 \| 0.6022 \| 0.8544 \| 0.9334 \| nan \| 0.9487 \| 0.8610 \| 0.9274 \| 0.6807 \| 0.0 \| 0.9211 \| 0.7029 \| 0.8738 \| 0.5130 \| \| 0.0587 \| 35.0 \| 4655 \| 0.3574 \| 0.6017 \| 0.8407 \| 0.9379 \| nan \| 0.9555 \| 0.8541 \| 0.9346 \| 0.6187 \| 0.0 \| 0.9269 \| 0.7228 \| 0.8627 \| 0.4962 \| \| 0.0557 \| 36.0 \| 4788 \| 0.3871 \| 0.5720 \| 0.8334 \| 0.9178 \| nan \| 0.9317 \| 0.8416 \| 0.9374 \| 0.6228 \| 0.0 \| 0.9051 \| 0.6479 \| 0.8160 \| 0.4911 \| \| 0.0567 \| 37.0 \| 4921 \| 0.4425 \| 0.5656 \| 0.8282 \| 0.9070 \| nan \| 0.9114 \| 0.8922 \| 0.9244 \| 0.5848 \| 0.0 \| 0.8889 \| 0.6100 \| 0.8575 \| 0.4718 \| \| 0.0537 \| 38.0 \| 5054 \| 0.3512 \| 0.5946 \| 0.8392 \| 0.9317 \| nan \| 0.9463 \| 0.8649 \| 0.9314 \| 0.6142 \| 0.0 \| 0.9187 \| 0.6984 \| 0.8637 \| 0.4921 \| \| 0.0559 \| 39.0 \| 5187 \| 0.3676 \| 0.5931 \| 0.8437 \| 0.9273 \| nan \| 0.9381 \| 0.8798 \| 0.9323 \| 0.6247 \| 0.0 \| 0.9129 \| 0.6779 \| 0.8786 \| 0.4959 \| \| 0.0502 \| 40.0 \| 5320 \| 0.4149 \| 0.5518 \| 0.8381 \| 0.8984 \| nan \| 0.9011 \| 0.8773 \| 0.9368 \| 0.6370 \| 0.0 \| 0.8793 \| 0.6069 \| 0.7741 \| 0.4989 \| \| 0.0559 \| 41.0 \| 5453 \| 0.4042 \| 0.5694 \| 0.8342 \| 0.9130 \| nan \| 0.9206 \| 0.8721 \| 0.9400 \| 0.6041 \| 0.0 \| 0.8971 \| 0.6319 \| 0.8286 \| 0.4896 \| \| 0.0523 \| 42.0 \| 5586 \| 0.3669 \| 0.5903 \| 0.8462 \| 0.9286 \| nan \| 0.9414 \| 0.8676 \| 0.9337 \| 0.6421 \| 0.0 \| 0.9162 \| 0.6883 \| 0.8370 \| 0.5102 \| \| 0.0525 \| 43.0 \| 5719 \| 0.4140 \| 0.5704 \| 0.8531 \| 0.9081 \| nan \| 0.9110 \| 0.8867 \| 0.9417 \| 0.6729 \| 0.0 \| 0.8898 \| 0.6220 \| 0.8366 \| 0.5035 \| \| 0.0508 \| 44.0 \| 5852 \| 0.3965 \| 0.5714 \| 0.8396 \| 0.9141 \| nan \| 0.9227 \| 0.8800 \| 0.9147 \| 0.6409 \| 0.0 \| 0.8989 \| 0.6513 \| 0.8007 \| 0.5060 \| \| 0.0507 \| 45.0 \| 5985 \| 0.3793 \| 0.5817 \| 0.8392 \| 0.9196 \| nan \| 0.9272 \| 0.8932 \| 0.9214 \| 0.6148 \| 0.0 \| 0.9042 \| 0.6627 \| 0.8407 \| 0.5011 \| \| 0.0494 \| 46.0 \| 6118 \| 0.3500 \| 0.6020 \| 0.8426 \| 0.9363 \| nan \| 0.9524 \| 0.8619 \| 0.9322 \| 0.6240 \| 0.0 \| 0.9247 \| 0.7142 \| 0.8653 \| 0.5058 \| \| 0.0462 \| 47.0 \| 6251 \| 0.3524 \| 0.6031 \| 0.8435 \| 0.9388 \| nan \| 0.9545 \| 0.8668 \| 0.9364 \| 0.6163 \| 0.0 \| 0.9274 \| 0.7269 \| 0.8703 \| 0.4909 \| \| 0.0486 \| 48.0 \| 6384 \| 0.3876 \| 0.5902 \| 0.8397 \| 0.9308 \| nan \| 0.9479 \| 0.8557 \| 0.9161 \| 0.6392 \| 0.0 \| 0.9203 \| 0.6928 \| 0.8334 \| 0.5046 \| \| 0.0461 \| 49.0 \| 6517 \| 0.3674 \| 0.5933 \| 0.8409 \| 0.9326 \| nan \| 0.9482 \| 0.8622 \| 0.9258 \| 0.6274 \| 0.0 \| 0.9214 \| 0.7053 \| 0.8367 \| 0.5030 \| \| 0.0497 \| 50.0 \| 6650 \| 0.4018 \| 0.5838 \| 0.8374 \| 0.9246 \| nan \| 0.9390 \| 0.8519 \| 0.9341 \| 0.6244 \| 0.0 \| 0.9102 \| 0.6733 \| 0.8361 \| 0.4992 \| \| 0.0491 \| 51.0 \| 6783 \| 0.4036 \| 0.5824 \| 0.8513 \| 0.9198 \| nan \| 0.9272 \| 0.8805 \| 0.9403 \| 0.6573 \| 0.0 \| 0.9037 \| 0.6712 \| 0.8169 \| 0.5203 \| \| 0.046 \| 52.0 \| 6916 \| 0.3913 \| 0.5820 \| 0.8395 \| 0.9243 \| nan \| 0.9347 \| 0.8771 \| 0.9336 \| 0.6126 \| 0.0 \| 0.9105 \| 0.6792 \| 0.8244 \| 0.4960 \| \| 0.0488 \| 53.0 \| 7049 \| 0.3441 \| 0.6010 \| 0.8504 \| 0.9362 \| nan \| 0.9523 \| 0.8521 \| 0.9457 \| 0.6517 \| 0.0 \| 0.9250 \| 0.7225 \| 0.8496 \| 0.5081 \| \| 0.0458 \| 54.0 \| 7182 \| 0.3784 \| 0.5977 \| 0.8382 \| 0.9378 \| nan \| 0.9603 \| 0.8212 \| 0.9375 \| 0.6337 \| 0.0 \| 0.9286 \| 0.7157 \| 0.8387 \| 0.5053 \| \| 0.0449 \| 55.0 \| 7315 \| 0.3506 \| 0.6068 \| 0.8493 \| 0.9404 \| nan \| 0.9579 \| 0.8554 \| 0.9385 \| 0.6456 \| 0.0 \| 0.9300 \| 0.7357 \| 0.8549 \| 0.5132 \| \| 0.0482 \| 56.0 \| 7448 \| 0.4005 \| 0.5819 \| 0.8414 \| 0.9249 \| nan \| 0.9374 \| 0.8642 \| 0.9337 \| 0.6303 \| 0.0 \| 0.9119 \| 0.6831 \| 0.8139 \| 0.5006 \| \| 0.0434 \| 57.0 \| 7581 \| 0.3749 \| 0.5914 \| 0.8465 \| 0.9294 \| nan \| 0.9423 \| 0.8675 \| 0.9339 \| 0.6421 \| 0.0 \| 0.9171 \| 0.6999 \| 0.8265 \| 0.5134 \| \| 0.0435 \| 58.0 \| 7714 \| 0.4195 \| 0.5722 \| 0.8400 \| 0.9172 \| nan \| 0.9274 \| 0.8700 \| 0.9234 \| 0.6392 \| 0.0 \| 0.9025 \| 0.6588 \| 0.7954 \| 0.5044 \| \| 0.0442 \| 59.0 \| 7847 \| 0.3975 \| 0.5828 \| 0.8407 \| 0.9257 \| nan \| 0.9398 \| 0.8563 \| 0.9312 \| 0.6356 \| 0.0 \| 0.9134 \| 0.6866 \| 0.8103 \| 0.5037 \| \| 0.0442 \| 60.0 \| 7980 \| 0.3845 \| 0.5929 \| 0.8457 \| 0.9315 \| nan \| 0.9459 \| 0.8603 \| 0.9363 \| 0.6404 \| 0.0 \| 0.9193 \| 0.7041 \| 0.8308 \| 0.5103 \| \| 0.0422 \| 61.0 \| 8113 \| 0.3875 \| 0.5963 \| 0.8465 \| 0.9338 \| nan \| 0.9489 \| 0.8616 \| 0.9340 \| 0.6413 \| 0.0 \| 0.9226 \| 0.7135 \| 0.8381 \| 0.5072 \| \| 0.0436 \| 62.0 \| 8246 \| 0.3859 \| 0.6022 \| 0.8497 \| 0.9385 \| nan \| 0.9566 \| 0.8477 \| 0.9382 \| 0.6562 \| 0.0 \| 0.9289 \| 0.7300 \| 0.8376 \| 0.5147 \| \| 0.0429 \| 63.0 \| 8379 \| 0.3857 \| 0.5956 \| 0.8425 \| 0.9357 \| nan \| 0.9534 \| 0.8481 \| 0.9357 \| 0.6327 \| 0.0 \| 0.9249 \| 0.7233 \| 0.8283 \| 0.5016 \| \| 0.0446 \| 64.0 \| 8512 \| 0.3778 \| 0.5976 \| 0.8495 \| 0.9343 \| nan \| 0.9492 \| 0.8602 \| 0.9399 \| 0.6489 \| 0.0 \| 0.9232 \| 0.7191 \| 0.8305 \| 0.5153 \| \| 0.0429 \| 65.0 \| 8645 \| 0.3889 \| 0.5948 \| 0.8478 \| 0.9330 \| nan \| 0.9490 \| 0.8548 \| 0.9325 \| 0.6549 \| 0.0 \| 0.9225 \| 0.7075 \| 0.8271 \| 0.5167 \| \| 0.0454 \| 66.0 \| 8778 \| 0.3915 \| 0.5941 \| 0.8470 \| 0.9329 \| nan \| 0.9490 \| 0.8571 \| 0.9271 \| 0.6547 \| 0.0 \| 0.9221 \| 0.7087 \| 0.8278 \| 0.5117 \| \| 0.0427 \| 67.0 \| 8911 \| 0.3924 \| 0.5967 \| 0.8455 \| 0.9349 \| nan \| 0.9518 \| 0.8520 \| 0.9350 \| 0.6433 \| 0.0 \| 0.9247 \| 0.7167 \| 0.8290 \| 0.5133 \| \| 0.0425 \| 68.0 \| 9044 \| 0.3990 \| 0.5992 \| 0.8491 \| 0.9358 \| nan \| 0.9524 \| 0.8545 \| 0.9355 \| 0.6541 \| 0.0 \| 0.9250 \| 0.7187 \| 0.8387 \| 0.5136 \| \| 0.0429 \| 69.0 \| 9177 \| 0.3911 \| 0.5909 \| 0.8499 \| 0.9303 \| nan \| 0.9451 \| 0.8532 \| 0.9394 \| 0.6619 \| 0.0 \| 0.9192 \| 0.7029 \| 0.8178 \| 0.5146 \| \| 0.0465 \| 70.0 \| 9310 \| 0.3840 \| 0.5977 \| 0.8481 \| 0.9332 \| nan \| 0.9473 \| 0.8700 \| 0.9278 \| 0.6473 \| 0.0 \| 0.9215 \| 0.7079 \| 0.8480 \| 0.5110 \| \| 0.0436 \| 71.0 \| 9443 \| 0.3862 \| 0.5974 \| 0.8456 \| 0.9351 \| nan \| 0.9518 \| 0.8534 \| 0.9359 \| 0.6413 \| 0.0 \| 0.9248 \| 0.7162 \| 0.8338 \| 0.5124 \| \| 0.0435 \| 72.0 \| 9576 \| 0.3926 \| 0.5952 \| 0.8448 \| 0.9328 \| nan \| 0.9484 \| 0.8585 \| 0.9318 \| 0.6405 \| 0.0 \| 0.9217 \| 0.7073 \| 0.8386 \| 0.5084 \| \| 0.0421 \| 73.0 \| 9709 \| 0.3961 \| 0.5984 \| 0.8467 \| 0.9348 \| nan \| 0.9513 \| 0.8564 \| 0.9309 \| 0.6482 \| 0.0 \| 0.9243 \| 0.7119 \| 0.8414 \| 0.5143 \| \| 0.0409 \| 74.0 \| 9842 \| 0.3973 \| 0.5982 \| 0.8494 \| 0.9341 \| nan \| 0.9498 \| 0.8596 \| 0.9306 \| 0.6578 \| 0.0 \| 0.9233 \| 0.7094 \| 0.8401 \| 0.5181 \| \| 0.041 \| 75.0 \| 9975 \| 0.3898 \| 0.5963 \| 0.8476 \| 0.9335 \| nan \| 0.9493 \| 0.8561 \| 0.9354 \| 0.6498 \| 0.0 \| 0.9227 \| 0.7108 \| 0.8329 \| 0.5153 \| \| 0.0436 \| 75.19 \| 10000 \| 0.3966 \| 0.5967 \| 0.8460 \| 0.9344 \| nan \| 0.9510 \| 0.8524 \| 0.9362 \| 0.6444 \| 0.0 \| 0.9239 \| 0.7125 \| 0.8335 \| 0.5139 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3
Masik001/combined-GI-RVC-models	Masik001	2023-07-10T19:43:01Z	0	1	null	[ "region:us" ]	null	2023-07-10T19:42:16Z	===== Application Startup at 2023-07-10 13:56:11 ===== 2023-07-10 17:36:30 \| INFO \| faiss.loader \| Loading faiss with AVX2 support. 2023-07-10 17:36:30 \| INFO \| faiss.loader \| Successfully loaded faiss with AVX2 support. 没有发现支持的N卡, 使用CPU进行推理 2023-07-10 17:36:31 \| INFO \| fairseq.tasks.hubert_pretraining \| current directory is /home/user/app 2023-07-10 17:36:31 \| INFO \| fairseq.tasks.hubert_pretraining \| HubertPretrainingTask Config {'_name': 'hubert_pretraining', 'data': 'metadata', 'fine_tuning': False, 'labels': ['km'], 'label_dir': 'label', 'label_rate': 50.0, 'sample_rate': 16000, 'normalize': False, 'enable_padding': False, 'max_keep_size': None, 'max_sample_size': 250000, 'min_sample_size': 32000, 'single_target': False, 'random_crop': True, 'pad_audio': False} 2023-07-10 17:36:31 \| INFO \| fairseq.models.hubert.hubert \| HubertModel Config: {'_name': 'hubert', 'label_rate': 50.0, 'extractor_mode': default, 'encoder_layers': 12, 'encoder_embed_dim': 768, 'encoder_ffn_embed_dim': 3072, 'encoder_attention_heads': 12, 'activation_fn': gelu, 'layer_type': transformer, 'dropout': 0.1, 'attention_dropout': 0.1, 'activation_dropout': 0.0, 'encoder_layerdrop': 0.05, 'dropout_input': 0.1, 'dropout_features': 0.1, 'final_dim': 256, 'untie_final_proj': True, 'layer_norm_first': False, 'conv_feature_layers': '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', 'conv_bias': False, 'logit_temp': 0.1, 'target_glu': False, 'feature_grad_mult': 0.1, 'mask_length': 10, 'mask_prob': 0.8, 'mask_selection': static, 'mask_other': 0.0, 'no_mask_overlap': False, 'mask_min_space': 1, 'mask_channel_length': 10, 'mask_channel_prob': 0.0, 'mask_channel_selection': static, 'mask_channel_other': 0.0, 'no_mask_channel_overlap': False, 'mask_channel_min_space': 1, 'conv_pos': 128, 'conv_pos_groups': 16, 'latent_temp': [2.0, 0.5, 0.999995], 'skip_masked': False, 'skip_nomask': False, 'checkpoint_activations': False, 'required_seq_len_multiple': 2, 'depthwise_conv_kernel_size': 31, 'attn_type': '', 'pos_enc_type': 'abs', 'fp16': False} gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: aether-jp / added_IVF865_Flat_nprobe_1_aether-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: albedo-jp / added_IVF641_Flat_nprobe_1_albedo-jp_v1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: alhaitham-jp / added_IVF519_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: ayaka-jp / added_IVF1018_Flat_nprobe_1_ayaka_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: ayato-jp / added_IVF1304_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: barbara-jp / added_IVF548_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: charlotte-jp / added_IVF1318_Flat_nprobe_1_charlotte-jp_v2_400.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: childe-jp / added_IVF684_Flat_nprobe_1_childe-v2_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: chongyun-jp / added_IVF545_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: cyno-jp / added_IVF380_Flat_nprobe_1_cyno-jp_v1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: diluc-jp / added_IVF1511_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: eula-jp / added_IVF2219_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: faruzan-jp / added_IVF256_Flat_nprobe_1_faruzan-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: fischl-jp / added_IVF1225_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: ganyu-jp / added_IVF1636_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: heizou-jp / added_IVF466_Flat_nprobe_1_heizou-jp_v1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: hutao-jp / added_IVF265_Flat_nprobe_5.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: itto-jp / added_IVF4454_Flat_nprobe_1_itto-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kaeya-jp / added_IVF1655_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kaveh-jp / added_IVF613_Flat_nprobe_1_kaveh_v2_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kazuha-jp / added_IVF860_Flat_nprobe_1_kazuha_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: keqing-jp / added_IVF1634_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kirara-jp / added_IVF672_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: klee-jp / added_IVF282_Flat_nprobe_5.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kokomi-jp / added_IVF934_Flat_nprobe_1_kokomi_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: lumine-jp / added_IVF938_Flat_nprobe_1_lumine-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: mona-jp / added_IVF2165_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: nahida-jp / added_IVF1062_Flat_nprobe_1_nahida-v2_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: nilou-jp / added_IVF218_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: paimon-jp / added_IVF3904_Flat_nprobe_1_paimon-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: raiden-jp / added_IVF4256_Flat_nprobe_1_raiden-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: signora-jp / added_IVF478_Flat_nprobe_1_signora-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: sucrose-jp / added_IVF884_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: thoma-jp / added_IVF366_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: tighnari-jp / added_IVF446_Flat_nprobe_1_tignari-jp_v1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: venti-jp / added_IVF3591_Flat_nprobe_1_venti-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: wanderer-jp / added_IVF953_Flat_nprobe_1_wanderer-v2_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: xiao-jp / added_IVF3205_Flat_nprobe_1_xiao-jp_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yae-jp / added_IVF1097_Flat_nprobe_1_yae-v2_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yanfei-jp / added_IVF1271_Flat_nprobe_1_yanfei-v2_v2.index \| (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yelan-jp / added_IVF2051_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yoimiya-jp / added_IVF2034_Flat_nprobe_1.index \| (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: zhongli-jp / added_IVF1672_Flat_nprobe_1.index \| (V1) Running on local URL: http://0.0.0.0:7860 To create a public link, set `share=True` in `launch()`. [2023-07-10 17:37]: npy: 2.0945026874542236, f0: 0.05994224548339844s, infer: 17.599822521209717s [2023-07-10 17:38]: npy: 3.1487624645233154, f0: 0.022048234939575195s, infer: 25.596487760543823s [2023-07-10 17:39]: npy: 3.693798780441284, f0: 0.017490386962890625s, infer: 32.087180376052856s [2023-07-10 17:39]: npy: 2.5506346225738525, f0: 0.013794660568237305s, infer: 26.60752511024475s [2023-07-10 17:40]: npy: 2.6092371940612793, f0: 0.03858685493469238s, infer: 26.312453031539917s [2023-07-10 17:41]: npy: 2.615102767944336, f0: 0.03931307792663574s, infer: 26.40330672264099s [2023-07-10 17:43]: npy: 3.1028923988342285, f0: 0.05546903610229492s, infer: 32.91775321960449s [2023-07-10 17:44]: npy: 2.839845657348633, f0: 0.046269893646240234s, infer: 27.98230767250061s [2023-07-10 17:44]: npy: 3.3039710521698, f0: 0.020084142684936523s, infer: 29.59837293624878s [2023-07-10 17:45]: npy: 3.30319881439209, f0: 0.03941464424133301s, infer: 32.42077875137329s [2023-07-10 17:46]: npy: 2.90372371673584, f0: 0.0513463020324707s, infer: 28.517998695373535s [2023-07-10 17:47]: npy: 3.4118876457214355, f0: 0.10508394241333008s, infer: 31.312357664108276s [2023-07-10 17:47]: npy: 4.102552890777588, f0: 0.02527928352355957s, infer: 33.81402325630188s [2023-07-10 17:48]: npy: 2.4004595279693604, f0: 0.09933662414550781s, infer: 29.89732074737549s [2023-07-10 17:49]: npy: 3.2991466522216797, f0: 0.03225088119506836s, infer: 29.510783195495605s [2023-07-10 17:49]: npy: 3.4149115085601807, f0: 0.04070758819580078s, infer: 30.8032488822937s
NasimB/gpt2-cocnat-aochildes-mod-sub-length-10k	NasimB	2023-07-10T19:27:45Z	16	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T17:32:01Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-aochildes-mod-sub-length-10k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-cocnat-aochildes-mod-sub-length-10k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3425 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6933 \| 0.29 \| 500 \| 5.6341 \| \| 5.3469 \| 0.59 \| 1000 \| 5.1996 \| \| 4.9864 \| 0.88 \| 1500 \| 4.9580 \| \| 4.7189 \| 1.18 \| 2000 \| 4.8083 \| \| 4.5609 \| 1.47 \| 2500 \| 4.6850 \| \| 4.4523 \| 1.77 \| 3000 \| 4.5821 \| \| 4.317 \| 2.06 \| 3500 \| 4.5146 \| \| 4.1329 \| 2.35 \| 4000 \| 4.4652 \| \| 4.1086 \| 2.65 \| 4500 \| 4.4071 \| \| 4.0635 \| 2.94 \| 5000 \| 4.3601 \| \| 3.8482 \| 3.24 \| 5500 \| 4.3553 \| \| 3.8055 \| 3.53 \| 6000 \| 4.3282 \| \| 3.7859 \| 3.83 \| 6500 \| 4.2926 \| \| 3.6619 \| 4.12 \| 7000 \| 4.2970 \| \| 3.5196 \| 4.41 \| 7500 \| 4.2933 \| \| 3.5139 \| 4.71 \| 8000 \| 4.2857 \| \| 3.4905 \| 5.0 \| 8500 \| 4.2710 \| \| 3.3203 \| 5.3 \| 9000 \| 4.2871 \| \| 3.322 \| 5.59 \| 9500 \| 4.2867 \| \| 3.3172 \| 5.89 \| 10000 \| 4.2863 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
aphi/ppo-SnowballTarget	aphi	2023-07-10T19:08:24Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-10T19:08:17Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: aphi/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
darthPanda/ppo-Huggy-v0	darthPanda	2023-07-10T18:57:02Z	14	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-10T18:55:55Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: darthPanda/ppo-Huggy-v0 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
43m1m4n/jpbrinx	43m1m4n	2023-07-10T18:53:46Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-04T20:40:20Z	--- license: creativeml-openrail-m ---
PraveenJesu/openai-whisper-medium-peft-lora-v2.2.5	PraveenJesu	2023-07-10T18:28:05Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-10T18:28:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
MaitreHibou/dqn-SpaceInvadersNoFrameskip-v4	MaitreHibou	2023-07-10T18:21:47Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T18:21:06Z	--- 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: 656.50 +/- 140.98 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 MaitreHibou -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 MaitreHibou -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 MaitreHibou ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('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', 0.0002), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
skrl/IsaacGymEnvs-AnymalTerrain-PPO	skrl	2023-07-10T18:15:29Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T20:41:55Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 19.88 +/- 0.5 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-AnymalTerrain type: IsaacGymEnvs-AnymalTerrain --- <!-- --- torch: 19.88 +/- 0.5 jax: 17.24 +/- 0.62 numpy: 17.8 +/- 0.29 --- --> # IsaacGymEnvs-AnymalTerrain-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: AnymalTerrain - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 24 # memory_size cfg["learning_epochs"] = 5 cfg["mini_batches"] = 6 # 24 * 4096 / 16384 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 3e-4 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.001 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
FerhatDk/wav2vec2-base-finetuned-ks	FerhatDk	2023-07-10T18:08:04Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2022-09-22T08:59:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3550 - Accuracy: 0.8727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 500 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 8 \| 0.6840 \| 0.6 \| \| 0.6867 \| 2.0 \| 16 \| 0.6780 \| 0.6364 \| \| 0.6742 \| 3.0 \| 24 \| 0.6601 \| 0.6182 \| \| 0.6446 \| 4.0 \| 32 \| 0.6294 \| 0.6364 \| \| 0.6299 \| 5.0 \| 40 \| 0.6002 \| 0.6727 \| \| 0.6299 \| 6.0 \| 48 \| 0.5755 \| 0.7091 \| \| 0.6021 \| 7.0 \| 56 \| 0.5530 \| 0.7273 \| \| 0.5678 \| 8.0 \| 64 \| 0.5036 \| 0.8182 \| \| 0.5512 \| 9.0 \| 72 \| 0.4753 \| 0.8545 \| \| 0.4784 \| 10.0 \| 80 \| 0.4184 \| 0.9273 \| \| 0.4784 \| 11.0 \| 88 \| 0.4102 \| 0.8909 \| \| 0.4515 \| 12.0 \| 96 \| 0.4444 \| 0.8182 \| \| 0.4878 \| 13.0 \| 104 \| 0.3780 \| 0.9091 \| \| 0.4418 \| 14.0 \| 112 \| 0.4570 \| 0.8 \| \| 0.4746 \| 15.0 \| 120 \| 0.3870 \| 0.8545 \| \| 0.4746 \| 16.0 \| 128 \| 0.3932 \| 0.8364 \| \| 0.4226 \| 17.0 \| 136 \| 0.2779 \| 0.9636 \| \| 0.4301 \| 18.0 \| 144 \| 0.3125 \| 0.9455 \| \| 0.3482 \| 19.0 \| 152 \| 0.3212 \| 0.9091 \| \| 0.3611 \| 20.0 \| 160 \| 0.3925 \| 0.8364 \| \| 0.3611 \| 21.0 \| 168 \| 0.3389 \| 0.8909 \| \| 0.3507 \| 22.0 \| 176 \| 0.3099 \| 0.8727 \| \| 0.3241 \| 23.0 \| 184 \| 0.3120 \| 0.8727 \| \| 0.2533 \| 24.0 \| 192 \| 0.2313 \| 0.9455 \| \| 0.2466 \| 25.0 \| 200 \| 0.3550 \| 0.8727 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Bellaaazzzzz/model_archive	Bellaaazzzzz	2023-07-10T18:00:43Z	2	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-10T17:41:57Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_archive These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png)
arpan-das-astrophysics/ppo-LunarLander-v2	arpan-das-astrophysics	2023-07-10T17:42:15Z	6	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T17:41:55Z	--- 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: 253.82 +/- 21.71 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 ... ```
RamonGOD/distilbert-base-uncased-finetuned-cola	RamonGOD	2023-07-10T17:32:17Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T17:00:10Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: RamonGOD/distilbert-base-uncased-finetuned-cola 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. --> # RamonGOD/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1891 - Validation Loss: 0.5654 - Train Matthews Correlation: 0.5209 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5243 \| 0.4596 \| 0.4917 \| 0 \| \| 0.3246 \| 0.5117 \| 0.4896 \| 1 \| \| 0.1891 \| 0.5654 \| 0.5209 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
AmbarB12/my_awesome_model	AmbarB12	2023-07-10T17:30:33Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-09T18:03:55Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: AmbarB12/my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # AmbarB12/my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0631 - Validation Loss: 0.2229 - Train Accuracy: 0.9306 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2523 \| 0.1891 \| 0.927 \| 0 \| \| 0.1327 \| 0.2007 \| 0.9298 \| 1 \| \| 0.0631 \| 0.2229 \| 0.9306 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
banden/ppo-Huggy	banden	2023-07-10T17:25:40Z	11	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-10T17:25:35Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: banden/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
cagarraz/rl_course_vizdoom_health_gathering_supreme	cagarraz	2023-07-10T17:23:21Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T17:23:08Z	--- 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: 3.94 +/- 0.20 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 cagarraz/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.
jordyvl/vit-small_tobacco3482_kd_CEKD_t1.5_a0.5	jordyvl	2023-07-10T17:17:44Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T16:39:33Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t1.5_a0.5 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. --> # vit-small_tobacco3482_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4258 - Accuracy: 0.825 - Brier Loss: 0.2707 - Nll: 0.8867 - F1 Micro: 0.825 - F1 Macro: 0.8116 - Ece: 0.2129 - Aurc: 0.0681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.7307 \| 0.22 \| 0.8748 \| 5.3766 \| 0.22 \| 0.1294 \| 0.2444 \| 0.6913 \| \| No log \| 2.0 \| 14 \| 1.3514 \| 0.405 \| 0.7426 \| 3.5573 \| 0.405 \| 0.2280 \| 0.2900 \| 0.4026 \| \| No log \| 3.0 \| 21 \| 0.9121 \| 0.62 \| 0.5647 \| 1.9398 \| 0.62 \| 0.5595 \| 0.2879 \| 0.2015 \| \| No log \| 4.0 \| 28 \| 0.7084 \| 0.695 \| 0.4179 \| 1.7042 \| 0.695 \| 0.6379 \| 0.2305 \| 0.1177 \| \| No log \| 5.0 \| 35 \| 0.7167 \| 0.735 \| 0.3862 \| 1.7929 \| 0.735 \| 0.7392 \| 0.2380 \| 0.1046 \| \| No log \| 6.0 \| 42 \| 0.6442 \| 0.765 \| 0.3625 \| 1.5688 \| 0.765 \| 0.7549 \| 0.2371 \| 0.1034 \| \| No log \| 7.0 \| 49 \| 0.6147 \| 0.805 \| 0.3410 \| 1.5975 \| 0.805 \| 0.7789 \| 0.2438 \| 0.1042 \| \| No log \| 8.0 \| 56 \| 0.6444 \| 0.775 \| 0.3446 \| 1.2309 \| 0.775 \| 0.7725 \| 0.2305 \| 0.0911 \| \| No log \| 9.0 \| 63 \| 0.5964 \| 0.8 \| 0.3219 \| 1.3613 \| 0.8000 \| 0.7784 \| 0.2446 \| 0.0734 \| \| No log \| 10.0 \| 70 \| 0.5700 \| 0.82 \| 0.3160 \| 1.2605 \| 0.82 \| 0.7860 \| 0.2301 \| 0.0632 \| \| No log \| 11.0 \| 77 \| 0.5663 \| 0.79 \| 0.3176 \| 1.2939 \| 0.79 \| 0.7643 \| 0.2315 \| 0.0666 \| \| No log \| 12.0 \| 84 \| 0.5111 \| 0.825 \| 0.3143 \| 1.1082 \| 0.825 \| 0.8082 \| 0.2519 \| 0.0844 \| \| No log \| 13.0 \| 91 \| 0.5228 \| 0.78 \| 0.3156 \| 0.9444 \| 0.78 \| 0.7773 \| 0.1941 \| 0.0650 \| \| No log \| 14.0 \| 98 \| 0.5792 \| 0.78 \| 0.3409 \| 1.5054 \| 0.78 \| 0.7725 \| 0.2061 \| 0.1019 \| \| No log \| 15.0 \| 105 \| 0.4905 \| 0.83 \| 0.2912 \| 1.0068 \| 0.83 \| 0.8266 \| 0.2324 \| 0.0545 \| \| No log \| 16.0 \| 112 \| 0.4990 \| 0.825 \| 0.2961 \| 1.1452 \| 0.825 \| 0.8140 \| 0.2188 \| 0.0632 \| \| No log \| 17.0 \| 119 \| 0.4900 \| 0.805 \| 0.2940 \| 1.2027 \| 0.805 \| 0.8018 \| 0.2188 \| 0.0860 \| \| No log \| 18.0 \| 126 \| 0.4755 \| 0.805 \| 0.2988 \| 1.0223 \| 0.805 \| 0.7789 \| 0.2229 \| 0.0792 \| \| No log \| 19.0 \| 133 \| 0.4398 \| 0.81 \| 0.2679 \| 0.9732 \| 0.81 \| 0.7830 \| 0.2085 \| 0.0585 \| \| No log \| 20.0 \| 140 \| 0.4766 \| 0.805 \| 0.2992 \| 0.9730 \| 0.805 \| 0.7934 \| 0.2141 \| 0.0662 \| \| No log \| 21.0 \| 147 \| 0.4615 \| 0.835 \| 0.2867 \| 0.9343 \| 0.835 \| 0.8219 \| 0.1999 \| 0.0751 \| \| No log \| 22.0 \| 154 \| 0.4343 \| 0.825 \| 0.2641 \| 1.1353 \| 0.825 \| 0.8070 \| 0.2095 \| 0.0603 \| \| No log \| 23.0 \| 161 \| 0.4291 \| 0.85 \| 0.2660 \| 1.0109 \| 0.85 \| 0.8365 \| 0.2435 \| 0.0615 \| \| No log \| 24.0 \| 168 \| 0.4263 \| 0.855 \| 0.2653 \| 0.9395 \| 0.855 \| 0.8440 \| 0.2445 \| 0.0623 \| \| No log \| 25.0 \| 175 \| 0.4338 \| 0.845 \| 0.2700 \| 0.8794 \| 0.845 \| 0.8349 \| 0.2254 \| 0.0584 \| \| No log \| 26.0 \| 182 \| 0.4305 \| 0.835 \| 0.2648 \| 0.9062 \| 0.835 \| 0.8322 \| 0.2113 \| 0.0658 \| \| No log \| 27.0 \| 189 \| 0.4262 \| 0.84 \| 0.2683 \| 0.9967 \| 0.8400 \| 0.8291 \| 0.2240 \| 0.0670 \| \| No log \| 28.0 \| 196 \| 0.4329 \| 0.83 \| 0.2724 \| 0.9016 \| 0.83 \| 0.8239 \| 0.2016 \| 0.0685 \| \| No log \| 29.0 \| 203 \| 0.4233 \| 0.845 \| 0.2653 \| 0.9115 \| 0.845 \| 0.8375 \| 0.2005 \| 0.0634 \| \| No log \| 30.0 \| 210 \| 0.4204 \| 0.84 \| 0.2638 \| 0.8892 \| 0.8400 \| 0.8348 \| 0.2175 \| 0.0633 \| \| No log \| 31.0 \| 217 \| 0.4240 \| 0.83 \| 0.2684 \| 0.8871 \| 0.83 \| 0.8217 \| 0.2128 \| 0.0660 \| \| No log \| 32.0 \| 224 \| 0.4246 \| 0.84 \| 0.2677 \| 0.8867 \| 0.8400 \| 0.8307 \| 0.2117 \| 0.0670 \| \| No log \| 33.0 \| 231 \| 0.4247 \| 0.83 \| 0.2690 \| 0.8917 \| 0.83 \| 0.8202 \| 0.2084 \| 0.0679 \| \| No log \| 34.0 \| 238 \| 0.4218 \| 0.84 \| 0.2660 \| 0.8848 \| 0.8400 \| 0.8326 \| 0.2138 \| 0.0663 \| \| No log \| 35.0 \| 245 \| 0.4220 \| 0.845 \| 0.2667 \| 0.8926 \| 0.845 \| 0.8354 \| 0.2109 \| 0.0655 \| \| No log \| 36.0 \| 252 \| 0.4247 \| 0.83 \| 0.2694 \| 0.8854 \| 0.83 \| 0.8202 \| 0.2213 \| 0.0683 \| \| No log \| 37.0 \| 259 \| 0.4239 \| 0.84 \| 0.2683 \| 0.8849 \| 0.8400 \| 0.8326 \| 0.2163 \| 0.0670 \| \| No log \| 38.0 \| 266 \| 0.4239 \| 0.835 \| 0.2689 \| 0.8876 \| 0.835 \| 0.8208 \| 0.2118 \| 0.0672 \| \| No log \| 39.0 \| 273 \| 0.4252 \| 0.83 \| 0.2696 \| 0.8885 \| 0.83 \| 0.8180 \| 0.2064 \| 0.0682 \| \| No log \| 40.0 \| 280 \| 0.4237 \| 0.835 \| 0.2686 \| 0.8867 \| 0.835 \| 0.8208 \| 0.2211 \| 0.0675 \| \| No log \| 41.0 \| 287 \| 0.4256 \| 0.83 \| 0.2700 \| 0.8847 \| 0.83 \| 0.8180 \| 0.2253 \| 0.0682 \| \| No log \| 42.0 \| 294 \| 0.4243 \| 0.835 \| 0.2692 \| 0.8839 \| 0.835 \| 0.8208 \| 0.2130 \| 0.0675 \| \| No log \| 43.0 \| 301 \| 0.4248 \| 0.83 \| 0.2695 \| 0.8850 \| 0.83 \| 0.8180 \| 0.2237 \| 0.0682 \| \| No log \| 44.0 \| 308 \| 0.4246 \| 0.83 \| 0.2694 \| 0.8847 \| 0.83 \| 0.8180 \| 0.2383 \| 0.0680 \| \| No log \| 45.0 \| 315 \| 0.4253 \| 0.83 \| 0.2699 \| 0.8858 \| 0.83 \| 0.8180 \| 0.2200 \| 0.0681 \| \| No log \| 46.0 \| 322 \| 0.4246 \| 0.83 \| 0.2694 \| 0.8857 \| 0.83 \| 0.8180 \| 0.2311 \| 0.0679 \| \| No log \| 47.0 \| 329 \| 0.4253 \| 0.83 \| 0.2700 \| 0.8843 \| 0.83 \| 0.8180 \| 0.2312 \| 0.0682 \| \| No log \| 48.0 \| 336 \| 0.4252 \| 0.83 \| 0.2698 \| 0.8830 \| 0.83 \| 0.8180 \| 0.2177 \| 0.0682 \| \| No log \| 49.0 \| 343 \| 0.4257 \| 0.83 \| 0.2703 \| 0.8848 \| 0.83 \| 0.8180 \| 0.2315 \| 0.0683 \| \| No log \| 50.0 \| 350 \| 0.4256 \| 0.83 \| 0.2703 \| 0.8833 \| 0.83 \| 0.8180 \| 0.2331 \| 0.0684 \| \| No log \| 51.0 \| 357 \| 0.4254 \| 0.83 \| 0.2703 \| 0.8863 \| 0.83 \| 0.8180 \| 0.2422 \| 0.0681 \| \| No log \| 52.0 \| 364 \| 0.4261 \| 0.83 \| 0.2707 \| 0.8864 \| 0.83 \| 0.8180 \| 0.2424 \| 0.0683 \| \| No log \| 53.0 \| 371 \| 0.4249 \| 0.83 \| 0.2700 \| 0.8855 \| 0.83 \| 0.8180 \| 0.2195 \| 0.0679 \| \| No log \| 54.0 \| 378 \| 0.4255 \| 0.83 \| 0.2704 \| 0.8846 \| 0.83 \| 0.8180 \| 0.2342 \| 0.0682 \| \| No log \| 55.0 \| 385 \| 0.4256 \| 0.825 \| 0.2704 \| 0.8861 \| 0.825 \| 0.8116 \| 0.2357 \| 0.0682 \| \| No log \| 56.0 \| 392 \| 0.4264 \| 0.83 \| 0.2708 \| 0.8853 \| 0.83 \| 0.8180 \| 0.2345 \| 0.0682 \| \| No log \| 57.0 \| 399 \| 0.4257 \| 0.825 \| 0.2706 \| 0.8864 \| 0.825 \| 0.8116 \| 0.2353 \| 0.0682 \| \| No log \| 58.0 \| 406 \| 0.4258 \| 0.825 \| 0.2704 \| 0.8841 \| 0.825 \| 0.8116 \| 0.2271 \| 0.0681 \| \| No log \| 59.0 \| 413 \| 0.4255 \| 0.825 \| 0.2703 \| 0.8856 \| 0.825 \| 0.8116 \| 0.2267 \| 0.0680 \| \| No log \| 60.0 \| 420 \| 0.4259 \| 0.825 \| 0.2709 \| 0.8842 \| 0.825 \| 0.8116 \| 0.2269 \| 0.0683 \| \| No log \| 61.0 \| 427 \| 0.4254 \| 0.83 \| 0.2702 \| 0.8852 \| 0.83 \| 0.8180 \| 0.2265 \| 0.0680 \| \| No log \| 62.0 \| 434 \| 0.4261 \| 0.83 \| 0.2707 \| 0.8851 \| 0.83 \| 0.8180 \| 0.2346 \| 0.0682 \| \| No log \| 63.0 \| 441 \| 0.4257 \| 0.825 \| 0.2704 \| 0.8854 \| 0.825 \| 0.8116 \| 0.2232 \| 0.0682 \| \| No log \| 64.0 \| 448 \| 0.4261 \| 0.825 \| 0.2708 \| 0.8845 \| 0.825 \| 0.8116 \| 0.2264 \| 0.0683 \| \| No log \| 65.0 \| 455 \| 0.4259 \| 0.825 \| 0.2706 \| 0.8862 \| 0.825 \| 0.8116 \| 0.2204 \| 0.0682 \| \| No log \| 66.0 \| 462 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8856 \| 0.825 \| 0.8116 \| 0.2193 \| 0.0682 \| \| No log \| 67.0 \| 469 \| 0.4255 \| 0.83 \| 0.2703 \| 0.8852 \| 0.83 \| 0.8180 \| 0.2190 \| 0.0681 \| \| No log \| 68.0 \| 476 \| 0.4260 \| 0.825 \| 0.2708 \| 0.8860 \| 0.825 \| 0.8116 \| 0.2196 \| 0.0682 \| \| No log \| 69.0 \| 483 \| 0.4259 \| 0.825 \| 0.2708 \| 0.8858 \| 0.825 \| 0.8116 \| 0.2195 \| 0.0682 \| \| No log \| 70.0 \| 490 \| 0.4255 \| 0.825 \| 0.2703 \| 0.8857 \| 0.825 \| 0.8116 \| 0.2135 \| 0.0682 \| \| No log \| 71.0 \| 497 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8857 \| 0.825 \| 0.8116 \| 0.2205 \| 0.0681 \| \| 0.1816 \| 72.0 \| 504 \| 0.4261 \| 0.825 \| 0.2708 \| 0.8857 \| 0.825 \| 0.8116 \| 0.2198 \| 0.0682 \| \| 0.1816 \| 73.0 \| 511 \| 0.4259 \| 0.825 \| 0.2706 \| 0.8852 \| 0.825 \| 0.8116 \| 0.2192 \| 0.0682 \| \| 0.1816 \| 74.0 \| 518 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8856 \| 0.825 \| 0.8116 \| 0.2290 \| 0.0681 \| \| 0.1816 \| 75.0 \| 525 \| 0.4257 \| 0.825 \| 0.2706 \| 0.8864 \| 0.825 \| 0.8116 \| 0.2337 \| 0.0681 \| \| 0.1816 \| 76.0 \| 532 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8855 \| 0.825 \| 0.8116 \| 0.2211 \| 0.0681 \| \| 0.1816 \| 77.0 \| 539 \| 0.4255 \| 0.825 \| 0.2704 \| 0.8860 \| 0.825 \| 0.8116 \| 0.2137 \| 0.0680 \| \| 0.1816 \| 78.0 \| 546 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8868 \| 0.825 \| 0.8116 \| 0.2274 \| 0.0682 \| \| 0.1816 \| 79.0 \| 553 \| 0.4260 \| 0.825 \| 0.2708 \| 0.8859 \| 0.825 \| 0.8116 \| 0.2209 \| 0.0682 \| \| 0.1816 \| 80.0 \| 560 \| 0.4260 \| 0.825 \| 0.2708 \| 0.8864 \| 0.825 \| 0.8116 \| 0.2135 \| 0.0681 \| \| 0.1816 \| 81.0 \| 567 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8859 \| 0.825 \| 0.8116 \| 0.2134 \| 0.0682 \| \| 0.1816 \| 82.0 \| 574 \| 0.4258 \| 0.825 \| 0.2706 \| 0.8862 \| 0.825 \| 0.8116 \| 0.2062 \| 0.0681 \| \| 0.1816 \| 83.0 \| 581 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8866 \| 0.825 \| 0.8116 \| 0.2204 \| 0.0681 \| \| 0.1816 \| 84.0 \| 588 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8868 \| 0.825 \| 0.8116 \| 0.2204 \| 0.0681 \| \| 0.1816 \| 85.0 \| 595 \| 0.4257 \| 0.825 \| 0.2706 \| 0.8861 \| 0.825 \| 0.8116 \| 0.2141 \| 0.0682 \| \| 0.1816 \| 86.0 \| 602 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8861 \| 0.825 \| 0.8116 \| 0.2140 \| 0.0682 \| \| 0.1816 \| 87.0 \| 609 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8867 \| 0.825 \| 0.8116 \| 0.2137 \| 0.0680 \| \| 0.1816 \| 88.0 \| 616 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8866 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0681 \| \| 0.1816 \| 89.0 \| 623 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8866 \| 0.825 \| 0.8116 \| 0.2205 \| 0.0681 \| \| 0.1816 \| 90.0 \| 630 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8865 \| 0.825 \| 0.8116 \| 0.2053 \| 0.0680 \| \| 0.1816 \| 91.0 \| 637 \| 0.4258 \| 0.825 \| 0.2706 \| 0.8868 \| 0.825 \| 0.8116 \| 0.2130 \| 0.0681 \| \| 0.1816 \| 92.0 \| 644 \| 0.4258 \| 0.825 \| 0.2706 \| 0.8870 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0680 \| \| 0.1816 \| 93.0 \| 651 \| 0.4258 \| 0.825 \| 0.2706 \| 0.8868 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0681 \| \| 0.1816 \| 94.0 \| 658 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8867 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0681 \| \| 0.1816 \| 95.0 \| 665 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8867 \| 0.825 \| 0.8116 \| 0.2053 \| 0.0680 \| \| 0.1816 \| 96.0 \| 672 \| 0.4259 \| 0.825 \| 0.2707 \| 0.8866 \| 0.825 \| 0.8116 \| 0.2053 \| 0.0681 \| \| 0.1816 \| 97.0 \| 679 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8868 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0681 \| \| 0.1816 \| 98.0 \| 686 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8868 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0680 \| \| 0.1816 \| 99.0 \| 693 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8868 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0681 \| \| 0.1816 \| 100.0 \| 700 \| 0.4258 \| 0.825 \| 0.2707 \| 0.8867 \| 0.825 \| 0.8116 \| 0.2129 \| 0.0681 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
NasimB/gpt2-concat-all-new-mod-datasets-rarity-all-iorder-13k-2p6k	NasimB	2023-07-10T17:09:06Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T15:25:56Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-all-new-mod-datasets-rarity-all-iorder-13k-2p6k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-all-new-mod-datasets-rarity-all-iorder-13k-2p6k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.7847 \| 0.32 \| 500 \| 5.6658 \| \| 5.4409 \| 0.63 \| 1000 \| 5.2342 \| \| 5.088 \| 0.95 \| 1500 \| 4.9831 \| \| 4.8091 \| 1.27 \| 2000 \| 4.8440 \| \| 4.6774 \| 1.59 \| 2500 \| 4.7254 \| \| 4.5641 \| 1.9 \| 3000 \| 4.6255 \| \| 4.3493 \| 2.22 \| 3500 \| 4.5674 \| \| 4.2735 \| 2.54 \| 4000 \| 4.5081 \| \| 4.2294 \| 2.86 \| 4500 \| 4.4480 \| \| 4.0526 \| 3.17 \| 5000 \| 4.4279 \| \| 3.9479 \| 3.49 \| 5500 \| 4.4002 \| \| 3.9223 \| 3.81 \| 6000 \| 4.3596 \| \| 3.8021 \| 4.13 \| 6500 \| 4.3586 \| \| 3.6504 \| 4.44 \| 7000 \| 4.3495 \| \| 3.6428 \| 4.76 \| 7500 \| 4.3416 \| \| 3.58 \| 5.08 \| 8000 \| 4.3470 \| \| 3.4494 \| 5.4 \| 8500 \| 4.3484 \| \| 3.4443 \| 5.71 \| 9000 \| 4.3455 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
Buth/fatuh	Buth	2023-07-10T16:50:46Z	0	0	adapter-transformers	[ "adapter-transformers", "en", "dataset:Open-Orca/OpenOrca", "license:openrail", "region:us" ]	null	2023-07-10T16:48:59Z	--- license: openrail datasets: - Open-Orca/OpenOrca language: - en metrics: - accuracy library_name: adapter-transformers ---
svalcin/q-FrozenLake-v1-4x4-noSlippery	svalcin	2023-07-10T16:39:14Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T16:39:10Z	--- 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="svalcin/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"]) ```
Umer1542/task-b-classification	Umer1542	2023-07-10T16:35:37Z	160	0	transformers	[ "transformers", "pytorch", "opt", "text-classification", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T15:47:23Z	--- license: other language: - en metrics: - accuracy - f1 - recall pipeline_tag: text-classification ---
TheBloke/MPT-30B-Dolphin-v2-GGML	TheBloke	2023-07-10T16:32:10Z	0	9	null	[ "license:other", "region:us" ]	null	2023-07-10T15:13:07Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Manoj Preveen's MPT 30B Dolphin v2 GGML These files are MPT GGML format model files for [Manoj Preveen's MPT 30B Dolphin v2](https://huggingface.co/manojpreveen/mpt-30b-dolphin-v2). Please note that these GGMLs are not compatible with llama.cpp, or currently with text-generation-webui. Please see below for a list of tools that work with this GGML model. ## Repositories available * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/MPT-30B-Dolphin-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/manojpreveen/mpt-30b-dolphin-v2) ## Prompt template: custom ``` <system>: You are a helpful assistant <human>: {prompt} <bot>: ``` <!-- compatibility_ggml start --> ## Compatibilty These files are not compatible with llama.cpp or text-generation-webui. They can be used with: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful inference engine based on llama.cpp with full GPU acceleration and good UI. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI for GGML inference on Windows and macOS. * [LoLLMs-WebUI](https://github.com/ParisNeo/LoLLMs-WebUI) a web UI which supports nearly every backend out there. Use ctransformers backend for support for this model. * [ctransformers](https://github.com/marella/ctransformers): for use in Python code, including LangChain support. * [rustformers' llm](https://github.com/rustformers/llm) * The example `mpt` binary provided with [ggml](https://github.com/ggerganov/ggml) As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!) ## Tutorial for using LoLLMs-WebUI: * [Video tutorial, by LoLLMs-WebUI's author ParisNeo](https://youtu.be/vBU1b5n0GMU) <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| mpt-30b-dolphin-v2.ggmlv1.q4_0.bin \| q4_0 \| 4 \| 16.85 GB\| 19.35 GB \| 4-bit. \| \| mpt-30b-dolphin-v2.ggmlv1.q4_1.bin \| q4_1 \| 4 \| 18.73 GB\| 21.23 GB \| 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| mpt-30b-dolphin-v2.ggmlv1.q5_0.bin \| q5_0 \| 5 \| 20.60 GB\| 23.10 GB \| 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| mpt-30b-dolphin-v2.ggmlv1.q5_1.bin \| q5_1 \| 5 \| 22.47 GB\| 24.97 GB \| 5-bit. Even higher accuracy, resource usage and slower inference. \| \| mpt-30b-dolphin-v2.ggmlv1.q8_0.bin \| q8_0 \| 8 \| 31.83 GB\| 34.33 GB \| 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz. Patreon special mentions: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Manoj Preveen's MPT 30B Dolphin v2 Base Model : mosaicml/mpt-30b Tool : MosaicML's llm-foundry (https://github.com/mosaicml/llm-foundry) Dataset : Entire flan3m-GPT3.5 dataset. Config yaml with Model Params : https://huggingface.co/manojpreveen/mpt-30b-orca-v2/blob/main/mpt-30b_orca.yaml Prompt Format : ``` <system>: [system prompt] <human>: [question] <bot>: ```
yhyhy3/open_llama_7b_v2_med_instruct	yhyhy3	2023-07-10T16:22:39Z	1,461	8	transformers	[ "transformers", "pytorch", "llama", "text-generation", "medical", "code", "en", "dataset:ehartford/dolphin", "dataset:LinhDuong/chatdoctor-200k", "dataset:sahil2801/code_instructions_120k", "dataset:medalpaca/medical_meadow_mediqa", "dataset:kaiokendev/SuperCOT-dataset", "dataset:tiiuae/falcon-refinedweb", "dataset:bigcode/starcoderdata", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-09T17:19:43Z	--- license: apache-2.0 datasets: - ehartford/dolphin - LinhDuong/chatdoctor-200k - sahil2801/code_instructions_120k - medalpaca/medical_meadow_mediqa - kaiokendev/SuperCOT-dataset - tiiuae/falcon-refinedweb - bigcode/starcoderdata - togethercomputer/RedPajama-Data-1T language: - en library_name: transformers pipeline_tag: text-generation tags: - medical - code --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This model is an instruction-tuned Open LLaMa model with 7B parameters, with specialities in medical QA and code instruction. ## Model Details <!-- Provide a longer summary of what this model is. --> - Model type: LlamaForCausalLM - Language(s) (NLP): English - License: Apache 2.0 - Finetuned from model (QLoRA): [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) ## How to Get Started with the Model Use the code below to get started with the model. ```py import torch from transformers import LlamaTokenizer, LlamaForCausalLM model_path = 'yhyhy3/open_llama_7b_v2_med_dolphin_qlora_merged' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) prompt = '''### Instruction: Answer the following question. ### Input: What is the capital of New Jersey? ### Response:''' input_ids = tokenizer(prompt, return_tensors="pt").input_ids generation_output = model.generate( input_ids=input_ids, max_new_tokens=32 ) print(tokenizer.decode(generation_output[0])) ``` ## Training Details ### Training Data Converted the following datasets to alpaca:instruction format. 1. [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) - ORCA style dataset generously created by [Eric Hartford](https://huggingface.co/ehartford) - Only used the 1 million GPT4 generated instructions file [flan1m-alpaca-uncensored.jsonl](https://huggingface.co/datasets/ehartford/dolphin/blob/main/flan1m-alpaca-uncensored.jsonl). 2. [LinhDuong/chatdoctor-200k](https://huggingface.co/datasets/LinhDuong/chatdoctor-200k) - Refined dataset sourced from icliniq medical QA forum 3. [sahil2801/code_instructions_120k](https://huggingface.co/datasets/sahil2801/code_instructions_120k) - Code instruction dataset generously created by Sahil Chaudhary from ThreeSixty AI 4. [medalpaca/medical_meadow_mediqa](https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa) - MEDIQA is a dataset of manually generated, question-driven summaries of multi and single document answers to consumer health questions from medalpaca group. 5. [kaiokendev/SuperCOT-dataset](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset) - Code instruction dataset generously created by Kaio Ken ### Training Procedure Trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) QLoRa on [RunPod](https://www.runpod.io/console/gpu-cloud) 8x A6000 on Community Cloud for 3 epochs (~14 hours - ~$70). <details> <summary>axolotl training config:</summary> ```yaml base_model: openlm-research/open_llama_7b_v2 base_model_config: openlm-research/open_llama_7b_v2 model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: true strict: false push_dataset_to_hub: hub_model_id: hf_use_auth_token: datasets: - path: json type: alpaca data_files: /disk/flan1m-alpaca-uncensored.jsonl shards: 8 - path: sahil2801/code_instructions_120k type: alpaca - path: LinhDuong/chatdoctor-200k type: alpaca shards: 2 - path: kaiokendev/SuperCOT-dataset type: alpaca - path: medalpaca/medical_meadow_mediqa type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.01 adapter: qlora lora_model_dir: sequence_len: 2048 max_packed_sequence_len: 2048 lora_r: 8 lora_alpha: 32 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_mode: true wandb_project: wandb_watch: wandb_run_id: wandb_log_model: 'openllama_checkpoint' output_dir: /disk/open_llama_7b_v2_dolphin_qlora gradient_accumulation_steps: 2 micro_batch_size: 16 num_epochs: 3 optimizer: paged_adamw_32bit torchdistx_path: lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: true flash_attention: gptq_groupsize: gptq_model_v1: warmup_steps: 1000 eval_steps: 5000 save_steps: debug: deepspeed: weight_decay: 0.0000001 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details>
aburnazy/opt125m_alpaca	aburnazy	2023-07-10T16:20:54Z	136	0	transformers	[ "transformers", "pytorch", "tensorboard", "opt", "text-generation", "generated_from_trainer", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T15:40:41Z	--- license: other tags: - generated_from_trainer model-index: - name: opt125m_alpaca 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. --> # opt125m_alpaca This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
tyavika/Distilbert-QA-Pytorch-seed	tyavika	2023-07-10T16:10:13Z	104	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-07-10T12:52:40Z	--- tags: - generated_from_trainer model-index: - name: Distilbert-QA-Pytorch-seed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Distilbert-QA-Pytorch-seed This model is a fine-tuned version of [tyavika/Distilbert-QA-Pytorch-seed](https://huggingface.co/tyavika/Distilbert-QA-Pytorch-seed) 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: 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: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
madoe001/ppo-LunarLander-v2	madoe001	2023-07-10T16:00:09Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-19T17:22:29Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 271.45 +/- 25.19 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
sianadouglas/ensembletest	sianadouglas	2023-07-10T15:48:14Z	0	0	null	[ "en", "license:other", "region:us" ]	null	2023-07-10T15:47:23Z	--- license: other language: - en ---
Khushnur/t5-base-end2end-questions-generation_squad	Khushnur	2023-07-10T15:47:50Z	164	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-10T15:02:34Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-end2end-questions-generation_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. --> # t5-base-end2end-questions-generation_squad This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5658 \| 0.67 \| 100 \| 1.8866 \| \| 1.958 \| 1.35 \| 200 \| 1.7150 \| \| 1.8516 \| 2.02 \| 300 \| 1.6701 \| \| 1.7965 \| 2.69 \| 400 \| 1.6560 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
mgmeskill/Pixelcopter-PLE-v0	mgmeskill	2023-07-10T15:38:32Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T15:26:11Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.50 +/- 37.13 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Suryabhan/openai-whisper-large-v2-LORA-colab	Suryabhan	2023-07-10T15:32:46Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-10T15:32:41Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
tyavika/LR1E4-BS16-Bert_CNN512LSTM256NoBid	tyavika	2023-07-10T15:31:42Z	77	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-09T20:06:29Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Bert_CNN512LSTM256NoBid 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. --> # LR1E4-BS16-Bert_CNN512LSTM256NoBid This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.7267 \| 1.0 \| 3290 \| 1.5092 \| \| 1.2394 \| 2.0 \| 6580 \| 1.3933 \| \| 0.8348 \| 3.0 \| 9870 \| 1.5591 \| \| 0.542 \| 4.0 \| 13160 \| 1.6667 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
MnLgt/textual_inversion_muir_1_5	MnLgt	2023-07-10T15:31:36Z	1	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T14:16:45Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - jordandavis/textual_inversion_muir_1_5 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
agercas/speecht5_finetuned_voxpopuli_nl	agercas	2023-07-10T15:27:22Z	78	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-10T09:21:57Z	--- license: mit tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4572 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5221 \| 4.3 \| 1000 \| 0.4774 \| \| 0.505 \| 8.61 \| 2000 \| 0.4648 \| \| 0.4929 \| 12.91 \| 3000 \| 0.4583 \| \| 0.4901 \| 17.21 \| 4000 \| 0.4572 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
grace-pro/afriberta-finetuned-hausa	grace-pro	2023-07-10T15:26:48Z	126	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T14:49:51Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-finetuned-hausa 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. --> # afriberta-finetuned-hausa This model is a fine-tuned version of [castorini/afriberta_large](https://huggingface.co/castorini/afriberta_large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1242 - Precision: 0.7104 - Recall: 0.5095 - F1: 0.5934 - Accuracy: 0.9647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1369 \| 1.0 \| 2624 \| 0.1256 \| 0.6856 \| 0.4541 \| 0.5463 \| 0.9614 \| \| 0.1103 \| 2.0 \| 5248 \| 0.1195 \| 0.7014 \| 0.4947 \| 0.5802 \| 0.9637 \| \| 0.0868 \| 3.0 \| 7872 \| 0.1242 \| 0.7104 \| 0.5095 \| 0.5934 \| 0.9647 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Birchlabs/llama-13b-stepwise-embeddings	Birchlabs	2023-07-10T15:17:11Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2023-07-10T13:55:53Z	--- license: apache-2.0 --- Fine-tuned input (`embed_tokens: Embedding`) and output (`lm_head: Linear`) embeddings layers, for use with [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter). Prior to finetuning: we grew the vocabulary of the tokenizer and embeddings layers. The new embeddings were average-initialized, and needed training, so we trained them. These are the weights from that training. Ordinarily a QLoRA finetune of an LLM would not finetune the `embed_tokens: Embedding` (you'd need to get a bit creative, because not only have the dimensions changed, but also I don't believe any way has been established to train _adapters_ over `Embedding`s). Nor apparently would it finetune `lm_head: Linear`. This is harder than it sounds (i.e. you can't handle it the same way you adapt the other Linear layers), because the dimensions have grown.
S1X3L4/Taxi-v3	S1X3L4	2023-07-10T15:04:55Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T15:04:50Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 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="S1X3L4/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"]) ```
dariowsz/wav2vec2-base-finetuned-gtzan	dariowsz	2023-07-10T15:03:27Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-04T13:47:36Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5537 - Accuracy: 0.88 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.7898 \| 1.0 \| 113 \| 1.8052 \| 0.45 \| \| 1.4297 \| 2.0 \| 226 \| 1.2229 \| 0.62 \| \| 1.041 \| 3.0 \| 339 \| 0.9934 \| 0.65 \| \| 1.3882 \| 4.0 \| 452 \| 1.1735 \| 0.62 \| \| 0.7248 \| 5.0 \| 565 \| 0.8461 \| 0.69 \| \| 0.6128 \| 6.0 \| 678 \| 0.7391 \| 0.75 \| \| 0.3225 \| 7.0 \| 791 \| 0.8754 \| 0.74 \| \| 0.6483 \| 8.0 \| 904 \| 0.8341 \| 0.79 \| \| 0.2755 \| 9.0 \| 1017 \| 0.5537 \| 0.88 \| \| 0.4398 \| 10.0 \| 1130 \| 0.6076 \| 0.85 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
NICFRU/bart-base-paraphrasing-news	NICFRU	2023-07-10T15:02:02Z	106	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-10T14:46:34Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-paraphrasing results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-paraphrasing This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6617 - Rouge1: 57.7088 - Rouge2: 51.0096 - Rougel: 54.7514 - Rougelsum: 56.3943 - Gen Len: 20.0 ## 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: 4e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 0.2 \| 10 \| 0.5263 \| 58.2676 \| 51.5842 \| 55.5057 \| 57.1584 \| 19.94 \| \| No log \| 0.4 \| 20 \| 0.5050 \| 56.1604 \| 48.7383 \| 54.0373 \| 55.372 \| 20.0 \| \| No log \| 0.6 \| 30 \| 0.4674 \| 58.0617 \| 51.4993 \| 56.0368 \| 56.9665 \| 20.0 \| \| No log \| 0.8 \| 40 \| 0.4545 \| 57.5375 \| 51.0203 \| 55.5247 \| 56.5761 \| 19.94 \| \| No log \| 1.0 \| 50 \| 0.4373 \| 57.7263 \| 50.8021 \| 55.0549 \| 56.35 \| 19.98 \| \| No log \| 1.2 \| 60 \| 0.4313 \| 57.87 \| 50.9904 \| 54.9727 \| 56.5379 \| 19.97 \| \| No log \| 1.4 \| 70 \| 0.4855 \| 56.5101 \| 49.3124 \| 54.1572 \| 55.0671 \| 20.0 \| \| No log \| 1.6 \| 80 \| 0.4202 \| 56.6535 \| 50.0302 \| 53.6891 \| 55.1016 \| 19.96 \| \| No log \| 1.8 \| 90 \| 0.4544 \| 57.315 \| 50.6289 \| 54.642 \| 55.7326 \| 19.95 \| \| 0.5858 \| 2.0 \| 100 \| 0.4157 \| 56.4569 \| 48.8105 \| 53.937 \| 55.3515 \| 20.0 \| \| 0.5858 \| 2.2 \| 110 \| 0.4555 \| 57.8424 \| 51.5966 \| 55.6655 \| 56.6862 \| 20.0 \| \| 0.5858 \| 2.4 \| 120 \| 0.4196 \| 58.2562 \| 51.7596 \| 55.5085 \| 57.1823 \| 19.97 \| \| 0.5858 \| 2.6 \| 130 \| 0.4334 \| 58.6906 \| 51.6106 \| 55.6631 \| 57.5254 \| 19.89 \| \| 0.5858 \| 2.8 \| 140 \| 0.4710 \| 56.5401 \| 49.33 \| 53.8792 \| 55.0282 \| 20.0 \| \| 0.5858 \| 3.0 \| 150 \| 0.4357 \| 58.2083 \| 52.0049 \| 55.9938 \| 57.1928 \| 20.0 \| \| 0.5858 \| 3.2 \| 160 \| 0.4735 \| 58.8112 \| 52.2196 \| 56.5004 \| 57.7703 \| 19.94 \| \| 0.5858 \| 3.4 \| 170 \| 0.4428 \| 57.6778 \| 50.6377 \| 54.8752 \| 56.4778 \| 20.0 \| \| 0.5858 \| 3.6 \| 180 \| 0.4983 \| 57.4124 \| 50.4244 \| 54.6163 \| 56.0992 \| 20.0 \| \| 0.5858 \| 3.8 \| 190 \| 0.4620 \| 58.0701 \| 51.5021 \| 55.7222 \| 56.8737 \| 20.0 \| \| 0.2865 \| 4.0 \| 200 \| 0.4502 \| 59.1191 \| 52.7516 \| 56.4389 \| 57.7153 \| 20.0 \| \| 0.2865 \| 4.2 \| 210 \| 0.4805 \| 58.9064 \| 52.7148 \| 56.1058 \| 57.6709 \| 20.0 \| \| 0.2865 \| 4.4 \| 220 \| 0.4755 \| 58.6883 \| 52.1464 \| 55.9164 \| 57.3825 \| 20.0 \| \| 0.2865 \| 4.6 \| 230 \| 0.4524 \| 58.9916 \| 52.1101 \| 56.4116 \| 57.9468 \| 19.9 \| \| 0.2865 \| 4.8 \| 240 \| 0.4726 \| 58.9953 \| 52.8173 \| 56.5846 \| 58.0805 \| 20.0 \| \| 0.2865 \| 5.0 \| 250 \| 0.4841 \| 58.1058 \| 51.614 \| 55.3374 \| 56.7617 \| 20.0 \| \| 0.2865 \| 5.2 \| 260 \| 0.5047 \| 58.2785 \| 51.1874 \| 55.5336 \| 56.8795 \| 20.0 \| \| 0.2865 \| 5.4 \| 270 \| 0.4658 \| 57.2753 \| 49.6038 \| 53.9588 \| 55.6038 \| 19.91 \| \| 0.2865 \| 5.6 \| 280 \| 0.5261 \| 58.1691 \| 51.5254 \| 55.2685 \| 56.7787 \| 20.0 \| \| 0.2865 \| 5.8 \| 290 \| 0.4833 \| 57.8088 \| 51.2838 \| 54.8739 \| 56.4374 \| 20.0 \| \| 0.1668 \| 6.0 \| 300 \| 0.5067 \| 58.2021 \| 51.3629 \| 55.3548 \| 56.9093 \| 19.99 \| \| 0.1668 \| 6.2 \| 310 \| 0.5461 \| 58.0327 \| 51.4051 \| 55.3426 \| 56.7923 \| 20.0 \| \| 0.1668 \| 6.4 \| 320 \| 0.5463 \| 58.1027 \| 51.3706 \| 55.1733 \| 56.7923 \| 19.9 \| \| 0.1668 \| 6.6 \| 330 \| 0.5837 \| 57.6284 \| 50.8245 \| 54.6253 \| 56.2127 \| 20.0 \| \| 0.1668 \| 6.8 \| 340 \| 0.5221 \| 58.0869 \| 51.5448 \| 55.4226 \| 56.7532 \| 20.0 \| \| 0.1668 \| 7.0 \| 350 \| 0.5433 \| 58.7676 \| 52.0403 \| 56.2634 \| 57.6441 \| 20.0 \| \| 0.1668 \| 7.2 \| 360 \| 0.5498 \| 57.9172 \| 50.9727 \| 55.1006 \| 56.6018 \| 20.0 \| \| 0.1668 \| 7.4 \| 370 \| 0.5581 \| 57.4669 \| 50.698 \| 54.6448 \| 56.1325 \| 20.0 \| \| 0.1668 \| 7.6 \| 380 \| 0.5526 \| 57.0821 \| 50.298 \| 54.1635 \| 55.8059 \| 20.0 \| \| 0.1668 \| 7.8 \| 390 \| 0.5548 \| 57.5422 \| 50.2734 \| 54.2446 \| 56.1223 \| 20.0 \| \| 0.1071 \| 8.0 \| 400 \| 0.5620 \| 57.4548 \| 50.2657 \| 54.5094 \| 55.9422 \| 20.0 \| \| 0.1071 \| 8.2 \| 410 \| 0.5772 \| 57.4144 \| 50.2443 \| 54.5173 \| 55.9331 \| 20.0 \| \| 0.1071 \| 8.4 \| 420 \| 0.5857 \| 57.2975 \| 50.2116 \| 54.5918 \| 55.9931 \| 20.0 \| \| 0.1071 \| 8.6 \| 430 \| 0.5827 \| 58.4767 \| 51.4318 \| 55.4792 \| 57.1284 \| 20.0 \| \| 0.1071 \| 8.8 \| 440 \| 0.5728 \| 58.4414 \| 51.3523 \| 55.2838 \| 57.202 \| 20.0 \| \| 0.1071 \| 9.0 \| 450 \| 0.5919 \| 58.0499 \| 51.3783 \| 55.0748 \| 56.6939 \| 20.0 \| \| 0.1071 \| 9.2 \| 460 \| 0.5937 \| 57.7604 \| 50.845 \| 54.8941 \| 56.351 \| 20.0 \| \| 0.1071 \| 9.4 \| 470 \| 0.5970 \| 57.3655 \| 50.4126 \| 54.4522 \| 55.7815 \| 20.0 \| \| 0.1071 \| 9.6 \| 480 \| 0.5911 \| 58.203 \| 51.0367 \| 55.3215 \| 56.8485 \| 20.0 \| \| 0.1071 \| 9.8 \| 490 \| 0.6121 \| 58.2898 \| 51.2749 \| 55.4292 \| 57.0241 \| 20.0 \| \| 0.0718 \| 10.0 \| 500 \| 0.5903 \| 58.2487 \| 51.3838 \| 55.4237 \| 56.8863 \| 20.0 \| \| 0.0718 \| 10.2 \| 510 \| 0.5983 \| 58.2681 \| 51.0925 \| 55.2887 \| 56.9562 \| 20.0 \| \| 0.0718 \| 10.4 \| 520 \| 0.6308 \| 57.9797 \| 50.7386 \| 54.995 \| 56.5939 \| 20.0 \| \| 0.0718 \| 10.6 \| 530 \| 0.6307 \| 57.6269 \| 50.5515 \| 54.446 \| 56.1544 \| 20.0 \| \| 0.0718 \| 10.8 \| 540 \| 0.6173 \| 57.9545 \| 51.1005 \| 54.9406 \| 56.5732 \| 20.0 \| \| 0.0718 \| 11.0 \| 550 \| 0.6322 \| 58.3718 \| 51.4321 \| 55.4241 \| 57.1879 \| 20.0 \| \| 0.0718 \| 11.2 \| 560 \| 0.6027 \| 58.6581 \| 51.8607 \| 55.6436 \| 57.32 \| 20.0 \| \| 0.0718 \| 11.4 \| 570 \| 0.6140 \| 58.6476 \| 51.7822 \| 55.5845 \| 57.3018 \| 20.0 \| \| 0.0718 \| 11.6 \| 580 \| 0.6184 \| 59.2454 \| 52.4204 \| 56.2174 \| 57.9278 \| 20.0 \| \| 0.0718 \| 11.8 \| 590 \| 0.6281 \| 59.2945 \| 52.8165 \| 56.547 \| 58.0674 \| 20.0 \| \| 0.0512 \| 12.0 \| 600 \| 0.6128 \| 58.2165 \| 51.3689 \| 55.37 \| 56.8342 \| 20.0 \| \| 0.0512 \| 12.2 \| 610 \| 0.6482 \| 57.9196 \| 50.9793 \| 55.0883 \| 56.6986 \| 20.0 \| \| 0.0512 \| 12.4 \| 620 \| 0.6267 \| 57.4782 \| 50.1118 \| 54.2802 \| 55.8872 \| 20.0 \| \| 0.0512 \| 12.6 \| 630 \| 0.6198 \| 57.457 \| 50.4079 \| 54.2449 \| 55.8118 \| 20.0 \| \| 0.0512 \| 12.8 \| 640 \| 0.6500 \| 57.6903 \| 51.0627 \| 55.0743 \| 56.3025 \| 20.0 \| \| 0.0512 \| 13.0 \| 650 \| 0.6265 \| 57.4394 \| 50.9013 \| 54.7936 \| 56.1688 \| 20.0 \| \| 0.0512 \| 13.2 \| 660 \| 0.6817 \| 58.4345 \| 51.7087 \| 55.291 \| 57.0057 \| 20.0 \| \| 0.0512 \| 13.4 \| 670 \| 0.6322 \| 57.869 \| 50.9503 \| 54.8937 \| 56.5178 \| 20.0 \| \| 0.0512 \| 13.6 \| 680 \| 0.6424 \| 57.8285 \| 51.1014 \| 55.0072 \| 56.5022 \| 20.0 \| \| 0.0512 \| 13.8 \| 690 \| 0.6668 \| 58.7067 \| 51.9929 \| 55.5044 \| 57.1517 \| 20.0 \| \| 0.0397 \| 14.0 \| 700 \| 0.6537 \| 58.8717 \| 52.4036 \| 55.6521 \| 57.4855 \| 20.0 \| \| 0.0397 \| 14.2 \| 710 \| 0.6463 \| 58.9623 \| 52.4749 \| 55.8145 \| 57.8095 \| 20.0 \| \| 0.0397 \| 14.4 \| 720 \| 0.6630 \| 58.8097 \| 52.1997 \| 55.8204 \| 57.6325 \| 20.0 \| \| 0.0397 \| 14.6 \| 730 \| 0.6839 \| 59.0479 \| 52.6573 \| 56.0439 \| 57.7322 \| 20.0 \| \| 0.0397 \| 14.8 \| 740 \| 0.6541 \| 59.2854 \| 52.6109 \| 56.1891 \| 57.9446 \| 20.0 \| \| 0.0397 \| 15.0 \| 750 \| 0.6486 \| 58.8419 \| 52.2004 \| 55.8071 \| 57.49 \| 20.0 \| \| 0.0397 \| 15.2 \| 760 \| 0.6578 \| 57.6161 \| 50.7276 \| 54.5514 \| 56.2359 \| 20.0 \| \| 0.0397 \| 15.4 \| 770 \| 0.6673 \| 57.5458 \| 50.8286 \| 54.4597 \| 56.1513 \| 20.0 \| \| 0.0397 \| 15.6 \| 780 \| 0.6624 \| 57.6634 \| 51.0017 \| 54.6769 \| 56.3837 \| 20.0 \| \| 0.0397 \| 15.8 \| 790 \| 0.6469 \| 57.9037 \| 51.137 \| 54.8939 \| 56.6427 \| 20.0 \| \| 0.0301 \| 16.0 \| 800 \| 0.6373 \| 57.8696 \| 51.0899 \| 54.8543 \| 56.4596 \| 20.0 \| \| 0.0301 \| 16.2 \| 810 \| 0.6712 \| 58.614 \| 52.0052 \| 55.6436 \| 57.3211 \| 20.0 \| \| 0.0301 \| 16.4 \| 820 \| 0.6812 \| 58.5214 \| 51.8911 \| 55.7447 \| 57.2663 \| 20.0 \| \| 0.0301 \| 16.6 \| 830 \| 0.6716 \| 58.5818 \| 51.929 \| 55.7993 \| 57.4064 \| 20.0 \| \| 0.0301 \| 16.8 \| 840 \| 0.6590 \| 57.745 \| 51.0481 \| 54.8545 \| 56.4781 \| 20.0 \| \| 0.0301 \| 17.0 \| 850 \| 0.6695 \| 57.6663 \| 50.9646 \| 54.7863 \| 56.3687 \| 20.0 \| \| 0.0301 \| 17.2 \| 860 \| 0.6858 \| 57.5552 \| 51.0436 \| 54.7092 \| 56.3079 \| 20.0 \| \| 0.0301 \| 17.4 \| 870 \| 0.6840 \| 57.9091 \| 51.3823 \| 54.8309 \| 56.6186 \| 20.0 \| \| 0.0301 \| 17.6 \| 880 \| 0.6751 \| 57.8223 \| 51.1688 \| 54.7562 \| 56.5558 \| 20.0 \| \| 0.0301 \| 17.8 \| 890 \| 0.6589 \| 57.9956 \| 51.1425 \| 54.9509 \| 56.6868 \| 20.0 \| \| 0.0482 \| 18.0 \| 900 \| 0.6634 \| 58.0392 \| 51.3121 \| 55.0726 \| 56.7878 \| 20.0 \| \| 0.0482 \| 18.2 \| 910 \| 0.6907 \| 58.2021 \| 51.4548 \| 55.1874 \| 56.91 \| 20.0 \| \| 0.0482 \| 18.4 \| 920 \| 0.6977 \| 58.1124 \| 51.4254 \| 55.062 \| 56.8412 \| 20.0 \| \| 0.0482 \| 18.6 \| 930 \| 0.6832 \| 58.0776 \| 51.3168 \| 55.0849 \| 56.8226 \| 20.0 \| \| 0.0482 \| 18.8 \| 940 \| 0.6672 \| 57.925 \| 51.2475 \| 54.9661 \| 56.655 \| 20.0 \| \| 0.0482 \| 19.0 \| 950 \| 0.6582 \| 57.9285 \| 51.2483 \| 54.9744 \| 56.6609 \| 20.0 \| \| 0.0482 \| 19.2 \| 960 \| 0.6575 \| 57.9285 \| 51.2483 \| 54.9744 \| 56.6609 \| 20.0 \| \| 0.0482 \| 19.4 \| 970 \| 0.6619 \| 57.8961 \| 51.2097 \| 54.9475 \| 56.6344 \| 20.0 \| \| 0.0482 \| 19.6 \| 980 \| 0.6658 \| 57.8961 \| 51.2097 \| 54.9475 \| 56.6344 \| 20.0 \| \| 0.0482 \| 19.8 \| 990 \| 0.6635 \| 57.7222 \| 51.0096 \| 54.8166 \| 56.4623 \| 20.0 \| \| 0.0201 \| 20.0 \| 1000 \| 0.6617 \| 57.7088 \| 51.0096 \| 54.7514 \| 56.3943 \| 20.0 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
SHENMU007/neunit_BASE_V10.19	SHENMU007	2023-07-10T15:01:47Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-07T08:50:51Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
NasimB/gpt2-cocnat-mod-datasets-txt-processing	NasimB	2023-07-10T15:01:23Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T12:29:02Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-mod-datasets-txt-processing results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-cocnat-mod-datasets-txt-processing This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3377 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6848 \| 0.3 \| 500 \| 5.6500 \| \| 5.3379 \| 0.59 \| 1000 \| 5.2204 \| \| 4.9909 \| 0.89 \| 1500 \| 4.9703 \| \| 4.7146 \| 1.19 \| 2000 \| 4.8200 \| \| 4.5695 \| 1.49 \| 2500 \| 4.7076 \| \| 4.4685 \| 1.78 \| 3000 \| 4.5985 \| \| 4.3237 \| 2.08 \| 3500 \| 4.5311 \| \| 4.1614 \| 2.38 \| 4000 \| 4.4731 \| \| 4.1267 \| 2.68 \| 4500 \| 4.4151 \| \| 4.082 \| 2.97 \| 5000 \| 4.3593 \| \| 3.8448 \| 3.27 \| 5500 \| 4.3575 \| \| 3.8261 \| 3.57 \| 6000 \| 4.3240 \| \| 3.8089 \| 3.86 \| 6500 \| 4.2887 \| \| 3.6462 \| 4.16 \| 7000 \| 4.2921 \| \| 3.5453 \| 4.46 \| 7500 \| 4.2840 \| \| 3.529 \| 4.76 \| 8000 \| 4.2688 \| \| 3.4926 \| 5.05 \| 8500 \| 4.2683 \| \| 3.3463 \| 5.35 \| 9000 \| 4.2715 \| \| 3.3453 \| 5.65 \| 9500 \| 4.2702 \| \| 3.3408 \| 5.95 \| 10000 \| 4.2694 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
ericNguyen0132/DepRoBERTa-2ndStage	ericNguyen0132	2023-07-10T14:56:14Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T13:42:58Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: DepRoBERTa-2ndStage 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. --> # DepRoBERTa-2ndStage This model is a fine-tuned version of [rafalposwiata/deproberta-large-v1](https://huggingface.co/rafalposwiata/deproberta-large-v1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6330 - Accuracy: 0.855 - F1: 0.9134 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 469 \| 0.3572 \| 0.8617 \| 0.9224 \| \| 0.4953 \| 2.0 \| 938 \| 0.3593 \| 0.8783 \| 0.9315 \| \| 0.3493 \| 3.0 \| 1407 \| 0.4274 \| 0.8483 \| 0.9091 \| \| 0.313 \| 4.0 \| 1876 \| 0.5488 \| 0.8617 \| 0.9187 \| \| 0.2622 \| 5.0 \| 2345 \| 0.6330 \| 0.855 \| 0.9134 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Nyme/textual_inversion_cat	Nyme	2023-07-10T14:49:16Z	5	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T09:17:48Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Nyme/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
Tasaloris13/falcon-7b-test	Tasaloris13	2023-07-10T14:31:30Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-10T14:31:29Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
medanis13/chatbot	medanis13	2023-07-10T14:25:42Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-10T14:22:19Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
jiuzhou/roop	jiuzhou	2023-07-10T14:14:43Z	0	1	null	[ "region:us" ]	null	2023-07-10T14:11:09Z	# Roop项目的Colab脚本使用谷歌免费的GPU在线运行一键换脸，[点击打开](roop_v1.ipynb)！ ![demo](4.jpg) # 更新 ![demo](new.jpg) # 原项目地址[roop](https://github.com/s0md3v/roop/) ![demo-gif](https://github.com/s0md3v/roop/blob/main/demo.gif) ![demo](cmp.gif) # 使用方法打开.ipynb文件，然后点击open in colab 就可以开始使用了，详细的使用教程，点[这里](https://www.tonyisstark.com/1240.html)
jordyvl/vit-_tobacco3482_kd_MSE_test_pretrain_student	jordyvl	2023-07-10T14:09:40Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T14:07:44Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: vit-_tobacco3482_kd_MSE_test_pretrain_student 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. --> # vit-_tobacco3482_kd_MSE_test_pretrain_student This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 0.8077 \| 0.4 \| 0.7439 \| 5.4442 \| 0.4000 \| 0.2755 \| 0.2844 \| 0.3738 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

asenella/mmnist_MMVAEPlusconfig_adapted_resnets_seed_0_ratio_0_c

asenella

2023-07-11T03:00:48Z

0

null

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

null

2023-07-11T03:00:34Z

--- 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") ```

Azizslanguagesmodels/turkishReviews-ds-mini

Azizslanguagesmodels

2023-07-11T02:48:42Z

61

0

transformers

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

text-generation

2023-07-11T02:43:12Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: Azizslanguagesmodels/turkishReviews-ds-mini results: [] ---  # Azizslanguagesmodels/turkishReviews-ds-mini This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.1781 - Validation Loss: 9.2629 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -896, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2821 | 9.9897 | 0 | | 9.6595 | 9.6377 | 1 | | 9.1781 | 9.2629 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

alex2awesome/source-role-model

alex2awesome

2023-07-11T02:46:00Z

1

0

transformers

[ "transformers", "pytorch", "roberta", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

null

2023-07-11T02:14:08Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: source-role-model results: [] ---  # source-role-model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5543 - F1: 0.5814 ## Model description More information needed ## Intended uses & 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.12 | 100 | 1.0000 | 0.3391 | | No log | 0.25 | 200 | 0.8371 | 0.5055 | | No log | 0.37 | 300 | 0.8684 | 0.5019 | | No log | 0.49 | 400 | 0.8668 | 0.5208 | | 0.9644 | 0.62 | 500 | 0.8473 | 0.5422 | | 0.9644 | 0.74 | 600 | 0.8852 | 0.4956 | | 0.9644 | 0.86 | 700 | 0.8368 | 0.5124 | | 0.9644 | 0.99 | 800 | 0.7913 | 0.5848 | | 0.9644 | 1.11 | 900 | 1.0570 | 0.4950 | | 0.8375 | 1.23 | 1000 | 0.9402 | 0.5280 | | 0.8375 | 1.35 | 1100 | 0.8023 | 0.5084 | | 0.8375 | 1.48 | 1200 | 0.9299 | 0.4807 | | 0.8375 | 1.6 | 1300 | 0.9661 | 0.5194 | | 0.8375 | 1.72 | 1400 | 0.8014 | 0.6016 | | 0.8149 | 1.85 | 1500 | 0.8608 | 0.6105 | | 0.8149 | 1.97 | 1600 | 0.9195 | 0.5741 | | 0.8149 | 2.09 | 1700 | 1.2378 | 0.5964 | | 0.8149 | 2.22 | 1800 | 1.0415 | 0.5902 | | 0.8149 | 2.34 | 1900 | 1.0499 | 0.5526 | | 0.6932 | 2.46 | 2000 | 1.0600 | 0.5832 | | 0.6932 | 2.59 | 2100 | 0.9368 | 0.6074 | | 0.6932 | 2.71 | 2200 | 1.0872 | 0.6270 | | 0.6932 | 2.83 | 2300 | 1.0912 | 0.5707 | | 0.6932 | 2.96 | 2400 | 0.8815 | 0.5602 | | 0.6214 | 3.08 | 2500 | 1.1650 | 0.5993 | | 0.6214 | 3.2 | 2600 | 1.4485 | 0.5821 | | 0.6214 | 3.33 | 2700 | 1.5382 | 0.5775 | | 0.6214 | 3.45 | 2800 | 1.3999 | 0.5696 | | 0.6214 | 3.57 | 2900 | 1.3702 | 0.6114 | | 0.5686 | 3.69 | 3000 | 1.3840 | 0.5635 | | 0.5686 | 3.82 | 3100 | 1.3547 | 0.5403 | | 0.5686 | 3.94 | 3200 | 1.0283 | 0.5723 | | 0.5686 | 4.06 | 3300 | 1.3593 | 0.6242 | | 0.5686 | 4.19 | 3400 | 1.5985 | 0.6004 | | 0.4807 | 4.31 | 3500 | 1.5351 | 0.6177 | | 0.4807 | 4.43 | 3600 | 1.4109 | 0.5779 | | 0.4807 | 4.56 | 3700 | 1.6972 | 0.5637 | | 0.4807 | 4.68 | 3800 | 1.5336 | 0.6047 | | 0.4807 | 4.8 | 3900 | 1.7811 | 0.5909 | | 0.4387 | 4.93 | 4000 | 1.5862 | 0.5869 | | 0.4387 | 5.05 | 4100 | 1.7106 | 0.5637 | | 0.4387 | 5.17 | 4200 | 1.5251 | 0.5624 | | 0.4387 | 5.3 | 4300 | 1.5519 | 0.5944 | | 0.4387 | 5.42 | 4400 | 1.7315 | 0.5908 | | 0.3219 | 5.54 | 4500 | 1.7588 | 0.6015 | | 0.3219 | 5.67 | 4600 | 1.9277 | 0.5635 | | 0.3219 | 5.79 | 4700 | 1.7663 | 0.5891 | | 0.3219 | 5.91 | 4800 | 1.8401 | 0.5917 | | 0.3219 | 6.03 | 4900 | 2.0516 | 0.5845 | | 0.2311 | 6.16 | 5000 | 2.0510 | 0.6166 | | 0.2311 | 6.28 | 5100 | 2.1673 | 0.5732 | | 0.2311 | 6.4 | 5200 | 2.0931 | 0.5819 | | 0.2311 | 6.53 | 5300 | 2.2803 | 0.5961 | | 0.2311 | 6.65 | 5400 | 1.9985 | 0.6010 | | 0.1669 | 6.77 | 5500 | 2.1742 | 0.5664 | | 0.1669 | 6.9 | 5600 | 2.1021 | 0.5732 | | 0.1669 | 7.02 | 5700 | 2.2043 | 0.5641 | | 0.1669 | 7.14 | 5800 | 2.2018 | 0.5837 | | 0.1669 | 7.27 | 5900 | 2.3575 | 0.5721 | | 0.1698 | 7.39 | 6000 | 2.4663 | 0.5662 | | 0.1698 | 7.51 | 6100 | 2.2658 | 0.5851 | | 0.1698 | 7.64 | 6200 | 2.1585 | 0.5676 | | 0.1698 | 7.76 | 6300 | 2.1755 | 0.5774 | | 0.1698 | 7.88 | 6400 | 2.2680 | 0.5696 | | 0.1378 | 8.0 | 6500 | 2.3505 | 0.5615 | | 0.1378 | 8.13 | 6600 | 2.2773 | 0.5705 | | 0.1378 | 8.25 | 6700 | 2.3112 | 0.5662 | | 0.1378 | 8.37 | 6800 | 2.4572 | 0.5679 | | 0.1378 | 8.5 | 6900 | 2.4642 | 0.5766 | | 0.0756 | 8.62 | 7000 | 2.4643 | 0.5885 | | 0.0756 | 8.74 | 7100 | 2.5096 | 0.5779 | | 0.0756 | 8.87 | 7200 | 2.4261 | 0.5789 | | 0.0756 | 8.99 | 7300 | 2.3973 | 0.5757 | | 0.0756 | 9.11 | 7400 | 2.4137 | 0.5906 | | 0.0842 | 9.24 | 7500 | 2.4577 | 0.5844 | | 0.0842 | 9.36 | 7600 | 2.5034 | 0.5840 | | 0.0842 | 9.48 | 7700 | 2.5176 | 0.5810 | | 0.0842 | 9.61 | 7800 | 2.5240 | 0.5852 | | 0.0842 | 9.73 | 7900 | 2.5141 | 0.5824 | | 0.0634 | 9.85 | 8000 | 2.5482 | 0.5814 | | 0.0634 | 9.98 | 8100 | 2.5543 | 0.5814 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

vimonteglione/ppo-Huggy

vimonteglione

2023-07-11T02:42:10Z

0

ml-agents

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

reinforcement-learning

2023-07-11T02:42:00Z

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

NasimB/gpt2-concat-cbt-mod-formatting-iorder-rarity-all-4k

NasimB

2023-07-11T02:39:48Z

4

0

transformers

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

text-generation

2023-07-11T00:45:49Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-cbt-mod-formatting-iorder-rarity-all-4k results: [] ---  # gpt2-concat-cbt-mod-formatting-iorder-rarity-all-4k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6962 | 0.29 | 500 | 5.6482 | | 5.3352 | 0.59 | 1000 | 5.2168 | | 4.9963 | 0.88 | 1500 | 4.9671 | | 4.7147 | 1.17 | 2000 | 4.8164 | | 4.5508 | 1.46 | 2500 | 4.6852 | | 4.4503 | 1.76 | 3000 | 4.5766 | | 4.3233 | 2.05 | 3500 | 4.4995 | | 4.1239 | 2.34 | 4000 | 4.4513 | | 4.0934 | 2.63 | 4500 | 4.3905 | | 4.0645 | 2.93 | 5000 | 4.3376 | | 3.8538 | 3.22 | 5500 | 4.3338 | | 3.7937 | 3.51 | 6000 | 4.3034 | | 3.781 | 3.8 | 6500 | 4.2718 | | 3.6821 | 4.1 | 7000 | 4.2702 | | 3.5082 | 4.39 | 7500 | 4.2633 | | 3.5078 | 4.68 | 8000 | 4.2471 | | 3.4936 | 4.97 | 8500 | 4.2346 | | 3.34 | 5.27 | 9000 | 4.2492 | | 3.3145 | 5.56 | 9500 | 4.2471 | | 3.315 | 5.85 | 10000 | 4.2463 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

alex2awesome/source-affiliation-model

alex2awesome

2023-07-11T02:37:57Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

null

2023-07-10T23:11:23Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: source-affiliation-model results: [] ---  # source-affiliation-model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3321 - F1: 0.5348 ## Model description More information needed ## Intended uses & 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.12 | 100 | 1.4535 | 0.2435 | | No log | 0.25 | 200 | 1.3128 | 0.3899 | | No log | 0.37 | 300 | 1.2888 | 0.4413 | | No log | 0.49 | 400 | 1.1560 | 0.4614 | | 1.4848 | 0.62 | 500 | 1.0988 | 0.4477 | | 1.4848 | 0.74 | 600 | 1.1211 | 0.4583 | | 1.4848 | 0.86 | 700 | 1.1152 | 0.4693 | | 1.4848 | 0.99 | 800 | 1.0176 | 0.5018 | | 1.4848 | 1.11 | 900 | 1.0942 | 0.4774 | | 1.1019 | 1.23 | 1000 | 1.1785 | 0.5119 | | 1.1019 | 1.35 | 1100 | 1.0751 | 0.4797 | | 1.1019 | 1.48 | 1200 | 1.0759 | 0.5206 | | 1.1019 | 1.6 | 1300 | 1.0756 | 0.5231 | | 1.1019 | 1.72 | 1400 | 1.1329 | 0.4547 | | 0.9431 | 1.85 | 1500 | 1.0617 | 0.4852 | | 0.9431 | 1.97 | 1600 | 1.1046 | 0.5254 | | 0.9431 | 2.09 | 1700 | 1.2489 | 0.5069 | | 0.9431 | 2.22 | 1800 | 1.2113 | 0.5363 | | 0.9431 | 2.34 | 1900 | 1.1782 | 0.5546 | | 0.7589 | 2.46 | 2000 | 1.0453 | 0.5862 | | 0.7589 | 2.59 | 2100 | 1.0810 | 0.5223 | | 0.7589 | 2.71 | 2200 | 1.1470 | 0.5872 | | 0.7589 | 2.83 | 2300 | 1.1522 | 0.5553 | | 0.7589 | 2.96 | 2400 | 1.0712 | 0.6273 | | 0.6875 | 3.08 | 2500 | 1.3458 | 0.5768 | | 0.6875 | 3.2 | 2600 | 1.7052 | 0.5491 | | 0.6875 | 3.33 | 2700 | 1.5080 | 0.6582 | | 0.6875 | 3.45 | 2800 | 1.5851 | 0.5965 | | 0.6875 | 3.57 | 2900 | 1.4771 | 0.5691 | | 0.5391 | 3.69 | 3000 | 1.6717 | 0.5350 | | 0.5391 | 3.82 | 3100 | 1.5607 | 0.5448 | | 0.5391 | 3.94 | 3200 | 1.5464 | 0.6062 | | 0.5391 | 4.06 | 3300 | 1.7645 | 0.5755 | | 0.5391 | 4.19 | 3400 | 1.6715 | 0.5504 | | 0.4928 | 4.31 | 3500 | 1.7604 | 0.5626 | | 0.4928 | 4.43 | 3600 | 1.8984 | 0.5142 | | 0.4928 | 4.56 | 3700 | 1.8012 | 0.5763 | | 0.4928 | 4.68 | 3800 | 1.7107 | 0.5671 | | 0.4928 | 4.8 | 3900 | 1.7697 | 0.5598 | | 0.4233 | 4.93 | 4000 | 1.6296 | 0.6084 | | 0.4233 | 5.05 | 4100 | 2.0418 | 0.5343 | | 0.4233 | 5.17 | 4200 | 1.8203 | 0.5526 | | 0.4233 | 5.3 | 4300 | 1.9760 | 0.5292 | | 0.4233 | 5.42 | 4400 | 2.0136 | 0.5153 | | 0.2518 | 5.54 | 4500 | 2.0137 | 0.5121 | | 0.2518 | 5.67 | 4600 | 2.0053 | 0.5257 | | 0.2518 | 5.79 | 4700 | 1.9539 | 0.5423 | | 0.2518 | 5.91 | 4800 | 2.0159 | 0.5686 | | 0.2518 | 6.03 | 4900 | 2.0411 | 0.5817 | | 0.2234 | 6.16 | 5000 | 2.0025 | 0.5780 | | 0.2234 | 6.28 | 5100 | 2.1189 | 0.5413 | | 0.2234 | 6.4 | 5200 | 2.1936 | 0.5628 | | 0.2234 | 6.53 | 5300 | 2.1825 | 0.5210 | | 0.2234 | 6.65 | 5400 | 2.0767 | 0.5471 | | 0.1829 | 6.77 | 5500 | 1.9747 | 0.5587 | | 0.1829 | 6.9 | 5600 | 2.1182 | 0.5847 | | 0.1829 | 7.02 | 5700 | 2.1597 | 0.5437 | | 0.1829 | 7.14 | 5800 | 2.0307 | 0.5629 | | 0.1829 | 7.27 | 5900 | 2.0912 | 0.5450 | | 0.1226 | 7.39 | 6000 | 2.2383 | 0.5379 | | 0.1226 | 7.51 | 6100 | 2.2311 | 0.5834 | | 0.1226 | 7.64 | 6200 | 2.2456 | 0.5438 | | 0.1226 | 7.76 | 6300 | 2.2423 | 0.5860 | | 0.1226 | 7.88 | 6400 | 2.2922 | 0.5245 | | 0.0883 | 8.0 | 6500 | 2.3304 | 0.5650 | | 0.0883 | 8.13 | 6600 | 2.3929 | 0.5288 | | 0.0883 | 8.25 | 6700 | 2.3928 | 0.5344 | | 0.0883 | 8.37 | 6800 | 2.3854 | 0.5266 | | 0.0883 | 8.5 | 6900 | 2.4275 | 0.5339 | | 0.044 | 8.62 | 7000 | 2.3929 | 0.5380 | | 0.044 | 8.74 | 7100 | 2.3587 | 0.5339 | | 0.044 | 8.87 | 7200 | 2.3372 | 0.5423 | | 0.044 | 8.99 | 7300 | 2.3488 | 0.5424 | | 0.044 | 9.11 | 7400 | 2.3543 | 0.5818 | | 0.0558 | 9.24 | 7500 | 2.3397 | 0.5554 | | 0.0558 | 9.36 | 7600 | 2.3255 | 0.5394 | | 0.0558 | 9.48 | 7700 | 2.3184 | 0.5557 | | 0.0558 | 9.61 | 7800 | 2.3293 | 0.5669 | | 0.0558 | 9.73 | 7900 | 2.3358 | 0.5666 | | 0.0323 | 9.85 | 8000 | 2.3307 | 0.5344 | | 0.0323 | 9.98 | 8100 | 2.3321 | 0.5348 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

IS4XD/Ris3

IS4XD

2023-07-11T02:36:03Z

0

null

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

null

2023-07-11T02:36:03Z

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

RavenFangsk/chronoborous-33B-GPTQ

RavenFangsk

2023-07-11T02:28:20Z

5

0

transformers

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

text-generation

2023-07-10T03:26:46Z

Auto-GPTQ'd version of https://huggingface.co/Henk717/chronoboros-33B

sl8425/troubleshooting_steps_classification_model

sl8425

2023-07-11T02:20:13Z

62

0

transformers

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

text-classification

2023-07-10T19:07:39Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: sl8425/troubleshooting_steps_classification_model results: [] ---  # sl8425/troubleshooting_steps_classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6664 - Validation Loss: 0.7197 - Train Accuracy: 0.7923 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 921, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 1.5468 | 0.9262 | 0.7317 | 0 | | 0.8223 | 0.7546 | 0.7830 | 1 | | 0.6664 | 0.7197 | 0.7923 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9

jordyvl

2023-07-11T02:14:34Z

163

0

transformers

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

image-classification

2023-07-11T01:01:03Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 results: [] ---  # vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2366 - Accuracy: 0.63 - Brier Loss: 0.5035 - Nll: 2.8588 - F1 Micro: 0.63 - F1 Macro: 0.6311 - Ece: 0.1649 - Aurc: 0.1472 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 2.8887 | 0.1225 | 0.9306 | 15.9457 | 0.1225 | 0.1226 | 0.1434 | 0.8620 | | No log | 2.0 | 50 | 2.2120 | 0.3775 | 0.7577 | 9.7500 | 0.3775 | 0.3483 | 0.1992 | 0.3776 | | No log | 3.0 | 75 | 1.7681 | 0.495 | 0.6387 | 5.6935 | 0.495 | 0.4838 | 0.1885 | 0.2491 | | No log | 4.0 | 100 | 1.6420 | 0.5225 | 0.6038 | 5.2427 | 0.5225 | 0.5242 | 0.1757 | 0.2301 | | No log | 5.0 | 125 | 1.5877 | 0.545 | 0.5986 | 4.6187 | 0.545 | 0.5282 | 0.1808 | 0.2248 | | No log | 6.0 | 150 | 1.6460 | 0.5125 | 0.6162 | 3.9942 | 0.5125 | 0.5060 | 0.1962 | 0.2295 | | No log | 7.0 | 175 | 1.8436 | 0.5125 | 0.6538 | 4.1740 | 0.5125 | 0.4932 | 0.2299 | 0.2451 | | No log | 8.0 | 200 | 1.8205 | 0.545 | 0.6453 | 5.0752 | 0.545 | 0.5234 | 0.2057 | 0.2432 | | No log | 9.0 | 225 | 1.7399 | 0.55 | 0.6260 | 4.5896 | 0.55 | 0.5460 | 0.2057 | 0.2258 | | No log | 10.0 | 250 | 1.8559 | 0.55 | 0.6521 | 5.0532 | 0.55 | 0.5368 | 0.2209 | 0.2560 | | No log | 11.0 | 275 | 1.8636 | 0.5625 | 0.6488 | 4.6642 | 0.5625 | 0.5544 | 0.2335 | 0.2187 | | No log | 12.0 | 300 | 1.7461 | 0.55 | 0.6356 | 4.1298 | 0.55 | 0.5638 | 0.2047 | 0.2313 | | No log | 13.0 | 325 | 1.7468 | 0.5625 | 0.6281 | 4.5451 | 0.5625 | 0.5570 | 0.2224 | 0.2214 | | No log | 14.0 | 350 | 1.9616 | 0.545 | 0.6884 | 3.7999 | 0.545 | 0.5484 | 0.2691 | 0.2624 | | No log | 15.0 | 375 | 2.0977 | 0.5175 | 0.7138 | 4.3792 | 0.5175 | 0.5055 | 0.2658 | 0.2917 | | No log | 16.0 | 400 | 2.0238 | 0.5275 | 0.6896 | 4.5299 | 0.5275 | 0.5177 | 0.2664 | 0.2603 | | No log | 17.0 | 425 | 1.8687 | 0.535 | 0.6534 | 3.7356 | 0.535 | 0.5388 | 0.2490 | 0.2448 | | No log | 18.0 | 450 | 1.8210 | 0.5575 | 0.6492 | 4.3823 | 0.5575 | 0.5537 | 0.2533 | 0.2268 | | No log | 19.0 | 475 | 1.7610 | 0.555 | 0.6325 | 3.9697 | 0.555 | 0.5503 | 0.2292 | 0.2161 | | 0.5398 | 20.0 | 500 | 1.7125 | 0.5825 | 0.6125 | 3.4176 | 0.5825 | 0.5731 | 0.2140 | 0.1859 | | 0.5398 | 21.0 | 525 | 1.6296 | 0.5775 | 0.6163 | 3.6014 | 0.5775 | 0.5871 | 0.2236 | 0.2051 | | 0.5398 | 22.0 | 550 | 1.5965 | 0.57 | 0.5908 | 3.7668 | 0.57 | 0.5712 | 0.2058 | 0.1883 | | 0.5398 | 23.0 | 575 | 1.4828 | 0.5875 | 0.5646 | 3.7028 | 0.5875 | 0.5854 | 0.1944 | 0.1714 | | 0.5398 | 24.0 | 600 | 1.3983 | 0.6075 | 0.5481 | 3.3608 | 0.6075 | 0.6107 | 0.1966 | 0.1628 | | 0.5398 | 25.0 | 625 | 1.5241 | 0.5925 | 0.5866 | 3.3669 | 0.5925 | 0.6019 | 0.2069 | 0.1886 | | 0.5398 | 26.0 | 650 | 1.5540 | 0.58 | 0.5780 | 3.5184 | 0.58 | 0.5710 | 0.2131 | 0.1857 | | 0.5398 | 27.0 | 675 | 1.4653 | 0.6 | 0.5768 | 2.9877 | 0.6 | 0.6043 | 0.2166 | 0.1781 | | 0.5398 | 28.0 | 700 | 1.4883 | 0.5925 | 0.5646 | 3.7789 | 0.5925 | 0.5910 | 0.2096 | 0.1746 | | 0.5398 | 29.0 | 725 | 1.5738 | 0.59 | 0.5914 | 4.0558 | 0.59 | 0.5879 | 0.2150 | 0.1957 | | 0.5398 | 30.0 | 750 | 1.4017 | 0.6025 | 0.5583 | 3.4791 | 0.6025 | 0.6023 | 0.2150 | 0.1752 | | 0.5398 | 31.0 | 775 | 1.3500 | 0.61 | 0.5365 | 3.2560 | 0.61 | 0.6157 | 0.1988 | 0.1579 | | 0.5398 | 32.0 | 800 | 1.2977 | 0.6375 | 0.5140 | 3.0503 | 0.6375 | 0.6395 | 0.1847 | 0.1534 | | 0.5398 | 33.0 | 825 | 1.3471 | 0.6175 | 0.5406 | 3.1888 | 0.6175 | 0.6104 | 0.2077 | 0.1689 | | 0.5398 | 34.0 | 850 | 1.2992 | 0.615 | 0.5219 | 2.8944 | 0.615 | 0.6191 | 0.1826 | 0.1574 | | 0.5398 | 35.0 | 875 | 1.2733 | 0.6225 | 0.5124 | 2.9352 | 0.6225 | 0.6238 | 0.1588 | 0.1505 | | 0.5398 | 36.0 | 900 | 1.2821 | 0.6175 | 0.5231 | 3.0142 | 0.6175 | 0.6169 | 0.1672 | 0.1553 | | 0.5398 | 37.0 | 925 | 1.2819 | 0.61 | 0.5200 | 2.6874 | 0.61 | 0.6116 | 0.1847 | 0.1540 | | 0.5398 | 38.0 | 950 | 1.2664 | 0.615 | 0.5145 | 2.9287 | 0.615 | 0.6159 | 0.1961 | 0.1528 | | 0.5398 | 39.0 | 975 | 1.2584 | 0.6225 | 0.5134 | 3.0058 | 0.6225 | 0.6230 | 0.1747 | 0.1508 | | 0.0507 | 40.0 | 1000 | 1.2562 | 0.615 | 0.5114 | 2.9269 | 0.615 | 0.6169 | 0.1815 | 0.1504 | | 0.0507 | 41.0 | 1025 | 1.2525 | 0.6225 | 0.5101 | 2.9199 | 0.6225 | 0.6239 | 0.1770 | 0.1496 | | 0.0507 | 42.0 | 1050 | 1.2573 | 0.62 | 0.5133 | 2.9195 | 0.62 | 0.6221 | 0.1824 | 0.1511 | | 0.0507 | 43.0 | 1075 | 1.2536 | 0.6125 | 0.5131 | 2.9026 | 0.6125 | 0.6121 | 0.1820 | 0.1511 | | 0.0507 | 44.0 | 1100 | 1.2543 | 0.6225 | 0.5109 | 3.0693 | 0.6225 | 0.6235 | 0.1647 | 0.1500 | | 0.0507 | 45.0 | 1125 | 1.2526 | 0.6125 | 0.5117 | 2.9018 | 0.6125 | 0.6141 | 0.1788 | 0.1500 | | 0.0507 | 46.0 | 1150 | 1.2432 | 0.615 | 0.5068 | 2.9042 | 0.615 | 0.6167 | 0.1762 | 0.1484 | | 0.0507 | 47.0 | 1175 | 1.2485 | 0.6275 | 0.5098 | 2.8927 | 0.6275 | 0.6251 | 0.1590 | 0.1496 | | 0.0507 | 48.0 | 1200 | 1.2576 | 0.6125 | 0.5140 | 2.8956 | 0.6125 | 0.6137 | 0.1824 | 0.1524 | | 0.0507 | 49.0 | 1225 | 1.2468 | 0.62 | 0.5094 | 2.8918 | 0.62 | 0.6204 | 0.1832 | 0.1496 | | 0.0507 | 50.0 | 1250 | 1.2479 | 0.6175 | 0.5102 | 2.8921 | 0.6175 | 0.6178 | 0.1706 | 0.1491 | | 0.0507 | 51.0 | 1275 | 1.2393 | 0.6225 | 0.5057 | 2.8813 | 0.6225 | 0.6229 | 0.1784 | 0.1486 | | 0.0507 | 52.0 | 1300 | 1.2463 | 0.6175 | 0.5085 | 2.8959 | 0.6175 | 0.6184 | 0.1669 | 0.1495 | | 0.0507 | 53.0 | 1325 | 1.2391 | 0.62 | 0.5061 | 2.8828 | 0.62 | 0.6215 | 0.1803 | 0.1471 | | 0.0507 | 54.0 | 1350 | 1.2538 | 0.6175 | 0.5121 | 2.8795 | 0.6175 | 0.6167 | 0.1680 | 0.1512 | | 0.0507 | 55.0 | 1375 | 1.2407 | 0.625 | 0.5064 | 2.8830 | 0.625 | 0.6259 | 0.1842 | 0.1482 | | 0.0507 | 56.0 | 1400 | 1.2488 | 0.62 | 0.5099 | 2.8769 | 0.62 | 0.6198 | 0.1568 | 0.1499 | | 0.0507 | 57.0 | 1425 | 1.2402 | 0.625 | 0.5052 | 2.8778 | 0.625 | 0.6260 | 0.1616 | 0.1481 | | 0.0507 | 58.0 | 1450 | 1.2457 | 0.625 | 0.5077 | 2.8786 | 0.625 | 0.6260 | 0.1759 | 0.1474 | | 0.0507 | 59.0 | 1475 | 1.2430 | 0.6275 | 0.5073 | 2.8744 | 0.6275 | 0.6266 | 0.1652 | 0.1486 | | 0.0319 | 60.0 | 1500 | 1.2399 | 0.625 | 0.5056 | 2.8767 | 0.625 | 0.6256 | 0.1701 | 0.1474 | | 0.0319 | 61.0 | 1525 | 1.2460 | 0.63 | 0.5087 | 2.8758 | 0.63 | 0.6329 | 0.1865 | 0.1491 | | 0.0319 | 62.0 | 1550 | 1.2410 | 0.6225 | 0.5058 | 2.8719 | 0.6225 | 0.6229 | 0.1752 | 0.1477 | | 0.0319 | 63.0 | 1575 | 1.2418 | 0.63 | 0.5060 | 2.8746 | 0.63 | 0.6319 | 0.1692 | 0.1484 | | 0.0319 | 64.0 | 1600 | 1.2424 | 0.6275 | 0.5069 | 2.8672 | 0.6275 | 0.6279 | 0.1903 | 0.1475 | | 0.0319 | 65.0 | 1625 | 1.2413 | 0.63 | 0.5061 | 2.8747 | 0.63 | 0.6304 | 0.1737 | 0.1471 | | 0.0319 | 66.0 | 1650 | 1.2385 | 0.6325 | 0.5039 | 2.8726 | 0.6325 | 0.6358 | 0.1792 | 0.1473 | | 0.0319 | 67.0 | 1675 | 1.2368 | 0.625 | 0.5047 | 2.8661 | 0.625 | 0.6261 | 0.1843 | 0.1467 | | 0.0319 | 68.0 | 1700 | 1.2370 | 0.6275 | 0.5039 | 2.8691 | 0.6275 | 0.6294 | 0.1724 | 0.1471 | | 0.0319 | 69.0 | 1725 | 1.2382 | 0.63 | 0.5050 | 2.8659 | 0.63 | 0.6317 | 0.1698 | 0.1472 | | 0.0319 | 70.0 | 1750 | 1.2396 | 0.6275 | 0.5051 | 2.8670 | 0.6275 | 0.6290 | 0.1790 | 0.1474 | | 0.0319 | 71.0 | 1775 | 1.2378 | 0.625 | 0.5045 | 2.8637 | 0.625 | 0.6268 | 0.1742 | 0.1476 | | 0.0319 | 72.0 | 1800 | 1.2360 | 0.625 | 0.5037 | 2.8669 | 0.625 | 0.6269 | 0.1778 | 0.1468 | | 0.0319 | 73.0 | 1825 | 1.2390 | 0.63 | 0.5049 | 2.8638 | 0.63 | 0.6310 | 0.1711 | 0.1474 | | 0.0319 | 74.0 | 1850 | 1.2372 | 0.625 | 0.5045 | 2.8640 | 0.625 | 0.6269 | 0.1817 | 0.1475 | | 0.0319 | 75.0 | 1875 | 1.2375 | 0.63 | 0.5044 | 2.8640 | 0.63 | 0.6313 | 0.1703 | 0.1472 | | 0.0319 | 76.0 | 1900 | 1.2372 | 0.6275 | 0.5041 | 2.8621 | 0.6275 | 0.6290 | 0.1794 | 0.1473 | | 0.0319 | 77.0 | 1925 | 1.2374 | 0.63 | 0.5041 | 2.8629 | 0.63 | 0.6313 | 0.1722 | 0.1472 | | 0.0319 | 78.0 | 1950 | 1.2367 | 0.6275 | 0.5039 | 2.8620 | 0.6275 | 0.6294 | 0.1704 | 0.1474 | | 0.0319 | 79.0 | 1975 | 1.2371 | 0.6275 | 0.5039 | 2.8619 | 0.6275 | 0.6294 | 0.1639 | 0.1474 | | 0.0314 | 80.0 | 2000 | 1.2372 | 0.63 | 0.5041 | 2.8612 | 0.63 | 0.6310 | 0.1750 | 0.1474 | | 0.0314 | 81.0 | 2025 | 1.2368 | 0.63 | 0.5038 | 2.8613 | 0.63 | 0.6309 | 0.1648 | 0.1473 | | 0.0314 | 82.0 | 2050 | 1.2370 | 0.63 | 0.5038 | 2.8607 | 0.63 | 0.6305 | 0.1782 | 0.1473 | | 0.0314 | 83.0 | 2075 | 1.2368 | 0.63 | 0.5038 | 2.8609 | 0.63 | 0.6307 | 0.1686 | 0.1472 | | 0.0314 | 84.0 | 2100 | 1.2368 | 0.63 | 0.5037 | 2.8603 | 0.63 | 0.6305 | 0.1667 | 0.1472 | | 0.0314 | 85.0 | 2125 | 1.2366 | 0.63 | 0.5036 | 2.8601 | 0.63 | 0.6309 | 0.1686 | 0.1473 | | 0.0314 | 86.0 | 2150 | 1.2367 | 0.6325 | 0.5037 | 2.8600 | 0.6325 | 0.6335 | 0.1751 | 0.1471 | | 0.0314 | 87.0 | 2175 | 1.2369 | 0.63 | 0.5037 | 2.8598 | 0.63 | 0.6307 | 0.1730 | 0.1473 | | 0.0314 | 88.0 | 2200 | 1.2367 | 0.63 | 0.5036 | 2.8595 | 0.63 | 0.6307 | 0.1657 | 0.1472 | | 0.0314 | 89.0 | 2225 | 1.2366 | 0.63 | 0.5036 | 2.8597 | 0.63 | 0.6307 | 0.1680 | 0.1472 | | 0.0314 | 90.0 | 2250 | 1.2366 | 0.63 | 0.5036 | 2.8594 | 0.63 | 0.6307 | 0.1580 | 0.1472 | | 0.0314 | 91.0 | 2275 | 1.2366 | 0.63 | 0.5035 | 2.8593 | 0.63 | 0.6307 | 0.1677 | 0.1472 | | 0.0314 | 92.0 | 2300 | 1.2367 | 0.63 | 0.5035 | 2.8593 | 0.63 | 0.6307 | 0.1616 | 0.1472 | | 0.0314 | 93.0 | 2325 | 1.2366 | 0.63 | 0.5035 | 2.8590 | 0.63 | 0.6307 | 0.1625 | 0.1472 | | 0.0314 | 94.0 | 2350 | 1.2366 | 0.6325 | 0.5035 | 2.8590 | 0.6325 | 0.6333 | 0.1586 | 0.1470 | | 0.0314 | 95.0 | 2375 | 1.2366 | 0.63 | 0.5035 | 2.8591 | 0.63 | 0.6307 | 0.1580 | 0.1472 | | 0.0314 | 96.0 | 2400 | 1.2366 | 0.63 | 0.5035 | 2.8589 | 0.63 | 0.6307 | 0.1695 | 0.1471 | | 0.0314 | 97.0 | 2425 | 1.2366 | 0.63 | 0.5035 | 2.8589 | 0.63 | 0.6311 | 0.1648 | 0.1472 | | 0.0314 | 98.0 | 2450 | 1.2366 | 0.63 | 0.5035 | 2.8588 | 0.63 | 0.6311 | 0.1695 | 0.1471 | | 0.0314 | 99.0 | 2475 | 1.2366 | 0.6325 | 0.5035 | 2.8589 | 0.6325 | 0.6337 | 0.1724 | 0.1470 | | 0.0312 | 100.0 | 2500 | 1.2366 | 0.63 | 0.5035 | 2.8588 | 0.63 | 0.6311 | 0.1649 | 0.1472 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented

hafidikhsan

2023-07-11T02:12:58Z

103

0

transformers

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

audio-classification

2023-07-11T02:10:43Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented results: [] ---  # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0403 - Accuracy: 0.744 - F1: 0.7432 - Precision: 0.7436 - Recall: 0.744 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.8567 | 1.0 | 313 | 0.9539 | 0.5388 | 0.5159 | 0.5387 | 0.5388 | | 0.665 | 2.0 | 626 | 0.7520 | 0.6512 | 0.6545 | 0.6625 | 0.6512 | | 0.629 | 3.0 | 939 | 0.7775 | 0.7008 | 0.6980 | 0.6978 | 0.7008 | | 0.4793 | 4.0 | 1252 | 0.8696 | 0.7268 | 0.7295 | 0.7365 | 0.7268 | | 0.2273 | 5.0 | 1565 | 1.0403 | 0.744 | 0.7432 | 0.7436 | 0.744 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

zwtharry/PPO-rocket

zwtharry

2023-07-11T02:09:34Z

2

0

stable-baselines3

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

reinforcement-learning

2023-07-11T02:09:13Z

--- 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: 234.64 +/- 40.86 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 ... ```

manhtt-079/vipubmed-deberta-base

manhtt-079

2023-07-11T01:59:35Z

7

0

transformers

[ "transformers", "pytorch", "tf", "deberta-v2", "transformer", "vietnamese", "nlp", "bert", "deberta", "fill-mask", "vi", "dataset:VietAI/vi_pubmed", "license:mit", "endpoints_compatible", "region:us" ]

fill-mask

2023-05-06T10:35:36Z

--- language: - vi metrics: - f1 pipeline_tag: fill-mask license: mit datasets: - VietAI/vi_pubmed tags: - transformer - vietnamese - nlp - bert - deberta - deberta-v2 --- # ViPubMedDeBERTa: A Vietnamese pretrained biomedical language representation model ## Model description ## Model variations - `vipubmed-deberta-xsmall`: 22M backbone parameters - `vipubmed-deberta-base`: 86M backbone parameters ## How to use You can use this model directly with a pipeline for masked language modeling:<br> **_NOTE:_** The input text should be already word-segmented, you can use [Pyvi](https://github.com/trungtv/pyvi) (Python Vietnamese Core NLP Toolkit) to segment word before passing to the model. ```python >>> from transformers import pipeline >>> model = pipeline('fill-mask', model='manhtt-079/vipubmed-deberta-base') >>> text_with_mask = """Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ) . FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm . Phẫu_thuật được coi là phương_thức điều_trị tốt nhất , tiếp_theo là hóa_trị . Trong trường_hợp của chúng_tôi , [MASK] cắt bỏ không_thể thực_hiện được , do đó bệnh_nhân được hóa_trị hai dòng , sau đó là cấy_ghép tủy xương , sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên""" >>> model(text_with_mask) [{'score': 0.8480948805809021, 'token': 1621, 'token_str': 'phẫu_thuật', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, phẫu_thuật cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.1136574074625969, 'token': 83, 'token_str': 'việc', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, việc cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.014141257852315903, 'token': 589, 'token_str': 'phương_pháp', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, phương_pháp cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.0024715897161513567, 'token': 454, 'token_str': 'điều_trị', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, điều_trị cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}, {'score': 0.002370780799537897, 'token': 485, 'token_str': 'quá_trình', 'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, quá_trình cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}] ``` #### Get features: - With PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('manhtt-079/vipubmed-deberta-base') model = AutoModel.from_pretrained("manhtt-079/vipubmed-deberta-base") text = "Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS )." model_inputs = tokenizer(text, return_tensors='pt') outputs = model(**model_inputs) ``` - With TensorFlow ```python from transformers import AutoTokenizer, TFAutoModel tokenizer = AutoTokenizer.from_pretrained('manhtt-079/vipubmed-deberta-base') model = TFAutoModel.from_pretrained("manhtt-079/vipubmed-deberta-base") text = "Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS )." model_inputs = tokenizer(text, return_tensors='tf') outputs = model(**model_inputs) ``` ## Pre-training data The ViPubMedDeBERTa model was pre-trained on [ViPubmed](https://github.com/vietai/ViPubmed), a dataset consisting of 20M Vietnamese Biomedical abstracts generated by large scale translation. ## Training procedure ### Data deduplication A fuzzy deduplication, targeting documents with high overlap, was conducted at the document level to enhance quality and address overfitting. Employing Locality Sensitive Hashing (LSH) with a threshold of 0.9 ensured the removal of documents with overlap exceeding 90%. This process resulted in an average reduction of the dataset's size by 3%. ### Pretraining We employ our model based on the [ViDeBERTa](https://github.com/HySonLab/ViDeBERTa) architecture and leverage its pre-trained checkpoint to continue pre-training. Our model was trained on a single A100 GPU (40GB) for 350 thousand steps, with a batch size of 16 and gradient accumulation steps set to 4 (resulting in a total of 64). The sequence length was limited to 512 tokens and the model peak learning rate of 1e-4. ## Evaluation results

casque/TemplarAssassinv0.2

casque

2023-07-11T01:29:41Z

0

null

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

null

2023-07-11T01:26:51Z

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

liyingjian/Reinforce-policy-gradient

liyingjian

2023-07-11T01:28:57Z

0

null

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

reinforcement-learning

2023-07-11T01:28:48Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-policy-gradient results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 403.00 +/- 194.00 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

AndreNasci/distilbert-base-uncased-finetuned-cola

AndreNasci

2023-07-11T01:24:44Z

62

0

transformers

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

text-classification

2023-07-10T23:58:13Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: AndreNasci/distilbert-base-uncased-finetuned-cola results: [] ---  # AndreNasci/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1932 - Validation Loss: 0.5147 - Train Matthews Correlation: 0.5469 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5120 | 0.4538 | 0.4858 | 0 | | 0.3206 | 0.4722 | 0.5116 | 1 | | 0.1932 | 0.5147 | 0.5469 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

MDelan/distilbert-base-uncased-finetuned-cola

MDelan

2023-07-11T01:19:40Z

61

0

transformers

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

text-classification

2023-07-11T01:14:40Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MDelan/distilbert-base-uncased-finetuned-cola results: [] ---  # MDelan/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1879 - Validation Loss: 0.5580 - Train Matthews Correlation: 0.5127 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5181 | 0.4661 | 0.4379 | 0 | | 0.3140 | 0.4981 | 0.4774 | 1 | | 0.1879 | 0.5580 | 0.5127 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

beomi/kollama-7b

beomi

2023-07-11T01:18:13Z

71

10

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "KoLLAMA", "KoreanGPT", "ko", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-04-10T15:43:35Z

--- license: mit language: - ko - en metrics: - perplexity - accuracy pipeline_tag: text-generation tags: - llama - KoLLAMA - KoreanGPT --- > 🚧 Note: this repo is under construction 🚧 ## Todo ✅ - finish ⏳ - currently working on it - ✅ Train new BBPE Tokenizer - ✅ Test train code on TPUv4 Pods (with model parallel) - ✅ Converting test (jax to PyTorch) - ✅ LM train validation on minimal dataset (1 sentence 1000 step) - ⏳ Build Data Shuffler (curriculum learning) - ⏳ Train 7B Model - Train 13B Model - Train 33B Model - Train 65B Model # KoLLaMA Model Card KoLLaMA (7B) trained on Korean/English/Code dataset with LLaMA Architecture via JAX, with the warm support from [Google TPU Research Cloud program](https://sites.research.google/trc/about/) for providing part of the computation resources. ## Model details **Researcher developing the model** Junbum Lee (aka Beomi) **Model date** KoLLaMA was trained between 2022.04~ **Model version** This is alpha version of the model. **Model type** LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters. (This repo contains 7B model!) **Paper or resources for more information** More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/. More info for KoAlpaca: [TBD] **Citations details** KoLLAMA: [TBD] LLAMA: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/ **License** MIT **Where to send questions or comments about the model** Questions and comments about KoLLaMA can be sent via the [GitHub repository](https://github.com/beomi/KoLLAMA) of the project , by opening an issue. ## Intended use **Primary intended uses** The primary use of KoLLaMA is research on Korean Opensource large language models **Primary intended users** The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence. **Out-of-scope use cases** LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers. ## Factors **Relevant factors** One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model. ## Evaluation datasets [TBD] ## Training dataset [TBD] ## Ethical considerations **Data** The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data. **Human life** The model is not intended to inform decisions about matters central to human life, and should not be used in such a way. **Risks and harms** Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard. **Use cases** LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.

lucs1265/distilbert-base-uncased-finetuned-cola

lucs1265

2023-07-11T01:11:57Z

61

0

transformers

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

text-classification

2023-07-11T01:06:54Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: lucs1265/distilbert-base-uncased-finetuned-cola results: [] ---  # lucs1265/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1898 - Validation Loss: 0.5233 - Train Matthews Correlation: 0.5286 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5194 | 0.4536 | 0.4725 | 0 | | 0.3249 | 0.4763 | 0.4867 | 1 | | 0.1898 | 0.5233 | 0.5286 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

mrovejaxd/ABL_b

mrovejaxd

2023-07-11T01:07:35Z

105

0

transformers

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

text-classification

2023-07-11T00:07:08Z

--- tags: - generated_from_trainer model-index: - name: ABL_b results: [] ---  # ABL_b This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.2

casque/Windrunnerv0.2

casque

2023-07-11T01:03:37Z

0

null

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

null

2023-07-11T01:00:46Z

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

hopkins/strict-small-4

hopkins

2023-07-11T00:43:51Z

5

0

transformers

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

text-generation

2023-06-13T21:25:31Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: strict-small-4 results: [] ---  # strict-small-4 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.8588 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 9 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.9925 | 1.83 | 1000 | 4.2033 | | 3.7647 | 3.67 | 2000 | 3.9152 | | 3.3569 | 5.5 | 3000 | 3.8495 | | 3.0079 | 7.34 | 4000 | 3.8588 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

casque/CrystalMaidenv0.2

casque

2023-07-11T00:42:48Z

0

null

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

null

2023-07-11T00:39:34Z

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

ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH

ALM-AHME

2023-07-11T00:40:15Z

5

1

transformers

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

image-classification

2023-07-10T02:43:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Augmented-Final split: train args: Augmented-Final metrics: - name: Accuracy type: accuracy value: 1.0 ---  # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0929 | 1.0 | 281 | 0.0919 | 0.9657 | | 0.0908 | 2.0 | 562 | 0.0127 | 0.9967 | | 0.0525 | 3.0 | 843 | 0.0133 | 0.9947 | | 0.1301 | 4.0 | 1125 | 0.0270 | 0.9927 | | 0.0624 | 5.0 | 1406 | 0.0064 | 0.9973 | | 0.0506 | 6.0 | 1687 | 0.0025 | 0.999 | | 0.0001 | 6.99 | 1967 | 0.0002 | 1.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Corran/all_mini_lm_paraphrase_L3_v2_12tr_5t

Corran

2023-07-11T00:37:54Z

4

0

sentence-transformers

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

text-classification

2023-07-11T00:37:49Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # Corran/all_mini_lm_paraphrase_L3_v2_12tr_5t 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("Corran/all_mini_lm_paraphrase_L3_v2_12tr_5t") # 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} } ```

jpherrerap/ner-bert-base-spanish-wwm-uncased

jpherrerap

2023-07-11T00:35:25Z

125

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:jpherrerap/competencia2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T23:53:37Z

--- language: - es tags: - generated_from_trainer datasets: - jpherrerap/competencia2 model-index: - name: ner-bert-base-spanish-wwm-uncased results: [] ---  # ner-bert-base-spanish-wwm-uncased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the jpherrerap/competencia2 dataset. It achieves the following results on the evaluation set: - Loss: 1.5112 - Body Part Precision: 0.0 - Body Part Recall: 0.0 - Body Part F1: 0.0 - Body Part Number: 0 - Disease Precision: 0.0 - Disease Recall: 0.0 - Disease F1: 0.0 - Disease Number: 0 - Family Member Precision: 0.0 - Family Member Recall: 0.0 - Family Member F1: 0.0 - Family Member Number: 0 - Medication Precision: 0.0 - Medication Recall: 0.0 - Medication F1: 0.0 - Medication Number: 0 - Procedure Precision: 0.0 - Procedure Recall: 0.0 - Procedure F1: 0.0 - Procedure Number: 0 - Overall Precision: 0.0 - Overall Recall: 0.0 - Overall F1: 0.0 - Overall Accuracy: 0.6713 ## Model description More information needed ## Intended uses & 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: 13 - 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 | Body Part Precision | Body Part Recall | Body Part F1 | Body Part Number | Disease Precision | Disease Recall | Disease F1 | Disease Number | Family Member Precision | Family Member Recall | Family Member F1 | Family Member Number | Medication Precision | Medication Recall | Medication F1 | Medication Number | Procedure Precision | Procedure Recall | Procedure F1 | Procedure Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.3372 | 1.0 | 1004 | 1.5112 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.6713 | | 0.1611 | 2.0 | 2008 | 1.7235 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.6705 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Unmand/procare_business_unit

Unmand

2023-07-11T00:33:12Z

4

0

spacy

[ "spacy", "text-classification", "en", "region:us" ]

text-classification

2023-07-11T00:31:49Z

--- tags: - spacy - text-classification language: - en model-index: - name: en_procare_business_unit results: [] --- | Feature | Description | | --- | --- | | **Name** | `en_procare_business_unit` | | **Version** | `0.0.0` | | **spaCy** | `>=3.5.4,<3.6.0` | | **Default Pipeline** | `textcat_multilabel` | | **Components** | `textcat_multilabel` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (30 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`textcat_multilabel`** | `Health and Wellbeing`, `NT Consulting`, `SA Workers Comp`, `VIC Workers Comp`, `ACT Consultancy`, `NSW Consulting`, `Staff Cards`, `NT MAC`, `NSW CTP`, `NSW Workers Comp`, `SA Consulting`, `ACT Workers Comp`, `Life and A&H`, `QLD Workers Comp`, `NT Workers Comp`, `Treatment`, `State Authorities Superannuation Scheme`, `ACT CTP`, `NULL`, `National Consulting`, `WA Workers Comp`, `QLD CTP`, `VIC TAC`, `WA Consulting`, `TAS Consulting`, `QLD Consulting`, `VIC Consulting`, `Comcare`, `TAS Workers Comp`, `SA CTP` | </details> ### Accuracy | Type | Score | | --- | --- | | `CATS_SCORE` | 81.21 | | `CATS_MICRO_P` | 89.37 | | `CATS_MICRO_R` | 59.54 | | `CATS_MICRO_F` | 71.47 | | `CATS_MACRO_P` | 67.87 | | `CATS_MACRO_R` | 33.52 | | `CATS_MACRO_F` | 42.52 | | `CATS_MACRO_AUC` | 81.21 | | `TEXTCAT_MULTILABEL_LOSS` | 73.72 |

layoric/openllama-7b-qlora-orca

layoric

2023-07-11T00:31:19Z

4

0

peft

[ "peft", "region:us" ]

null

2023-07-09T23:58:03Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0

bobobert4/poca-SoccerTwos

bobobert4

2023-07-11T00:18:04Z

5

0

ml-agents

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

reinforcement-learning

2023-07-11T00:16:06Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: bobobert4/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

bastianchinchon/nominal-groups-recognition-beto-clinical-wl-es

bastianchinchon

2023-07-10T23:58:42Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:bastianchinchon/spanish_nominal_groups_conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T22:32:12Z

--- language: - es tags: - generated_from_trainer datasets: - bastianchinchon/spanish_nominal_groups_conll2003 model-index: - name: nominal-groups-recognition-beto-clinical-wl-es results: [] ---  # nominal-groups-recognition-beto-clinical-wl-es This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the bastianchinchon/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2338 - Body Part Precision: 0.7894 - Body Part Recall: 0.8257 - Body Part F1: 0.8071 - Body Part Number: 413 - Disease Precision: 0.7790 - Disease Recall: 0.8133 - Disease F1: 0.7958 - Disease Number: 975 - Family Member Precision: 0.8286 - Family Member Recall: 0.9667 - Family Member F1: 0.8923 - Family Member Number: 30 - Medication Precision: 0.8913 - Medication Recall: 0.8817 - Medication F1: 0.8865 - Medication Number: 93 - Procedure Precision: 0.7130 - Procedure Recall: 0.7910 - Procedure F1: 0.75 - Procedure Number: 311 - Overall Precision: 0.7758 - Overall Recall: 0.8183 - Overall F1: 0.7965 - Overall Accuracy: 0.9382 ## Model description More information needed ## Intended uses & 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: 13 - 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 | Body Part Precision | Body Part Recall | Body Part F1 | Body Part Number | Disease Precision | Disease Recall | Disease F1 | Disease Number | Family Member Precision | Family Member Recall | Family Member F1 | Family Member Number | Medication Precision | Medication Recall | Medication F1 | Medication Number | Procedure Precision | Procedure Recall | Procedure F1 | Procedure Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2998 | 1.0 | 1004 | 0.2127 | 0.7460 | 0.7893 | 0.7671 | 413 | 0.7612 | 0.7815 | 0.7713 | 975 | 0.9062 | 0.9667 | 0.9355 | 30 | 0.8462 | 0.8280 | 0.8370 | 93 | 0.6583 | 0.7556 | 0.7036 | 311 | 0.7450 | 0.7843 | 0.7642 | 0.9331 | | 0.1566 | 2.0 | 2008 | 0.2278 | 0.7780 | 0.8232 | 0.8 | 413 | 0.7847 | 0.8 | 0.7923 | 975 | 0.8529 | 0.9667 | 0.9062 | 30 | 0.8710 | 0.8710 | 0.8710 | 93 | 0.7346 | 0.7653 | 0.7496 | 311 | 0.7800 | 0.8057 | 0.7927 | 0.9367 | | 0.1089 | 3.0 | 3012 | 0.2338 | 0.7894 | 0.8257 | 0.8071 | 413 | 0.7790 | 0.8133 | 0.7958 | 975 | 0.8286 | 0.9667 | 0.8923 | 30 | 0.8913 | 0.8817 | 0.8865 | 93 | 0.7130 | 0.7910 | 0.75 | 311 | 0.7758 | 0.8183 | 0.7965 | 0.9382 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jz0214/sd-class-butterflies-64

jz0214

2023-07-10T23:52:24Z

30

0

diffusers

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

unconditional-image-generation

2023-07-10T23:50:42Z

--- 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('jz0214/sd-class-butterflies-64') image = pipeline().images[0] image ```

aliceBG/distilbert-base-uncased-finetuned-cola

aliceBG

2023-07-10T23:38:28Z

61

0

transformers

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

text-classification

2023-07-09T23:52:39Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: aliceBG/distilbert-base-uncased-finetuned-cola results: [] ---  # aliceBG/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1834 - Validation Loss: 0.5540 - Train Matthews Correlation: 0.5495 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5170 | 0.4723 | 0.4122 | 0 | | 0.3177 | 0.4714 | 0.5232 | 1 | | 0.1834 | 0.5540 | 0.5495 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

digiplay/Juggernaut_final

digiplay

2023-07-10T23:21:23Z

1,591

15

diffusers

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

text-to-image

2023-07-10T22:56:03Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Models info : https://civitai.com/models/46422?modelVersionId=114770 Sample image I made thru huggingface's API: ![1396153a-5288-40f0-a52a-1e3b8cd9ad29.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/xIDqdaAoABeyKlucmgtXN.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/92fd888b-9f8f-4230-ac30-3ad36fb4800b/01H50CX4KX66D605VM2QMK1EP2-0.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5b10f2a0-f162-497a-80b1-ea5875d99ad8/01H50KNH2P89XCA1EV4EC35GGC-0.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7a4ee832-3d5d-4c69-85a8-235206e07b18/01H50K2G4JSN79V68A4B3RCTQ9-0.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/64b7297e-208f-476d-bbc3-f3420b159ee0/01H50MNJPGKZ7M889HWVK8KKK6-0.jpeg)

jz0214/sd-class-butterflies-32

jz0214

2023-07-10T23:09:47Z

30

0

diffusers

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

unconditional-image-generation

2023-07-10T23:08:46Z

--- 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('jz0214/sd-class-butterflies-32') image = pipeline().images[0] image ```

PistonPalacios/Piston

PistonPalacios

2023-07-10T23:04:10Z

0

diffusers

[ "diffusers", "legal", "es", "dataset:fka/awesome-chatgpt-prompts", "doi:10.57967/hf/0876", "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T22:50:18Z

--- license: creativeml-openrail-m datasets: - fka/awesome-chatgpt-prompts language: - es library_name: diffusers tags: - legal ---

trevorj/dqn-SpaceInvadersNoFrameskip-v4

trevorj

2023-07-10T22:41:49Z

0

stable-baselines3

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

reinforcement-learning

2023-07-10T22:41:13Z

--- 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: 523.00 +/- 142.73 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 trevorj -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 trevorj -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 trevorj ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.9

jordyvl

2023-07-10T22:40:13Z

161

0

transformers

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

image-classification

2023-07-10T22:00:19Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5373 - Accuracy: 0.85 - Brier Loss: 0.2432 - Nll: 1.1157 - F1 Micro: 0.85 - F1 Macro: 0.8450 - Ece: 0.1621 - Aurc: 0.0427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 2.1036 | 0.215 | 0.8753 | 5.3195 | 0.2150 | 0.1264 | 0.2571 | 0.6923 | | No log | 2.0 | 14 | 1.6952 | 0.405 | 0.7407 | 3.4929 | 0.405 | 0.2416 | 0.2907 | 0.4040 | | No log | 3.0 | 21 | 1.1843 | 0.62 | 0.5633 | 2.0113 | 0.62 | 0.5725 | 0.2740 | 0.2014 | | No log | 4.0 | 28 | 0.8797 | 0.71 | 0.4080 | 1.7043 | 0.7100 | 0.6683 | 0.2024 | 0.1125 | | No log | 5.0 | 35 | 0.8570 | 0.715 | 0.3837 | 1.6476 | 0.715 | 0.7280 | 0.2189 | 0.1079 | | No log | 6.0 | 42 | 0.7484 | 0.775 | 0.3285 | 1.5962 | 0.775 | 0.7668 | 0.1873 | 0.0816 | | No log | 7.0 | 49 | 0.7337 | 0.79 | 0.3131 | 1.5377 | 0.79 | 0.7779 | 0.1904 | 0.0771 | | No log | 8.0 | 56 | 0.6709 | 0.795 | 0.3012 | 1.2156 | 0.795 | 0.7776 | 0.1939 | 0.0761 | | No log | 9.0 | 63 | 0.6901 | 0.795 | 0.3069 | 1.4725 | 0.795 | 0.7916 | 0.1882 | 0.0769 | | No log | 10.0 | 70 | 0.7960 | 0.75 | 0.3586 | 1.4426 | 0.75 | 0.7406 | 0.1868 | 0.0976 | | No log | 11.0 | 77 | 0.7489 | 0.77 | 0.3296 | 1.6202 | 0.7700 | 0.7794 | 0.2020 | 0.0878 | | No log | 12.0 | 84 | 0.7068 | 0.785 | 0.3270 | 1.4127 | 0.785 | 0.7812 | 0.1922 | 0.0759 | | No log | 13.0 | 91 | 0.6687 | 0.79 | 0.3050 | 1.3820 | 0.79 | 0.7945 | 0.1818 | 0.0625 | | No log | 14.0 | 98 | 0.6052 | 0.79 | 0.2854 | 1.0602 | 0.79 | 0.7716 | 0.1702 | 0.0590 | | No log | 15.0 | 105 | 0.6369 | 0.795 | 0.2959 | 1.0580 | 0.795 | 0.7953 | 0.1709 | 0.0603 | | No log | 16.0 | 112 | 0.6204 | 0.81 | 0.2816 | 1.1886 | 0.81 | 0.8050 | 0.1657 | 0.0702 | | No log | 17.0 | 119 | 0.5648 | 0.83 | 0.2475 | 1.2506 | 0.83 | 0.8241 | 0.1347 | 0.0612 | | No log | 18.0 | 126 | 0.5849 | 0.83 | 0.2672 | 1.2245 | 0.83 | 0.8155 | 0.1646 | 0.0601 | | No log | 19.0 | 133 | 0.5536 | 0.835 | 0.2475 | 1.0514 | 0.835 | 0.8254 | 0.1683 | 0.0531 | | No log | 20.0 | 140 | 0.5689 | 0.835 | 0.2513 | 1.2369 | 0.835 | 0.8437 | 0.1722 | 0.0489 | | No log | 21.0 | 147 | 0.5540 | 0.83 | 0.2485 | 1.2139 | 0.83 | 0.8165 | 0.1641 | 0.0608 | | No log | 22.0 | 154 | 0.5352 | 0.835 | 0.2402 | 1.0108 | 0.835 | 0.8295 | 0.1408 | 0.0430 | | No log | 23.0 | 161 | 0.5380 | 0.84 | 0.2403 | 1.2280 | 0.8400 | 0.8347 | 0.1405 | 0.0436 | | No log | 24.0 | 168 | 0.5422 | 0.835 | 0.2471 | 1.0204 | 0.835 | 0.8324 | 0.1606 | 0.0445 | | No log | 25.0 | 175 | 0.5342 | 0.85 | 0.2404 | 1.0767 | 0.85 | 0.8487 | 0.1469 | 0.0432 | | No log | 26.0 | 182 | 0.5374 | 0.84 | 0.2429 | 1.0774 | 0.8400 | 0.8334 | 0.1420 | 0.0462 | | No log | 27.0 | 189 | 0.5311 | 0.85 | 0.2395 | 1.0748 | 0.85 | 0.8487 | 0.1439 | 0.0446 | | No log | 28.0 | 196 | 0.5298 | 0.85 | 0.2384 | 1.1337 | 0.85 | 0.8487 | 0.1570 | 0.0437 | | No log | 29.0 | 203 | 0.5387 | 0.845 | 0.2435 | 1.1319 | 0.845 | 0.8424 | 0.1539 | 0.0458 | | No log | 30.0 | 210 | 0.5361 | 0.85 | 0.2430 | 1.0648 | 0.85 | 0.8450 | 0.1679 | 0.0431 | | No log | 31.0 | 217 | 0.5339 | 0.85 | 0.2413 | 1.0676 | 0.85 | 0.8487 | 0.1646 | 0.0428 | | No log | 32.0 | 224 | 0.5345 | 0.85 | 0.2421 | 1.0709 | 0.85 | 0.8487 | 0.1476 | 0.0440 | | No log | 33.0 | 231 | 0.5343 | 0.85 | 0.2421 | 1.1236 | 0.85 | 0.8450 | 0.1621 | 0.0431 | | No log | 34.0 | 238 | 0.5353 | 0.845 | 0.2426 | 1.1244 | 0.845 | 0.8424 | 0.1710 | 0.0428 | | No log | 35.0 | 245 | 0.5346 | 0.85 | 0.2423 | 1.0649 | 0.85 | 0.8487 | 0.1520 | 0.0440 | | No log | 36.0 | 252 | 0.5356 | 0.855 | 0.2422 | 1.1241 | 0.855 | 0.8517 | 0.1814 | 0.0429 | | No log | 37.0 | 259 | 0.5357 | 0.85 | 0.2426 | 1.1237 | 0.85 | 0.8450 | 0.1670 | 0.0425 | | No log | 38.0 | 266 | 0.5356 | 0.845 | 0.2426 | 1.1226 | 0.845 | 0.8419 | 0.1607 | 0.0435 | | No log | 39.0 | 273 | 0.5347 | 0.855 | 0.2420 | 1.0739 | 0.855 | 0.8517 | 0.1597 | 0.0427 | | No log | 40.0 | 280 | 0.5356 | 0.855 | 0.2423 | 1.1203 | 0.855 | 0.8517 | 0.1676 | 0.0435 | | No log | 41.0 | 287 | 0.5365 | 0.85 | 0.2431 | 1.1199 | 0.85 | 0.8450 | 0.1780 | 0.0429 | | No log | 42.0 | 294 | 0.5356 | 0.85 | 0.2426 | 1.1173 | 0.85 | 0.8450 | 0.1653 | 0.0430 | | No log | 43.0 | 301 | 0.5363 | 0.85 | 0.2428 | 1.1189 | 0.85 | 0.8450 | 0.1550 | 0.0435 | | No log | 44.0 | 308 | 0.5345 | 0.85 | 0.2418 | 1.1193 | 0.85 | 0.8450 | 0.1590 | 0.0428 | | No log | 45.0 | 315 | 0.5374 | 0.85 | 0.2435 | 1.1202 | 0.85 | 0.8450 | 0.1633 | 0.0435 | | No log | 46.0 | 322 | 0.5355 | 0.85 | 0.2423 | 1.1183 | 0.85 | 0.8450 | 0.1564 | 0.0428 | | No log | 47.0 | 329 | 0.5354 | 0.85 | 0.2425 | 1.1176 | 0.85 | 0.8450 | 0.1509 | 0.0429 | | No log | 48.0 | 336 | 0.5369 | 0.85 | 0.2433 | 1.1177 | 0.85 | 0.8450 | 0.1517 | 0.0432 | | No log | 49.0 | 343 | 0.5361 | 0.85 | 0.2428 | 1.1182 | 0.85 | 0.8450 | 0.1490 | 0.0428 | | No log | 50.0 | 350 | 0.5364 | 0.85 | 0.2431 | 1.1179 | 0.85 | 0.8450 | 0.1654 | 0.0430 | | No log | 51.0 | 357 | 0.5365 | 0.85 | 0.2428 | 1.1185 | 0.85 | 0.8450 | 0.1729 | 0.0432 | | No log | 52.0 | 364 | 0.5364 | 0.85 | 0.2430 | 1.1165 | 0.85 | 0.8450 | 0.1614 | 0.0429 | | No log | 53.0 | 371 | 0.5362 | 0.85 | 0.2429 | 1.1167 | 0.85 | 0.8450 | 0.1694 | 0.0430 | | No log | 54.0 | 378 | 0.5369 | 0.85 | 0.2432 | 1.1170 | 0.85 | 0.8450 | 0.1597 | 0.0432 | | No log | 55.0 | 385 | 0.5368 | 0.85 | 0.2430 | 1.1168 | 0.85 | 0.8450 | 0.1670 | 0.0429 | | No log | 56.0 | 392 | 0.5367 | 0.85 | 0.2430 | 1.1180 | 0.85 | 0.8450 | 0.1619 | 0.0430 | | No log | 57.0 | 399 | 0.5364 | 0.85 | 0.2429 | 1.1163 | 0.85 | 0.8450 | 0.1649 | 0.0429 | | No log | 58.0 | 406 | 0.5364 | 0.85 | 0.2430 | 1.1156 | 0.85 | 0.8450 | 0.1611 | 0.0429 | | No log | 59.0 | 413 | 0.5365 | 0.85 | 0.2428 | 1.1163 | 0.85 | 0.8450 | 0.1591 | 0.0429 | | No log | 60.0 | 420 | 0.5364 | 0.85 | 0.2429 | 1.1155 | 0.85 | 0.8450 | 0.1588 | 0.0429 | | No log | 61.0 | 427 | 0.5370 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1772 | 0.0432 | | No log | 62.0 | 434 | 0.5367 | 0.85 | 0.2429 | 1.1167 | 0.85 | 0.8450 | 0.1622 | 0.0429 | | No log | 63.0 | 441 | 0.5362 | 0.85 | 0.2428 | 1.1162 | 0.85 | 0.8450 | 0.1503 | 0.0428 | | No log | 64.0 | 448 | 0.5372 | 0.85 | 0.2433 | 1.1161 | 0.85 | 0.8450 | 0.1616 | 0.0432 | | No log | 65.0 | 455 | 0.5371 | 0.85 | 0.2431 | 1.1162 | 0.85 | 0.8450 | 0.1499 | 0.0429 | | No log | 66.0 | 462 | 0.5367 | 0.85 | 0.2430 | 1.1160 | 0.85 | 0.8450 | 0.1591 | 0.0427 | | No log | 67.0 | 469 | 0.5367 | 0.85 | 0.2430 | 1.1164 | 0.85 | 0.8450 | 0.1562 | 0.0428 | | No log | 68.0 | 476 | 0.5368 | 0.85 | 0.2430 | 1.1168 | 0.85 | 0.8450 | 0.1556 | 0.0427 | | No log | 69.0 | 483 | 0.5368 | 0.85 | 0.2431 | 1.1158 | 0.85 | 0.8450 | 0.1593 | 0.0428 | | No log | 70.0 | 490 | 0.5372 | 0.85 | 0.2432 | 1.1162 | 0.85 | 0.8450 | 0.1628 | 0.0428 | | No log | 71.0 | 497 | 0.5371 | 0.85 | 0.2432 | 1.1163 | 0.85 | 0.8450 | 0.1599 | 0.0429 | | 0.1708 | 72.0 | 504 | 0.5370 | 0.85 | 0.2430 | 1.1161 | 0.85 | 0.8450 | 0.1559 | 0.0430 | | 0.1708 | 73.0 | 511 | 0.5372 | 0.85 | 0.2433 | 1.1154 | 0.85 | 0.8450 | 0.1556 | 0.0428 | | 0.1708 | 74.0 | 518 | 0.5370 | 0.85 | 0.2429 | 1.1165 | 0.85 | 0.8450 | 0.1540 | 0.0428 | | 0.1708 | 75.0 | 525 | 0.5371 | 0.85 | 0.2431 | 1.1161 | 0.85 | 0.8450 | 0.1616 | 0.0427 | | 0.1708 | 76.0 | 532 | 0.5369 | 0.85 | 0.2431 | 1.1161 | 0.85 | 0.8450 | 0.1619 | 0.0427 | | 0.1708 | 77.0 | 539 | 0.5369 | 0.85 | 0.2430 | 1.1156 | 0.85 | 0.8450 | 0.1623 | 0.0429 | | 0.1708 | 78.0 | 546 | 0.5372 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1619 | 0.0427 | | 0.1708 | 79.0 | 553 | 0.5375 | 0.85 | 0.2433 | 1.1162 | 0.85 | 0.8450 | 0.1688 | 0.0429 | | 0.1708 | 80.0 | 560 | 0.5372 | 0.85 | 0.2432 | 1.1160 | 0.85 | 0.8450 | 0.1623 | 0.0429 | | 0.1708 | 81.0 | 567 | 0.5373 | 0.85 | 0.2432 | 1.1162 | 0.85 | 0.8450 | 0.1620 | 0.0428 | | 0.1708 | 82.0 | 574 | 0.5374 | 0.85 | 0.2433 | 1.1160 | 0.85 | 0.8450 | 0.1622 | 0.0428 | | 0.1708 | 83.0 | 581 | 0.5372 | 0.85 | 0.2432 | 1.1159 | 0.85 | 0.8450 | 0.1622 | 0.0428 | | 0.1708 | 84.0 | 588 | 0.5371 | 0.85 | 0.2431 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 85.0 | 595 | 0.5372 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1687 | 0.0426 | | 0.1708 | 86.0 | 602 | 0.5372 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1619 | 0.0426 | | 0.1708 | 87.0 | 609 | 0.5374 | 0.85 | 0.2432 | 1.1159 | 0.85 | 0.8450 | 0.1687 | 0.0428 | | 0.1708 | 88.0 | 616 | 0.5373 | 0.85 | 0.2432 | 1.1160 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 89.0 | 623 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 90.0 | 630 | 0.5373 | 0.85 | 0.2432 | 1.1156 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 91.0 | 637 | 0.5372 | 0.85 | 0.2432 | 1.1156 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 92.0 | 644 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 93.0 | 651 | 0.5372 | 0.85 | 0.2432 | 1.1156 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 94.0 | 658 | 0.5373 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 95.0 | 665 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 96.0 | 672 | 0.5372 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 97.0 | 679 | 0.5372 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 98.0 | 686 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 99.0 | 693 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 100.0 | 700 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

Raizel123/SNoonzlora

Raizel123

2023-07-10T22:35:30Z

0

null

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

null

2023-07-10T22:32:26Z

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

Raizel123/Mbyonglora

Raizel123

2023-07-10T22:31:20Z

0

null

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

null

2023-07-10T22:27:47Z

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

jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_MSE

jordyvl

2023-07-10T22:30:03Z

163

0

transformers

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

image-classification

2023-07-10T21:13:38Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_MSE results: [] ---  # vit-small_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4673 - Accuracy: 0.6425 - Brier Loss: 0.4763 - Nll: 3.0680 - F1 Micro: 0.6425 - F1 Macro: 0.6485 - Ece: 0.1946 - Aurc: 0.1381 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 4.4851 | 0.06 | 0.9565 | 13.8276 | 0.06 | 0.0556 | 0.1688 | 0.9385 | | No log | 2.0 | 50 | 3.5619 | 0.3775 | 0.7827 | 6.2649 | 0.3775 | 0.3611 | 0.2331 | 0.3882 | | No log | 3.0 | 75 | 2.8990 | 0.5025 | 0.6453 | 4.7376 | 0.5025 | 0.4858 | 0.1689 | 0.2658 | | No log | 4.0 | 100 | 2.5972 | 0.515 | 0.5980 | 4.4210 | 0.515 | 0.4895 | 0.1605 | 0.2249 | | No log | 5.0 | 125 | 2.4353 | 0.56 | 0.5762 | 3.4885 | 0.56 | 0.5566 | 0.1548 | 0.2100 | | No log | 6.0 | 150 | 2.4157 | 0.5475 | 0.5864 | 3.8261 | 0.5475 | 0.5323 | 0.1837 | 0.2167 | | No log | 7.0 | 175 | 2.1786 | 0.6075 | 0.5203 | 3.4565 | 0.6075 | 0.6103 | 0.1403 | 0.1670 | | No log | 8.0 | 200 | 2.1082 | 0.63 | 0.5040 | 3.3570 | 0.63 | 0.6246 | 0.1580 | 0.1530 | | No log | 9.0 | 225 | 2.0472 | 0.625 | 0.5042 | 3.8572 | 0.625 | 0.6184 | 0.1552 | 0.1530 | | No log | 10.0 | 250 | 2.0589 | 0.6025 | 0.5468 | 3.5723 | 0.6025 | 0.5982 | 0.1781 | 0.1785 | | No log | 11.0 | 275 | 1.8965 | 0.65 | 0.4755 | 3.4466 | 0.65 | 0.6497 | 0.1605 | 0.1475 | | No log | 12.0 | 300 | 1.9014 | 0.6325 | 0.5066 | 3.0881 | 0.6325 | 0.6359 | 0.1658 | 0.1591 | | No log | 13.0 | 325 | 1.7904 | 0.6175 | 0.5162 | 3.4673 | 0.6175 | 0.6141 | 0.1525 | 0.1598 | | No log | 14.0 | 350 | 1.8624 | 0.625 | 0.5173 | 3.6824 | 0.625 | 0.6179 | 0.1567 | 0.1624 | | No log | 15.0 | 375 | 1.7083 | 0.6625 | 0.4817 | 3.1296 | 0.6625 | 0.6686 | 0.1651 | 0.1405 | | No log | 16.0 | 400 | 1.8848 | 0.59 | 0.5478 | 4.3761 | 0.59 | 0.5913 | 0.2083 | 0.1696 | | No log | 17.0 | 425 | 1.7238 | 0.6125 | 0.5229 | 3.1232 | 0.6125 | 0.6052 | 0.1833 | 0.1553 | | No log | 18.0 | 450 | 1.7126 | 0.625 | 0.5152 | 2.9267 | 0.625 | 0.6284 | 0.1747 | 0.1565 | | No log | 19.0 | 475 | 1.6459 | 0.6275 | 0.5024 | 2.9078 | 0.6275 | 0.6219 | 0.1766 | 0.1527 | | 1.0542 | 20.0 | 500 | 1.6029 | 0.6275 | 0.4855 | 3.0931 | 0.6275 | 0.6316 | 0.1720 | 0.1414 | | 1.0542 | 21.0 | 525 | 1.6566 | 0.6525 | 0.4847 | 3.0998 | 0.6525 | 0.6479 | 0.1558 | 0.1438 | | 1.0542 | 22.0 | 550 | 1.6169 | 0.645 | 0.4894 | 3.0081 | 0.645 | 0.6471 | 0.1687 | 0.1400 | | 1.0542 | 23.0 | 575 | 1.5322 | 0.6525 | 0.4557 | 3.3587 | 0.6525 | 0.6520 | 0.1428 | 0.1247 | | 1.0542 | 24.0 | 600 | 1.5991 | 0.6475 | 0.4787 | 2.9349 | 0.6475 | 0.6444 | 0.1580 | 0.1450 | | 1.0542 | 25.0 | 625 | 1.5625 | 0.6375 | 0.4926 | 3.0245 | 0.6375 | 0.6378 | 0.1641 | 0.1433 | | 1.0542 | 26.0 | 650 | 1.5366 | 0.64 | 0.4884 | 3.3388 | 0.64 | 0.6461 | 0.1595 | 0.1453 | | 1.0542 | 27.0 | 675 | 1.5686 | 0.65 | 0.4765 | 3.5120 | 0.65 | 0.6504 | 0.1625 | 0.1359 | | 1.0542 | 28.0 | 700 | 1.5562 | 0.6475 | 0.4817 | 3.0348 | 0.6475 | 0.6488 | 0.1459 | 0.1388 | | 1.0542 | 29.0 | 725 | 1.5213 | 0.6475 | 0.4719 | 3.2628 | 0.6475 | 0.6475 | 0.1634 | 0.1326 | | 1.0542 | 30.0 | 750 | 1.5492 | 0.6675 | 0.4730 | 3.1693 | 0.6675 | 0.6679 | 0.1469 | 0.1415 | | 1.0542 | 31.0 | 775 | 1.5311 | 0.65 | 0.4896 | 3.0881 | 0.65 | 0.6504 | 0.1815 | 0.1380 | | 1.0542 | 32.0 | 800 | 1.5556 | 0.6475 | 0.4821 | 3.1829 | 0.6475 | 0.6491 | 0.1640 | 0.1405 | | 1.0542 | 33.0 | 825 | 1.5471 | 0.6375 | 0.4846 | 3.4190 | 0.6375 | 0.6407 | 0.1628 | 0.1415 | | 1.0542 | 34.0 | 850 | 1.4809 | 0.6575 | 0.4714 | 2.9136 | 0.6575 | 0.6612 | 0.1729 | 0.1338 | | 1.0542 | 35.0 | 875 | 1.5256 | 0.66 | 0.4773 | 3.2303 | 0.66 | 0.6650 | 0.1746 | 0.1368 | | 1.0542 | 36.0 | 900 | 1.4929 | 0.6675 | 0.4671 | 3.2360 | 0.6675 | 0.6698 | 0.1698 | 0.1309 | | 1.0542 | 37.0 | 925 | 1.4923 | 0.645 | 0.4880 | 3.0567 | 0.645 | 0.6564 | 0.1764 | 0.1395 | | 1.0542 | 38.0 | 950 | 1.5038 | 0.665 | 0.4672 | 3.2116 | 0.665 | 0.6661 | 0.1588 | 0.1343 | | 1.0542 | 39.0 | 975 | 1.4708 | 0.6625 | 0.4669 | 3.1420 | 0.6625 | 0.6675 | 0.1683 | 0.1301 | | 0.0522 | 40.0 | 1000 | 1.5153 | 0.6475 | 0.4865 | 3.1796 | 0.6475 | 0.6447 | 0.1639 | 0.1400 | | 0.0522 | 41.0 | 1025 | 1.4705 | 0.6575 | 0.4642 | 3.2196 | 0.6575 | 0.6626 | 0.1440 | 0.1308 | | 0.0522 | 42.0 | 1050 | 1.4844 | 0.6575 | 0.4722 | 3.2445 | 0.6575 | 0.6595 | 0.1746 | 0.1328 | | 0.0522 | 43.0 | 1075 | 1.4957 | 0.6425 | 0.4828 | 3.1456 | 0.6425 | 0.6468 | 0.1499 | 0.1417 | | 0.0522 | 44.0 | 1100 | 1.5179 | 0.645 | 0.4910 | 3.3921 | 0.645 | 0.6470 | 0.1861 | 0.1433 | | 0.0522 | 45.0 | 1125 | 1.4878 | 0.6425 | 0.4839 | 3.2139 | 0.6425 | 0.6478 | 0.1720 | 0.1403 | | 0.0522 | 46.0 | 1150 | 1.4666 | 0.655 | 0.4741 | 2.9333 | 0.655 | 0.6601 | 0.1813 | 0.1347 | | 0.0522 | 47.0 | 1175 | 1.4954 | 0.6575 | 0.4776 | 3.2102 | 0.6575 | 0.6604 | 0.1842 | 0.1390 | | 0.0522 | 48.0 | 1200 | 1.4976 | 0.645 | 0.4856 | 3.1539 | 0.645 | 0.6493 | 0.1549 | 0.1407 | | 0.0522 | 49.0 | 1225 | 1.4772 | 0.64 | 0.4780 | 2.9845 | 0.64 | 0.6445 | 0.1826 | 0.1388 | | 0.0522 | 50.0 | 1250 | 1.4584 | 0.65 | 0.4703 | 3.0776 | 0.65 | 0.6533 | 0.1685 | 0.1352 | | 0.0522 | 51.0 | 1275 | 1.4828 | 0.6325 | 0.4844 | 3.1425 | 0.6325 | 0.6377 | 0.1641 | 0.1409 | | 0.0522 | 52.0 | 1300 | 1.4676 | 0.6525 | 0.4737 | 3.1483 | 0.6525 | 0.6565 | 0.1773 | 0.1358 | | 0.0522 | 53.0 | 1325 | 1.4675 | 0.6475 | 0.4791 | 3.1411 | 0.6475 | 0.6515 | 0.1820 | 0.1388 | | 0.0522 | 54.0 | 1350 | 1.4724 | 0.645 | 0.4764 | 3.0744 | 0.645 | 0.6499 | 0.1847 | 0.1382 | | 0.0522 | 55.0 | 1375 | 1.4689 | 0.6425 | 0.4769 | 3.2256 | 0.6425 | 0.6476 | 0.1839 | 0.1376 | | 0.0522 | 56.0 | 1400 | 1.4660 | 0.6425 | 0.4760 | 2.9907 | 0.6425 | 0.6479 | 0.1906 | 0.1378 | | 0.0522 | 57.0 | 1425 | 1.4663 | 0.645 | 0.4757 | 3.0722 | 0.645 | 0.6514 | 0.1705 | 0.1367 | | 0.0522 | 58.0 | 1450 | 1.4678 | 0.65 | 0.4770 | 3.0710 | 0.65 | 0.6546 | 0.1794 | 0.1371 | | 0.0522 | 59.0 | 1475 | 1.4717 | 0.64 | 0.4786 | 3.0737 | 0.64 | 0.6455 | 0.1889 | 0.1392 | | 0.0064 | 60.0 | 1500 | 1.4691 | 0.645 | 0.4768 | 3.0688 | 0.645 | 0.6499 | 0.1815 | 0.1378 | | 0.0064 | 61.0 | 1525 | 1.4689 | 0.64 | 0.4767 | 3.0688 | 0.64 | 0.6452 | 0.1846 | 0.1382 | | 0.0064 | 62.0 | 1550 | 1.4689 | 0.64 | 0.4770 | 3.0674 | 0.64 | 0.6455 | 0.1937 | 0.1383 | | 0.0064 | 63.0 | 1575 | 1.4687 | 0.6425 | 0.4767 | 3.0700 | 0.6425 | 0.6485 | 0.1897 | 0.1381 | | 0.0064 | 64.0 | 1600 | 1.4674 | 0.6425 | 0.4764 | 3.0675 | 0.6425 | 0.6472 | 0.1855 | 0.1375 | | 0.0064 | 65.0 | 1625 | 1.4681 | 0.6425 | 0.4766 | 3.0694 | 0.6425 | 0.6485 | 0.1917 | 0.1381 | | 0.0064 | 66.0 | 1650 | 1.4681 | 0.6425 | 0.4766 | 3.0687 | 0.6425 | 0.6472 | 0.1905 | 0.1378 | | 0.0064 | 67.0 | 1675 | 1.4667 | 0.645 | 0.4757 | 3.0681 | 0.645 | 0.6505 | 0.1899 | 0.1375 | | 0.0064 | 68.0 | 1700 | 1.4683 | 0.6425 | 0.4771 | 3.0686 | 0.6425 | 0.6474 | 0.1871 | 0.1379 | | 0.0064 | 69.0 | 1725 | 1.4672 | 0.64 | 0.4760 | 3.0679 | 0.64 | 0.6455 | 0.1932 | 0.1380 | | 0.0064 | 70.0 | 1750 | 1.4673 | 0.6425 | 0.4763 | 3.0683 | 0.6425 | 0.6474 | 0.1955 | 0.1376 | | 0.0064 | 71.0 | 1775 | 1.4676 | 0.645 | 0.4763 | 3.0680 | 0.645 | 0.6505 | 0.1921 | 0.1376 | | 0.0064 | 72.0 | 1800 | 1.4674 | 0.6425 | 0.4763 | 3.0683 | 0.6425 | 0.6474 | 0.1946 | 0.1376 | | 0.0064 | 73.0 | 1825 | 1.4675 | 0.6425 | 0.4763 | 3.0682 | 0.6425 | 0.6474 | 0.1946 | 0.1377 | | 0.0064 | 74.0 | 1850 | 1.4674 | 0.6425 | 0.4763 | 3.0682 | 0.6425 | 0.6485 | 0.1945 | 0.1380 | | 0.0064 | 75.0 | 1875 | 1.4674 | 0.64 | 0.4763 | 3.0680 | 0.64 | 0.6455 | 0.1960 | 0.1380 | | 0.0064 | 76.0 | 1900 | 1.4675 | 0.64 | 0.4764 | 3.0682 | 0.64 | 0.6455 | 0.1972 | 0.1381 | | 0.0064 | 77.0 | 1925 | 1.4675 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1947 | 0.1380 | | 0.0064 | 78.0 | 1950 | 1.4674 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1381 | | 0.0064 | 79.0 | 1975 | 1.4674 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6474 | 0.1935 | 0.1376 | | 0.0 | 80.0 | 2000 | 1.4673 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1380 | | 0.0 | 81.0 | 2025 | 1.4674 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1946 | 0.1380 | | 0.0 | 82.0 | 2050 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1935 | 0.1380 | | 0.0 | 83.0 | 2075 | 1.4674 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 84.0 | 2100 | 1.4674 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1381 | | 0.0 | 85.0 | 2125 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 86.0 | 2150 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 87.0 | 2175 | 1.4673 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1381 | | 0.0 | 88.0 | 2200 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 89.0 | 2225 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 90.0 | 2250 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 91.0 | 2275 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 92.0 | 2300 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 93.0 | 2325 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 94.0 | 2350 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1909 | 0.1381 | | 0.0 | 95.0 | 2375 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 96.0 | 2400 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 97.0 | 2425 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 98.0 | 2450 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 99.0 | 2475 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 100.0 | 2500 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

Raizel123/Alfilora

Raizel123

2023-07-10T22:23:13Z

0

null

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

null

2023-07-10T22:18:35Z

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

NasimB/gpt2-dp-mod-datasets-txt-processing-rarity-all

NasimB

2023-07-10T22:14:46Z

4

0

transformers

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

text-generation

2023-07-10T19:52:59Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-mod-datasets-txt-processing-rarity-all results: [] ---  # gpt2-dp-mod-datasets-txt-processing-rarity-all This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7606 | 0.29 | 500 | 5.6933 | | 5.4375 | 0.59 | 1000 | 5.2559 | | 5.0937 | 0.88 | 1500 | 5.0171 | | 4.8204 | 1.18 | 2000 | 4.8701 | | 4.6728 | 1.47 | 2500 | 4.7593 | | 4.574 | 1.77 | 3000 | 4.6587 | | 4.4456 | 2.06 | 3500 | 4.5885 | | 4.258 | 2.36 | 4000 | 4.5468 | | 4.2423 | 2.65 | 4500 | 4.4860 | | 4.2036 | 2.94 | 5000 | 4.4302 | | 3.9737 | 3.24 | 5500 | 4.4364 | | 3.9439 | 3.53 | 6000 | 4.4019 | | 3.9271 | 3.83 | 6500 | 4.3632 | | 3.7901 | 4.12 | 7000 | 4.3689 | | 3.6474 | 4.42 | 7500 | 4.3662 | | 3.6414 | 4.71 | 8000 | 4.3472 | | 3.6338 | 5.01 | 8500 | 4.3344 | | 3.3764 | 5.3 | 9000 | 4.3618 | | 3.3821 | 5.59 | 9500 | 4.3568 | | 3.3777 | 5.89 | 10000 | 4.3513 | | 3.2752 | 6.18 | 10500 | 4.3602 | | 3.2228 | 6.48 | 11000 | 4.3652 | | 3.2172 | 6.77 | 11500 | 4.3656 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

MnLgt/swivel_inversion

MnLgt

2023-07-10T22:11:42Z

0

null

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

null

2023-07-10T22:11:41Z

--- license: mit --- ### swivel_inversion on Stable Diffusion This is the `<swivel-chair>` 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`: ![<swivel-chair> 0](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/942493f105b42e65e9bbb2afb8fd24ee.jpg) ![<swivel-chair> 1](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/25a60f0b7e1df4480da0096f4855d3cd.jpg) ![<swivel-chair> 2](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/099ba1cdff4a7d6d76437ec3b9d48743.jpg) ![<swivel-chair> 3](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a95a8487c048359027c5dc1f2f4231cd.jpg) ![<swivel-chair> 4](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e2258228f0c125fc4f0d2b3c27c4b5b5.jpg) ![<swivel-chair> 5](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/7692300d5457b1ad0b9b77bb4370a7b5.jpg) ![<swivel-chair> 6](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3b7fb905cd512af41d664db5b5c9c489.jpg) ![<swivel-chair> 7](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/89803df4905f81d2c1f70a1a7faf68fd.jpg) ![<swivel-chair> 8](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/570e6f83c4b0cd052893aee8e7030521.jpg) ![<swivel-chair> 9](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/465e22dc7bbfd6f42a803e8ab35c0609.jpg) ![<swivel-chair> 10](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a6490c193d2815bd520a2478fcdb543f.jpg) ![<swivel-chair> 11](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/c789ecc814b35df75187611633dbd84a.jpg) ![<swivel-chair> 12](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/11ca822a037aa86f3316564ac212ac1c.jpg) ![<swivel-chair> 13](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/5dea3e2b3148e21a4cb4dfe8dea7af08.jpg) ![<swivel-chair> 14](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/46b89058d8c2342f3c152b50039cb0c9.jpg) ![<swivel-chair> 15](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/749bf590f4dd9f4c3ef1ffd58e7db3e8.jpg) ![<swivel-chair> 16](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6e32521663ac1cd6d0999e4a09dbf5a1.jpg) ![<swivel-chair> 17](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/bd3580a999d8ec073f2e9e7584fb1479.jpg) ![<swivel-chair> 18](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/fa116ff22175ba831f641af9bc1b44c8.jpg) ![<swivel-chair> 19](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/f32510be7c6e3d2d540d53ef0c0b5536.jpg) ![<swivel-chair> 20](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3aab159b96dbcc1d403eeeea81191fb2.jpg) ![<swivel-chair> 21](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6405e9bae158bf99eab384a36468c0cc.jpg) ![<swivel-chair> 22](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e727d064c7c19b510acaacb2637c195e.jpg) ![<swivel-chair> 23](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ebf0269fca456ea8e2e307f6de6222ef.jpg) ![<swivel-chair> 24](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ac4a58c646a756d07608c485bbe7fa45.jpg)

jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.7

jordyvl

2023-07-10T21:59:33Z

161

0

transformers

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

image-classification

2023-07-10T21:19:56Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4918 - Accuracy: 0.85 - Brier Loss: 0.2583 - Nll: 1.0894 - F1 Micro: 0.85 - F1 Macro: 0.8374 - Ece: 0.1917 - Aurc: 0.0470 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.8329 | 0.225 | 0.8761 | 5.2731 | 0.225 | 0.1384 | 0.2607 | 0.6977 | | No log | 2.0 | 14 | 1.4785 | 0.405 | 0.7460 | 3.4067 | 0.405 | 0.2289 | 0.3097 | 0.4085 | | No log | 3.0 | 21 | 1.0406 | 0.6 | 0.5725 | 1.8722 | 0.6 | 0.5345 | 0.3050 | 0.2010 | | No log | 4.0 | 28 | 0.8087 | 0.725 | 0.4192 | 1.6096 | 0.7250 | 0.6767 | 0.2345 | 0.1149 | | No log | 5.0 | 35 | 0.7666 | 0.735 | 0.3731 | 1.6189 | 0.735 | 0.7350 | 0.2377 | 0.1011 | | No log | 6.0 | 42 | 0.6960 | 0.78 | 0.3413 | 1.5230 | 0.78 | 0.7592 | 0.2295 | 0.0868 | | No log | 7.0 | 49 | 0.6490 | 0.805 | 0.3110 | 1.4861 | 0.805 | 0.7864 | 0.2138 | 0.0785 | | No log | 8.0 | 56 | 0.6238 | 0.795 | 0.3069 | 1.2098 | 0.795 | 0.7816 | 0.2065 | 0.0698 | | No log | 9.0 | 63 | 0.5755 | 0.83 | 0.2866 | 1.1943 | 0.83 | 0.8117 | 0.1937 | 0.0694 | | No log | 10.0 | 70 | 0.6360 | 0.77 | 0.3164 | 1.2608 | 0.7700 | 0.7550 | 0.1785 | 0.0677 | | No log | 11.0 | 77 | 0.6548 | 0.785 | 0.3335 | 1.4895 | 0.785 | 0.7707 | 0.2281 | 0.0885 | | No log | 12.0 | 84 | 0.5847 | 0.805 | 0.3002 | 1.4317 | 0.805 | 0.7807 | 0.2264 | 0.0756 | | No log | 13.0 | 91 | 0.5956 | 0.81 | 0.3040 | 1.2590 | 0.81 | 0.7928 | 0.2241 | 0.0556 | | No log | 14.0 | 98 | 0.5692 | 0.81 | 0.3025 | 1.2119 | 0.81 | 0.8043 | 0.2235 | 0.0665 | | No log | 15.0 | 105 | 0.5223 | 0.83 | 0.2762 | 1.1162 | 0.83 | 0.8221 | 0.1798 | 0.0552 | | No log | 16.0 | 112 | 0.4981 | 0.84 | 0.2523 | 1.0864 | 0.8400 | 0.8372 | 0.1868 | 0.0396 | | No log | 17.0 | 119 | 0.5207 | 0.805 | 0.2741 | 1.0416 | 0.805 | 0.7897 | 0.1960 | 0.0551 | | No log | 18.0 | 126 | 0.5165 | 0.84 | 0.2723 | 1.1596 | 0.8400 | 0.8325 | 0.1942 | 0.0506 | | No log | 19.0 | 133 | 0.4979 | 0.845 | 0.2573 | 1.2329 | 0.845 | 0.8297 | 0.1825 | 0.0444 | | No log | 20.0 | 140 | 0.4953 | 0.855 | 0.2565 | 1.1213 | 0.855 | 0.8442 | 0.1844 | 0.0474 | | No log | 21.0 | 147 | 0.5296 | 0.82 | 0.2792 | 1.0000 | 0.82 | 0.8218 | 0.1768 | 0.0523 | | No log | 22.0 | 154 | 0.5027 | 0.835 | 0.2625 | 0.9926 | 0.835 | 0.8238 | 0.2035 | 0.0481 | | No log | 23.0 | 161 | 0.5027 | 0.84 | 0.2642 | 1.0500 | 0.8400 | 0.8299 | 0.1616 | 0.0482 | | No log | 24.0 | 168 | 0.5017 | 0.84 | 0.2616 | 1.0560 | 0.8400 | 0.8314 | 0.1819 | 0.0497 | | No log | 25.0 | 175 | 0.4942 | 0.85 | 0.2594 | 1.1003 | 0.85 | 0.8407 | 0.1793 | 0.0483 | | No log | 26.0 | 182 | 0.4943 | 0.83 | 0.2586 | 1.0436 | 0.83 | 0.8140 | 0.1869 | 0.0518 | | No log | 27.0 | 189 | 0.4950 | 0.835 | 0.2613 | 1.0817 | 0.835 | 0.8224 | 0.2039 | 0.0504 | | No log | 28.0 | 196 | 0.4957 | 0.85 | 0.2599 | 1.1109 | 0.85 | 0.8309 | 0.2058 | 0.0485 | | No log | 29.0 | 203 | 0.4956 | 0.845 | 0.2599 | 1.0914 | 0.845 | 0.8304 | 0.1916 | 0.0492 | | No log | 30.0 | 210 | 0.4893 | 0.84 | 0.2561 | 1.0890 | 0.8400 | 0.8214 | 0.2071 | 0.0482 | | No log | 31.0 | 217 | 0.4920 | 0.835 | 0.2587 | 1.0907 | 0.835 | 0.8270 | 0.2031 | 0.0482 | | No log | 32.0 | 224 | 0.4927 | 0.83 | 0.2601 | 1.0879 | 0.83 | 0.8157 | 0.2093 | 0.0500 | | No log | 33.0 | 231 | 0.4925 | 0.835 | 0.2593 | 1.0886 | 0.835 | 0.8270 | 0.1810 | 0.0484 | | No log | 34.0 | 238 | 0.4909 | 0.845 | 0.2578 | 1.0871 | 0.845 | 0.8304 | 0.1916 | 0.0478 | | No log | 35.0 | 245 | 0.4927 | 0.845 | 0.2591 | 1.0866 | 0.845 | 0.8378 | 0.1943 | 0.0473 | | No log | 36.0 | 252 | 0.4919 | 0.85 | 0.2581 | 1.0891 | 0.85 | 0.8342 | 0.2193 | 0.0475 | | No log | 37.0 | 259 | 0.4908 | 0.845 | 0.2579 | 1.0867 | 0.845 | 0.8346 | 0.2215 | 0.0474 | | No log | 38.0 | 266 | 0.4929 | 0.85 | 0.2590 | 1.0873 | 0.85 | 0.8407 | 0.1884 | 0.0471 | | No log | 39.0 | 273 | 0.4913 | 0.85 | 0.2584 | 1.0861 | 0.85 | 0.8374 | 0.1944 | 0.0474 | | No log | 40.0 | 280 | 0.4933 | 0.835 | 0.2595 | 1.0871 | 0.835 | 0.8248 | 0.1893 | 0.0491 | | No log | 41.0 | 287 | 0.4936 | 0.84 | 0.2599 | 1.0863 | 0.8400 | 0.8276 | 0.1860 | 0.0486 | | No log | 42.0 | 294 | 0.4911 | 0.85 | 0.2580 | 1.0861 | 0.85 | 0.8374 | 0.2186 | 0.0474 | | No log | 43.0 | 301 | 0.4915 | 0.85 | 0.2581 | 1.0860 | 0.85 | 0.8374 | 0.2023 | 0.0475 | | No log | 44.0 | 308 | 0.4921 | 0.85 | 0.2586 | 1.0874 | 0.85 | 0.8374 | 0.2013 | 0.0477 | | No log | 45.0 | 315 | 0.4915 | 0.85 | 0.2583 | 1.0862 | 0.85 | 0.8374 | 0.1941 | 0.0475 | | No log | 46.0 | 322 | 0.4918 | 0.85 | 0.2584 | 1.0878 | 0.85 | 0.8374 | 0.1852 | 0.0473 | | No log | 47.0 | 329 | 0.4916 | 0.85 | 0.2583 | 1.0873 | 0.85 | 0.8374 | 0.2089 | 0.0473 | | No log | 48.0 | 336 | 0.4921 | 0.85 | 0.2586 | 1.0879 | 0.85 | 0.8374 | 0.2026 | 0.0477 | | No log | 49.0 | 343 | 0.4918 | 0.845 | 0.2584 | 1.0884 | 0.845 | 0.8282 | 0.1963 | 0.0478 | | No log | 50.0 | 350 | 0.4922 | 0.85 | 0.2587 | 1.0871 | 0.85 | 0.8374 | 0.2102 | 0.0474 | | No log | 51.0 | 357 | 0.4920 | 0.85 | 0.2585 | 1.0879 | 0.85 | 0.8374 | 0.2095 | 0.0474 | | No log | 52.0 | 364 | 0.4926 | 0.85 | 0.2589 | 1.0878 | 0.85 | 0.8374 | 0.2022 | 0.0477 | | No log | 53.0 | 371 | 0.4920 | 0.85 | 0.2586 | 1.0888 | 0.85 | 0.8374 | 0.2027 | 0.0475 | | No log | 54.0 | 378 | 0.4921 | 0.85 | 0.2586 | 1.0886 | 0.85 | 0.8374 | 0.2020 | 0.0474 | | No log | 55.0 | 385 | 0.4921 | 0.85 | 0.2587 | 1.0890 | 0.85 | 0.8374 | 0.1929 | 0.0471 | | No log | 56.0 | 392 | 0.4925 | 0.85 | 0.2589 | 1.0881 | 0.85 | 0.8374 | 0.1946 | 0.0473 | | No log | 57.0 | 399 | 0.4917 | 0.85 | 0.2583 | 1.0893 | 0.85 | 0.8374 | 0.1932 | 0.0472 | | No log | 58.0 | 406 | 0.4921 | 0.85 | 0.2586 | 1.0877 | 0.85 | 0.8374 | 0.1948 | 0.0476 | | No log | 59.0 | 413 | 0.4917 | 0.85 | 0.2583 | 1.0883 | 0.85 | 0.8374 | 0.1931 | 0.0472 | | No log | 60.0 | 420 | 0.4918 | 0.85 | 0.2583 | 1.0882 | 0.85 | 0.8374 | 0.1945 | 0.0475 | | No log | 61.0 | 427 | 0.4916 | 0.85 | 0.2582 | 1.0883 | 0.85 | 0.8374 | 0.1936 | 0.0472 | | No log | 62.0 | 434 | 0.4920 | 0.85 | 0.2586 | 1.0882 | 0.85 | 0.8374 | 0.1942 | 0.0473 | | No log | 63.0 | 441 | 0.4922 | 0.85 | 0.2587 | 1.0889 | 0.85 | 0.8374 | 0.1935 | 0.0473 | | No log | 64.0 | 448 | 0.4921 | 0.85 | 0.2586 | 1.0885 | 0.85 | 0.8374 | 0.1848 | 0.0473 | | No log | 65.0 | 455 | 0.4916 | 0.85 | 0.2582 | 1.0887 | 0.85 | 0.8374 | 0.1848 | 0.0474 | | No log | 66.0 | 462 | 0.4917 | 0.85 | 0.2583 | 1.0883 | 0.85 | 0.8374 | 0.1849 | 0.0472 | | No log | 67.0 | 469 | 0.4917 | 0.85 | 0.2584 | 1.0887 | 0.85 | 0.8374 | 0.1848 | 0.0472 | | No log | 68.0 | 476 | 0.4920 | 0.85 | 0.2585 | 1.0888 | 0.85 | 0.8374 | 0.2011 | 0.0471 | | No log | 69.0 | 483 | 0.4918 | 0.85 | 0.2584 | 1.0889 | 0.85 | 0.8374 | 0.2007 | 0.0471 | | No log | 70.0 | 490 | 0.4919 | 0.85 | 0.2584 | 1.0886 | 0.85 | 0.8374 | 0.1848 | 0.0474 | | No log | 71.0 | 497 | 0.4920 | 0.85 | 0.2585 | 1.0888 | 0.85 | 0.8374 | 0.1940 | 0.0474 | | 0.1824 | 72.0 | 504 | 0.4919 | 0.85 | 0.2584 | 1.0889 | 0.85 | 0.8374 | 0.2011 | 0.0471 | | 0.1824 | 73.0 | 511 | 0.4917 | 0.85 | 0.2583 | 1.0887 | 0.85 | 0.8374 | 0.1848 | 0.0472 | | 0.1824 | 74.0 | 518 | 0.4920 | 0.85 | 0.2585 | 1.0890 | 0.85 | 0.8374 | 0.1848 | 0.0472 | | 0.1824 | 75.0 | 525 | 0.4920 | 0.85 | 0.2585 | 1.0892 | 0.85 | 0.8374 | 0.1846 | 0.0472 | | 0.1824 | 76.0 | 532 | 0.4918 | 0.85 | 0.2583 | 1.0889 | 0.85 | 0.8374 | 0.1930 | 0.0472 | | 0.1824 | 77.0 | 539 | 0.4917 | 0.85 | 0.2582 | 1.0891 | 0.85 | 0.8374 | 0.2005 | 0.0472 | | 0.1824 | 78.0 | 546 | 0.4919 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1928 | 0.0472 | | 0.1824 | 79.0 | 553 | 0.4920 | 0.85 | 0.2585 | 1.0893 | 0.85 | 0.8374 | 0.1845 | 0.0473 | | 0.1824 | 80.0 | 560 | 0.4919 | 0.85 | 0.2584 | 1.0890 | 0.85 | 0.8374 | 0.1929 | 0.0473 | | 0.1824 | 81.0 | 567 | 0.4920 | 0.85 | 0.2585 | 1.0892 | 0.85 | 0.8374 | 0.1925 | 0.0471 | | 0.1824 | 82.0 | 574 | 0.4920 | 0.85 | 0.2585 | 1.0895 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 83.0 | 581 | 0.4919 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1916 | 0.0471 | | 0.1824 | 84.0 | 588 | 0.4918 | 0.85 | 0.2584 | 1.0890 | 0.85 | 0.8374 | 0.1926 | 0.0471 | | 0.1824 | 85.0 | 595 | 0.4918 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 86.0 | 602 | 0.4918 | 0.85 | 0.2584 | 1.0893 | 0.85 | 0.8374 | 0.1927 | 0.0472 | | 0.1824 | 87.0 | 609 | 0.4918 | 0.85 | 0.2584 | 1.0895 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 88.0 | 616 | 0.4918 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 89.0 | 623 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1917 | 0.0471 | | 0.1824 | 90.0 | 630 | 0.4919 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1998 | 0.0471 | | 0.1824 | 91.0 | 637 | 0.4919 | 0.85 | 0.2584 | 1.0894 | 0.85 | 0.8374 | 0.1916 | 0.0471 | | 0.1824 | 92.0 | 644 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 93.0 | 651 | 0.4918 | 0.85 | 0.2583 | 1.0893 | 0.85 | 0.8374 | 0.1917 | 0.0471 | | 0.1824 | 94.0 | 658 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 95.0 | 665 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 96.0 | 672 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 97.0 | 679 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1916 | 0.0471 | | 0.1824 | 98.0 | 686 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 99.0 | 693 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 100.0 | 700 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

umanlp/babelbert-ft-xlm-r

umanlp

2023-07-10T21:57:04Z

160

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]

feature-extraction

2023-07-07T21:22:09Z

This model is one of the artifacts of the paper [Massively Multilingual Lexical Specialization of Multilingual Transformers](https://aclanthology.org/2023.acl-long.426/). It was obtained by fine-tuning the representations of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the dataset [babelbert-dataset](https://huggingface.co/datasets/umanlp/babelbert-dataset).

voyzan/unit1-bonus1-Huggy-A01

voyzan

2023-07-10T21:19:37Z

0

ml-agents

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

reinforcement-learning

2023-07-10T21:19:36Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: voyzan/unit1-bonus1-Huggy-A01 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE

jordyvl

2023-07-10T21:13:05Z

164

0

transformers

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

image-classification

2023-07-10T20:08:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE results: [] ---  # vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7723 - Accuracy: 0.6025 - Brier Loss: 0.5295 - Nll: 3.6748 - F1 Micro: 0.6025 - F1 Macro: 0.6055 - Ece: 0.1688 - Aurc: 0.1708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 4.7870 | 0.065 | 0.9655 | 17.0930 | 0.065 | 0.0550 | 0.1747 | 0.9357 | | No log | 2.0 | 50 | 3.9498 | 0.205 | 0.8858 | 9.5780 | 0.205 | 0.1863 | 0.1692 | 0.6618 | | No log | 3.0 | 75 | 3.3698 | 0.3675 | 0.7672 | 6.4908 | 0.3675 | 0.3392 | 0.1676 | 0.4195 | | No log | 4.0 | 100 | 2.9935 | 0.4075 | 0.6958 | 5.5595 | 0.4075 | 0.3820 | 0.1828 | 0.3327 | | No log | 5.0 | 125 | 2.8351 | 0.455 | 0.6591 | 4.8619 | 0.455 | 0.4351 | 0.1561 | 0.2833 | | No log | 6.0 | 150 | 2.8196 | 0.4725 | 0.6595 | 4.7785 | 0.4725 | 0.4367 | 0.1808 | 0.2790 | | No log | 7.0 | 175 | 2.6352 | 0.5075 | 0.6234 | 4.9881 | 0.5075 | 0.4886 | 0.1563 | 0.2493 | | No log | 8.0 | 200 | 2.5325 | 0.525 | 0.6162 | 4.3297 | 0.525 | 0.5026 | 0.1724 | 0.2365 | | No log | 9.0 | 225 | 2.5459 | 0.53 | 0.6099 | 5.1608 | 0.53 | 0.5148 | 0.1944 | 0.2350 | | No log | 10.0 | 250 | 2.5573 | 0.5325 | 0.6161 | 5.4495 | 0.5325 | 0.5212 | 0.2052 | 0.2397 | | No log | 11.0 | 275 | 2.3199 | 0.5675 | 0.5828 | 4.1247 | 0.5675 | 0.5626 | 0.1849 | 0.2071 | | No log | 12.0 | 300 | 2.2917 | 0.565 | 0.5758 | 4.1738 | 0.565 | 0.5694 | 0.1992 | 0.2023 | | No log | 13.0 | 325 | 2.2744 | 0.555 | 0.5974 | 4.2323 | 0.555 | 0.5544 | 0.1982 | 0.2203 | | No log | 14.0 | 350 | 2.1638 | 0.5625 | 0.5807 | 4.2049 | 0.5625 | 0.5629 | 0.1868 | 0.2049 | | No log | 15.0 | 375 | 2.1934 | 0.5575 | 0.5903 | 4.3813 | 0.5575 | 0.5614 | 0.1868 | 0.2022 | | No log | 16.0 | 400 | 2.1092 | 0.5625 | 0.5702 | 3.6094 | 0.5625 | 0.5700 | 0.1846 | 0.2011 | | No log | 17.0 | 425 | 2.0379 | 0.5875 | 0.5642 | 4.4351 | 0.5875 | 0.5822 | 0.2036 | 0.1959 | | No log | 18.0 | 450 | 2.0303 | 0.5825 | 0.5558 | 3.6847 | 0.5825 | 0.5820 | 0.1684 | 0.1881 | | No log | 19.0 | 475 | 2.0506 | 0.57 | 0.5749 | 4.0014 | 0.57 | 0.5708 | 0.1725 | 0.2027 | | 1.5026 | 20.0 | 500 | 1.9932 | 0.5875 | 0.5524 | 3.8003 | 0.5875 | 0.5914 | 0.1843 | 0.1831 | | 1.5026 | 21.0 | 525 | 2.0131 | 0.565 | 0.5643 | 4.0681 | 0.565 | 0.5635 | 0.1776 | 0.1957 | | 1.5026 | 22.0 | 550 | 2.0162 | 0.5725 | 0.5712 | 3.7068 | 0.5725 | 0.5766 | 0.1934 | 0.1955 | | 1.5026 | 23.0 | 575 | 1.9093 | 0.605 | 0.5381 | 3.7930 | 0.605 | 0.6032 | 0.1539 | 0.1749 | | 1.5026 | 24.0 | 600 | 1.9607 | 0.575 | 0.5561 | 4.5740 | 0.575 | 0.5789 | 0.1782 | 0.1902 | | 1.5026 | 25.0 | 625 | 1.8971 | 0.5825 | 0.5408 | 3.7290 | 0.5825 | 0.5754 | 0.1836 | 0.1751 | | 1.5026 | 26.0 | 650 | 1.9217 | 0.5775 | 0.5537 | 3.8085 | 0.5775 | 0.5844 | 0.1725 | 0.1843 | | 1.5026 | 27.0 | 675 | 1.9493 | 0.585 | 0.5606 | 3.6743 | 0.585 | 0.5953 | 0.1755 | 0.1882 | | 1.5026 | 28.0 | 700 | 1.8884 | 0.585 | 0.5437 | 3.7865 | 0.585 | 0.5828 | 0.1801 | 0.1822 | | 1.5026 | 29.0 | 725 | 1.9242 | 0.585 | 0.5479 | 3.9607 | 0.585 | 0.5856 | 0.1619 | 0.1817 | | 1.5026 | 30.0 | 750 | 1.8767 | 0.5975 | 0.5470 | 3.7995 | 0.5975 | 0.5966 | 0.1599 | 0.1790 | | 1.5026 | 31.0 | 775 | 1.8723 | 0.5925 | 0.5337 | 3.8962 | 0.5925 | 0.5972 | 0.1678 | 0.1729 | | 1.5026 | 32.0 | 800 | 1.9093 | 0.585 | 0.5545 | 3.8776 | 0.585 | 0.5830 | 0.1902 | 0.1841 | | 1.5026 | 33.0 | 825 | 1.8667 | 0.595 | 0.5363 | 3.8926 | 0.595 | 0.5917 | 0.1772 | 0.1745 | | 1.5026 | 34.0 | 850 | 1.8403 | 0.59 | 0.5521 | 3.8560 | 0.59 | 0.5953 | 0.1711 | 0.1800 | | 1.5026 | 35.0 | 875 | 1.8464 | 0.5925 | 0.5380 | 4.0376 | 0.5925 | 0.5970 | 0.1719 | 0.1756 | | 1.5026 | 36.0 | 900 | 1.8441 | 0.5975 | 0.5411 | 3.7193 | 0.5975 | 0.6008 | 0.1569 | 0.1753 | | 1.5026 | 37.0 | 925 | 1.8599 | 0.5875 | 0.5402 | 3.9139 | 0.5875 | 0.5908 | 0.1779 | 0.1789 | | 1.5026 | 38.0 | 950 | 1.8559 | 0.6 | 0.5458 | 3.8970 | 0.6 | 0.5991 | 0.1583 | 0.1804 | | 1.5026 | 39.0 | 975 | 1.8285 | 0.61 | 0.5370 | 3.6292 | 0.61 | 0.6155 | 0.1623 | 0.1722 | | 0.0745 | 40.0 | 1000 | 1.8309 | 0.5975 | 0.5432 | 3.6865 | 0.5975 | 0.6017 | 0.1663 | 0.1821 | | 0.0745 | 41.0 | 1025 | 1.8237 | 0.59 | 0.5348 | 3.6213 | 0.59 | 0.5921 | 0.1695 | 0.1738 | | 0.0745 | 42.0 | 1050 | 1.8421 | 0.605 | 0.5360 | 3.8592 | 0.605 | 0.6048 | 0.1601 | 0.1743 | | 0.0745 | 43.0 | 1075 | 1.8158 | 0.5975 | 0.5300 | 3.4537 | 0.5975 | 0.5953 | 0.1696 | 0.1707 | | 0.0745 | 44.0 | 1100 | 1.8238 | 0.5875 | 0.5358 | 3.7706 | 0.5875 | 0.5923 | 0.1797 | 0.1754 | | 0.0745 | 45.0 | 1125 | 1.8214 | 0.595 | 0.5463 | 3.4742 | 0.595 | 0.5981 | 0.1800 | 0.1770 | | 0.0745 | 46.0 | 1150 | 1.8162 | 0.5925 | 0.5317 | 3.9260 | 0.5925 | 0.5950 | 0.1646 | 0.1733 | | 0.0745 | 47.0 | 1175 | 1.8050 | 0.5975 | 0.5392 | 3.8322 | 0.5975 | 0.5979 | 0.1794 | 0.1763 | | 0.0745 | 48.0 | 1200 | 1.8214 | 0.5975 | 0.5347 | 3.7965 | 0.5975 | 0.6009 | 0.1555 | 0.1746 | | 0.0745 | 49.0 | 1225 | 1.7813 | 0.6 | 0.5294 | 3.8398 | 0.6 | 0.6005 | 0.1674 | 0.1688 | | 0.0745 | 50.0 | 1250 | 1.8179 | 0.6075 | 0.5336 | 3.4690 | 0.6075 | 0.6112 | 0.1743 | 0.1748 | | 0.0745 | 51.0 | 1275 | 1.7953 | 0.595 | 0.5380 | 3.7781 | 0.595 | 0.5990 | 0.1380 | 0.1727 | | 0.0745 | 52.0 | 1300 | 1.7897 | 0.6 | 0.5323 | 3.7412 | 0.6 | 0.6013 | 0.1603 | 0.1707 | | 0.0745 | 53.0 | 1325 | 1.8072 | 0.59 | 0.5428 | 3.5993 | 0.59 | 0.5947 | 0.1571 | 0.1773 | | 0.0745 | 54.0 | 1350 | 1.7834 | 0.605 | 0.5219 | 3.7600 | 0.605 | 0.6049 | 0.1563 | 0.1671 | | 0.0745 | 55.0 | 1375 | 1.7920 | 0.595 | 0.5361 | 3.5986 | 0.595 | 0.5978 | 0.1512 | 0.1717 | | 0.0745 | 56.0 | 1400 | 1.8074 | 0.5925 | 0.5387 | 3.5383 | 0.5925 | 0.5962 | 0.1669 | 0.1741 | | 0.0745 | 57.0 | 1425 | 1.7893 | 0.605 | 0.5346 | 3.6929 | 0.605 | 0.6039 | 0.1641 | 0.1681 | | 0.0745 | 58.0 | 1450 | 1.7787 | 0.6 | 0.5317 | 3.7652 | 0.6 | 0.6004 | 0.1850 | 0.1726 | | 0.0745 | 59.0 | 1475 | 1.7888 | 0.595 | 0.5323 | 3.4558 | 0.595 | 0.5975 | 0.1797 | 0.1732 | | 0.0231 | 60.0 | 1500 | 1.8064 | 0.58 | 0.5332 | 3.7773 | 0.58 | 0.5839 | 0.1819 | 0.1762 | | 0.0231 | 61.0 | 1525 | 1.7795 | 0.6075 | 0.5298 | 3.7998 | 0.6075 | 0.6086 | 0.1678 | 0.1704 | | 0.0231 | 62.0 | 1550 | 1.7826 | 0.595 | 0.5318 | 3.6741 | 0.595 | 0.5916 | 0.1550 | 0.1715 | | 0.0231 | 63.0 | 1575 | 1.7704 | 0.5925 | 0.5325 | 3.5942 | 0.5925 | 0.5941 | 0.1619 | 0.1712 | | 0.0231 | 64.0 | 1600 | 1.7901 | 0.6025 | 0.5289 | 3.4459 | 0.6025 | 0.6054 | 0.2022 | 0.1712 | | 0.0231 | 65.0 | 1625 | 1.7944 | 0.59 | 0.5381 | 3.7591 | 0.59 | 0.5910 | 0.1599 | 0.1756 | | 0.0231 | 66.0 | 1650 | 1.7721 | 0.605 | 0.5256 | 3.5227 | 0.605 | 0.6045 | 0.1525 | 0.1677 | | 0.0231 | 67.0 | 1675 | 1.7779 | 0.5975 | 0.5306 | 3.6792 | 0.5975 | 0.5994 | 0.1667 | 0.1714 | | 0.0231 | 68.0 | 1700 | 1.7724 | 0.6 | 0.5250 | 3.7552 | 0.6 | 0.6022 | 0.1818 | 0.1683 | | 0.0231 | 69.0 | 1725 | 1.7765 | 0.6025 | 0.5283 | 3.4264 | 0.6025 | 0.6019 | 0.1671 | 0.1700 | | 0.0231 | 70.0 | 1750 | 1.7784 | 0.6 | 0.5276 | 3.6887 | 0.6 | 0.6053 | 0.1715 | 0.1703 | | 0.0231 | 71.0 | 1775 | 1.7659 | 0.6 | 0.5282 | 3.6051 | 0.6 | 0.6006 | 0.1722 | 0.1691 | | 0.0231 | 72.0 | 1800 | 1.7882 | 0.5975 | 0.5329 | 3.5950 | 0.5975 | 0.6016 | 0.1981 | 0.1716 | | 0.0231 | 73.0 | 1825 | 1.7678 | 0.6 | 0.5287 | 3.6691 | 0.6 | 0.6032 | 0.1733 | 0.1696 | | 0.0231 | 74.0 | 1850 | 1.7716 | 0.6 | 0.5286 | 3.7576 | 0.6 | 0.6013 | 0.1734 | 0.1692 | | 0.0231 | 75.0 | 1875 | 1.7704 | 0.6 | 0.5299 | 3.5917 | 0.6 | 0.6016 | 0.1645 | 0.1709 | | 0.0231 | 76.0 | 1900 | 1.7729 | 0.6 | 0.5298 | 3.6758 | 0.6 | 0.6024 | 0.1766 | 0.1710 | | 0.0231 | 77.0 | 1925 | 1.7749 | 0.6 | 0.5308 | 3.6022 | 0.6 | 0.6030 | 0.1604 | 0.1717 | | 0.0231 | 78.0 | 1950 | 1.7720 | 0.6 | 0.5294 | 3.6759 | 0.6 | 0.6017 | 0.1786 | 0.1708 | | 0.0231 | 79.0 | 1975 | 1.7734 | 0.6025 | 0.5288 | 3.6765 | 0.6025 | 0.6048 | 0.1673 | 0.1698 | | 0.0059 | 80.0 | 2000 | 1.7709 | 0.6 | 0.5286 | 3.6755 | 0.6 | 0.6020 | 0.1749 | 0.1704 | | 0.0059 | 81.0 | 2025 | 1.7730 | 0.6 | 0.5295 | 3.6760 | 0.6 | 0.6020 | 0.1677 | 0.1708 | | 0.0059 | 82.0 | 2050 | 1.7723 | 0.6025 | 0.5295 | 3.6756 | 0.6025 | 0.6055 | 0.1626 | 0.1708 | | 0.0059 | 83.0 | 2075 | 1.7721 | 0.6025 | 0.5295 | 3.6741 | 0.6025 | 0.6055 | 0.1709 | 0.1708 | | 0.0059 | 84.0 | 2100 | 1.7725 | 0.6025 | 0.5297 | 3.6747 | 0.6025 | 0.6048 | 0.1627 | 0.1709 | | 0.0059 | 85.0 | 2125 | 1.7724 | 0.6025 | 0.5295 | 3.6751 | 0.6025 | 0.6055 | 0.1639 | 0.1707 | | 0.0059 | 86.0 | 2150 | 1.7724 | 0.6025 | 0.5296 | 3.6751 | 0.6025 | 0.6055 | 0.1630 | 0.1708 | | 0.0059 | 87.0 | 2175 | 1.7724 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1638 | 0.1707 | | 0.0059 | 88.0 | 2200 | 1.7722 | 0.6025 | 0.5295 | 3.6752 | 0.6025 | 0.6055 | 0.1645 | 0.1708 | | 0.0059 | 89.0 | 2225 | 1.7723 | 0.6025 | 0.5295 | 3.6747 | 0.6025 | 0.6055 | 0.1639 | 0.1708 | | 0.0059 | 90.0 | 2250 | 1.7723 | 0.6025 | 0.5294 | 3.6750 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 91.0 | 2275 | 1.7723 | 0.6025 | 0.5294 | 3.6750 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 92.0 | 2300 | 1.7723 | 0.6025 | 0.5295 | 3.6747 | 0.6025 | 0.6055 | 0.1639 | 0.1708 | | 0.0059 | 93.0 | 2325 | 1.7723 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1637 | 0.1707 | | 0.0059 | 94.0 | 2350 | 1.7722 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | | 0.0059 | 95.0 | 2375 | 1.7723 | 0.6025 | 0.5295 | 3.6748 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 96.0 | 2400 | 1.7723 | 0.6025 | 0.5294 | 3.6748 | 0.6025 | 0.6055 | 0.1643 | 0.1707 | | 0.0059 | 97.0 | 2425 | 1.7723 | 0.6025 | 0.5295 | 3.6748 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | | 0.0059 | 98.0 | 2450 | 1.7723 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 99.0 | 2475 | 1.7723 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | | 0.0 | 100.0 | 2500 | 1.7723 | 0.6025 | 0.5295 | 3.6748 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO

skrl

2023-07-10T21:06:55Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T19:47:47Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -8.89 +/- 10.3 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltScrew type: IsaacGymEnvs-FactoryTaskNutBoltScrew ---  # IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** FactoryTaskNutBoltScrew - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 128 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 32 # 128 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

skrl/IsaacGymEnvs-FactoryTaskNutBoltPick-PPO

skrl

2023-07-10T20:49:13Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T19:46:39Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -13.83 +/- 0.26 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltPick type: IsaacGymEnvs-FactoryTaskNutBoltPick ---  # IsaacGymEnvs-FactoryTaskNutBoltPick-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** FactoryTaskNutBoltPick - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPick-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPick-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 120 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 30 # 120 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

vk21/ppo-SnowballTarget-unit5

vk21

2023-07-10T20:34:28Z

10

0

ml-agents

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

reinforcement-learning

2023-07-10T20:34:22Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vk21/ppo-SnowballTarget-unit5 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

DarkAirforce/dqn-SpaceInvadersNoFrameskip-v4

DarkAirforce

2023-07-10T20:33:23Z

0

stable-baselines3

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

reinforcement-learning

2023-07-07T19:24:04Z

--- 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: 534.00 +/- 175.24 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 DarkAirforce -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 DarkAirforce -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 DarkAirforce ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

grace-pro/afriberta-large-finetuned-hausa

grace-pro

2023-07-10T20:28:21Z

127

0

transformers

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

token-classification

2023-07-10T19:28:18Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-large-finetuned-hausa results: [] ---  # afriberta-large-finetuned-hausa This model is a fine-tuned version of [castorini/afriberta_large](https://huggingface.co/castorini/afriberta_large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Precision: 0.7114 - Recall: 0.5238 - F1: 0.6034 - Accuracy: 0.9652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1373 | 1.0 | 2624 | 0.1267 | 0.6804 | 0.4519 | 0.5431 | 0.9612 | | 0.1102 | 2.0 | 5248 | 0.1186 | 0.6927 | 0.5020 | 0.5821 | 0.9635 | | 0.0849 | 3.0 | 7872 | 0.1269 | 0.7114 | 0.5036 | 0.5897 | 0.9645 | | 0.0683 | 4.0 | 10496 | 0.1341 | 0.7159 | 0.5078 | 0.5941 | 0.9650 | | 0.0567 | 5.0 | 13120 | 0.1448 | 0.7114 | 0.5238 | 0.6034 | 0.9652 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO

skrl

2023-07-10T20:15:49Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T19:47:18Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -38.54 +/- 17.49 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltPlace type: IsaacGymEnvs-FactoryTaskNutBoltPlace ---  # IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** FactoryTaskNutBoltPlace - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 120 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 30 # 120 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

MaitreHibou/Reinforce-Pixelcopter-PLE-v0

MaitreHibou

2023-07-10T20:12:10Z

0

null

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

reinforcement-learning

2023-07-10T19:26:33Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.00 +/- 20.11 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

blzncz/segformer-finetuned-4ss1st3r_s3gs3m-10k-steps

blzncz

2023-07-10T20:04:14Z

188

0

transformers

[ "transformers", "pytorch", "segformer", "image-segmentation", "vision", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2023-07-10T10:49:12Z

--- license: other tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-finetuned-4ss1st3r_s3gs3m-10k-steps results: [] ---  # segformer-finetuned-4ss1st3r_s3gs3m-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m dataset. It achieves the following results on the evaluation set: - Loss: 0.3966 - Mean Iou: 0.5967 - Mean Accuracy: 0.8460 - Overall Accuracy: 0.9344 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.9510 - Accuracy Fallo malla: 0.8524 - Accuracy Fallo adhesivo: 0.9362 - Accuracy Fallo burbuja: 0.6444 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.9239 - Iou Fallo malla: 0.7125 - Iou Fallo adhesivo: 0.8335 - Iou Fallo burbuja: 0.5139 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:| | 0.4796 | 1.0 | 133 | 0.4190 | 0.4518 | 0.6689 | 0.9049 | nan | 0.9277 | 0.8091 | 0.9381 | 0.0008 | 0.0 | 0.8866 | 0.6536 | 0.7179 | 0.0008 | | 0.2665 | 2.0 | 266 | 0.3667 | 0.5096 | 0.7283 | 0.9001 | nan | 0.9111 | 0.8964 | 0.8731 | 0.2324 | 0.0 | 0.8802 | 0.6013 | 0.8467 | 0.2197 | | 0.2158 | 3.0 | 399 | 0.3210 | 0.5505 | 0.7807 | 0.9142 | nan | 0.9250 | 0.8685 | 0.9414 | 0.3878 | 0.0 | 0.8952 | 0.6239 | 0.8901 | 0.3432 | | 0.1737 | 4.0 | 532 | 0.3572 | 0.5370 | 0.7851 | 0.8905 | nan | 0.8905 | 0.9102 | 0.9121 | 0.4277 | 0.0 | 0.8671 | 0.5637 | 0.8777 | 0.3764 | | 0.1602 | 5.0 | 665 | 0.6273 | 0.4086 | 0.7632 | 0.7743 | nan | 0.7333 | 0.9343 | 0.9685 | 0.4168 | 0.0 | 0.7198 | 0.4460 | 0.5324 | 0.3449 | | 0.1707 | 6.0 | 798 | 0.3534 | 0.5442 | 0.7953 | 0.9025 | nan | 0.9056 | 0.9031 | 0.9234 | 0.4492 | 0.0 | 0.8812 | 0.5985 | 0.8629 | 0.3783 | | 0.1376 | 7.0 | 931 | 0.3266 | 0.5513 | 0.7634 | 0.9262 | nan | 0.9434 | 0.8621 | 0.9288 | 0.3195 | 0.0 | 0.9109 | 0.6623 | 0.8866 | 0.2968 | | 0.1346 | 8.0 | 1064 | 0.4976 | 0.4916 | 0.7900 | 0.8396 | nan | 0.8190 | 0.9133 | 0.9713 | 0.4565 | 0.0 | 0.8041 | 0.4662 | 0.7906 | 0.3970 | | 0.1319 | 9.0 | 1197 | 0.3650 | 0.5652 | 0.8404 | 0.9043 | nan | 0.9053 | 0.8856 | 0.9593 | 0.6113 | 0.0 | 0.8829 | 0.5992 | 0.8734 | 0.4706 | | 0.1229 | 10.0 | 1330 | 0.3201 | 0.5666 | 0.7963 | 0.9299 | nan | 0.9435 | 0.8764 | 0.9389 | 0.4265 | 0.0 | 0.9171 | 0.6896 | 0.8499 | 0.3763 | | 0.1142 | 11.0 | 1463 | 0.3824 | 0.5576 | 0.8204 | 0.9020 | nan | 0.8988 | 0.9231 | 0.9456 | 0.5142 | 0.0 | 0.8795 | 0.6001 | 0.8711 | 0.4374 | | 0.0983 | 12.0 | 1596 | 0.3133 | 0.5812 | 0.8297 | 0.9293 | nan | 0.9354 | 0.9046 | 0.9558 | 0.5229 | 0.0 | 0.9136 | 0.6969 | 0.8618 | 0.4335 | | 0.1058 | 13.0 | 1729 | 0.2965 | 0.5860 | 0.8250 | 0.9364 | nan | 0.9528 | 0.8496 | 0.9598 | 0.5378 | 0.0 | 0.9253 | 0.7162 | 0.8502 | 0.4383 | | 0.1052 | 14.0 | 1862 | 0.2839 | 0.6064 | 0.8275 | 0.9460 | nan | 0.9674 | 0.8517 | 0.9290 | 0.5621 | 0.0 | 0.9355 | 0.7492 | 0.8930 | 0.4540 | | 0.0911 | 15.0 | 1995 | 0.3245 | 0.5853 | 0.8116 | 0.9368 | nan | 0.9565 | 0.8504 | 0.9298 | 0.5099 | 0.0 | 0.9243 | 0.7171 | 0.8534 | 0.4318 | | 0.0889 | 16.0 | 2128 | 0.3094 | 0.5969 | 0.8225 | 0.9422 | nan | 0.9615 | 0.8559 | 0.9376 | 0.5351 | 0.0 | 0.9313 | 0.7353 | 0.8726 | 0.4451 | | 0.0827 | 17.0 | 2261 | 0.4776 | 0.5187 | 0.8195 | 0.8547 | nan | 0.8390 | 0.9163 | 0.9440 | 0.5786 | 0.0 | 0.8207 | 0.4920 | 0.8216 | 0.4590 | | 0.0939 | 18.0 | 2394 | 0.3923 | 0.5364 | 0.8375 | 0.8948 | nan | 0.8950 | 0.8831 | 0.9437 | 0.6282 | 0.0 | 0.8746 | 0.6268 | 0.7090 | 0.4717 | | 0.0799 | 19.0 | 2527 | 0.3560 | 0.5776 | 0.8252 | 0.9254 | nan | 0.9337 | 0.8933 | 0.9409 | 0.5331 | 0.0 | 0.9096 | 0.6846 | 0.8519 | 0.4422 | | 0.075 | 20.0 | 2660 | 0.3803 | 0.5796 | 0.8338 | 0.9194 | nan | 0.9249 | 0.9078 | 0.9238 | 0.5788 | 0.0 | 0.9032 | 0.6459 | 0.8821 | 0.4670 | | 0.0844 | 21.0 | 2793 | 0.2885 | 0.6170 | 0.8334 | 0.9507 | nan | 0.9757 | 0.8296 | 0.9390 | 0.5892 | 0.0 | 0.9412 | 0.7654 | 0.8933 | 0.4852 | | 0.0746 | 22.0 | 2926 | 0.3222 | 0.5831 | 0.8160 | 0.9331 | nan | 0.9481 | 0.8685 | 0.9370 | 0.5105 | 0.0 | 0.9193 | 0.7032 | 0.8716 | 0.4215 | | 0.072 | 23.0 | 3059 | 0.3481 | 0.5878 | 0.8336 | 0.9266 | nan | 0.9357 | 0.8952 | 0.9271 | 0.5764 | 0.0 | 0.9123 | 0.6824 | 0.8720 | 0.4725 | | 0.0735 | 24.0 | 3192 | 0.3196 | 0.5974 | 0.8403 | 0.9353 | nan | 0.9496 | 0.8666 | 0.9430 | 0.6018 | 0.0 | 0.9225 | 0.7165 | 0.8649 | 0.4832 | | 0.0674 | 25.0 | 3325 | 0.3407 | 0.5927 | 0.8435 | 0.9282 | nan | 0.9401 | 0.8786 | 0.9246 | 0.6304 | 0.0 | 0.9141 | 0.6844 | 0.8696 | 0.4953 | | 0.0712 | 26.0 | 3458 | 0.3356 | 0.5906 | 0.8420 | 0.9301 | nan | 0.9405 | 0.8895 | 0.9299 | 0.6080 | 0.0 | 0.9160 | 0.6905 | 0.8743 | 0.4722 | | 0.072 | 27.0 | 3591 | 0.3491 | 0.5833 | 0.8372 | 0.9286 | nan | 0.9415 | 0.8636 | 0.9425 | 0.6012 | 0.0 | 0.9161 | 0.6966 | 0.8246 | 0.4790 | | 0.0641 | 28.0 | 3724 | 0.3130 | 0.6087 | 0.8422 | 0.9473 | nan | 0.9697 | 0.8357 | 0.9427 | 0.6208 | 0.0 | 0.9386 | 0.7613 | 0.8599 | 0.4837 | | 0.0597 | 29.0 | 3857 | 0.3828 | 0.5666 | 0.8394 | 0.9107 | nan | 0.9141 | 0.8934 | 0.9411 | 0.6092 | 0.0 | 0.8924 | 0.6327 | 0.8343 | 0.4735 | | 0.0648 | 30.0 | 3990 | 0.3435 | 0.6001 | 0.8372 | 0.9403 | nan | 0.9569 | 0.8708 | 0.9276 | 0.5935 | 0.0 | 0.9292 | 0.7312 | 0.8779 | 0.4623 | | 0.0618 | 31.0 | 4123 | 0.3531 | 0.5963 | 0.8521 | 0.9303 | nan | 0.9450 | 0.8621 | 0.9240 | 0.6773 | 0.0 | 0.9179 | 0.6842 | 0.8730 | 0.5063 | | 0.0556 | 32.0 | 4256 | 0.3307 | 0.6037 | 0.8417 | 0.9401 | nan | 0.9576 | 0.8637 | 0.9271 | 0.6183 | 0.0 | 0.9298 | 0.7274 | 0.8637 | 0.4974 | | 0.0616 | 33.0 | 4389 | 0.3510 | 0.5911 | 0.8347 | 0.9298 | nan | 0.9424 | 0.8714 | 0.9388 | 0.5863 | 0.0 | 0.9158 | 0.6914 | 0.8745 | 0.4740 | | 0.0603 | 34.0 | 4522 | 0.3467 | 0.6022 | 0.8544 | 0.9334 | nan | 0.9487 | 0.8610 | 0.9274 | 0.6807 | 0.0 | 0.9211 | 0.7029 | 0.8738 | 0.5130 | | 0.0587 | 35.0 | 4655 | 0.3574 | 0.6017 | 0.8407 | 0.9379 | nan | 0.9555 | 0.8541 | 0.9346 | 0.6187 | 0.0 | 0.9269 | 0.7228 | 0.8627 | 0.4962 | | 0.0557 | 36.0 | 4788 | 0.3871 | 0.5720 | 0.8334 | 0.9178 | nan | 0.9317 | 0.8416 | 0.9374 | 0.6228 | 0.0 | 0.9051 | 0.6479 | 0.8160 | 0.4911 | | 0.0567 | 37.0 | 4921 | 0.4425 | 0.5656 | 0.8282 | 0.9070 | nan | 0.9114 | 0.8922 | 0.9244 | 0.5848 | 0.0 | 0.8889 | 0.6100 | 0.8575 | 0.4718 | | 0.0537 | 38.0 | 5054 | 0.3512 | 0.5946 | 0.8392 | 0.9317 | nan | 0.9463 | 0.8649 | 0.9314 | 0.6142 | 0.0 | 0.9187 | 0.6984 | 0.8637 | 0.4921 | | 0.0559 | 39.0 | 5187 | 0.3676 | 0.5931 | 0.8437 | 0.9273 | nan | 0.9381 | 0.8798 | 0.9323 | 0.6247 | 0.0 | 0.9129 | 0.6779 | 0.8786 | 0.4959 | | 0.0502 | 40.0 | 5320 | 0.4149 | 0.5518 | 0.8381 | 0.8984 | nan | 0.9011 | 0.8773 | 0.9368 | 0.6370 | 0.0 | 0.8793 | 0.6069 | 0.7741 | 0.4989 | | 0.0559 | 41.0 | 5453 | 0.4042 | 0.5694 | 0.8342 | 0.9130 | nan | 0.9206 | 0.8721 | 0.9400 | 0.6041 | 0.0 | 0.8971 | 0.6319 | 0.8286 | 0.4896 | | 0.0523 | 42.0 | 5586 | 0.3669 | 0.5903 | 0.8462 | 0.9286 | nan | 0.9414 | 0.8676 | 0.9337 | 0.6421 | 0.0 | 0.9162 | 0.6883 | 0.8370 | 0.5102 | | 0.0525 | 43.0 | 5719 | 0.4140 | 0.5704 | 0.8531 | 0.9081 | nan | 0.9110 | 0.8867 | 0.9417 | 0.6729 | 0.0 | 0.8898 | 0.6220 | 0.8366 | 0.5035 | | 0.0508 | 44.0 | 5852 | 0.3965 | 0.5714 | 0.8396 | 0.9141 | nan | 0.9227 | 0.8800 | 0.9147 | 0.6409 | 0.0 | 0.8989 | 0.6513 | 0.8007 | 0.5060 | | 0.0507 | 45.0 | 5985 | 0.3793 | 0.5817 | 0.8392 | 0.9196 | nan | 0.9272 | 0.8932 | 0.9214 | 0.6148 | 0.0 | 0.9042 | 0.6627 | 0.8407 | 0.5011 | | 0.0494 | 46.0 | 6118 | 0.3500 | 0.6020 | 0.8426 | 0.9363 | nan | 0.9524 | 0.8619 | 0.9322 | 0.6240 | 0.0 | 0.9247 | 0.7142 | 0.8653 | 0.5058 | | 0.0462 | 47.0 | 6251 | 0.3524 | 0.6031 | 0.8435 | 0.9388 | nan | 0.9545 | 0.8668 | 0.9364 | 0.6163 | 0.0 | 0.9274 | 0.7269 | 0.8703 | 0.4909 | | 0.0486 | 48.0 | 6384 | 0.3876 | 0.5902 | 0.8397 | 0.9308 | nan | 0.9479 | 0.8557 | 0.9161 | 0.6392 | 0.0 | 0.9203 | 0.6928 | 0.8334 | 0.5046 | | 0.0461 | 49.0 | 6517 | 0.3674 | 0.5933 | 0.8409 | 0.9326 | nan | 0.9482 | 0.8622 | 0.9258 | 0.6274 | 0.0 | 0.9214 | 0.7053 | 0.8367 | 0.5030 | | 0.0497 | 50.0 | 6650 | 0.4018 | 0.5838 | 0.8374 | 0.9246 | nan | 0.9390 | 0.8519 | 0.9341 | 0.6244 | 0.0 | 0.9102 | 0.6733 | 0.8361 | 0.4992 | | 0.0491 | 51.0 | 6783 | 0.4036 | 0.5824 | 0.8513 | 0.9198 | nan | 0.9272 | 0.8805 | 0.9403 | 0.6573 | 0.0 | 0.9037 | 0.6712 | 0.8169 | 0.5203 | | 0.046 | 52.0 | 6916 | 0.3913 | 0.5820 | 0.8395 | 0.9243 | nan | 0.9347 | 0.8771 | 0.9336 | 0.6126 | 0.0 | 0.9105 | 0.6792 | 0.8244 | 0.4960 | | 0.0488 | 53.0 | 7049 | 0.3441 | 0.6010 | 0.8504 | 0.9362 | nan | 0.9523 | 0.8521 | 0.9457 | 0.6517 | 0.0 | 0.9250 | 0.7225 | 0.8496 | 0.5081 | | 0.0458 | 54.0 | 7182 | 0.3784 | 0.5977 | 0.8382 | 0.9378 | nan | 0.9603 | 0.8212 | 0.9375 | 0.6337 | 0.0 | 0.9286 | 0.7157 | 0.8387 | 0.5053 | | 0.0449 | 55.0 | 7315 | 0.3506 | 0.6068 | 0.8493 | 0.9404 | nan | 0.9579 | 0.8554 | 0.9385 | 0.6456 | 0.0 | 0.9300 | 0.7357 | 0.8549 | 0.5132 | | 0.0482 | 56.0 | 7448 | 0.4005 | 0.5819 | 0.8414 | 0.9249 | nan | 0.9374 | 0.8642 | 0.9337 | 0.6303 | 0.0 | 0.9119 | 0.6831 | 0.8139 | 0.5006 | | 0.0434 | 57.0 | 7581 | 0.3749 | 0.5914 | 0.8465 | 0.9294 | nan | 0.9423 | 0.8675 | 0.9339 | 0.6421 | 0.0 | 0.9171 | 0.6999 | 0.8265 | 0.5134 | | 0.0435 | 58.0 | 7714 | 0.4195 | 0.5722 | 0.8400 | 0.9172 | nan | 0.9274 | 0.8700 | 0.9234 | 0.6392 | 0.0 | 0.9025 | 0.6588 | 0.7954 | 0.5044 | | 0.0442 | 59.0 | 7847 | 0.3975 | 0.5828 | 0.8407 | 0.9257 | nan | 0.9398 | 0.8563 | 0.9312 | 0.6356 | 0.0 | 0.9134 | 0.6866 | 0.8103 | 0.5037 | | 0.0442 | 60.0 | 7980 | 0.3845 | 0.5929 | 0.8457 | 0.9315 | nan | 0.9459 | 0.8603 | 0.9363 | 0.6404 | 0.0 | 0.9193 | 0.7041 | 0.8308 | 0.5103 | | 0.0422 | 61.0 | 8113 | 0.3875 | 0.5963 | 0.8465 | 0.9338 | nan | 0.9489 | 0.8616 | 0.9340 | 0.6413 | 0.0 | 0.9226 | 0.7135 | 0.8381 | 0.5072 | | 0.0436 | 62.0 | 8246 | 0.3859 | 0.6022 | 0.8497 | 0.9385 | nan | 0.9566 | 0.8477 | 0.9382 | 0.6562 | 0.0 | 0.9289 | 0.7300 | 0.8376 | 0.5147 | | 0.0429 | 63.0 | 8379 | 0.3857 | 0.5956 | 0.8425 | 0.9357 | nan | 0.9534 | 0.8481 | 0.9357 | 0.6327 | 0.0 | 0.9249 | 0.7233 | 0.8283 | 0.5016 | | 0.0446 | 64.0 | 8512 | 0.3778 | 0.5976 | 0.8495 | 0.9343 | nan | 0.9492 | 0.8602 | 0.9399 | 0.6489 | 0.0 | 0.9232 | 0.7191 | 0.8305 | 0.5153 | | 0.0429 | 65.0 | 8645 | 0.3889 | 0.5948 | 0.8478 | 0.9330 | nan | 0.9490 | 0.8548 | 0.9325 | 0.6549 | 0.0 | 0.9225 | 0.7075 | 0.8271 | 0.5167 | | 0.0454 | 66.0 | 8778 | 0.3915 | 0.5941 | 0.8470 | 0.9329 | nan | 0.9490 | 0.8571 | 0.9271 | 0.6547 | 0.0 | 0.9221 | 0.7087 | 0.8278 | 0.5117 | | 0.0427 | 67.0 | 8911 | 0.3924 | 0.5967 | 0.8455 | 0.9349 | nan | 0.9518 | 0.8520 | 0.9350 | 0.6433 | 0.0 | 0.9247 | 0.7167 | 0.8290 | 0.5133 | | 0.0425 | 68.0 | 9044 | 0.3990 | 0.5992 | 0.8491 | 0.9358 | nan | 0.9524 | 0.8545 | 0.9355 | 0.6541 | 0.0 | 0.9250 | 0.7187 | 0.8387 | 0.5136 | | 0.0429 | 69.0 | 9177 | 0.3911 | 0.5909 | 0.8499 | 0.9303 | nan | 0.9451 | 0.8532 | 0.9394 | 0.6619 | 0.0 | 0.9192 | 0.7029 | 0.8178 | 0.5146 | | 0.0465 | 70.0 | 9310 | 0.3840 | 0.5977 | 0.8481 | 0.9332 | nan | 0.9473 | 0.8700 | 0.9278 | 0.6473 | 0.0 | 0.9215 | 0.7079 | 0.8480 | 0.5110 | | 0.0436 | 71.0 | 9443 | 0.3862 | 0.5974 | 0.8456 | 0.9351 | nan | 0.9518 | 0.8534 | 0.9359 | 0.6413 | 0.0 | 0.9248 | 0.7162 | 0.8338 | 0.5124 | | 0.0435 | 72.0 | 9576 | 0.3926 | 0.5952 | 0.8448 | 0.9328 | nan | 0.9484 | 0.8585 | 0.9318 | 0.6405 | 0.0 | 0.9217 | 0.7073 | 0.8386 | 0.5084 | | 0.0421 | 73.0 | 9709 | 0.3961 | 0.5984 | 0.8467 | 0.9348 | nan | 0.9513 | 0.8564 | 0.9309 | 0.6482 | 0.0 | 0.9243 | 0.7119 | 0.8414 | 0.5143 | | 0.0409 | 74.0 | 9842 | 0.3973 | 0.5982 | 0.8494 | 0.9341 | nan | 0.9498 | 0.8596 | 0.9306 | 0.6578 | 0.0 | 0.9233 | 0.7094 | 0.8401 | 0.5181 | | 0.041 | 75.0 | 9975 | 0.3898 | 0.5963 | 0.8476 | 0.9335 | nan | 0.9493 | 0.8561 | 0.9354 | 0.6498 | 0.0 | 0.9227 | 0.7108 | 0.8329 | 0.5153 | | 0.0436 | 75.19 | 10000 | 0.3966 | 0.5967 | 0.8460 | 0.9344 | nan | 0.9510 | 0.8524 | 0.9362 | 0.6444 | 0.0 | 0.9239 | 0.7125 | 0.8335 | 0.5139 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

Masik001/combined-GI-RVC-models

Masik001

2023-07-10T19:43:01Z

0

1

null

[ "region:us" ]

null

2023-07-10T19:42:16Z

===== Application Startup at 2023-07-10 13:56:11 ===== 2023-07-10 17:36:30 | INFO | faiss.loader | Loading faiss with AVX2 support. 2023-07-10 17:36:30 | INFO | faiss.loader | Successfully loaded faiss with AVX2 support. 没有发现支持的N卡, 使用CPU进行推理 2023-07-10 17:36:31 | INFO | fairseq.tasks.hubert_pretraining | current directory is /home/user/app 2023-07-10 17:36:31 | INFO | fairseq.tasks.hubert_pretraining | HubertPretrainingTask Config {'_name': 'hubert_pretraining', 'data': 'metadata', 'fine_tuning': False, 'labels': ['km'], 'label_dir': 'label', 'label_rate': 50.0, 'sample_rate': 16000, 'normalize': False, 'enable_padding': False, 'max_keep_size': None, 'max_sample_size': 250000, 'min_sample_size': 32000, 'single_target': False, 'random_crop': True, 'pad_audio': False} 2023-07-10 17:36:31 | INFO | fairseq.models.hubert.hubert | HubertModel Config: {'_name': 'hubert', 'label_rate': 50.0, 'extractor_mode': default, 'encoder_layers': 12, 'encoder_embed_dim': 768, 'encoder_ffn_embed_dim': 3072, 'encoder_attention_heads': 12, 'activation_fn': gelu, 'layer_type': transformer, 'dropout': 0.1, 'attention_dropout': 0.1, 'activation_dropout': 0.0, 'encoder_layerdrop': 0.05, 'dropout_input': 0.1, 'dropout_features': 0.1, 'final_dim': 256, 'untie_final_proj': True, 'layer_norm_first': False, 'conv_feature_layers': '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', 'conv_bias': False, 'logit_temp': 0.1, 'target_glu': False, 'feature_grad_mult': 0.1, 'mask_length': 10, 'mask_prob': 0.8, 'mask_selection': static, 'mask_other': 0.0, 'no_mask_overlap': False, 'mask_min_space': 1, 'mask_channel_length': 10, 'mask_channel_prob': 0.0, 'mask_channel_selection': static, 'mask_channel_other': 0.0, 'no_mask_channel_overlap': False, 'mask_channel_min_space': 1, 'conv_pos': 128, 'conv_pos_groups': 16, 'latent_temp': [2.0, 0.5, 0.999995], 'skip_masked': False, 'skip_nomask': False, 'checkpoint_activations': False, 'required_seq_len_multiple': 2, 'depthwise_conv_kernel_size': 31, 'attn_type': '', 'pos_enc_type': 'abs', 'fp16': False} gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: aether-jp / added_IVF865_Flat_nprobe_1_aether-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: albedo-jp / added_IVF641_Flat_nprobe_1_albedo-jp_v1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: alhaitham-jp / added_IVF519_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: ayaka-jp / added_IVF1018_Flat_nprobe_1_ayaka_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: ayato-jp / added_IVF1304_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: barbara-jp / added_IVF548_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: charlotte-jp / added_IVF1318_Flat_nprobe_1_charlotte-jp_v2_400.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: childe-jp / added_IVF684_Flat_nprobe_1_childe-v2_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: chongyun-jp / added_IVF545_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: cyno-jp / added_IVF380_Flat_nprobe_1_cyno-jp_v1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: diluc-jp / added_IVF1511_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: eula-jp / added_IVF2219_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: faruzan-jp / added_IVF256_Flat_nprobe_1_faruzan-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: fischl-jp / added_IVF1225_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: ganyu-jp / added_IVF1636_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: heizou-jp / added_IVF466_Flat_nprobe_1_heizou-jp_v1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: hutao-jp / added_IVF265_Flat_nprobe_5.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: itto-jp / added_IVF4454_Flat_nprobe_1_itto-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kaeya-jp / added_IVF1655_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kaveh-jp / added_IVF613_Flat_nprobe_1_kaveh_v2_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kazuha-jp / added_IVF860_Flat_nprobe_1_kazuha_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: keqing-jp / added_IVF1634_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kirara-jp / added_IVF672_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: klee-jp / added_IVF282_Flat_nprobe_5.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: kokomi-jp / added_IVF934_Flat_nprobe_1_kokomi_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: lumine-jp / added_IVF938_Flat_nprobe_1_lumine-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: mona-jp / added_IVF2165_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: nahida-jp / added_IVF1062_Flat_nprobe_1_nahida-v2_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: nilou-jp / added_IVF218_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: paimon-jp / added_IVF3904_Flat_nprobe_1_paimon-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: raiden-jp / added_IVF4256_Flat_nprobe_1_raiden-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: signora-jp / added_IVF478_Flat_nprobe_1_signora-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: sucrose-jp / added_IVF884_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: thoma-jp / added_IVF366_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: tighnari-jp / added_IVF446_Flat_nprobe_1_tignari-jp_v1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: venti-jp / added_IVF3591_Flat_nprobe_1_venti-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: wanderer-jp / added_IVF953_Flat_nprobe_1_wanderer-v2_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: xiao-jp / added_IVF3205_Flat_nprobe_1_xiao-jp_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yae-jp / added_IVF1097_Flat_nprobe_1_yae-v2_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yanfei-jp / added_IVF1271_Flat_nprobe_1_yanfei-v2_v2.index | (V2) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yelan-jp / added_IVF2051_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: yoimiya-jp / added_IVF2034_Flat_nprobe_1.index | (V1) gin_channels: 256 self.spk_embed_dim: 109 <All keys matched successfully> Model loaded: zhongli-jp / added_IVF1672_Flat_nprobe_1.index | (V1) Running on local URL: http://0.0.0.0:7860 To create a public link, set `share=True` in `launch()`. [2023-07-10 17:37]: npy: 2.0945026874542236, f0: 0.05994224548339844s, infer: 17.599822521209717s [2023-07-10 17:38]: npy: 3.1487624645233154, f0: 0.022048234939575195s, infer: 25.596487760543823s [2023-07-10 17:39]: npy: 3.693798780441284, f0: 0.017490386962890625s, infer: 32.087180376052856s [2023-07-10 17:39]: npy: 2.5506346225738525, f0: 0.013794660568237305s, infer: 26.60752511024475s [2023-07-10 17:40]: npy: 2.6092371940612793, f0: 0.03858685493469238s, infer: 26.312453031539917s [2023-07-10 17:41]: npy: 2.615102767944336, f0: 0.03931307792663574s, infer: 26.40330672264099s [2023-07-10 17:43]: npy: 3.1028923988342285, f0: 0.05546903610229492s, infer: 32.91775321960449s [2023-07-10 17:44]: npy: 2.839845657348633, f0: 0.046269893646240234s, infer: 27.98230767250061s [2023-07-10 17:44]: npy: 3.3039710521698, f0: 0.020084142684936523s, infer: 29.59837293624878s [2023-07-10 17:45]: npy: 3.30319881439209, f0: 0.03941464424133301s, infer: 32.42077875137329s [2023-07-10 17:46]: npy: 2.90372371673584, f0: 0.0513463020324707s, infer: 28.517998695373535s [2023-07-10 17:47]: npy: 3.4118876457214355, f0: 0.10508394241333008s, infer: 31.312357664108276s [2023-07-10 17:47]: npy: 4.102552890777588, f0: 0.02527928352355957s, infer: 33.81402325630188s [2023-07-10 17:48]: npy: 2.4004595279693604, f0: 0.09933662414550781s, infer: 29.89732074737549s [2023-07-10 17:49]: npy: 3.2991466522216797, f0: 0.03225088119506836s, infer: 29.510783195495605s [2023-07-10 17:49]: npy: 3.4149115085601807, f0: 0.04070758819580078s, infer: 30.8032488822937s

NasimB/gpt2-cocnat-aochildes-mod-sub-length-10k

NasimB

2023-07-10T19:27:45Z

16

0

transformers

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

text-generation

2023-07-10T17:32:01Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-aochildes-mod-sub-length-10k results: [] ---  # gpt2-cocnat-aochildes-mod-sub-length-10k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3425 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6933 | 0.29 | 500 | 5.6341 | | 5.3469 | 0.59 | 1000 | 5.1996 | | 4.9864 | 0.88 | 1500 | 4.9580 | | 4.7189 | 1.18 | 2000 | 4.8083 | | 4.5609 | 1.47 | 2500 | 4.6850 | | 4.4523 | 1.77 | 3000 | 4.5821 | | 4.317 | 2.06 | 3500 | 4.5146 | | 4.1329 | 2.35 | 4000 | 4.4652 | | 4.1086 | 2.65 | 4500 | 4.4071 | | 4.0635 | 2.94 | 5000 | 4.3601 | | 3.8482 | 3.24 | 5500 | 4.3553 | | 3.8055 | 3.53 | 6000 | 4.3282 | | 3.7859 | 3.83 | 6500 | 4.2926 | | 3.6619 | 4.12 | 7000 | 4.2970 | | 3.5196 | 4.41 | 7500 | 4.2933 | | 3.5139 | 4.71 | 8000 | 4.2857 | | 3.4905 | 5.0 | 8500 | 4.2710 | | 3.3203 | 5.3 | 9000 | 4.2871 | | 3.322 | 5.59 | 9500 | 4.2867 | | 3.3172 | 5.89 | 10000 | 4.2863 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

aphi/ppo-SnowballTarget

aphi

2023-07-10T19:08:24Z

0

ml-agents

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

reinforcement-learning

2023-07-10T19:08:17Z

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

darthPanda/ppo-Huggy-v0

darthPanda

2023-07-10T18:57:02Z

14

0

ml-agents

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

reinforcement-learning

2023-07-10T18:55:55Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: darthPanda/ppo-Huggy-v0 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

43m1m4n/jpbrinx

43m1m4n

2023-07-10T18:53:46Z

0

null

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

null

2023-06-04T20:40:20Z

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

PraveenJesu/openai-whisper-medium-peft-lora-v2.2.5

PraveenJesu

2023-07-10T18:28:05Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-10T18:28:04Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

MaitreHibou/dqn-SpaceInvadersNoFrameskip-v4

MaitreHibou

2023-07-10T18:21:47Z

2

0

stable-baselines3

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

reinforcement-learning

2023-07-10T18:21:06Z

--- 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: 656.50 +/- 140.98 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 MaitreHibou -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 MaitreHibou -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 MaitreHibou ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('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', 0.0002), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

skrl/IsaacGymEnvs-AnymalTerrain-PPO

skrl

2023-07-10T18:15:29Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-01-24T20:41:55Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 19.88 +/- 0.5 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-AnymalTerrain type: IsaacGymEnvs-AnymalTerrain ---  # IsaacGymEnvs-AnymalTerrain-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** AnymalTerrain - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 24 # memory_size cfg["learning_epochs"] = 5 cfg["mini_batches"] = 6 # 24 * 4096 / 16384 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 3e-4 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.001 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

FerhatDk/wav2vec2-base-finetuned-ks

FerhatDk

2023-07-10T18:08:04Z

4

0

transformers

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

audio-classification

2022-09-22T08:59:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks results: [] ---  # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3550 - Accuracy: 0.8727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 500 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 8 | 0.6840 | 0.6 | | 0.6867 | 2.0 | 16 | 0.6780 | 0.6364 | | 0.6742 | 3.0 | 24 | 0.6601 | 0.6182 | | 0.6446 | 4.0 | 32 | 0.6294 | 0.6364 | | 0.6299 | 5.0 | 40 | 0.6002 | 0.6727 | | 0.6299 | 6.0 | 48 | 0.5755 | 0.7091 | | 0.6021 | 7.0 | 56 | 0.5530 | 0.7273 | | 0.5678 | 8.0 | 64 | 0.5036 | 0.8182 | | 0.5512 | 9.0 | 72 | 0.4753 | 0.8545 | | 0.4784 | 10.0 | 80 | 0.4184 | 0.9273 | | 0.4784 | 11.0 | 88 | 0.4102 | 0.8909 | | 0.4515 | 12.0 | 96 | 0.4444 | 0.8182 | | 0.4878 | 13.0 | 104 | 0.3780 | 0.9091 | | 0.4418 | 14.0 | 112 | 0.4570 | 0.8 | | 0.4746 | 15.0 | 120 | 0.3870 | 0.8545 | | 0.4746 | 16.0 | 128 | 0.3932 | 0.8364 | | 0.4226 | 17.0 | 136 | 0.2779 | 0.9636 | | 0.4301 | 18.0 | 144 | 0.3125 | 0.9455 | | 0.3482 | 19.0 | 152 | 0.3212 | 0.9091 | | 0.3611 | 20.0 | 160 | 0.3925 | 0.8364 | | 0.3611 | 21.0 | 168 | 0.3389 | 0.8909 | | 0.3507 | 22.0 | 176 | 0.3099 | 0.8727 | | 0.3241 | 23.0 | 184 | 0.3120 | 0.8727 | | 0.2533 | 24.0 | 192 | 0.2313 | 0.9455 | | 0.2466 | 25.0 | 200 | 0.3550 | 0.8727 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Bellaaazzzzz/model_archive

Bellaaazzzzz

2023-07-10T18:00:43Z

2

0

diffusers

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

text-to-image

2023-07-10T17:41:57Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_archive These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png)

arpan-das-astrophysics/ppo-LunarLander-v2

arpan-das-astrophysics

2023-07-10T17:42:15Z

6

0

stable-baselines3

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

reinforcement-learning

2023-07-10T17:41:55Z

--- 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: 253.82 +/- 21.71 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 ... ```

RamonGOD/distilbert-base-uncased-finetuned-cola

RamonGOD

2023-07-10T17:32:17Z

62

0

transformers

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

text-classification

2023-07-10T17:00:10Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: RamonGOD/distilbert-base-uncased-finetuned-cola results: [] ---  # RamonGOD/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1891 - Validation Loss: 0.5654 - Train Matthews Correlation: 0.5209 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5243 | 0.4596 | 0.4917 | 0 | | 0.3246 | 0.5117 | 0.4896 | 1 | | 0.1891 | 0.5654 | 0.5209 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

AmbarB12/my_awesome_model

AmbarB12

2023-07-10T17:30:33Z

62

0

transformers

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

text-classification

2023-07-09T18:03:55Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: AmbarB12/my_awesome_model results: [] ---  # AmbarB12/my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0631 - Validation Loss: 0.2229 - Train Accuracy: 0.9306 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2523 | 0.1891 | 0.927 | 0 | | 0.1327 | 0.2007 | 0.9298 | 1 | | 0.0631 | 0.2229 | 0.9306 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

banden/ppo-Huggy

banden

2023-07-10T17:25:40Z

11

0

ml-agents

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

reinforcement-learning

2023-07-10T17:25:35Z

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

cagarraz/rl_course_vizdoom_health_gathering_supreme

cagarraz

2023-07-10T17:23:21Z

0

sample-factory

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

reinforcement-learning

2023-07-10T17:23:08Z

--- 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: 3.94 +/- 0.20 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 cagarraz/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.

jordyvl/vit-small_tobacco3482_kd_CEKD_t1.5_a0.5

jordyvl

2023-07-10T17:17:44Z

163

0

transformers

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

image-classification

2023-07-10T16:39:33Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t1.5_a0.5 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t1.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4258 - Accuracy: 0.825 - Brier Loss: 0.2707 - Nll: 0.8867 - F1 Micro: 0.825 - F1 Macro: 0.8116 - Ece: 0.2129 - Aurc: 0.0681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.7307 | 0.22 | 0.8748 | 5.3766 | 0.22 | 0.1294 | 0.2444 | 0.6913 | | No log | 2.0 | 14 | 1.3514 | 0.405 | 0.7426 | 3.5573 | 0.405 | 0.2280 | 0.2900 | 0.4026 | | No log | 3.0 | 21 | 0.9121 | 0.62 | 0.5647 | 1.9398 | 0.62 | 0.5595 | 0.2879 | 0.2015 | | No log | 4.0 | 28 | 0.7084 | 0.695 | 0.4179 | 1.7042 | 0.695 | 0.6379 | 0.2305 | 0.1177 | | No log | 5.0 | 35 | 0.7167 | 0.735 | 0.3862 | 1.7929 | 0.735 | 0.7392 | 0.2380 | 0.1046 | | No log | 6.0 | 42 | 0.6442 | 0.765 | 0.3625 | 1.5688 | 0.765 | 0.7549 | 0.2371 | 0.1034 | | No log | 7.0 | 49 | 0.6147 | 0.805 | 0.3410 | 1.5975 | 0.805 | 0.7789 | 0.2438 | 0.1042 | | No log | 8.0 | 56 | 0.6444 | 0.775 | 0.3446 | 1.2309 | 0.775 | 0.7725 | 0.2305 | 0.0911 | | No log | 9.0 | 63 | 0.5964 | 0.8 | 0.3219 | 1.3613 | 0.8000 | 0.7784 | 0.2446 | 0.0734 | | No log | 10.0 | 70 | 0.5700 | 0.82 | 0.3160 | 1.2605 | 0.82 | 0.7860 | 0.2301 | 0.0632 | | No log | 11.0 | 77 | 0.5663 | 0.79 | 0.3176 | 1.2939 | 0.79 | 0.7643 | 0.2315 | 0.0666 | | No log | 12.0 | 84 | 0.5111 | 0.825 | 0.3143 | 1.1082 | 0.825 | 0.8082 | 0.2519 | 0.0844 | | No log | 13.0 | 91 | 0.5228 | 0.78 | 0.3156 | 0.9444 | 0.78 | 0.7773 | 0.1941 | 0.0650 | | No log | 14.0 | 98 | 0.5792 | 0.78 | 0.3409 | 1.5054 | 0.78 | 0.7725 | 0.2061 | 0.1019 | | No log | 15.0 | 105 | 0.4905 | 0.83 | 0.2912 | 1.0068 | 0.83 | 0.8266 | 0.2324 | 0.0545 | | No log | 16.0 | 112 | 0.4990 | 0.825 | 0.2961 | 1.1452 | 0.825 | 0.8140 | 0.2188 | 0.0632 | | No log | 17.0 | 119 | 0.4900 | 0.805 | 0.2940 | 1.2027 | 0.805 | 0.8018 | 0.2188 | 0.0860 | | No log | 18.0 | 126 | 0.4755 | 0.805 | 0.2988 | 1.0223 | 0.805 | 0.7789 | 0.2229 | 0.0792 | | No log | 19.0 | 133 | 0.4398 | 0.81 | 0.2679 | 0.9732 | 0.81 | 0.7830 | 0.2085 | 0.0585 | | No log | 20.0 | 140 | 0.4766 | 0.805 | 0.2992 | 0.9730 | 0.805 | 0.7934 | 0.2141 | 0.0662 | | No log | 21.0 | 147 | 0.4615 | 0.835 | 0.2867 | 0.9343 | 0.835 | 0.8219 | 0.1999 | 0.0751 | | No log | 22.0 | 154 | 0.4343 | 0.825 | 0.2641 | 1.1353 | 0.825 | 0.8070 | 0.2095 | 0.0603 | | No log | 23.0 | 161 | 0.4291 | 0.85 | 0.2660 | 1.0109 | 0.85 | 0.8365 | 0.2435 | 0.0615 | | No log | 24.0 | 168 | 0.4263 | 0.855 | 0.2653 | 0.9395 | 0.855 | 0.8440 | 0.2445 | 0.0623 | | No log | 25.0 | 175 | 0.4338 | 0.845 | 0.2700 | 0.8794 | 0.845 | 0.8349 | 0.2254 | 0.0584 | | No log | 26.0 | 182 | 0.4305 | 0.835 | 0.2648 | 0.9062 | 0.835 | 0.8322 | 0.2113 | 0.0658 | | No log | 27.0 | 189 | 0.4262 | 0.84 | 0.2683 | 0.9967 | 0.8400 | 0.8291 | 0.2240 | 0.0670 | | No log | 28.0 | 196 | 0.4329 | 0.83 | 0.2724 | 0.9016 | 0.83 | 0.8239 | 0.2016 | 0.0685 | | No log | 29.0 | 203 | 0.4233 | 0.845 | 0.2653 | 0.9115 | 0.845 | 0.8375 | 0.2005 | 0.0634 | | No log | 30.0 | 210 | 0.4204 | 0.84 | 0.2638 | 0.8892 | 0.8400 | 0.8348 | 0.2175 | 0.0633 | | No log | 31.0 | 217 | 0.4240 | 0.83 | 0.2684 | 0.8871 | 0.83 | 0.8217 | 0.2128 | 0.0660 | | No log | 32.0 | 224 | 0.4246 | 0.84 | 0.2677 | 0.8867 | 0.8400 | 0.8307 | 0.2117 | 0.0670 | | No log | 33.0 | 231 | 0.4247 | 0.83 | 0.2690 | 0.8917 | 0.83 | 0.8202 | 0.2084 | 0.0679 | | No log | 34.0 | 238 | 0.4218 | 0.84 | 0.2660 | 0.8848 | 0.8400 | 0.8326 | 0.2138 | 0.0663 | | No log | 35.0 | 245 | 0.4220 | 0.845 | 0.2667 | 0.8926 | 0.845 | 0.8354 | 0.2109 | 0.0655 | | No log | 36.0 | 252 | 0.4247 | 0.83 | 0.2694 | 0.8854 | 0.83 | 0.8202 | 0.2213 | 0.0683 | | No log | 37.0 | 259 | 0.4239 | 0.84 | 0.2683 | 0.8849 | 0.8400 | 0.8326 | 0.2163 | 0.0670 | | No log | 38.0 | 266 | 0.4239 | 0.835 | 0.2689 | 0.8876 | 0.835 | 0.8208 | 0.2118 | 0.0672 | | No log | 39.0 | 273 | 0.4252 | 0.83 | 0.2696 | 0.8885 | 0.83 | 0.8180 | 0.2064 | 0.0682 | | No log | 40.0 | 280 | 0.4237 | 0.835 | 0.2686 | 0.8867 | 0.835 | 0.8208 | 0.2211 | 0.0675 | | No log | 41.0 | 287 | 0.4256 | 0.83 | 0.2700 | 0.8847 | 0.83 | 0.8180 | 0.2253 | 0.0682 | | No log | 42.0 | 294 | 0.4243 | 0.835 | 0.2692 | 0.8839 | 0.835 | 0.8208 | 0.2130 | 0.0675 | | No log | 43.0 | 301 | 0.4248 | 0.83 | 0.2695 | 0.8850 | 0.83 | 0.8180 | 0.2237 | 0.0682 | | No log | 44.0 | 308 | 0.4246 | 0.83 | 0.2694 | 0.8847 | 0.83 | 0.8180 | 0.2383 | 0.0680 | | No log | 45.0 | 315 | 0.4253 | 0.83 | 0.2699 | 0.8858 | 0.83 | 0.8180 | 0.2200 | 0.0681 | | No log | 46.0 | 322 | 0.4246 | 0.83 | 0.2694 | 0.8857 | 0.83 | 0.8180 | 0.2311 | 0.0679 | | No log | 47.0 | 329 | 0.4253 | 0.83 | 0.2700 | 0.8843 | 0.83 | 0.8180 | 0.2312 | 0.0682 | | No log | 48.0 | 336 | 0.4252 | 0.83 | 0.2698 | 0.8830 | 0.83 | 0.8180 | 0.2177 | 0.0682 | | No log | 49.0 | 343 | 0.4257 | 0.83 | 0.2703 | 0.8848 | 0.83 | 0.8180 | 0.2315 | 0.0683 | | No log | 50.0 | 350 | 0.4256 | 0.83 | 0.2703 | 0.8833 | 0.83 | 0.8180 | 0.2331 | 0.0684 | | No log | 51.0 | 357 | 0.4254 | 0.83 | 0.2703 | 0.8863 | 0.83 | 0.8180 | 0.2422 | 0.0681 | | No log | 52.0 | 364 | 0.4261 | 0.83 | 0.2707 | 0.8864 | 0.83 | 0.8180 | 0.2424 | 0.0683 | | No log | 53.0 | 371 | 0.4249 | 0.83 | 0.2700 | 0.8855 | 0.83 | 0.8180 | 0.2195 | 0.0679 | | No log | 54.0 | 378 | 0.4255 | 0.83 | 0.2704 | 0.8846 | 0.83 | 0.8180 | 0.2342 | 0.0682 | | No log | 55.0 | 385 | 0.4256 | 0.825 | 0.2704 | 0.8861 | 0.825 | 0.8116 | 0.2357 | 0.0682 | | No log | 56.0 | 392 | 0.4264 | 0.83 | 0.2708 | 0.8853 | 0.83 | 0.8180 | 0.2345 | 0.0682 | | No log | 57.0 | 399 | 0.4257 | 0.825 | 0.2706 | 0.8864 | 0.825 | 0.8116 | 0.2353 | 0.0682 | | No log | 58.0 | 406 | 0.4258 | 0.825 | 0.2704 | 0.8841 | 0.825 | 0.8116 | 0.2271 | 0.0681 | | No log | 59.0 | 413 | 0.4255 | 0.825 | 0.2703 | 0.8856 | 0.825 | 0.8116 | 0.2267 | 0.0680 | | No log | 60.0 | 420 | 0.4259 | 0.825 | 0.2709 | 0.8842 | 0.825 | 0.8116 | 0.2269 | 0.0683 | | No log | 61.0 | 427 | 0.4254 | 0.83 | 0.2702 | 0.8852 | 0.83 | 0.8180 | 0.2265 | 0.0680 | | No log | 62.0 | 434 | 0.4261 | 0.83 | 0.2707 | 0.8851 | 0.83 | 0.8180 | 0.2346 | 0.0682 | | No log | 63.0 | 441 | 0.4257 | 0.825 | 0.2704 | 0.8854 | 0.825 | 0.8116 | 0.2232 | 0.0682 | | No log | 64.0 | 448 | 0.4261 | 0.825 | 0.2708 | 0.8845 | 0.825 | 0.8116 | 0.2264 | 0.0683 | | No log | 65.0 | 455 | 0.4259 | 0.825 | 0.2706 | 0.8862 | 0.825 | 0.8116 | 0.2204 | 0.0682 | | No log | 66.0 | 462 | 0.4258 | 0.825 | 0.2707 | 0.8856 | 0.825 | 0.8116 | 0.2193 | 0.0682 | | No log | 67.0 | 469 | 0.4255 | 0.83 | 0.2703 | 0.8852 | 0.83 | 0.8180 | 0.2190 | 0.0681 | | No log | 68.0 | 476 | 0.4260 | 0.825 | 0.2708 | 0.8860 | 0.825 | 0.8116 | 0.2196 | 0.0682 | | No log | 69.0 | 483 | 0.4259 | 0.825 | 0.2708 | 0.8858 | 0.825 | 0.8116 | 0.2195 | 0.0682 | | No log | 70.0 | 490 | 0.4255 | 0.825 | 0.2703 | 0.8857 | 0.825 | 0.8116 | 0.2135 | 0.0682 | | No log | 71.0 | 497 | 0.4258 | 0.825 | 0.2707 | 0.8857 | 0.825 | 0.8116 | 0.2205 | 0.0681 | | 0.1816 | 72.0 | 504 | 0.4261 | 0.825 | 0.2708 | 0.8857 | 0.825 | 0.8116 | 0.2198 | 0.0682 | | 0.1816 | 73.0 | 511 | 0.4259 | 0.825 | 0.2706 | 0.8852 | 0.825 | 0.8116 | 0.2192 | 0.0682 | | 0.1816 | 74.0 | 518 | 0.4259 | 0.825 | 0.2707 | 0.8856 | 0.825 | 0.8116 | 0.2290 | 0.0681 | | 0.1816 | 75.0 | 525 | 0.4257 | 0.825 | 0.2706 | 0.8864 | 0.825 | 0.8116 | 0.2337 | 0.0681 | | 0.1816 | 76.0 | 532 | 0.4259 | 0.825 | 0.2707 | 0.8855 | 0.825 | 0.8116 | 0.2211 | 0.0681 | | 0.1816 | 77.0 | 539 | 0.4255 | 0.825 | 0.2704 | 0.8860 | 0.825 | 0.8116 | 0.2137 | 0.0680 | | 0.1816 | 78.0 | 546 | 0.4258 | 0.825 | 0.2707 | 0.8868 | 0.825 | 0.8116 | 0.2274 | 0.0682 | | 0.1816 | 79.0 | 553 | 0.4260 | 0.825 | 0.2708 | 0.8859 | 0.825 | 0.8116 | 0.2209 | 0.0682 | | 0.1816 | 80.0 | 560 | 0.4260 | 0.825 | 0.2708 | 0.8864 | 0.825 | 0.8116 | 0.2135 | 0.0681 | | 0.1816 | 81.0 | 567 | 0.4259 | 0.825 | 0.2707 | 0.8859 | 0.825 | 0.8116 | 0.2134 | 0.0682 | | 0.1816 | 82.0 | 574 | 0.4258 | 0.825 | 0.2706 | 0.8862 | 0.825 | 0.8116 | 0.2062 | 0.0681 | | 0.1816 | 83.0 | 581 | 0.4259 | 0.825 | 0.2707 | 0.8866 | 0.825 | 0.8116 | 0.2204 | 0.0681 | | 0.1816 | 84.0 | 588 | 0.4259 | 0.825 | 0.2707 | 0.8868 | 0.825 | 0.8116 | 0.2204 | 0.0681 | | 0.1816 | 85.0 | 595 | 0.4257 | 0.825 | 0.2706 | 0.8861 | 0.825 | 0.8116 | 0.2141 | 0.0682 | | 0.1816 | 86.0 | 602 | 0.4258 | 0.825 | 0.2707 | 0.8861 | 0.825 | 0.8116 | 0.2140 | 0.0682 | | 0.1816 | 87.0 | 609 | 0.4258 | 0.825 | 0.2707 | 0.8867 | 0.825 | 0.8116 | 0.2137 | 0.0680 | | 0.1816 | 88.0 | 616 | 0.4259 | 0.825 | 0.2707 | 0.8866 | 0.825 | 0.8116 | 0.2129 | 0.0681 | | 0.1816 | 89.0 | 623 | 0.4258 | 0.825 | 0.2707 | 0.8866 | 0.825 | 0.8116 | 0.2205 | 0.0681 | | 0.1816 | 90.0 | 630 | 0.4259 | 0.825 | 0.2707 | 0.8865 | 0.825 | 0.8116 | 0.2053 | 0.0680 | | 0.1816 | 91.0 | 637 | 0.4258 | 0.825 | 0.2706 | 0.8868 | 0.825 | 0.8116 | 0.2130 | 0.0681 | | 0.1816 | 92.0 | 644 | 0.4258 | 0.825 | 0.2706 | 0.8870 | 0.825 | 0.8116 | 0.2129 | 0.0680 | | 0.1816 | 93.0 | 651 | 0.4258 | 0.825 | 0.2706 | 0.8868 | 0.825 | 0.8116 | 0.2129 | 0.0681 | | 0.1816 | 94.0 | 658 | 0.4258 | 0.825 | 0.2707 | 0.8867 | 0.825 | 0.8116 | 0.2129 | 0.0681 | | 0.1816 | 95.0 | 665 | 0.4258 | 0.825 | 0.2707 | 0.8867 | 0.825 | 0.8116 | 0.2053 | 0.0680 | | 0.1816 | 96.0 | 672 | 0.4259 | 0.825 | 0.2707 | 0.8866 | 0.825 | 0.8116 | 0.2053 | 0.0681 | | 0.1816 | 97.0 | 679 | 0.4258 | 0.825 | 0.2707 | 0.8868 | 0.825 | 0.8116 | 0.2129 | 0.0681 | | 0.1816 | 98.0 | 686 | 0.4258 | 0.825 | 0.2707 | 0.8868 | 0.825 | 0.8116 | 0.2129 | 0.0680 | | 0.1816 | 99.0 | 693 | 0.4258 | 0.825 | 0.2707 | 0.8868 | 0.825 | 0.8116 | 0.2129 | 0.0681 | | 0.1816 | 100.0 | 700 | 0.4258 | 0.825 | 0.2707 | 0.8867 | 0.825 | 0.8116 | 0.2129 | 0.0681 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

NasimB/gpt2-concat-all-new-mod-datasets-rarity-all-iorder-13k-2p6k

NasimB

2023-07-10T17:09:06Z

5

0

transformers

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

text-generation

2023-07-10T15:25:56Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-all-new-mod-datasets-rarity-all-iorder-13k-2p6k results: [] ---  # gpt2-concat-all-new-mod-datasets-rarity-all-iorder-13k-2p6k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.7847 | 0.32 | 500 | 5.6658 | | 5.4409 | 0.63 | 1000 | 5.2342 | | 5.088 | 0.95 | 1500 | 4.9831 | | 4.8091 | 1.27 | 2000 | 4.8440 | | 4.6774 | 1.59 | 2500 | 4.7254 | | 4.5641 | 1.9 | 3000 | 4.6255 | | 4.3493 | 2.22 | 3500 | 4.5674 | | 4.2735 | 2.54 | 4000 | 4.5081 | | 4.2294 | 2.86 | 4500 | 4.4480 | | 4.0526 | 3.17 | 5000 | 4.4279 | | 3.9479 | 3.49 | 5500 | 4.4002 | | 3.9223 | 3.81 | 6000 | 4.3596 | | 3.8021 | 4.13 | 6500 | 4.3586 | | 3.6504 | 4.44 | 7000 | 4.3495 | | 3.6428 | 4.76 | 7500 | 4.3416 | | 3.58 | 5.08 | 8000 | 4.3470 | | 3.4494 | 5.4 | 8500 | 4.3484 | | 3.4443 | 5.71 | 9000 | 4.3455 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

Buth/fatuh

Buth

2023-07-10T16:50:46Z

0

adapter-transformers

[ "adapter-transformers", "en", "dataset:Open-Orca/OpenOrca", "license:openrail", "region:us" ]

null

2023-07-10T16:48:59Z

--- license: openrail datasets: - Open-Orca/OpenOrca language: - en metrics: - accuracy library_name: adapter-transformers ---

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

svalcin

2023-07-10T16:39:14Z

0

null

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

reinforcement-learning

2023-07-10T16:39:10Z

--- 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="svalcin/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"]) ```

Umer1542/task-b-classification

Umer1542

2023-07-10T16:35:37Z

160

0

transformers

[ "transformers", "pytorch", "opt", "text-classification", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2023-07-10T15:47:23Z

--- license: other language: - en metrics: - accuracy - f1 - recall pipeline_tag: text-classification ---

TheBloke/MPT-30B-Dolphin-v2-GGML

TheBloke

2023-07-10T16:32:10Z

0

9

null

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

null

2023-07-10T15:13:07Z

--- inference: false license: other ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div>  # Manoj Preveen's MPT 30B Dolphin v2 GGML These files are MPT GGML format model files for [Manoj Preveen's MPT 30B Dolphin v2](https://huggingface.co/manojpreveen/mpt-30b-dolphin-v2). Please note that these GGMLs are **not compatible with llama.cpp, or currently with text-generation-webui**. Please see below for a list of tools that work with this GGML model. ## Repositories available * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/MPT-30B-Dolphin-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/manojpreveen/mpt-30b-dolphin-v2) ## Prompt template: custom ``` <system>: You are a helpful assistant <human>: {prompt} <bot>: ```  ## Compatibilty These files are **not** compatible with llama.cpp or text-generation-webui. They can be used with: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful inference engine based on llama.cpp with full GPU acceleration and good UI. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI for GGML inference on Windows and macOS. * [LoLLMs-WebUI](https://github.com/ParisNeo/LoLLMs-WebUI) a web UI which supports nearly every backend out there. Use ctransformers backend for support for this model. * [ctransformers](https://github.com/marella/ctransformers): for use in Python code, including LangChain support. * [rustformers' llm](https://github.com/rustformers/llm) * The example `mpt` binary provided with [ggml](https://github.com/ggerganov/ggml) As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!) ## Tutorial for using LoLLMs-WebUI: * [Video tutorial, by LoLLMs-WebUI's author **ParisNeo**](https://youtu.be/vBU1b5n0GMU)  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | mpt-30b-dolphin-v2.ggmlv1.q4_0.bin | q4_0 | 4 | 16.85 GB| 19.35 GB | 4-bit. | | mpt-30b-dolphin-v2.ggmlv1.q4_1.bin | q4_1 | 4 | 18.73 GB| 21.23 GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | mpt-30b-dolphin-v2.ggmlv1.q5_0.bin | q5_0 | 5 | 20.60 GB| 23.10 GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | | mpt-30b-dolphin-v2.ggmlv1.q5_1.bin | q5_1 | 5 | 22.47 GB| 24.97 GB | 5-bit. Even higher accuracy, resource usage and slower inference. | | mpt-30b-dolphin-v2.ggmlv1.q8_0.bin | q8_0 | 8 | 31.83 GB| 34.33 GB | 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz. **Patreon special mentions**: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. Thank you to all my generous patrons and donaters!  # Original model card: Manoj Preveen's MPT 30B Dolphin v2 **Base Model :** mosaicml/mpt-30b **Tool :** MosaicML's llm-foundry (https://github.com/mosaicml/llm-foundry) **Dataset :** Entire flan3m-GPT3.5 dataset. **Config yaml with Model Params :** https://huggingface.co/manojpreveen/mpt-30b-orca-v2/blob/main/mpt-30b_orca.yaml **Prompt Format :** ``` <system>: [system prompt] <human>: [question] <bot>: ```

yhyhy3/open_llama_7b_v2_med_instruct

yhyhy3

2023-07-10T16:22:39Z

1,461

8

transformers

[ "transformers", "pytorch", "llama", "text-generation", "medical", "code", "en", "dataset:ehartford/dolphin", "dataset:LinhDuong/chatdoctor-200k", "dataset:sahil2801/code_instructions_120k", "dataset:medalpaca/medical_meadow_mediqa", "dataset:kaiokendev/SuperCOT-dataset", "dataset:tiiuae/falcon-refinedweb", "dataset:bigcode/starcoderdata", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-09T17:19:43Z

--- license: apache-2.0 datasets: - ehartford/dolphin - LinhDuong/chatdoctor-200k - sahil2801/code_instructions_120k - medalpaca/medical_meadow_mediqa - kaiokendev/SuperCOT-dataset - tiiuae/falcon-refinedweb - bigcode/starcoderdata - togethercomputer/RedPajama-Data-1T language: - en library_name: transformers pipeline_tag: text-generation tags: - medical - code --- # Model Card for Model ID  This model is an instruction-tuned Open LLaMa model with 7B parameters, with specialities in medical QA and code instruction. ## Model Details  - **Model type:** LlamaForCausalLM - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Finetuned from model (QLoRA):** [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) ## How to Get Started with the Model Use the code below to get started with the model. ```py import torch from transformers import LlamaTokenizer, LlamaForCausalLM model_path = 'yhyhy3/open_llama_7b_v2_med_dolphin_qlora_merged' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) prompt = '''### Instruction: Answer the following question. ### Input: What is the capital of New Jersey? ### Response:''' input_ids = tokenizer(prompt, return_tensors="pt").input_ids generation_output = model.generate( input_ids=input_ids, max_new_tokens=32 ) print(tokenizer.decode(generation_output[0])) ``` ## Training Details ### Training Data Converted the following datasets to alpaca:instruction format. 1. [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) - ORCA style dataset generously created by [Eric Hartford](https://huggingface.co/ehartford) - Only used the 1 million GPT4 generated instructions file [flan1m-alpaca-uncensored.jsonl](https://huggingface.co/datasets/ehartford/dolphin/blob/main/flan1m-alpaca-uncensored.jsonl). 2. [LinhDuong/chatdoctor-200k](https://huggingface.co/datasets/LinhDuong/chatdoctor-200k) - Refined dataset sourced from icliniq medical QA forum 3. [sahil2801/code_instructions_120k](https://huggingface.co/datasets/sahil2801/code_instructions_120k) - Code instruction dataset generously created by Sahil Chaudhary from ThreeSixty AI 4. [medalpaca/medical_meadow_mediqa](https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa) - MEDIQA is a dataset of manually generated, question-driven summaries of multi and single document answers to consumer health questions from medalpaca group. 5. [kaiokendev/SuperCOT-dataset](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset) - Code instruction dataset generously created by Kaio Ken ### Training Procedure Trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) QLoRa on [RunPod](https://www.runpod.io/console/gpu-cloud) 8x A6000 on Community Cloud for 3 epochs (~14 hours - ~$70). <details> <summary>axolotl training config:</summary> ```yaml base_model: openlm-research/open_llama_7b_v2 base_model_config: openlm-research/open_llama_7b_v2 model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: true strict: false push_dataset_to_hub: hub_model_id: hf_use_auth_token: datasets: - path: json type: alpaca data_files: /disk/flan1m-alpaca-uncensored.jsonl shards: 8 - path: sahil2801/code_instructions_120k type: alpaca - path: LinhDuong/chatdoctor-200k type: alpaca shards: 2 - path: kaiokendev/SuperCOT-dataset type: alpaca - path: medalpaca/medical_meadow_mediqa type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.01 adapter: qlora lora_model_dir: sequence_len: 2048 max_packed_sequence_len: 2048 lora_r: 8 lora_alpha: 32 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_mode: true wandb_project: wandb_watch: wandb_run_id: wandb_log_model: 'openllama_checkpoint' output_dir: /disk/open_llama_7b_v2_dolphin_qlora gradient_accumulation_steps: 2 micro_batch_size: 16 num_epochs: 3 optimizer: paged_adamw_32bit torchdistx_path: lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: true flash_attention: gptq_groupsize: gptq_model_v1: warmup_steps: 1000 eval_steps: 5000 save_steps: debug: deepspeed: weight_decay: 0.0000001 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details>

aburnazy/opt125m_alpaca

aburnazy

2023-07-10T16:20:54Z

136

0

transformers

[ "transformers", "pytorch", "tensorboard", "opt", "text-generation", "generated_from_trainer", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T15:40:41Z

--- license: other tags: - generated_from_trainer model-index: - name: opt125m_alpaca results: [] ---  # opt125m_alpaca This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3

tyavika/Distilbert-QA-Pytorch-seed

tyavika

2023-07-10T16:10:13Z

104

0

transformers

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

question-answering

2023-07-10T12:52:40Z

--- tags: - generated_from_trainer model-index: - name: Distilbert-QA-Pytorch-seed results: [] ---  # Distilbert-QA-Pytorch-seed This model is a fine-tuned version of [tyavika/Distilbert-QA-Pytorch-seed](https://huggingface.co/tyavika/Distilbert-QA-Pytorch-seed) 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: 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: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

madoe001/ppo-LunarLander-v2

madoe001

2023-07-10T16:00:09Z

2

0

stable-baselines3

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

reinforcement-learning

2023-03-19T17:22:29Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 271.45 +/- 25.19 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

sianadouglas/ensembletest

sianadouglas

2023-07-10T15:48:14Z

0

null

[ "en", "license:other", "region:us" ]

null

2023-07-10T15:47:23Z

--- license: other language: - en ---

Khushnur/t5-base-end2end-questions-generation_squad

Khushnur

2023-07-10T15:47:50Z

164

0

transformers

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

text2text-generation

2023-07-10T15:02:34Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-end2end-questions-generation_squad results: [] ---  # t5-base-end2end-questions-generation_squad This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6560 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.5658 | 0.67 | 100 | 1.8866 | | 1.958 | 1.35 | 200 | 1.7150 | | 1.8516 | 2.02 | 300 | 1.6701 | | 1.7965 | 2.69 | 400 | 1.6560 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

mgmeskill/Pixelcopter-PLE-v0

mgmeskill

2023-07-10T15:38:32Z

0

null

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

reinforcement-learning

2023-07-10T15:26:11Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.50 +/- 37.13 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Suryabhan/openai-whisper-large-v2-LORA-colab

Suryabhan

2023-07-10T15:32:46Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-10T15:32:41Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

tyavika/LR1E4-BS16-Bert_CNN512LSTM256NoBid

tyavika

2023-07-10T15:31:42Z

77

0

transformers

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

question-answering

2023-07-09T20:06:29Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Bert_CNN512LSTM256NoBid results: [] ---  # LR1E4-BS16-Bert_CNN512LSTM256NoBid This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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 | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7267 | 1.0 | 3290 | 1.5092 | | 1.2394 | 2.0 | 6580 | 1.3933 | | 0.8348 | 3.0 | 9870 | 1.5591 | | 0.542 | 4.0 | 13160 | 1.6667 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

MnLgt/textual_inversion_muir_1_5

MnLgt

2023-07-10T15:31:36Z

1

0

diffusers

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

text-to-image

2023-07-10T14:16:45Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - jordandavis/textual_inversion_muir_1_5 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

agercas/speecht5_finetuned_voxpopuli_nl

agercas

2023-07-10T15:27:22Z

78

0

transformers

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

text-to-audio

2023-07-10T09:21:57Z

--- license: mit tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] ---  # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4572 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5221 | 4.3 | 1000 | 0.4774 | | 0.505 | 8.61 | 2000 | 0.4648 | | 0.4929 | 12.91 | 3000 | 0.4583 | | 0.4901 | 17.21 | 4000 | 0.4572 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

grace-pro/afriberta-finetuned-hausa

grace-pro

2023-07-10T15:26:48Z

126

0

transformers

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

token-classification

2023-07-10T14:49:51Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-finetuned-hausa results: [] ---  # afriberta-finetuned-hausa This model is a fine-tuned version of [castorini/afriberta_large](https://huggingface.co/castorini/afriberta_large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1242 - Precision: 0.7104 - Recall: 0.5095 - F1: 0.5934 - Accuracy: 0.9647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1369 | 1.0 | 2624 | 0.1256 | 0.6856 | 0.4541 | 0.5463 | 0.9614 | | 0.1103 | 2.0 | 5248 | 0.1195 | 0.7014 | 0.4947 | 0.5802 | 0.9637 | | 0.0868 | 3.0 | 7872 | 0.1242 | 0.7104 | 0.5095 | 0.5934 | 0.9647 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Birchlabs/llama-13b-stepwise-embeddings

Birchlabs

2023-07-10T15:17:11Z

0

null

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

null

2023-07-10T13:55:53Z

--- license: apache-2.0 --- Fine-tuned input (`embed_tokens: Embedding`) and output (`lm_head: Linear`) embeddings layers, for use with [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter). Prior to finetuning: we grew the vocabulary of the tokenizer and embeddings layers. The new embeddings were average-initialized, and needed training, so we trained them. These are the weights from that training. Ordinarily a QLoRA finetune of an LLM would not finetune the `embed_tokens: Embedding` (you'd need to get a bit creative, because not only have the dimensions changed, but also I don't believe any way has been established to train _adapters_ over `Embedding`s). Nor apparently would it finetune `lm_head: Linear`. This is harder than it sounds (i.e. you can't handle it the same way you adapt the other Linear layers), because the dimensions have grown.

S1X3L4/Taxi-v3

S1X3L4

2023-07-10T15:04:55Z

0

null

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

reinforcement-learning

2023-07-10T15:04:50Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 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="S1X3L4/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"]) ```

dariowsz/wav2vec2-base-finetuned-gtzan

dariowsz

2023-07-10T15:03:27Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-04T13:47:36Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: [] ---  # wav2vec2-base-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5537 - Accuracy: 0.88 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7898 | 1.0 | 113 | 1.8052 | 0.45 | | 1.4297 | 2.0 | 226 | 1.2229 | 0.62 | | 1.041 | 3.0 | 339 | 0.9934 | 0.65 | | 1.3882 | 4.0 | 452 | 1.1735 | 0.62 | | 0.7248 | 5.0 | 565 | 0.8461 | 0.69 | | 0.6128 | 6.0 | 678 | 0.7391 | 0.75 | | 0.3225 | 7.0 | 791 | 0.8754 | 0.74 | | 0.6483 | 8.0 | 904 | 0.8341 | 0.79 | | 0.2755 | 9.0 | 1017 | 0.5537 | 0.88 | | 0.4398 | 10.0 | 1130 | 0.6076 | 0.85 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

NICFRU/bart-base-paraphrasing-news

NICFRU

2023-07-10T15:02:02Z

106

0

transformers

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

text2text-generation

2023-07-10T14:46:34Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-paraphrasing results: [] ---  # bart-base-paraphrasing This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6617 - Rouge1: 57.7088 - Rouge2: 51.0096 - Rougel: 54.7514 - Rougelsum: 56.3943 - Gen Len: 20.0 ## 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: 4e-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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 0.2 | 10 | 0.5263 | 58.2676 | 51.5842 | 55.5057 | 57.1584 | 19.94 | | No log | 0.4 | 20 | 0.5050 | 56.1604 | 48.7383 | 54.0373 | 55.372 | 20.0 | | No log | 0.6 | 30 | 0.4674 | 58.0617 | 51.4993 | 56.0368 | 56.9665 | 20.0 | | No log | 0.8 | 40 | 0.4545 | 57.5375 | 51.0203 | 55.5247 | 56.5761 | 19.94 | | No log | 1.0 | 50 | 0.4373 | 57.7263 | 50.8021 | 55.0549 | 56.35 | 19.98 | | No log | 1.2 | 60 | 0.4313 | 57.87 | 50.9904 | 54.9727 | 56.5379 | 19.97 | | No log | 1.4 | 70 | 0.4855 | 56.5101 | 49.3124 | 54.1572 | 55.0671 | 20.0 | | No log | 1.6 | 80 | 0.4202 | 56.6535 | 50.0302 | 53.6891 | 55.1016 | 19.96 | | No log | 1.8 | 90 | 0.4544 | 57.315 | 50.6289 | 54.642 | 55.7326 | 19.95 | | 0.5858 | 2.0 | 100 | 0.4157 | 56.4569 | 48.8105 | 53.937 | 55.3515 | 20.0 | | 0.5858 | 2.2 | 110 | 0.4555 | 57.8424 | 51.5966 | 55.6655 | 56.6862 | 20.0 | | 0.5858 | 2.4 | 120 | 0.4196 | 58.2562 | 51.7596 | 55.5085 | 57.1823 | 19.97 | | 0.5858 | 2.6 | 130 | 0.4334 | 58.6906 | 51.6106 | 55.6631 | 57.5254 | 19.89 | | 0.5858 | 2.8 | 140 | 0.4710 | 56.5401 | 49.33 | 53.8792 | 55.0282 | 20.0 | | 0.5858 | 3.0 | 150 | 0.4357 | 58.2083 | 52.0049 | 55.9938 | 57.1928 | 20.0 | | 0.5858 | 3.2 | 160 | 0.4735 | 58.8112 | 52.2196 | 56.5004 | 57.7703 | 19.94 | | 0.5858 | 3.4 | 170 | 0.4428 | 57.6778 | 50.6377 | 54.8752 | 56.4778 | 20.0 | | 0.5858 | 3.6 | 180 | 0.4983 | 57.4124 | 50.4244 | 54.6163 | 56.0992 | 20.0 | | 0.5858 | 3.8 | 190 | 0.4620 | 58.0701 | 51.5021 | 55.7222 | 56.8737 | 20.0 | | 0.2865 | 4.0 | 200 | 0.4502 | 59.1191 | 52.7516 | 56.4389 | 57.7153 | 20.0 | | 0.2865 | 4.2 | 210 | 0.4805 | 58.9064 | 52.7148 | 56.1058 | 57.6709 | 20.0 | | 0.2865 | 4.4 | 220 | 0.4755 | 58.6883 | 52.1464 | 55.9164 | 57.3825 | 20.0 | | 0.2865 | 4.6 | 230 | 0.4524 | 58.9916 | 52.1101 | 56.4116 | 57.9468 | 19.9 | | 0.2865 | 4.8 | 240 | 0.4726 | 58.9953 | 52.8173 | 56.5846 | 58.0805 | 20.0 | | 0.2865 | 5.0 | 250 | 0.4841 | 58.1058 | 51.614 | 55.3374 | 56.7617 | 20.0 | | 0.2865 | 5.2 | 260 | 0.5047 | 58.2785 | 51.1874 | 55.5336 | 56.8795 | 20.0 | | 0.2865 | 5.4 | 270 | 0.4658 | 57.2753 | 49.6038 | 53.9588 | 55.6038 | 19.91 | | 0.2865 | 5.6 | 280 | 0.5261 | 58.1691 | 51.5254 | 55.2685 | 56.7787 | 20.0 | | 0.2865 | 5.8 | 290 | 0.4833 | 57.8088 | 51.2838 | 54.8739 | 56.4374 | 20.0 | | 0.1668 | 6.0 | 300 | 0.5067 | 58.2021 | 51.3629 | 55.3548 | 56.9093 | 19.99 | | 0.1668 | 6.2 | 310 | 0.5461 | 58.0327 | 51.4051 | 55.3426 | 56.7923 | 20.0 | | 0.1668 | 6.4 | 320 | 0.5463 | 58.1027 | 51.3706 | 55.1733 | 56.7923 | 19.9 | | 0.1668 | 6.6 | 330 | 0.5837 | 57.6284 | 50.8245 | 54.6253 | 56.2127 | 20.0 | | 0.1668 | 6.8 | 340 | 0.5221 | 58.0869 | 51.5448 | 55.4226 | 56.7532 | 20.0 | | 0.1668 | 7.0 | 350 | 0.5433 | 58.7676 | 52.0403 | 56.2634 | 57.6441 | 20.0 | | 0.1668 | 7.2 | 360 | 0.5498 | 57.9172 | 50.9727 | 55.1006 | 56.6018 | 20.0 | | 0.1668 | 7.4 | 370 | 0.5581 | 57.4669 | 50.698 | 54.6448 | 56.1325 | 20.0 | | 0.1668 | 7.6 | 380 | 0.5526 | 57.0821 | 50.298 | 54.1635 | 55.8059 | 20.0 | | 0.1668 | 7.8 | 390 | 0.5548 | 57.5422 | 50.2734 | 54.2446 | 56.1223 | 20.0 | | 0.1071 | 8.0 | 400 | 0.5620 | 57.4548 | 50.2657 | 54.5094 | 55.9422 | 20.0 | | 0.1071 | 8.2 | 410 | 0.5772 | 57.4144 | 50.2443 | 54.5173 | 55.9331 | 20.0 | | 0.1071 | 8.4 | 420 | 0.5857 | 57.2975 | 50.2116 | 54.5918 | 55.9931 | 20.0 | | 0.1071 | 8.6 | 430 | 0.5827 | 58.4767 | 51.4318 | 55.4792 | 57.1284 | 20.0 | | 0.1071 | 8.8 | 440 | 0.5728 | 58.4414 | 51.3523 | 55.2838 | 57.202 | 20.0 | | 0.1071 | 9.0 | 450 | 0.5919 | 58.0499 | 51.3783 | 55.0748 | 56.6939 | 20.0 | | 0.1071 | 9.2 | 460 | 0.5937 | 57.7604 | 50.845 | 54.8941 | 56.351 | 20.0 | | 0.1071 | 9.4 | 470 | 0.5970 | 57.3655 | 50.4126 | 54.4522 | 55.7815 | 20.0 | | 0.1071 | 9.6 | 480 | 0.5911 | 58.203 | 51.0367 | 55.3215 | 56.8485 | 20.0 | | 0.1071 | 9.8 | 490 | 0.6121 | 58.2898 | 51.2749 | 55.4292 | 57.0241 | 20.0 | | 0.0718 | 10.0 | 500 | 0.5903 | 58.2487 | 51.3838 | 55.4237 | 56.8863 | 20.0 | | 0.0718 | 10.2 | 510 | 0.5983 | 58.2681 | 51.0925 | 55.2887 | 56.9562 | 20.0 | | 0.0718 | 10.4 | 520 | 0.6308 | 57.9797 | 50.7386 | 54.995 | 56.5939 | 20.0 | | 0.0718 | 10.6 | 530 | 0.6307 | 57.6269 | 50.5515 | 54.446 | 56.1544 | 20.0 | | 0.0718 | 10.8 | 540 | 0.6173 | 57.9545 | 51.1005 | 54.9406 | 56.5732 | 20.0 | | 0.0718 | 11.0 | 550 | 0.6322 | 58.3718 | 51.4321 | 55.4241 | 57.1879 | 20.0 | | 0.0718 | 11.2 | 560 | 0.6027 | 58.6581 | 51.8607 | 55.6436 | 57.32 | 20.0 | | 0.0718 | 11.4 | 570 | 0.6140 | 58.6476 | 51.7822 | 55.5845 | 57.3018 | 20.0 | | 0.0718 | 11.6 | 580 | 0.6184 | 59.2454 | 52.4204 | 56.2174 | 57.9278 | 20.0 | | 0.0718 | 11.8 | 590 | 0.6281 | 59.2945 | 52.8165 | 56.547 | 58.0674 | 20.0 | | 0.0512 | 12.0 | 600 | 0.6128 | 58.2165 | 51.3689 | 55.37 | 56.8342 | 20.0 | | 0.0512 | 12.2 | 610 | 0.6482 | 57.9196 | 50.9793 | 55.0883 | 56.6986 | 20.0 | | 0.0512 | 12.4 | 620 | 0.6267 | 57.4782 | 50.1118 | 54.2802 | 55.8872 | 20.0 | | 0.0512 | 12.6 | 630 | 0.6198 | 57.457 | 50.4079 | 54.2449 | 55.8118 | 20.0 | | 0.0512 | 12.8 | 640 | 0.6500 | 57.6903 | 51.0627 | 55.0743 | 56.3025 | 20.0 | | 0.0512 | 13.0 | 650 | 0.6265 | 57.4394 | 50.9013 | 54.7936 | 56.1688 | 20.0 | | 0.0512 | 13.2 | 660 | 0.6817 | 58.4345 | 51.7087 | 55.291 | 57.0057 | 20.0 | | 0.0512 | 13.4 | 670 | 0.6322 | 57.869 | 50.9503 | 54.8937 | 56.5178 | 20.0 | | 0.0512 | 13.6 | 680 | 0.6424 | 57.8285 | 51.1014 | 55.0072 | 56.5022 | 20.0 | | 0.0512 | 13.8 | 690 | 0.6668 | 58.7067 | 51.9929 | 55.5044 | 57.1517 | 20.0 | | 0.0397 | 14.0 | 700 | 0.6537 | 58.8717 | 52.4036 | 55.6521 | 57.4855 | 20.0 | | 0.0397 | 14.2 | 710 | 0.6463 | 58.9623 | 52.4749 | 55.8145 | 57.8095 | 20.0 | | 0.0397 | 14.4 | 720 | 0.6630 | 58.8097 | 52.1997 | 55.8204 | 57.6325 | 20.0 | | 0.0397 | 14.6 | 730 | 0.6839 | 59.0479 | 52.6573 | 56.0439 | 57.7322 | 20.0 | | 0.0397 | 14.8 | 740 | 0.6541 | 59.2854 | 52.6109 | 56.1891 | 57.9446 | 20.0 | | 0.0397 | 15.0 | 750 | 0.6486 | 58.8419 | 52.2004 | 55.8071 | 57.49 | 20.0 | | 0.0397 | 15.2 | 760 | 0.6578 | 57.6161 | 50.7276 | 54.5514 | 56.2359 | 20.0 | | 0.0397 | 15.4 | 770 | 0.6673 | 57.5458 | 50.8286 | 54.4597 | 56.1513 | 20.0 | | 0.0397 | 15.6 | 780 | 0.6624 | 57.6634 | 51.0017 | 54.6769 | 56.3837 | 20.0 | | 0.0397 | 15.8 | 790 | 0.6469 | 57.9037 | 51.137 | 54.8939 | 56.6427 | 20.0 | | 0.0301 | 16.0 | 800 | 0.6373 | 57.8696 | 51.0899 | 54.8543 | 56.4596 | 20.0 | | 0.0301 | 16.2 | 810 | 0.6712 | 58.614 | 52.0052 | 55.6436 | 57.3211 | 20.0 | | 0.0301 | 16.4 | 820 | 0.6812 | 58.5214 | 51.8911 | 55.7447 | 57.2663 | 20.0 | | 0.0301 | 16.6 | 830 | 0.6716 | 58.5818 | 51.929 | 55.7993 | 57.4064 | 20.0 | | 0.0301 | 16.8 | 840 | 0.6590 | 57.745 | 51.0481 | 54.8545 | 56.4781 | 20.0 | | 0.0301 | 17.0 | 850 | 0.6695 | 57.6663 | 50.9646 | 54.7863 | 56.3687 | 20.0 | | 0.0301 | 17.2 | 860 | 0.6858 | 57.5552 | 51.0436 | 54.7092 | 56.3079 | 20.0 | | 0.0301 | 17.4 | 870 | 0.6840 | 57.9091 | 51.3823 | 54.8309 | 56.6186 | 20.0 | | 0.0301 | 17.6 | 880 | 0.6751 | 57.8223 | 51.1688 | 54.7562 | 56.5558 | 20.0 | | 0.0301 | 17.8 | 890 | 0.6589 | 57.9956 | 51.1425 | 54.9509 | 56.6868 | 20.0 | | 0.0482 | 18.0 | 900 | 0.6634 | 58.0392 | 51.3121 | 55.0726 | 56.7878 | 20.0 | | 0.0482 | 18.2 | 910 | 0.6907 | 58.2021 | 51.4548 | 55.1874 | 56.91 | 20.0 | | 0.0482 | 18.4 | 920 | 0.6977 | 58.1124 | 51.4254 | 55.062 | 56.8412 | 20.0 | | 0.0482 | 18.6 | 930 | 0.6832 | 58.0776 | 51.3168 | 55.0849 | 56.8226 | 20.0 | | 0.0482 | 18.8 | 940 | 0.6672 | 57.925 | 51.2475 | 54.9661 | 56.655 | 20.0 | | 0.0482 | 19.0 | 950 | 0.6582 | 57.9285 | 51.2483 | 54.9744 | 56.6609 | 20.0 | | 0.0482 | 19.2 | 960 | 0.6575 | 57.9285 | 51.2483 | 54.9744 | 56.6609 | 20.0 | | 0.0482 | 19.4 | 970 | 0.6619 | 57.8961 | 51.2097 | 54.9475 | 56.6344 | 20.0 | | 0.0482 | 19.6 | 980 | 0.6658 | 57.8961 | 51.2097 | 54.9475 | 56.6344 | 20.0 | | 0.0482 | 19.8 | 990 | 0.6635 | 57.7222 | 51.0096 | 54.8166 | 56.4623 | 20.0 | | 0.0201 | 20.0 | 1000 | 0.6617 | 57.7088 | 51.0096 | 54.7514 | 56.3943 | 20.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

SHENMU007/neunit_BASE_V10.19

SHENMU007

2023-07-10T15:01:47Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-07T08:50:51Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

NasimB/gpt2-cocnat-mod-datasets-txt-processing

NasimB

2023-07-10T15:01:23Z

5

0

transformers

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

text-generation

2023-07-10T12:29:02Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-mod-datasets-txt-processing results: [] ---  # gpt2-cocnat-mod-datasets-txt-processing This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3377 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6848 | 0.3 | 500 | 5.6500 | | 5.3379 | 0.59 | 1000 | 5.2204 | | 4.9909 | 0.89 | 1500 | 4.9703 | | 4.7146 | 1.19 | 2000 | 4.8200 | | 4.5695 | 1.49 | 2500 | 4.7076 | | 4.4685 | 1.78 | 3000 | 4.5985 | | 4.3237 | 2.08 | 3500 | 4.5311 | | 4.1614 | 2.38 | 4000 | 4.4731 | | 4.1267 | 2.68 | 4500 | 4.4151 | | 4.082 | 2.97 | 5000 | 4.3593 | | 3.8448 | 3.27 | 5500 | 4.3575 | | 3.8261 | 3.57 | 6000 | 4.3240 | | 3.8089 | 3.86 | 6500 | 4.2887 | | 3.6462 | 4.16 | 7000 | 4.2921 | | 3.5453 | 4.46 | 7500 | 4.2840 | | 3.529 | 4.76 | 8000 | 4.2688 | | 3.4926 | 5.05 | 8500 | 4.2683 | | 3.3463 | 5.35 | 9000 | 4.2715 | | 3.3453 | 5.65 | 9500 | 4.2702 | | 3.3408 | 5.95 | 10000 | 4.2694 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

ericNguyen0132/DepRoBERTa-2ndStage

ericNguyen0132

2023-07-10T14:56:14Z

105

0

transformers

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

text-classification

2023-07-10T13:42:58Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: DepRoBERTa-2ndStage results: [] ---  # DepRoBERTa-2ndStage This model is a fine-tuned version of [rafalposwiata/deproberta-large-v1](https://huggingface.co/rafalposwiata/deproberta-large-v1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6330 - Accuracy: 0.855 - F1: 0.9134 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 469 | 0.3572 | 0.8617 | 0.9224 | | 0.4953 | 2.0 | 938 | 0.3593 | 0.8783 | 0.9315 | | 0.3493 | 3.0 | 1407 | 0.4274 | 0.8483 | 0.9091 | | 0.313 | 4.0 | 1876 | 0.5488 | 0.8617 | 0.9187 | | 0.2622 | 5.0 | 2345 | 0.6330 | 0.855 | 0.9134 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Nyme/textual_inversion_cat

Nyme

2023-07-10T14:49:16Z

5

0

diffusers

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

text-to-image

2023-07-10T09:17:48Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Nyme/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

Tasaloris13/falcon-7b-test

Tasaloris13

2023-07-10T14:31:30Z

0

peft

[ "peft", "region:us" ]

null

2023-07-10T14:31:29Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

medanis13/chatbot

medanis13

2023-07-10T14:25:42Z

0

peft

[ "peft", "region:us" ]

null

2023-07-10T14:22:19Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

jiuzhou/roop

jiuzhou

2023-07-10T14:14:43Z

0

1

null

[ "region:us" ]

null

2023-07-10T14:11:09Z

# Roop项目的Colab脚本使用谷歌免费的GPU在线运行一键换脸，[点击打开](roop_v1.ipynb)！ ![demo](4.jpg) # 更新 ![demo](new.jpg) # 原项目地址[roop](https://github.com/s0md3v/roop/) ![demo-gif](https://github.com/s0md3v/roop/blob/main/demo.gif) ![demo](cmp.gif) # 使用方法打开.ipynb文件，然后点击open in colab 就可以开始使用了，详细的使用教程，点[这里](https://www.tonyisstark.com/1240.html)

jordyvl/vit-_tobacco3482_kd_MSE_test_pretrain_student

jordyvl

2023-07-10T14:09:40Z

163

0

transformers

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

image-classification

2023-07-10T14:07:44Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: vit-_tobacco3482_kd_MSE_test_pretrain_student results: [] ---  # vit-_tobacco3482_kd_MSE_test_pretrain_student This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 0.8077 | 0.4 | 0.7439 | 5.4442 | 0.4000 | 0.2755 | 0.2844 | 0.3738 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1