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 2025-07-15 06:27:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 06:27:26	card stringlengths 11 1.01M
tvarella/q-FrozenLake-v1-4x4-noSlippery	tvarella	2023-02-14T21:10:59Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T21:08:24Z	--- 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="tvarella/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"]) ```
enlacinglines/q-FrozenLake-v1-4x4-noSlippery	enlacinglines	2023-02-14T21:08:21Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T21:08:18Z	--- 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="enlacinglines/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"]) ```
dor88/Reinforce-pixelcopter	dor88	2023-02-14T20:00:08Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T13:33:37Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 58.50 +/- 53.06 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
Deisler/poca-SoccerTwos	Deisler	2023-02-14T19:50:10Z	47	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-02-09T22:49:24Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: Deisler/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
fpsandnoob/mae-vit-base-patch32-224-ct	fpsandnoob	2023-02-14T19:43:11Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit_mae", "pretraining", "masked-auto-encoding", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-02-14T12:05:25Z	--- license: apache-2.0 tags: - masked-auto-encoding - generated_from_trainer datasets: - imagefolder model-index: - name: mae-vit-base-patch32-224-ct 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. --> # mae-vit-base-patch32-224-ct This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3803 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - train_batch_size: 256 - eval_batch_size: 256 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1200.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 1.1257 \| 1.0 \| 51 \| 1.1119 \| \| 1.0507 \| 2.0 \| 102 \| 1.0434 \| \| 1.0046 \| 3.0 \| 153 \| 0.9988 \| \| 0.9761 \| 4.0 \| 204 \| 0.9725 \| \| 0.9572 \| 5.0 \| 255 \| 0.9529 \| \| 0.9357 \| 6.0 \| 306 \| 0.9304 \| \| 0.9128 \| 7.0 \| 357 \| 0.9100 \| \| 0.9037 \| 8.0 \| 408 \| 0.9004 \| \| 0.8984 \| 9.0 \| 459 \| 0.8941 \| \| 0.8904 \| 10.0 \| 510 \| 0.8896 \| \| 0.8846 \| 11.0 \| 561 \| 0.8802 \| \| 0.8748 \| 12.0 \| 612 \| 0.8775 \| \| 0.8692 \| 13.0 \| 663 \| 0.8685 \| \| 0.8656 \| 14.0 \| 714 \| 0.8665 \| \| 0.8634 \| 15.0 \| 765 \| 0.8607 \| \| 0.8565 \| 16.0 \| 816 \| 0.8561 \| \| 0.8555 \| 17.0 \| 867 \| 0.8548 \| \| 0.8521 \| 18.0 \| 918 \| 0.8464 \| \| 0.8478 \| 19.0 \| 969 \| 0.8449 \| \| 0.847 \| 20.0 \| 1020 \| 0.8455 \| \| 0.842 \| 21.0 \| 1071 \| 0.8378 \| \| 0.8385 \| 22.0 \| 1122 \| 0.8358 \| \| 0.8319 \| 23.0 \| 1173 \| 0.8332 \| \| 0.8267 \| 24.0 \| 1224 \| 0.8347 \| \| 0.8266 \| 25.0 \| 1275 \| 0.8247 \| \| 0.8242 \| 26.0 \| 1326 \| 0.8242 \| \| 0.8215 \| 27.0 \| 1377 \| 0.8192 \| \| 0.8171 \| 28.0 \| 1428 \| 0.8213 \| \| 0.8176 \| 29.0 \| 1479 \| 0.8160 \| \| 0.8122 \| 30.0 \| 1530 \| 0.8128 \| \| 0.8107 \| 31.0 \| 1581 \| 0.8036 \| \| 0.8069 \| 32.0 \| 1632 \| 0.8069 \| \| 0.8081 \| 33.0 \| 1683 \| 0.8023 \| \| 0.8043 \| 34.0 \| 1734 \| 0.8048 \| \| 0.8071 \| 35.0 \| 1785 \| 0.8082 \| \| 0.8017 \| 36.0 \| 1836 \| 0.7971 \| \| 0.7965 \| 37.0 \| 1887 \| 0.7953 \| \| 0.7953 \| 38.0 \| 1938 \| 0.8112 \| \| 0.7979 \| 39.0 \| 1989 \| 0.7955 \| \| 0.7887 \| 40.0 \| 2040 \| 0.7966 \| \| 0.7866 \| 41.0 \| 2091 \| 0.7879 \| \| 0.7862 \| 42.0 \| 2142 \| 0.7828 \| \| 0.7836 \| 43.0 \| 2193 \| 0.7865 \| \| 0.7851 \| 44.0 \| 2244 \| 0.7830 \| \| 0.7813 \| 45.0 \| 2295 \| 0.7840 \| \| 0.78 \| 46.0 \| 2346 \| 0.7749 \| \| 0.779 \| 47.0 \| 2397 \| 0.7825 \| \| 0.7762 \| 48.0 \| 2448 \| 0.7712 \| \| 0.7676 \| 49.0 \| 2499 \| 0.7675 \| \| 0.7638 \| 50.0 \| 2550 \| 0.7645 \| \| 0.7826 \| 51.0 \| 2601 \| 0.7879 \| \| 0.7728 \| 52.0 \| 2652 \| 0.7730 \| \| 0.7629 \| 53.0 \| 2703 \| 0.7606 \| \| 0.7819 \| 54.0 \| 2754 \| 0.7718 \| \| 0.7802 \| 55.0 \| 2805 \| 0.7809 \| \| 0.7632 \| 56.0 \| 2856 \| 0.7577 \| \| 0.7567 \| 57.0 \| 2907 \| 0.7654 \| \| 0.7564 \| 58.0 \| 2958 \| 0.7574 \| \| 0.7535 \| 59.0 \| 3009 \| 0.7555 \| \| 0.75 \| 60.0 \| 3060 \| 0.7484 \| \| 0.7512 \| 61.0 \| 3111 \| 0.7487 \| \| 0.7493 \| 62.0 \| 3162 \| 0.7462 \| \| 0.742 \| 63.0 \| 3213 \| 0.7450 \| \| 0.7469 \| 64.0 \| 3264 \| 0.7464 \| \| 0.7449 \| 65.0 \| 3315 \| 0.7393 \| \| 0.7321 \| 66.0 \| 3366 \| 0.7425 \| \| 0.7411 \| 67.0 \| 3417 \| 0.7391 \| \| 0.7394 \| 68.0 \| 3468 \| 0.7413 \| \| 0.7301 \| 69.0 \| 3519 \| 0.7344 \| \| 0.7208 \| 70.0 \| 3570 \| 0.7256 \| \| 0.7211 \| 71.0 \| 3621 \| 0.7225 \| \| 0.7273 \| 72.0 \| 3672 \| 0.7264 \| \| 0.7267 \| 73.0 \| 3723 \| 0.7221 \| \| 0.7222 \| 74.0 \| 3774 \| 0.7256 \| \| 0.7175 \| 75.0 \| 3825 \| 0.7202 \| \| 0.7174 \| 76.0 \| 3876 \| 0.7149 \| \| 0.7143 \| 77.0 \| 3927 \| 0.7127 \| \| 0.7106 \| 78.0 \| 3978 \| 0.7061 \| \| 0.7188 \| 79.0 \| 4029 \| 0.7153 \| \| 0.7103 \| 80.0 \| 4080 \| 0.7086 \| \| 0.7055 \| 81.0 \| 4131 \| 0.7098 \| \| 0.7026 \| 82.0 \| 4182 \| 0.7075 \| \| 0.7191 \| 83.0 \| 4233 \| 0.7127 \| \| 0.7027 \| 84.0 \| 4284 \| 0.7172 \| \| 0.6981 \| 85.0 \| 4335 \| 0.7070 \| \| 0.7064 \| 86.0 \| 4386 \| 0.7029 \| \| 0.6943 \| 87.0 \| 4437 \| 0.7046 \| \| 0.7025 \| 88.0 \| 4488 \| 0.7036 \| \| 0.6959 \| 89.0 \| 4539 \| 0.7094 \| \| 0.6988 \| 90.0 \| 4590 \| 0.6917 \| \| 0.6912 \| 91.0 \| 4641 \| 0.6926 \| \| 0.689 \| 92.0 \| 4692 \| 0.6881 \| \| 0.687 \| 93.0 \| 4743 \| 0.6866 \| \| 0.6867 \| 94.0 \| 4794 \| 0.6873 \| \| 0.6832 \| 95.0 \| 4845 \| 0.6820 \| \| 0.6863 \| 96.0 \| 4896 \| 0.6809 \| \| 0.6908 \| 97.0 \| 4947 \| 0.6792 \| \| 0.6891 \| 98.0 \| 4998 \| 0.6796 \| \| 0.6803 \| 99.0 \| 5049 \| 0.6793 \| \| 0.6755 \| 100.0 \| 5100 \| 0.6738 \| \| 0.6735 \| 101.0 \| 5151 \| 0.6750 \| \| 0.6727 \| 102.0 \| 5202 \| 0.6729 \| \| 0.6695 \| 103.0 \| 5253 \| 0.6734 \| \| 0.6678 \| 104.0 \| 5304 \| 0.6702 \| \| 0.671 \| 105.0 \| 5355 \| 0.6720 \| \| 0.6654 \| 106.0 \| 5406 \| 0.6686 \| \| 0.669 \| 107.0 \| 5457 \| 0.6683 \| \| 0.6628 \| 108.0 \| 5508 \| 0.6639 \| \| 0.6655 \| 109.0 \| 5559 \| 0.6663 \| \| 0.6637 \| 110.0 \| 5610 \| 0.6651 \| \| 0.6643 \| 111.0 \| 5661 \| 0.6639 \| \| 0.6607 \| 112.0 \| 5712 \| 0.6561 \| \| 0.6598 \| 113.0 \| 5763 \| 0.6591 \| \| 0.6589 \| 114.0 \| 5814 \| 0.6610 \| \| 0.6566 \| 115.0 \| 5865 \| 0.6566 \| \| 0.6706 \| 116.0 \| 5916 \| 0.6749 \| \| 0.6688 \| 117.0 \| 5967 \| 0.6670 \| \| 0.6657 \| 118.0 \| 6018 \| 0.6599 \| \| 0.6611 \| 119.0 \| 6069 \| 0.6567 \| \| 0.6528 \| 120.0 \| 6120 \| 0.6591 \| \| 0.652 \| 121.0 \| 6171 \| 0.6566 \| \| 0.6488 \| 122.0 \| 6222 \| 0.6528 \| \| 0.6538 \| 123.0 \| 6273 \| 0.6558 \| \| 0.6457 \| 124.0 \| 6324 \| 0.6509 \| \| 0.643 \| 125.0 \| 6375 \| 0.6462 \| \| 0.6433 \| 126.0 \| 6426 \| 0.6459 \| \| 0.6451 \| 127.0 \| 6477 \| 0.6454 \| \| 0.6413 \| 128.0 \| 6528 \| 0.6441 \| \| 0.6407 \| 129.0 \| 6579 \| 0.6409 \| \| 0.6381 \| 130.0 \| 6630 \| 0.6422 \| \| 0.6408 \| 131.0 \| 6681 \| 0.6432 \| \| 0.6404 \| 132.0 \| 6732 \| 0.6408 \| \| 0.6412 \| 133.0 \| 6783 \| 0.6354 \| \| 0.6348 \| 134.0 \| 6834 \| 0.6350 \| \| 0.6307 \| 135.0 \| 6885 \| 0.6389 \| \| 0.639 \| 136.0 \| 6936 \| 0.6417 \| \| 0.6319 \| 137.0 \| 6987 \| 0.6353 \| \| 0.6306 \| 138.0 \| 7038 \| 0.6385 \| \| 0.6307 \| 139.0 \| 7089 \| 0.6412 \| \| 0.6343 \| 140.0 \| 7140 \| 0.6308 \| \| 0.6289 \| 141.0 \| 7191 \| 0.6337 \| \| 0.6298 \| 142.0 \| 7242 \| 0.6342 \| \| 0.6284 \| 143.0 \| 7293 \| 0.6287 \| \| 0.624 \| 144.0 \| 7344 \| 0.6305 \| \| 0.6266 \| 145.0 \| 7395 \| 0.6338 \| \| 0.6253 \| 146.0 \| 7446 \| 0.6281 \| \| 0.6204 \| 147.0 \| 7497 \| 0.6241 \| \| 0.6232 \| 148.0 \| 7548 \| 0.6222 \| \| 0.6213 \| 149.0 \| 7599 \| 0.6201 \| \| 0.6225 \| 150.0 \| 7650 \| 0.6237 \| \| 0.6228 \| 151.0 \| 7701 \| 0.6193 \| \| 0.6191 \| 152.0 \| 7752 \| 0.6200 \| \| 0.6198 \| 153.0 \| 7803 \| 0.6229 \| \| 0.6183 \| 154.0 \| 7854 \| 0.6213 \| \| 0.6181 \| 155.0 \| 7905 \| 0.6213 \| \| 0.6168 \| 156.0 \| 7956 \| 0.6164 \| \| 0.6156 \| 157.0 \| 8007 \| 0.6160 \| \| 0.6125 \| 158.0 \| 8058 \| 0.6153 \| \| 0.6126 \| 159.0 \| 8109 \| 0.6151 \| \| 0.6115 \| 160.0 \| 8160 \| 0.6163 \| \| 0.611 \| 161.0 \| 8211 \| 0.6167 \| \| 0.6099 \| 162.0 \| 8262 \| 0.6083 \| \| 0.6089 \| 163.0 \| 8313 \| 0.6104 \| \| 0.6091 \| 164.0 \| 8364 \| 0.6140 \| \| 0.6105 \| 165.0 \| 8415 \| 0.6122 \| \| 0.61 \| 166.0 \| 8466 \| 0.6106 \| \| 0.6104 \| 167.0 \| 8517 \| 0.6062 \| \| 0.6067 \| 168.0 \| 8568 \| 0.6095 \| \| 0.6056 \| 169.0 \| 8619 \| 0.6067 \| \| 0.607 \| 170.0 \| 8670 \| 0.6091 \| \| 0.6032 \| 171.0 \| 8721 \| 0.6041 \| \| 0.6038 \| 172.0 \| 8772 \| 0.6104 \| \| 0.605 \| 173.0 \| 8823 \| 0.6068 \| \| 0.6036 \| 174.0 \| 8874 \| 0.6005 \| \| 0.6035 \| 175.0 \| 8925 \| 0.6055 \| \| 0.6026 \| 176.0 \| 8976 \| 0.6014 \| \| 0.6012 \| 177.0 \| 9027 \| 0.6029 \| \| 0.5945 \| 178.0 \| 9078 \| 0.5967 \| \| 0.6011 \| 179.0 \| 9129 \| 0.5921 \| \| 0.5929 \| 180.0 \| 9180 \| 0.5991 \| \| 0.5981 \| 181.0 \| 9231 \| 0.5954 \| \| 0.6011 \| 182.0 \| 9282 \| 0.6007 \| \| 0.5977 \| 183.0 \| 9333 \| 0.6013 \| \| 0.5947 \| 184.0 \| 9384 \| 0.6023 \| \| 0.59 \| 185.0 \| 9435 \| 0.5968 \| \| 0.5924 \| 186.0 \| 9486 \| 0.5987 \| \| 0.5906 \| 187.0 \| 9537 \| 0.5915 \| \| 0.5928 \| 188.0 \| 9588 \| 0.5877 \| \| 0.5849 \| 189.0 \| 9639 \| 0.5911 \| \| 0.5913 \| 190.0 \| 9690 \| 0.5954 \| \| 0.5863 \| 191.0 \| 9741 \| 0.5906 \| \| 0.588 \| 192.0 \| 9792 \| 0.5942 \| \| 0.5906 \| 193.0 \| 9843 \| 0.5924 \| \| 0.5927 \| 194.0 \| 9894 \| 0.5911 \| \| 0.5857 \| 195.0 \| 9945 \| 0.5852 \| \| 0.5859 \| 196.0 \| 9996 \| 0.5910 \| \| 0.5775 \| 197.0 \| 10047 \| 0.5853 \| \| 0.586 \| 198.0 \| 10098 \| 0.5877 \| \| 0.5853 \| 199.0 \| 10149 \| 0.5848 \| \| 0.5824 \| 200.0 \| 10200 \| 0.5854 \| \| 0.5797 \| 201.0 \| 10251 \| 0.5834 \| \| 0.5857 \| 202.0 \| 10302 \| 0.5792 \| \| 0.5863 \| 203.0 \| 10353 \| 0.5824 \| \| 0.5826 \| 204.0 \| 10404 \| 0.5838 \| \| 0.579 \| 205.0 \| 10455 \| 0.5808 \| \| 0.5758 \| 206.0 \| 10506 \| 0.5810 \| \| 0.5798 \| 207.0 \| 10557 \| 0.5782 \| \| 0.576 \| 208.0 \| 10608 \| 0.5818 \| \| 0.5717 \| 209.0 \| 10659 \| 0.5826 \| \| 0.5774 \| 210.0 \| 10710 \| 0.5800 \| \| 0.5724 \| 211.0 \| 10761 \| 0.5813 \| \| 0.5706 \| 212.0 \| 10812 \| 0.5755 \| \| 0.5737 \| 213.0 \| 10863 \| 0.5788 \| \| 0.5791 \| 214.0 \| 10914 \| 0.5769 \| \| 0.5712 \| 215.0 \| 10965 \| 0.5767 \| \| 0.567 \| 216.0 \| 11016 \| 0.5790 \| \| 0.5671 \| 217.0 \| 11067 \| 0.5734 \| \| 0.5733 \| 218.0 \| 11118 \| 0.5722 \| \| 0.5673 \| 219.0 \| 11169 \| 0.5806 \| \| 0.5713 \| 220.0 \| 11220 \| 0.5764 \| \| 0.5669 \| 221.0 \| 11271 \| 0.5694 \| \| 0.5669 \| 222.0 \| 11322 \| 0.5749 \| \| 0.5665 \| 223.0 \| 11373 \| 0.5732 \| \| 0.5676 \| 224.0 \| 11424 \| 0.5676 \| \| 0.5621 \| 225.0 \| 11475 \| 0.5677 \| \| 0.5623 \| 226.0 \| 11526 \| 0.5715 \| \| 0.5695 \| 227.0 \| 11577 \| 0.5676 \| \| 0.5657 \| 228.0 \| 11628 \| 0.5667 \| \| 0.565 \| 229.0 \| 11679 \| 0.5644 \| \| 0.5617 \| 230.0 \| 11730 \| 0.5650 \| \| 0.5587 \| 231.0 \| 11781 \| 0.5637 \| \| 0.5591 \| 232.0 \| 11832 \| 0.5652 \| \| 0.5607 \| 233.0 \| 11883 \| 0.5648 \| \| 0.559 \| 234.0 \| 11934 \| 0.5681 \| \| 0.5601 \| 235.0 \| 11985 \| 0.5637 \| \| 0.5605 \| 236.0 \| 12036 \| 0.5697 \| \| 0.5555 \| 237.0 \| 12087 \| 0.5593 \| \| 0.5602 \| 238.0 \| 12138 \| 0.5683 \| \| 0.5647 \| 239.0 \| 12189 \| 0.5629 \| \| 0.5575 \| 240.0 \| 12240 \| 0.5611 \| \| 0.5577 \| 241.0 \| 12291 \| 0.5588 \| \| 0.5514 \| 242.0 \| 12342 \| 0.5584 \| \| 0.5581 \| 243.0 \| 12393 \| 0.5566 \| \| 0.555 \| 244.0 \| 12444 \| 0.5563 \| \| 0.5571 \| 245.0 \| 12495 \| 0.5541 \| \| 0.5549 \| 246.0 \| 12546 \| 0.5541 \| \| 0.5521 \| 247.0 \| 12597 \| 0.5521 \| \| 0.55 \| 248.0 \| 12648 \| 0.5567 \| \| 0.5518 \| 249.0 \| 12699 \| 0.5559 \| \| 0.5522 \| 250.0 \| 12750 \| 0.5536 \| \| 0.5481 \| 251.0 \| 12801 \| 0.5504 \| \| 0.5516 \| 252.0 \| 12852 \| 0.5563 \| \| 0.5524 \| 253.0 \| 12903 \| 0.5503 \| \| 0.5582 \| 254.0 \| 12954 \| 0.5519 \| \| 0.5514 \| 255.0 \| 13005 \| 0.5504 \| \| 0.5498 \| 256.0 \| 13056 \| 0.5520 \| \| 0.5481 \| 257.0 \| 13107 \| 0.5540 \| \| 0.551 \| 258.0 \| 13158 \| 0.5503 \| \| 0.5495 \| 259.0 \| 13209 \| 0.5491 \| \| 0.5483 \| 260.0 \| 13260 \| 0.5461 \| \| 0.5468 \| 261.0 \| 13311 \| 0.5586 \| \| 0.5454 \| 262.0 \| 13362 \| 0.5495 \| \| 0.5447 \| 263.0 \| 13413 \| 0.5455 \| \| 0.5475 \| 264.0 \| 13464 \| 0.5511 \| \| 0.5439 \| 265.0 \| 13515 \| 0.5453 \| \| 0.542 \| 266.0 \| 13566 \| 0.5477 \| \| 0.5437 \| 267.0 \| 13617 \| 0.5502 \| \| 0.5452 \| 268.0 \| 13668 \| 0.5432 \| \| 0.5397 \| 269.0 \| 13719 \| 0.5443 \| \| 0.5424 \| 270.0 \| 13770 \| 0.5410 \| \| 0.5391 \| 271.0 \| 13821 \| 0.5420 \| \| 0.5368 \| 272.0 \| 13872 \| 0.5402 \| \| 0.5387 \| 273.0 \| 13923 \| 0.5401 \| \| 0.5362 \| 274.0 \| 13974 \| 0.5414 \| \| 0.5374 \| 275.0 \| 14025 \| 0.5418 \| \| 0.5375 \| 276.0 \| 14076 \| 0.5415 \| \| 0.5427 \| 277.0 \| 14127 \| 0.5436 \| \| 0.5382 \| 278.0 \| 14178 \| 0.5366 \| \| 0.5341 \| 279.0 \| 14229 \| 0.5411 \| \| 0.5348 \| 280.0 \| 14280 \| 0.5377 \| \| 0.5339 \| 281.0 \| 14331 \| 0.5393 \| \| 0.5359 \| 282.0 \| 14382 \| 0.5359 \| \| 0.536 \| 283.0 \| 14433 \| 0.5368 \| \| 0.5362 \| 284.0 \| 14484 \| 0.5384 \| \| 0.532 \| 285.0 \| 14535 \| 0.5346 \| \| 0.5298 \| 286.0 \| 14586 \| 0.5376 \| \| 0.5352 \| 287.0 \| 14637 \| 0.5373 \| \| 0.5344 \| 288.0 \| 14688 \| 0.5359 \| \| 0.5399 \| 289.0 \| 14739 \| 0.5427 \| \| 0.5329 \| 290.0 \| 14790 \| 0.5349 \| \| 0.531 \| 291.0 \| 14841 \| 0.5321 \| \| 0.5317 \| 292.0 \| 14892 \| 0.5361 \| \| 0.5303 \| 293.0 \| 14943 \| 0.5296 \| \| 0.5291 \| 294.0 \| 14994 \| 0.5312 \| \| 0.5335 \| 295.0 \| 15045 \| 0.5244 \| \| 0.5309 \| 296.0 \| 15096 \| 0.5252 \| \| 0.5251 \| 297.0 \| 15147 \| 0.5310 \| \| 0.5266 \| 298.0 \| 15198 \| 0.5301 \| \| 0.5279 \| 299.0 \| 15249 \| 0.5308 \| \| 0.5261 \| 300.0 \| 15300 \| 0.5250 \| \| 0.5214 \| 301.0 \| 15351 \| 0.5252 \| \| 0.5269 \| 302.0 \| 15402 \| 0.5306 \| \| 0.5229 \| 303.0 \| 15453 \| 0.5264 \| \| 0.5234 \| 304.0 \| 15504 \| 0.5263 \| \| 0.5271 \| 305.0 \| 15555 \| 0.5280 \| \| 0.525 \| 306.0 \| 15606 \| 0.5233 \| \| 0.5216 \| 307.0 \| 15657 \| 0.5211 \| \| 0.5247 \| 308.0 \| 15708 \| 0.5246 \| \| 0.5203 \| 309.0 \| 15759 \| 0.5279 \| \| 0.5201 \| 310.0 \| 15810 \| 0.5246 \| \| 0.5254 \| 311.0 \| 15861 \| 0.5306 \| \| 0.5166 \| 312.0 \| 15912 \| 0.5224 \| \| 0.525 \| 313.0 \| 15963 \| 0.5192 \| \| 0.5224 \| 314.0 \| 16014 \| 0.5247 \| \| 0.5195 \| 315.0 \| 16065 \| 0.5230 \| \| 0.5189 \| 316.0 \| 16116 \| 0.5239 \| \| 0.5226 \| 317.0 \| 16167 \| 0.5180 \| \| 0.5166 \| 318.0 \| 16218 \| 0.5197 \| \| 0.5159 \| 319.0 \| 16269 \| 0.5156 \| \| 0.5156 \| 320.0 \| 16320 \| 0.5204 \| \| 0.5179 \| 321.0 \| 16371 \| 0.5215 \| \| 0.5194 \| 322.0 \| 16422 \| 0.5211 \| \| 0.519 \| 323.0 \| 16473 \| 0.5212 \| \| 0.5112 \| 324.0 \| 16524 \| 0.5175 \| \| 0.5163 \| 325.0 \| 16575 \| 0.5225 \| \| 0.5165 \| 326.0 \| 16626 \| 0.5172 \| \| 0.5104 \| 327.0 \| 16677 \| 0.5200 \| \| 0.51 \| 328.0 \| 16728 \| 0.5156 \| \| 0.5129 \| 329.0 \| 16779 \| 0.5160 \| \| 0.5084 \| 330.0 \| 16830 \| 0.5207 \| \| 0.5159 \| 331.0 \| 16881 \| 0.5147 \| \| 0.5126 \| 332.0 \| 16932 \| 0.5159 \| \| 0.5132 \| 333.0 \| 16983 \| 0.5156 \| \| 0.5092 \| 334.0 \| 17034 \| 0.5151 \| \| 0.5116 \| 335.0 \| 17085 \| 0.5147 \| \| 0.5113 \| 336.0 \| 17136 \| 0.5121 \| \| 0.5076 \| 337.0 \| 17187 \| 0.5101 \| \| 0.5106 \| 338.0 \| 17238 \| 0.5111 \| \| 0.5117 \| 339.0 \| 17289 \| 0.5094 \| \| 0.5086 \| 340.0 \| 17340 \| 0.5132 \| \| 0.5034 \| 341.0 \| 17391 \| 0.5162 \| \| 0.5061 \| 342.0 \| 17442 \| 0.5142 \| \| 0.5101 \| 343.0 \| 17493 \| 0.5136 \| \| 0.5042 \| 344.0 \| 17544 \| 0.5135 \| \| 0.5091 \| 345.0 \| 17595 \| 0.5083 \| \| 0.5095 \| 346.0 \| 17646 \| 0.5112 \| \| 0.5058 \| 347.0 \| 17697 \| 0.5121 \| \| 0.504 \| 348.0 \| 17748 \| 0.5082 \| \| 0.5016 \| 349.0 \| 17799 \| 0.5075 \| \| 0.5042 \| 350.0 \| 17850 \| 0.5090 \| \| 0.5036 \| 351.0 \| 17901 \| 0.5089 \| \| 0.5045 \| 352.0 \| 17952 \| 0.5095 \| \| 0.5067 \| 353.0 \| 18003 \| 0.5087 \| \| 0.5026 \| 354.0 \| 18054 \| 0.5064 \| \| 0.5001 \| 355.0 \| 18105 \| 0.5055 \| \| 0.5036 \| 356.0 \| 18156 \| 0.5057 \| \| 0.5012 \| 357.0 \| 18207 \| 0.5083 \| \| 0.5031 \| 358.0 \| 18258 \| 0.5110 \| \| 0.5021 \| 359.0 \| 18309 \| 0.5128 \| \| 0.4973 \| 360.0 \| 18360 \| 0.5014 \| \| 0.4988 \| 361.0 \| 18411 \| 0.5028 \| \| 0.5013 \| 362.0 \| 18462 \| 0.5035 \| \| 0.5001 \| 363.0 \| 18513 \| 0.5040 \| \| 0.4972 \| 364.0 \| 18564 \| 0.5056 \| \| 0.4994 \| 365.0 \| 18615 \| 0.5070 \| \| 0.5005 \| 366.0 \| 18666 \| 0.5070 \| \| 0.4993 \| 367.0 \| 18717 \| 0.5053 \| \| 0.4975 \| 368.0 \| 18768 \| 0.5036 \| \| 0.4967 \| 369.0 \| 18819 \| 0.5026 \| \| 0.4968 \| 370.0 \| 18870 \| 0.5011 \| \| 0.498 \| 371.0 \| 18921 \| 0.4990 \| \| 0.5022 \| 372.0 \| 18972 \| 0.5032 \| \| 0.4959 \| 373.0 \| 19023 \| 0.4972 \| \| 0.4921 \| 374.0 \| 19074 \| 0.4967 \| \| 0.4936 \| 375.0 \| 19125 \| 0.4967 \| \| 0.496 \| 376.0 \| 19176 \| 0.5000 \| \| 0.4941 \| 377.0 \| 19227 \| 0.4980 \| \| 0.4937 \| 378.0 \| 19278 \| 0.4975 \| \| 0.4979 \| 379.0 \| 19329 \| 0.4975 \| \| 0.4996 \| 380.0 \| 19380 \| 0.4932 \| \| 0.4961 \| 381.0 \| 19431 \| 0.4983 \| \| 0.4903 \| 382.0 \| 19482 \| 0.4974 \| \| 0.4899 \| 383.0 \| 19533 \| 0.4953 \| \| 0.4924 \| 384.0 \| 19584 \| 0.4953 \| \| 0.4895 \| 385.0 \| 19635 \| 0.4964 \| \| 0.4965 \| 386.0 \| 19686 \| 0.5006 \| \| 0.4896 \| 387.0 \| 19737 \| 0.4938 \| \| 0.497 \| 388.0 \| 19788 \| 0.4956 \| \| 0.4924 \| 389.0 \| 19839 \| 0.4960 \| \| 0.4904 \| 390.0 \| 19890 \| 0.4972 \| \| 0.5 \| 391.0 \| 19941 \| 0.4958 \| \| 0.4961 \| 392.0 \| 19992 \| 0.4906 \| \| 0.491 \| 393.0 \| 20043 \| 0.4918 \| \| 0.4878 \| 394.0 \| 20094 \| 0.4954 \| \| 0.4881 \| 395.0 \| 20145 \| 0.4916 \| \| 0.49 \| 396.0 \| 20196 \| 0.4946 \| \| 0.4881 \| 397.0 \| 20247 \| 0.4924 \| \| 0.4871 \| 398.0 \| 20298 \| 0.4959 \| \| 0.492 \| 399.0 \| 20349 \| 0.4867 \| \| 0.4883 \| 400.0 \| 20400 \| 0.4891 \| \| 0.4864 \| 401.0 \| 20451 \| 0.4946 \| \| 0.4898 \| 402.0 \| 20502 \| 0.4922 \| \| 0.4841 \| 403.0 \| 20553 \| 0.4902 \| \| 0.4879 \| 404.0 \| 20604 \| 0.4921 \| \| 0.4801 \| 405.0 \| 20655 \| 0.4914 \| \| 0.4877 \| 406.0 \| 20706 \| 0.4882 \| \| 0.4858 \| 407.0 \| 20757 \| 0.4882 \| \| 0.4856 \| 408.0 \| 20808 \| 0.4872 \| \| 0.4825 \| 409.0 \| 20859 \| 0.4871 \| \| 0.4865 \| 410.0 \| 20910 \| 0.4853 \| \| 0.4834 \| 411.0 \| 20961 \| 0.4908 \| \| 0.4815 \| 412.0 \| 21012 \| 0.4847 \| \| 0.4828 \| 413.0 \| 21063 \| 0.4919 \| \| 0.487 \| 414.0 \| 21114 \| 0.4899 \| \| 0.4842 \| 415.0 \| 21165 \| 0.4876 \| \| 0.4902 \| 416.0 \| 21216 \| 0.4873 \| \| 0.4809 \| 417.0 \| 21267 \| 0.4913 \| \| 0.4825 \| 418.0 \| 21318 \| 0.4832 \| \| 0.4797 \| 419.0 \| 21369 \| 0.4872 \| \| 0.4852 \| 420.0 \| 21420 \| 0.4868 \| \| 0.4879 \| 421.0 \| 21471 \| 0.4833 \| \| 0.4823 \| 422.0 \| 21522 \| 0.4824 \| \| 0.4729 \| 423.0 \| 21573 \| 0.4793 \| \| 0.4825 \| 424.0 \| 21624 \| 0.4812 \| \| 0.4739 \| 425.0 \| 21675 \| 0.4831 \| \| 0.4767 \| 426.0 \| 21726 \| 0.4848 \| \| 0.4806 \| 427.0 \| 21777 \| 0.4858 \| \| 0.4736 \| 428.0 \| 21828 \| 0.4831 \| \| 0.4857 \| 429.0 \| 21879 \| 0.4785 \| \| 0.4819 \| 430.0 \| 21930 \| 0.4805 \| \| 0.4767 \| 431.0 \| 21981 \| 0.4845 \| \| 0.4765 \| 432.0 \| 22032 \| 0.4803 \| \| 0.4785 \| 433.0 \| 22083 \| 0.4826 \| \| 0.4758 \| 434.0 \| 22134 \| 0.4814 \| \| 0.4677 \| 435.0 \| 22185 \| 0.4815 \| \| 0.4735 \| 436.0 \| 22236 \| 0.4811 \| \| 0.4764 \| 437.0 \| 22287 \| 0.4749 \| \| 0.4743 \| 438.0 \| 22338 \| 0.4846 \| \| 0.4736 \| 439.0 \| 22389 \| 0.4825 \| \| 0.4732 \| 440.0 \| 22440 \| 0.4783 \| \| 0.4706 \| 441.0 \| 22491 \| 0.4810 \| \| 0.4735 \| 442.0 \| 22542 \| 0.4780 \| \| 0.4796 \| 443.0 \| 22593 \| 0.4881 \| \| 0.4724 \| 444.0 \| 22644 \| 0.4785 \| \| 0.4701 \| 445.0 \| 22695 \| 0.4753 \| \| 0.4764 \| 446.0 \| 22746 \| 0.4787 \| \| 0.4729 \| 447.0 \| 22797 \| 0.4824 \| \| 0.4726 \| 448.0 \| 22848 \| 0.4742 \| \| 0.4736 \| 449.0 \| 22899 \| 0.4775 \| \| 0.4764 \| 450.0 \| 22950 \| 0.4755 \| \| 0.4701 \| 451.0 \| 23001 \| 0.4755 \| \| 0.4746 \| 452.0 \| 23052 \| 0.4750 \| \| 0.4727 \| 453.0 \| 23103 \| 0.4731 \| \| 0.4691 \| 454.0 \| 23154 \| 0.4686 \| \| 0.4673 \| 455.0 \| 23205 \| 0.4761 \| \| 0.4726 \| 456.0 \| 23256 \| 0.4763 \| \| 0.4726 \| 457.0 \| 23307 \| 0.4807 \| \| 0.4696 \| 458.0 \| 23358 \| 0.4738 \| \| 0.4689 \| 459.0 \| 23409 \| 0.4727 \| \| 0.4702 \| 460.0 \| 23460 \| 0.4793 \| \| 0.4692 \| 461.0 \| 23511 \| 0.4696 \| \| 0.4694 \| 462.0 \| 23562 \| 0.4713 \| \| 0.4628 \| 463.0 \| 23613 \| 0.4747 \| \| 0.4677 \| 464.0 \| 23664 \| 0.4787 \| \| 0.4673 \| 465.0 \| 23715 \| 0.4682 \| \| 0.4709 \| 466.0 \| 23766 \| 0.4692 \| \| 0.463 \| 467.0 \| 23817 \| 0.4676 \| \| 0.4654 \| 468.0 \| 23868 \| 0.4696 \| \| 0.4648 \| 469.0 \| 23919 \| 0.4675 \| \| 0.4642 \| 470.0 \| 23970 \| 0.4700 \| \| 0.4687 \| 471.0 \| 24021 \| 0.4691 \| \| 0.469 \| 472.0 \| 24072 \| 0.4749 \| \| 0.4692 \| 473.0 \| 24123 \| 0.4672 \| \| 0.4635 \| 474.0 \| 24174 \| 0.4707 \| \| 0.4635 \| 475.0 \| 24225 \| 0.4696 \| \| 0.4655 \| 476.0 \| 24276 \| 0.4652 \| \| 0.4633 \| 477.0 \| 24327 \| 0.4702 \| \| 0.4622 \| 478.0 \| 24378 \| 0.4637 \| \| 0.4571 \| 479.0 \| 24429 \| 0.4678 \| \| 0.4645 \| 480.0 \| 24480 \| 0.4635 \| \| 0.4654 \| 481.0 \| 24531 \| 0.4655 \| \| 0.4588 \| 482.0 \| 24582 \| 0.4688 \| \| 0.4608 \| 483.0 \| 24633 \| 0.4639 \| \| 0.4606 \| 484.0 \| 24684 \| 0.4654 \| \| 0.4624 \| 485.0 \| 24735 \| 0.4661 \| \| 0.4612 \| 486.0 \| 24786 \| 0.4669 \| \| 0.46 \| 487.0 \| 24837 \| 0.4653 \| \| 0.4623 \| 488.0 \| 24888 \| 0.4688 \| \| 0.4648 \| 489.0 \| 24939 \| 0.4648 \| \| 0.4602 \| 490.0 \| 24990 \| 0.4620 \| \| 0.4587 \| 491.0 \| 25041 \| 0.4652 \| \| 0.4627 \| 492.0 \| 25092 \| 0.4694 \| \| 0.4638 \| 493.0 \| 25143 \| 0.4620 \| \| 0.4565 \| 494.0 \| 25194 \| 0.4653 \| \| 0.4588 \| 495.0 \| 25245 \| 0.4598 \| \| 0.4568 \| 496.0 \| 25296 \| 0.4617 \| \| 0.4524 \| 497.0 \| 25347 \| 0.4631 \| \| 0.4635 \| 498.0 \| 25398 \| 0.4640 \| \| 0.4534 \| 499.0 \| 25449 \| 0.4643 \| \| 0.4599 \| 500.0 \| 25500 \| 0.4663 \| \| 0.4549 \| 501.0 \| 25551 \| 0.4588 \| \| 0.4595 \| 502.0 \| 25602 \| 0.4661 \| \| 0.46 \| 503.0 \| 25653 \| 0.4626 \| \| 0.4504 \| 504.0 \| 25704 \| 0.4591 \| \| 0.459 \| 505.0 \| 25755 \| 0.4623 \| \| 0.4582 \| 506.0 \| 25806 \| 0.4617 \| \| 0.4532 \| 507.0 \| 25857 \| 0.4580 \| \| 0.4555 \| 508.0 \| 25908 \| 0.4615 \| \| 0.4571 \| 509.0 \| 25959 \| 0.4617 \| \| 0.4561 \| 510.0 \| 26010 \| 0.4579 \| \| 0.4541 \| 511.0 \| 26061 \| 0.4601 \| \| 0.4534 \| 512.0 \| 26112 \| 0.4627 \| \| 0.4569 \| 513.0 \| 26163 \| 0.4615 \| \| 0.4583 \| 514.0 \| 26214 \| 0.4527 \| \| 0.4498 \| 515.0 \| 26265 \| 0.4587 \| \| 0.4511 \| 516.0 \| 26316 \| 0.4552 \| \| 0.4535 \| 517.0 \| 26367 \| 0.4579 \| \| 0.4551 \| 518.0 \| 26418 \| 0.4543 \| \| 0.4581 \| 519.0 \| 26469 \| 0.4597 \| \| 0.4573 \| 520.0 \| 26520 \| 0.4540 \| \| 0.4495 \| 521.0 \| 26571 \| 0.4578 \| \| 0.4532 \| 522.0 \| 26622 \| 0.4605 \| \| 0.4474 \| 523.0 \| 26673 \| 0.4579 \| \| 0.4504 \| 524.0 \| 26724 \| 0.4563 \| \| 0.4529 \| 525.0 \| 26775 \| 0.4583 \| \| 0.4475 \| 526.0 \| 26826 \| 0.4616 \| \| 0.4457 \| 527.0 \| 26877 \| 0.4558 \| \| 0.4532 \| 528.0 \| 26928 \| 0.4584 \| \| 0.4566 \| 529.0 \| 26979 \| 0.4573 \| \| 0.4546 \| 530.0 \| 27030 \| 0.4563 \| \| 0.4479 \| 531.0 \| 27081 \| 0.4628 \| \| 0.4485 \| 532.0 \| 27132 \| 0.4547 \| \| 0.4491 \| 533.0 \| 27183 \| 0.4539 \| \| 0.4522 \| 534.0 \| 27234 \| 0.4536 \| \| 0.4477 \| 535.0 \| 27285 \| 0.4561 \| \| 0.45 \| 536.0 \| 27336 \| 0.4530 \| \| 0.4522 \| 537.0 \| 27387 \| 0.4525 \| \| 0.4475 \| 538.0 \| 27438 \| 0.4554 \| \| 0.4475 \| 539.0 \| 27489 \| 0.4486 \| \| 0.4512 \| 540.0 \| 27540 \| 0.4584 \| \| 0.445 \| 541.0 \| 27591 \| 0.4543 \| \| 0.4478 \| 542.0 \| 27642 \| 0.4507 \| \| 0.4472 \| 543.0 \| 27693 \| 0.4520 \| \| 0.448 \| 544.0 \| 27744 \| 0.4507 \| \| 0.4447 \| 545.0 \| 27795 \| 0.4514 \| \| 0.4485 \| 546.0 \| 27846 \| 0.4553 \| \| 0.4482 \| 547.0 \| 27897 \| 0.4532 \| \| 0.4448 \| 548.0 \| 27948 \| 0.4533 \| \| 0.4467 \| 549.0 \| 27999 \| 0.4511 \| \| 0.4473 \| 550.0 \| 28050 \| 0.4531 \| \| 0.4423 \| 551.0 \| 28101 \| 0.4462 \| \| 0.4473 \| 552.0 \| 28152 \| 0.4538 \| \| 0.4463 \| 553.0 \| 28203 \| 0.4472 \| \| 0.4459 \| 554.0 \| 28254 \| 0.4486 \| \| 0.4432 \| 555.0 \| 28305 \| 0.4470 \| \| 0.4448 \| 556.0 \| 28356 \| 0.4522 \| \| 0.4406 \| 557.0 \| 28407 \| 0.4528 \| \| 0.4433 \| 558.0 \| 28458 \| 0.4502 \| \| 0.4447 \| 559.0 \| 28509 \| 0.4471 \| \| 0.4438 \| 560.0 \| 28560 \| 0.4500 \| \| 0.4433 \| 561.0 \| 28611 \| 0.4471 \| \| 0.4412 \| 562.0 \| 28662 \| 0.4491 \| \| 0.4357 \| 563.0 \| 28713 \| 0.4474 \| \| 0.4424 \| 564.0 \| 28764 \| 0.4481 \| \| 0.4412 \| 565.0 \| 28815 \| 0.4480 \| \| 0.4483 \| 566.0 \| 28866 \| 0.4453 \| \| 0.4397 \| 567.0 \| 28917 \| 0.4435 \| \| 0.4377 \| 568.0 \| 28968 \| 0.4460 \| \| 0.4424 \| 569.0 \| 29019 \| 0.4475 \| \| 0.4412 \| 570.0 \| 29070 \| 0.4445 \| \| 0.4435 \| 571.0 \| 29121 \| 0.4418 \| \| 0.4398 \| 572.0 \| 29172 \| 0.4434 \| \| 0.4427 \| 573.0 \| 29223 \| 0.4417 \| \| 0.4409 \| 574.0 \| 29274 \| 0.4410 \| \| 0.4425 \| 575.0 \| 29325 \| 0.4434 \| \| 0.4402 \| 576.0 \| 29376 \| 0.4489 \| \| 0.4394 \| 577.0 \| 29427 \| 0.4435 \| \| 0.4379 \| 578.0 \| 29478 \| 0.4447 \| \| 0.4391 \| 579.0 \| 29529 \| 0.4471 \| \| 0.4404 \| 580.0 \| 29580 \| 0.4435 \| \| 0.4399 \| 581.0 \| 29631 \| 0.4411 \| \| 0.4353 \| 582.0 \| 29682 \| 0.4416 \| \| 0.4417 \| 583.0 \| 29733 \| 0.4417 \| \| 0.4389 \| 584.0 \| 29784 \| 0.4399 \| \| 0.4378 \| 585.0 \| 29835 \| 0.4432 \| \| 0.439 \| 586.0 \| 29886 \| 0.4427 \| \| 0.431 \| 587.0 \| 29937 \| 0.4403 \| \| 0.4348 \| 588.0 \| 29988 \| 0.4409 \| \| 0.4363 \| 589.0 \| 30039 \| 0.4425 \| \| 0.4399 \| 590.0 \| 30090 \| 0.4394 \| \| 0.4342 \| 591.0 \| 30141 \| 0.4412 \| \| 0.4342 \| 592.0 \| 30192 \| 0.4399 \| \| 0.4348 \| 593.0 \| 30243 \| 0.4420 \| \| 0.4326 \| 594.0 \| 30294 \| 0.4446 \| \| 0.4333 \| 595.0 \| 30345 \| 0.4430 \| \| 0.4336 \| 596.0 \| 30396 \| 0.4397 \| \| 0.4314 \| 597.0 \| 30447 \| 0.4418 \| \| 0.4371 \| 598.0 \| 30498 \| 0.4411 \| \| 0.4333 \| 599.0 \| 30549 \| 0.4385 \| \| 0.4337 \| 600.0 \| 30600 \| 0.4394 \| \| 0.4371 \| 601.0 \| 30651 \| 0.4407 \| \| 0.4294 \| 602.0 \| 30702 \| 0.4395 \| \| 0.4323 \| 603.0 \| 30753 \| 0.4404 \| \| 0.4303 \| 604.0 \| 30804 \| 0.4422 \| \| 0.4325 \| 605.0 \| 30855 \| 0.4376 \| \| 0.44 \| 606.0 \| 30906 \| 0.4399 \| \| 0.4343 \| 607.0 \| 30957 \| 0.4403 \| \| 0.4313 \| 608.0 \| 31008 \| 0.4397 \| \| 0.4338 \| 609.0 \| 31059 \| 0.4379 \| \| 0.4299 \| 610.0 \| 31110 \| 0.4349 \| \| 0.4325 \| 611.0 \| 31161 \| 0.4370 \| \| 0.429 \| 612.0 \| 31212 \| 0.4371 \| \| 0.4291 \| 613.0 \| 31263 \| 0.4299 \| \| 0.4349 \| 614.0 \| 31314 \| 0.4364 \| \| 0.4308 \| 615.0 \| 31365 \| 0.4336 \| \| 0.4305 \| 616.0 \| 31416 \| 0.4343 \| \| 0.4267 \| 617.0 \| 31467 \| 0.4391 \| \| 0.4329 \| 618.0 \| 31518 \| 0.4365 \| \| 0.4269 \| 619.0 \| 31569 \| 0.4333 \| \| 0.4251 \| 620.0 \| 31620 \| 0.4343 \| \| 0.427 \| 621.0 \| 31671 \| 0.4344 \| \| 0.4327 \| 622.0 \| 31722 \| 0.4345 \| \| 0.4263 \| 623.0 \| 31773 \| 0.4370 \| \| 0.4288 \| 624.0 \| 31824 \| 0.4323 \| \| 0.4316 \| 625.0 \| 31875 \| 0.4325 \| \| 0.431 \| 626.0 \| 31926 \| 0.4328 \| \| 0.4316 \| 627.0 \| 31977 \| 0.4316 \| \| 0.4325 \| 628.0 \| 32028 \| 0.4311 \| \| 0.4287 \| 629.0 \| 32079 \| 0.4323 \| \| 0.4267 \| 630.0 \| 32130 \| 0.4302 \| \| 0.426 \| 631.0 \| 32181 \| 0.4342 \| \| 0.4259 \| 632.0 \| 32232 \| 0.4324 \| \| 0.427 \| 633.0 \| 32283 \| 0.4315 \| \| 0.4268 \| 634.0 \| 32334 \| 0.4300 \| \| 0.4251 \| 635.0 \| 32385 \| 0.4385 \| \| 0.4291 \| 636.0 \| 32436 \| 0.4358 \| \| 0.4273 \| 637.0 \| 32487 \| 0.4342 \| \| 0.4238 \| 638.0 \| 32538 \| 0.4311 \| \| 0.4262 \| 639.0 \| 32589 \| 0.4327 \| \| 0.4251 \| 640.0 \| 32640 \| 0.4329 \| \| 0.4276 \| 641.0 \| 32691 \| 0.4344 \| \| 0.4274 \| 642.0 \| 32742 \| 0.4304 \| \| 0.4269 \| 643.0 \| 32793 \| 0.4263 \| \| 0.4217 \| 644.0 \| 32844 \| 0.4305 \| \| 0.4204 \| 645.0 \| 32895 \| 0.4314 \| \| 0.4268 \| 646.0 \| 32946 \| 0.4284 \| \| 0.4227 \| 647.0 \| 32997 \| 0.4281 \| \| 0.4236 \| 648.0 \| 33048 \| 0.4320 \| \| 0.4245 \| 649.0 \| 33099 \| 0.4295 \| \| 0.4229 \| 650.0 \| 33150 \| 0.4262 \| \| 0.423 \| 651.0 \| 33201 \| 0.4239 \| \| 0.4209 \| 652.0 \| 33252 \| 0.4294 \| \| 0.4209 \| 653.0 \| 33303 \| 0.4315 \| \| 0.425 \| 654.0 \| 33354 \| 0.4299 \| \| 0.418 \| 655.0 \| 33405 \| 0.4282 \| \| 0.423 \| 656.0 \| 33456 \| 0.4264 \| \| 0.4267 \| 657.0 \| 33507 \| 0.4296 \| \| 0.4226 \| 658.0 \| 33558 \| 0.4269 \| \| 0.4213 \| 659.0 \| 33609 \| 0.4296 \| \| 0.4192 \| 660.0 \| 33660 \| 0.4259 \| \| 0.4234 \| 661.0 \| 33711 \| 0.4243 \| \| 0.4205 \| 662.0 \| 33762 \| 0.4256 \| \| 0.4185 \| 663.0 \| 33813 \| 0.4251 \| \| 0.4212 \| 664.0 \| 33864 \| 0.4231 \| \| 0.4228 \| 665.0 \| 33915 \| 0.4250 \| \| 0.421 \| 666.0 \| 33966 \| 0.4284 \| \| 0.4226 \| 667.0 \| 34017 \| 0.4243 \| \| 0.4201 \| 668.0 \| 34068 \| 0.4279 \| \| 0.4213 \| 669.0 \| 34119 \| 0.4210 \| \| 0.4237 \| 670.0 \| 34170 \| 0.4264 \| \| 0.4228 \| 671.0 \| 34221 \| 0.4237 \| \| 0.4181 \| 672.0 \| 34272 \| 0.4245 \| \| 0.4242 \| 673.0 \| 34323 \| 0.4244 \| \| 0.4178 \| 674.0 \| 34374 \| 0.4250 \| \| 0.4184 \| 675.0 \| 34425 \| 0.4274 \| \| 0.4163 \| 676.0 \| 34476 \| 0.4221 \| \| 0.4288 \| 677.0 \| 34527 \| 0.4245 \| \| 0.4205 \| 678.0 \| 34578 \| 0.4258 \| \| 0.4167 \| 679.0 \| 34629 \| 0.4243 \| \| 0.4172 \| 680.0 \| 34680 \| 0.4241 \| \| 0.4212 \| 681.0 \| 34731 \| 0.4216 \| \| 0.4164 \| 682.0 \| 34782 \| 0.4214 \| \| 0.4171 \| 683.0 \| 34833 \| 0.4230 \| \| 0.4166 \| 684.0 \| 34884 \| 0.4261 \| \| 0.4172 \| 685.0 \| 34935 \| 0.4224 \| \| 0.4188 \| 686.0 \| 34986 \| 0.4209 \| \| 0.4187 \| 687.0 \| 35037 \| 0.4168 \| \| 0.4174 \| 688.0 \| 35088 \| 0.4201 \| \| 0.4184 \| 689.0 \| 35139 \| 0.4177 \| \| 0.4126 \| 690.0 \| 35190 \| 0.4192 \| \| 0.4168 \| 691.0 \| 35241 \| 0.4171 \| \| 0.4152 \| 692.0 \| 35292 \| 0.4202 \| \| 0.4137 \| 693.0 \| 35343 \| 0.4210 \| \| 0.4139 \| 694.0 \| 35394 \| 0.4143 \| \| 0.418 \| 695.0 \| 35445 \| 0.4250 \| \| 0.4116 \| 696.0 \| 35496 \| 0.4237 \| \| 0.4113 \| 697.0 \| 35547 \| 0.4172 \| \| 0.4131 \| 698.0 \| 35598 \| 0.4219 \| \| 0.4148 \| 699.0 \| 35649 \| 0.4179 \| \| 0.4117 \| 700.0 \| 35700 \| 0.4264 \| \| 0.4115 \| 701.0 \| 35751 \| 0.4244 \| \| 0.4149 \| 702.0 \| 35802 \| 0.4223 \| \| 0.4129 \| 703.0 \| 35853 \| 0.4190 \| \| 0.4134 \| 704.0 \| 35904 \| 0.4197 \| \| 0.4155 \| 705.0 \| 35955 \| 0.4203 \| \| 0.4112 \| 706.0 \| 36006 \| 0.4206 \| \| 0.4113 \| 707.0 \| 36057 \| 0.4176 \| \| 0.4117 \| 708.0 \| 36108 \| 0.4202 \| \| 0.4128 \| 709.0 \| 36159 \| 0.4186 \| \| 0.4111 \| 710.0 \| 36210 \| 0.4196 \| \| 0.4168 \| 711.0 \| 36261 \| 0.4225 \| \| 0.408 \| 712.0 \| 36312 \| 0.4146 \| \| 0.4117 \| 713.0 \| 36363 \| 0.4185 \| \| 0.4089 \| 714.0 \| 36414 \| 0.4214 \| \| 0.408 \| 715.0 \| 36465 \| 0.4196 \| \| 0.4126 \| 716.0 \| 36516 \| 0.4175 \| \| 0.4106 \| 717.0 \| 36567 \| 0.4145 \| \| 0.4112 \| 718.0 \| 36618 \| 0.4160 \| \| 0.4064 \| 719.0 \| 36669 \| 0.4175 \| \| 0.41 \| 720.0 \| 36720 \| 0.4181 \| \| 0.4046 \| 721.0 \| 36771 \| 0.4159 \| \| 0.4141 \| 722.0 \| 36822 \| 0.4119 \| \| 0.414 \| 723.0 \| 36873 \| 0.4167 \| \| 0.4118 \| 724.0 \| 36924 \| 0.4166 \| \| 0.4106 \| 725.0 \| 36975 \| 0.4157 \| \| 0.4079 \| 726.0 \| 37026 \| 0.4176 \| \| 0.4114 \| 727.0 \| 37077 \| 0.4108 \| \| 0.4117 \| 728.0 \| 37128 \| 0.4135 \| \| 0.4155 \| 729.0 \| 37179 \| 0.4171 \| \| 0.4117 \| 730.0 \| 37230 \| 0.4147 \| \| 0.4092 \| 731.0 \| 37281 \| 0.4094 \| \| 0.4091 \| 732.0 \| 37332 \| 0.4133 \| \| 0.4081 \| 733.0 \| 37383 \| 0.4142 \| \| 0.4084 \| 734.0 \| 37434 \| 0.4170 \| \| 0.4082 \| 735.0 \| 37485 \| 0.4158 \| \| 0.4097 \| 736.0 \| 37536 \| 0.4118 \| \| 0.4082 \| 737.0 \| 37587 \| 0.4105 \| \| 0.4043 \| 738.0 \| 37638 \| 0.4162 \| \| 0.4011 \| 739.0 \| 37689 \| 0.4122 \| \| 0.4082 \| 740.0 \| 37740 \| 0.4158 \| \| 0.4098 \| 741.0 \| 37791 \| 0.4153 \| \| 0.4082 \| 742.0 \| 37842 \| 0.4107 \| \| 0.4073 \| 743.0 \| 37893 \| 0.4117 \| \| 0.403 \| 744.0 \| 37944 \| 0.4163 \| \| 0.4024 \| 745.0 \| 37995 \| 0.4080 \| \| 0.4098 \| 746.0 \| 38046 \| 0.4082 \| \| 0.4072 \| 747.0 \| 38097 \| 0.4111 \| \| 0.4065 \| 748.0 \| 38148 \| 0.4119 \| \| 0.404 \| 749.0 \| 38199 \| 0.4087 \| \| 0.4024 \| 750.0 \| 38250 \| 0.4093 \| \| 0.4054 \| 751.0 \| 38301 \| 0.4111 \| \| 0.403 \| 752.0 \| 38352 \| 0.4093 \| \| 0.4042 \| 753.0 \| 38403 \| 0.4117 \| \| 0.4025 \| 754.0 \| 38454 \| 0.4088 \| \| 0.4025 \| 755.0 \| 38505 \| 0.4102 \| \| 0.4056 \| 756.0 \| 38556 \| 0.4135 \| \| 0.4025 \| 757.0 \| 38607 \| 0.4125 \| \| 0.4035 \| 758.0 \| 38658 \| 0.4110 \| \| 0.4026 \| 759.0 \| 38709 \| 0.4127 \| \| 0.4028 \| 760.0 \| 38760 \| 0.4107 \| \| 0.4007 \| 761.0 \| 38811 \| 0.4079 \| \| 0.4043 \| 762.0 \| 38862 \| 0.4106 \| \| 0.3979 \| 763.0 \| 38913 \| 0.4084 \| \| 0.4071 \| 764.0 \| 38964 \| 0.4093 \| \| 0.4097 \| 765.0 \| 39015 \| 0.4130 \| \| 0.4052 \| 766.0 \| 39066 \| 0.4118 \| \| 0.4063 \| 767.0 \| 39117 \| 0.4055 \| \| 0.4051 \| 768.0 \| 39168 \| 0.4056 \| \| 0.403 \| 769.0 \| 39219 \| 0.4054 \| \| 0.4061 \| 770.0 \| 39270 \| 0.4102 \| \| 0.3989 \| 771.0 \| 39321 \| 0.4141 \| \| 0.4022 \| 772.0 \| 39372 \| 0.4050 \| \| 0.4018 \| 773.0 \| 39423 \| 0.4098 \| \| 0.3993 \| 774.0 \| 39474 \| 0.4090 \| \| 0.3984 \| 775.0 \| 39525 \| 0.4074 \| \| 0.4034 \| 776.0 \| 39576 \| 0.4068 \| \| 0.4036 \| 777.0 \| 39627 \| 0.4043 \| \| 0.4027 \| 778.0 \| 39678 \| 0.4056 \| \| 0.3999 \| 779.0 \| 39729 \| 0.4104 \| \| 0.401 \| 780.0 \| 39780 \| 0.4033 \| \| 0.4058 \| 781.0 \| 39831 \| 0.4058 \| \| 0.3977 \| 782.0 \| 39882 \| 0.4094 \| \| 0.402 \| 783.0 \| 39933 \| 0.4057 \| \| 0.3972 \| 784.0 \| 39984 \| 0.4044 \| \| 0.3997 \| 785.0 \| 40035 \| 0.4075 \| \| 0.4003 \| 786.0 \| 40086 \| 0.4074 \| \| 0.3973 \| 787.0 \| 40137 \| 0.4045 \| \| 0.3989 \| 788.0 \| 40188 \| 0.4078 \| \| 0.4029 \| 789.0 \| 40239 \| 0.4092 \| \| 0.4011 \| 790.0 \| 40290 \| 0.4051 \| \| 0.3975 \| 791.0 \| 40341 \| 0.4008 \| \| 0.3952 \| 792.0 \| 40392 \| 0.4049 \| \| 0.4032 \| 793.0 \| 40443 \| 0.4054 \| \| 0.4027 \| 794.0 \| 40494 \| 0.4034 \| \| 0.397 \| 795.0 \| 40545 \| 0.4042 \| \| 0.3941 \| 796.0 \| 40596 \| 0.4030 \| \| 0.3929 \| 797.0 \| 40647 \| 0.4031 \| \| 0.4016 \| 798.0 \| 40698 \| 0.4003 \| \| 0.3926 \| 799.0 \| 40749 \| 0.4026 \| \| 0.3985 \| 800.0 \| 40800 \| 0.4046 \| \| 0.3978 \| 801.0 \| 40851 \| 0.4002 \| \| 0.3972 \| 802.0 \| 40902 \| 0.4058 \| \| 0.3993 \| 803.0 \| 40953 \| 0.4026 \| \| 0.3935 \| 804.0 \| 41004 \| 0.4049 \| \| 0.3973 \| 805.0 \| 41055 \| 0.3989 \| \| 0.4002 \| 806.0 \| 41106 \| 0.4003 \| \| 0.3918 \| 807.0 \| 41157 \| 0.4006 \| \| 0.4001 \| 808.0 \| 41208 \| 0.3997 \| \| 0.397 \| 809.0 \| 41259 \| 0.4018 \| \| 0.3984 \| 810.0 \| 41310 \| 0.4030 \| \| 0.3925 \| 811.0 \| 41361 \| 0.4074 \| \| 0.398 \| 812.0 \| 41412 \| 0.4032 \| \| 0.4 \| 813.0 \| 41463 \| 0.3987 \| \| 0.3943 \| 814.0 \| 41514 \| 0.4015 \| \| 0.3973 \| 815.0 \| 41565 \| 0.3962 \| \| 0.3922 \| 816.0 \| 41616 \| 0.4032 \| \| 0.3902 \| 817.0 \| 41667 \| 0.3993 \| \| 0.3942 \| 818.0 \| 41718 \| 0.4018 \| \| 0.3994 \| 819.0 \| 41769 \| 0.4031 \| \| 0.3959 \| 820.0 \| 41820 \| 0.4008 \| \| 0.3911 \| 821.0 \| 41871 \| 0.4036 \| \| 0.3941 \| 822.0 \| 41922 \| 0.3997 \| \| 0.3936 \| 823.0 \| 41973 \| 0.3971 \| \| 0.397 \| 824.0 \| 42024 \| 0.4011 \| \| 0.3974 \| 825.0 \| 42075 \| 0.3964 \| \| 0.3921 \| 826.0 \| 42126 \| 0.4010 \| \| 0.3961 \| 827.0 \| 42177 \| 0.4019 \| \| 0.3912 \| 828.0 \| 42228 \| 0.4004 \| \| 0.3939 \| 829.0 \| 42279 \| 0.3980 \| \| 0.3917 \| 830.0 \| 42330 \| 0.4027 \| \| 0.3977 \| 831.0 \| 42381 \| 0.4005 \| \| 0.3881 \| 832.0 \| 42432 \| 0.3983 \| \| 0.3939 \| 833.0 \| 42483 \| 0.4026 \| \| 0.393 \| 834.0 \| 42534 \| 0.3991 \| \| 0.3928 \| 835.0 \| 42585 \| 0.3980 \| \| 0.394 \| 836.0 \| 42636 \| 0.3953 \| \| 0.3908 \| 837.0 \| 42687 \| 0.4002 \| \| 0.3926 \| 838.0 \| 42738 \| 0.4015 \| \| 0.3947 \| 839.0 \| 42789 \| 0.3991 \| \| 0.3965 \| 840.0 \| 42840 \| 0.3969 \| \| 0.3934 \| 841.0 \| 42891 \| 0.4002 \| \| 0.3916 \| 842.0 \| 42942 \| 0.3969 \| \| 0.3887 \| 843.0 \| 42993 \| 0.3941 \| \| 0.3938 \| 844.0 \| 43044 \| 0.3972 \| \| 0.3928 \| 845.0 \| 43095 \| 0.4015 \| \| 0.3948 \| 846.0 \| 43146 \| 0.3976 \| \| 0.3925 \| 847.0 \| 43197 \| 0.3953 \| \| 0.3876 \| 848.0 \| 43248 \| 0.3958 \| \| 0.3857 \| 849.0 \| 43299 \| 0.3967 \| \| 0.389 \| 850.0 \| 43350 \| 0.3975 \| \| 0.3905 \| 851.0 \| 43401 \| 0.3916 \| \| 0.389 \| 852.0 \| 43452 \| 0.3987 \| \| 0.3872 \| 853.0 \| 43503 \| 0.3965 \| \| 0.3902 \| 854.0 \| 43554 \| 0.3963 \| \| 0.3883 \| 855.0 \| 43605 \| 0.3941 \| \| 0.393 \| 856.0 \| 43656 \| 0.3945 \| \| 0.3908 \| 857.0 \| 43707 \| 0.3987 \| \| 0.3891 \| 858.0 \| 43758 \| 0.3970 \| \| 0.39 \| 859.0 \| 43809 \| 0.3934 \| \| 0.3894 \| 860.0 \| 43860 \| 0.3981 \| \| 0.3859 \| 861.0 \| 43911 \| 0.3940 \| \| 0.3896 \| 862.0 \| 43962 \| 0.3956 \| \| 0.3897 \| 863.0 \| 44013 \| 0.3952 \| \| 0.385 \| 864.0 \| 44064 \| 0.3941 \| \| 0.3876 \| 865.0 \| 44115 \| 0.3937 \| \| 0.3889 \| 866.0 \| 44166 \| 0.3975 \| \| 0.3926 \| 867.0 \| 44217 \| 0.3953 \| \| 0.3895 \| 868.0 \| 44268 \| 0.3918 \| \| 0.3926 \| 869.0 \| 44319 \| 0.3926 \| \| 0.3861 \| 870.0 \| 44370 \| 0.3933 \| \| 0.3881 \| 871.0 \| 44421 \| 0.3941 \| \| 0.3863 \| 872.0 \| 44472 \| 0.3939 \| \| 0.3863 \| 873.0 \| 44523 \| 0.3913 \| \| 0.386 \| 874.0 \| 44574 \| 0.3919 \| \| 0.382 \| 875.0 \| 44625 \| 0.3879 \| \| 0.384 \| 876.0 \| 44676 \| 0.3938 \| \| 0.3898 \| 877.0 \| 44727 \| 0.3949 \| \| 0.3913 \| 878.0 \| 44778 \| 0.3947 \| \| 0.3859 \| 879.0 \| 44829 \| 0.3952 \| \| 0.385 \| 880.0 \| 44880 \| 0.3950 \| \| 0.3872 \| 881.0 \| 44931 \| 0.3877 \| \| 0.383 \| 882.0 \| 44982 \| 0.3905 \| \| 0.387 \| 883.0 \| 45033 \| 0.3939 \| \| 0.3834 \| 884.0 \| 45084 \| 0.3947 \| \| 0.3866 \| 885.0 \| 45135 \| 0.3935 \| \| 0.3834 \| 886.0 \| 45186 \| 0.3925 \| \| 0.3848 \| 887.0 \| 45237 \| 0.3903 \| \| 0.3896 \| 888.0 \| 45288 \| 0.3918 \| \| 0.3863 \| 889.0 \| 45339 \| 0.3880 \| \| 0.384 \| 890.0 \| 45390 \| 0.3884 \| \| 0.3844 \| 891.0 \| 45441 \| 0.3907 \| \| 0.3863 \| 892.0 \| 45492 \| 0.3954 \| \| 0.3872 \| 893.0 \| 45543 \| 0.3919 \| \| 0.3869 \| 894.0 \| 45594 \| 0.3928 \| \| 0.3801 \| 895.0 \| 45645 \| 0.3941 \| \| 0.3832 \| 896.0 \| 45696 \| 0.3930 \| \| 0.3886 \| 897.0 \| 45747 \| 0.3933 \| \| 0.3871 \| 898.0 \| 45798 \| 0.3917 \| \| 0.3892 \| 899.0 \| 45849 \| 0.3927 \| \| 0.3864 \| 900.0 \| 45900 \| 0.3934 \| \| 0.3827 \| 901.0 \| 45951 \| 0.3916 \| \| 0.3838 \| 902.0 \| 46002 \| 0.3932 \| \| 0.3859 \| 903.0 \| 46053 \| 0.3901 \| \| 0.382 \| 904.0 \| 46104 \| 0.3918 \| \| 0.3824 \| 905.0 \| 46155 \| 0.3939 \| \| 0.3799 \| 906.0 \| 46206 \| 0.3907 \| \| 0.3851 \| 907.0 \| 46257 \| 0.3891 \| \| 0.3854 \| 908.0 \| 46308 \| 0.3885 \| \| 0.3855 \| 909.0 \| 46359 \| 0.3912 \| \| 0.3855 \| 910.0 \| 46410 \| 0.3912 \| \| 0.3799 \| 911.0 \| 46461 \| 0.3882 \| \| 0.387 \| 912.0 \| 46512 \| 0.3894 \| \| 0.3792 \| 913.0 \| 46563 \| 0.3887 \| \| 0.3831 \| 914.0 \| 46614 \| 0.3875 \| \| 0.3821 \| 915.0 \| 46665 \| 0.3863 \| \| 0.3853 \| 916.0 \| 46716 \| 0.3884 \| \| 0.381 \| 917.0 \| 46767 \| 0.3873 \| \| 0.3847 \| 918.0 \| 46818 \| 0.3850 \| \| 0.3813 \| 919.0 \| 46869 \| 0.3875 \| \| 0.3853 \| 920.0 \| 46920 \| 0.3860 \| \| 0.3849 \| 921.0 \| 46971 \| 0.3880 \| \| 0.3771 \| 922.0 \| 47022 \| 0.3891 \| \| 0.3815 \| 923.0 \| 47073 \| 0.3887 \| \| 0.3827 \| 924.0 \| 47124 \| 0.3902 \| \| 0.3828 \| 925.0 \| 47175 \| 0.3900 \| \| 0.3861 \| 926.0 \| 47226 \| 0.3915 \| \| 0.383 \| 927.0 \| 47277 \| 0.3911 \| \| 0.3785 \| 928.0 \| 47328 \| 0.3837 \| \| 0.3825 \| 929.0 \| 47379 \| 0.3879 \| \| 0.3793 \| 930.0 \| 47430 \| 0.3921 \| \| 0.3836 \| 931.0 \| 47481 \| 0.3893 \| \| 0.3858 \| 932.0 \| 47532 \| 0.3874 \| \| 0.387 \| 933.0 \| 47583 \| 0.3881 \| \| 0.3855 \| 934.0 \| 47634 \| 0.3863 \| \| 0.3813 \| 935.0 \| 47685 \| 0.3833 \| \| 0.3787 \| 936.0 \| 47736 \| 0.3876 \| \| 0.3834 \| 937.0 \| 47787 \| 0.3870 \| \| 0.3807 \| 938.0 \| 47838 \| 0.3839 \| \| 0.3788 \| 939.0 \| 47889 \| 0.3863 \| \| 0.3788 \| 940.0 \| 47940 \| 0.3847 \| \| 0.3819 \| 941.0 \| 47991 \| 0.3876 \| \| 0.3814 \| 942.0 \| 48042 \| 0.3845 \| \| 0.3817 \| 943.0 \| 48093 \| 0.3830 \| \| 0.3838 \| 944.0 \| 48144 \| 0.3880 \| \| 0.3787 \| 945.0 \| 48195 \| 0.3880 \| \| 0.3812 \| 946.0 \| 48246 \| 0.3884 \| \| 0.3806 \| 947.0 \| 48297 \| 0.3891 \| \| 0.3816 \| 948.0 \| 48348 \| 0.3855 \| \| 0.3813 \| 949.0 \| 48399 \| 0.3847 \| \| 0.3811 \| 950.0 \| 48450 \| 0.3847 \| \| 0.3776 \| 951.0 \| 48501 \| 0.3831 \| \| 0.3794 \| 952.0 \| 48552 \| 0.3867 \| \| 0.3782 \| 953.0 \| 48603 \| 0.3812 \| \| 0.3834 \| 954.0 \| 48654 \| 0.3852 \| \| 0.3785 \| 955.0 \| 48705 \| 0.3830 \| \| 0.3789 \| 956.0 \| 48756 \| 0.3852 \| \| 0.3801 \| 957.0 \| 48807 \| 0.3882 \| \| 0.3771 \| 958.0 \| 48858 \| 0.3842 \| \| 0.3808 \| 959.0 \| 48909 \| 0.3840 \| \| 0.3762 \| 960.0 \| 48960 \| 0.3849 \| \| 0.3777 \| 961.0 \| 49011 \| 0.3842 \| \| 0.3781 \| 962.0 \| 49062 \| 0.3874 \| \| 0.3781 \| 963.0 \| 49113 \| 0.3838 \| \| 0.376 \| 964.0 \| 49164 \| 0.3863 \| \| 0.3777 \| 965.0 \| 49215 \| 0.3827 \| \| 0.3808 \| 966.0 \| 49266 \| 0.3853 \| \| 0.3835 \| 967.0 \| 49317 \| 0.3869 \| \| 0.3801 \| 968.0 \| 49368 \| 0.3859 \| \| 0.3839 \| 969.0 \| 49419 \| 0.3841 \| \| 0.3768 \| 970.0 \| 49470 \| 0.3849 \| \| 0.3797 \| 971.0 \| 49521 \| 0.3844 \| \| 0.3763 \| 972.0 \| 49572 \| 0.3855 \| \| 0.3788 \| 973.0 \| 49623 \| 0.3832 \| \| 0.374 \| 974.0 \| 49674 \| 0.3858 \| \| 0.3785 \| 975.0 \| 49725 \| 0.3805 \| \| 0.3752 \| 976.0 \| 49776 \| 0.3855 \| \| 0.3752 \| 977.0 \| 49827 \| 0.3827 \| \| 0.3779 \| 978.0 \| 49878 \| 0.3826 \| \| 0.3769 \| 979.0 \| 49929 \| 0.3824 \| \| 0.3778 \| 980.0 \| 49980 \| 0.3848 \| \| 0.3749 \| 981.0 \| 50031 \| 0.3831 \| \| 0.3756 \| 982.0 \| 50082 \| 0.3879 \| \| 0.3739 \| 983.0 \| 50133 \| 0.3830 \| \| 0.3769 \| 984.0 \| 50184 \| 0.3845 \| \| 0.3737 \| 985.0 \| 50235 \| 0.3894 \| \| 0.3769 \| 986.0 \| 50286 \| 0.3815 \| \| 0.373 \| 987.0 \| 50337 \| 0.3797 \| \| 0.374 \| 988.0 \| 50388 \| 0.3827 \| \| 0.3778 \| 989.0 \| 50439 \| 0.3844 \| \| 0.3773 \| 990.0 \| 50490 \| 0.3846 \| \| 0.3759 \| 991.0 \| 50541 \| 0.3826 \| \| 0.3752 \| 992.0 \| 50592 \| 0.3843 \| \| 0.3747 \| 993.0 \| 50643 \| 0.3817 \| \| 0.3781 \| 994.0 \| 50694 \| 0.3784 \| \| 0.3751 \| 995.0 \| 50745 \| 0.3832 \| \| 0.3758 \| 996.0 \| 50796 \| 0.3800 \| \| 0.3718 \| 997.0 \| 50847 \| 0.3837 \| \| 0.3745 \| 998.0 \| 50898 \| 0.3823 \| \| 0.3757 \| 999.0 \| 50949 \| 0.3798 \| \| 0.3786 \| 1000.0 \| 51000 \| 0.3794 \| \| 0.3738 \| 1001.0 \| 51051 \| 0.3781 \| \| 0.3779 \| 1002.0 \| 51102 \| 0.3851 \| \| 0.3735 \| 1003.0 \| 51153 \| 0.3844 \| \| 0.3753 \| 1004.0 \| 51204 \| 0.3841 \| \| 0.3701 \| 1005.0 \| 51255 \| 0.3805 \| \| 0.3738 \| 1006.0 \| 51306 \| 0.3826 \| \| 0.3729 \| 1007.0 \| 51357 \| 0.3793 \| \| 0.3765 \| 1008.0 \| 51408 \| 0.3825 \| \| 0.3725 \| 1009.0 \| 51459 \| 0.3817 \| \| 0.3766 \| 1010.0 \| 51510 \| 0.3813 \| \| 0.3736 \| 1011.0 \| 51561 \| 0.3834 \| \| 0.3747 \| 1012.0 \| 51612 \| 0.3800 \| \| 0.3726 \| 1013.0 \| 51663 \| 0.3817 \| \| 0.3819 \| 1014.0 \| 51714 \| 0.3840 \| \| 0.3799 \| 1015.0 \| 51765 \| 0.3834 \| \| 0.3754 \| 1016.0 \| 51816 \| 0.3818 \| \| 0.3762 \| 1017.0 \| 51867 \| 0.3769 \| \| 0.3718 \| 1018.0 \| 51918 \| 0.3794 \| \| 0.3785 \| 1019.0 \| 51969 \| 0.3825 \| \| 0.3754 \| 1020.0 \| 52020 \| 0.3827 \| \| 0.374 \| 1021.0 \| 52071 \| 0.3818 \| \| 0.3785 \| 1022.0 \| 52122 \| 0.3780 \| \| 0.3735 \| 1023.0 \| 52173 \| 0.3815 \| \| 0.3726 \| 1024.0 \| 52224 \| 0.3794 \| \| 0.3798 \| 1025.0 \| 52275 \| 0.3787 \| \| 0.3714 \| 1026.0 \| 52326 \| 0.3810 \| \| 0.3776 \| 1027.0 \| 52377 \| 0.3787 \| \| 0.3688 \| 1028.0 \| 52428 \| 0.3771 \| \| 0.375 \| 1029.0 \| 52479 \| 0.3776 \| \| 0.372 \| 1030.0 \| 52530 \| 0.3795 \| \| 0.3736 \| 1031.0 \| 52581 \| 0.3781 \| \| 0.3713 \| 1032.0 \| 52632 \| 0.3815 \| \| 0.3772 \| 1033.0 \| 52683 \| 0.3802 \| \| 0.375 \| 1034.0 \| 52734 \| 0.3788 \| \| 0.3725 \| 1035.0 \| 52785 \| 0.3819 \| \| 0.3696 \| 1036.0 \| 52836 \| 0.3836 \| \| 0.3741 \| 1037.0 \| 52887 \| 0.3814 \| \| 0.3734 \| 1038.0 \| 52938 \| 0.3799 \| \| 0.3759 \| 1039.0 \| 52989 \| 0.3789 \| \| 0.3726 \| 1040.0 \| 53040 \| 0.3802 \| \| 0.3693 \| 1041.0 \| 53091 \| 0.3769 \| \| 0.3705 \| 1042.0 \| 53142 \| 0.3812 \| \| 0.3691 \| 1043.0 \| 53193 \| 0.3806 \| \| 0.3736 \| 1044.0 \| 53244 \| 0.3796 \| \| 0.3707 \| 1045.0 \| 53295 \| 0.3784 \| \| 0.3735 \| 1046.0 \| 53346 \| 0.3752 \| \| 0.3773 \| 1047.0 \| 53397 \| 0.3801 \| \| 0.3714 \| 1048.0 \| 53448 \| 0.3800 \| \| 0.3747 \| 1049.0 \| 53499 \| 0.3787 \| \| 0.3735 \| 1050.0 \| 53550 \| 0.3775 \| \| 0.3727 \| 1051.0 \| 53601 \| 0.3771 \| \| 0.3736 \| 1052.0 \| 53652 \| 0.3833 \| \| 0.3676 \| 1053.0 \| 53703 \| 0.3796 \| \| 0.3688 \| 1054.0 \| 53754 \| 0.3758 \| \| 0.369 \| 1055.0 \| 53805 \| 0.3775 \| \| 0.3696 \| 1056.0 \| 53856 \| 0.3811 \| \| 0.3707 \| 1057.0 \| 53907 \| 0.3776 \| \| 0.3765 \| 1058.0 \| 53958 \| 0.3804 \| \| 0.3697 \| 1059.0 \| 54009 \| 0.3813 \| \| 0.3718 \| 1060.0 \| 54060 \| 0.3722 \| \| 0.3699 \| 1061.0 \| 54111 \| 0.3771 \| \| 0.3725 \| 1062.0 \| 54162 \| 0.3780 \| \| 0.3705 \| 1063.0 \| 54213 \| 0.3767 \| \| 0.3698 \| 1064.0 \| 54264 \| 0.3783 \| \| 0.374 \| 1065.0 \| 54315 \| 0.3775 \| \| 0.3665 \| 1066.0 \| 54366 \| 0.3813 \| \| 0.3695 \| 1067.0 \| 54417 \| 0.3801 \| \| 0.3705 \| 1068.0 \| 54468 \| 0.3805 \| \| 0.3709 \| 1069.0 \| 54519 \| 0.3780 \| \| 0.3762 \| 1070.0 \| 54570 \| 0.3758 \| \| 0.3718 \| 1071.0 \| 54621 \| 0.3801 \| \| 0.3736 \| 1072.0 \| 54672 \| 0.3769 \| \| 0.3702 \| 1073.0 \| 54723 \| 0.3763 \| \| 0.3716 \| 1074.0 \| 54774 \| 0.3791 \| \| 0.3684 \| 1075.0 \| 54825 \| 0.3745 \| \| 0.3682 \| 1076.0 \| 54876 \| 0.3796 \| \| 0.3699 \| 1077.0 \| 54927 \| 0.3784 \| \| 0.3745 \| 1078.0 \| 54978 \| 0.3794 \| \| 0.3721 \| 1079.0 \| 55029 \| 0.3780 \| \| 0.3758 \| 1080.0 \| 55080 \| 0.3792 \| \| 0.3742 \| 1081.0 \| 55131 \| 0.3781 \| \| 0.3693 \| 1082.0 \| 55182 \| 0.3819 \| \| 0.3676 \| 1083.0 \| 55233 \| 0.3746 \| \| 0.3684 \| 1084.0 \| 55284 \| 0.3812 \| \| 0.3727 \| 1085.0 \| 55335 \| 0.3745 \| \| 0.3689 \| 1086.0 \| 55386 \| 0.3743 \| \| 0.3704 \| 1087.0 \| 55437 \| 0.3785 \| \| 0.3664 \| 1088.0 \| 55488 \| 0.3774 \| \| 0.3704 \| 1089.0 \| 55539 \| 0.3757 \| \| 0.3702 \| 1090.0 \| 55590 \| 0.3790 \| \| 0.3747 \| 1091.0 \| 55641 \| 0.3798 \| \| 0.3704 \| 1092.0 \| 55692 \| 0.3756 \| \| 0.3749 \| 1093.0 \| 55743 \| 0.3783 \| \| 0.3686 \| 1094.0 \| 55794 \| 0.3759 \| \| 0.369 \| 1095.0 \| 55845 \| 0.3762 \| \| 0.3671 \| 1096.0 \| 55896 \| 0.3783 \| \| 0.3686 \| 1097.0 \| 55947 \| 0.3780 \| \| 0.3693 \| 1098.0 \| 55998 \| 0.3778 \| \| 0.3728 \| 1099.0 \| 56049 \| 0.3759 \| \| 0.3715 \| 1100.0 \| 56100 \| 0.3777 \| \| 0.3712 \| 1101.0 \| 56151 \| 0.3775 \| \| 0.3695 \| 1102.0 \| 56202 \| 0.3767 \| \| 0.3715 \| 1103.0 \| 56253 \| 0.3762 \| \| 0.3728 \| 1104.0 \| 56304 \| 0.3775 \| \| 0.368 \| 1105.0 \| 56355 \| 0.3783 \| \| 0.3705 \| 1106.0 \| 56406 \| 0.3797 \| \| 0.3705 \| 1107.0 \| 56457 \| 0.3771 \| \| 0.3734 \| 1108.0 \| 56508 \| 0.3754 \| \| 0.3701 \| 1109.0 \| 56559 \| 0.3793 \| \| 0.3707 \| 1110.0 \| 56610 \| 0.3729 \| \| 0.3677 \| 1111.0 \| 56661 \| 0.3763 \| \| 0.3734 \| 1112.0 \| 56712 \| 0.3813 \| \| 0.3714 \| 1113.0 \| 56763 \| 0.3772 \| \| 0.3654 \| 1114.0 \| 56814 \| 0.3765 \| \| 0.3692 \| 1115.0 \| 56865 \| 0.3757 \| \| 0.3721 \| 1116.0 \| 56916 \| 0.3749 \| \| 0.3741 \| 1117.0 \| 56967 \| 0.3769 \| \| 0.3649 \| 1118.0 \| 57018 \| 0.3806 \| \| 0.3709 \| 1119.0 \| 57069 \| 0.3720 \| \| 0.3721 \| 1120.0 \| 57120 \| 0.3794 \| \| 0.3701 \| 1121.0 \| 57171 \| 0.3748 \| \| 0.3674 \| 1122.0 \| 57222 \| 0.3787 \| \| 0.3669 \| 1123.0 \| 57273 \| 0.3736 \| \| 0.3726 \| 1124.0 \| 57324 \| 0.3789 \| \| 0.3672 \| 1125.0 \| 57375 \| 0.3774 \| \| 0.3674 \| 1126.0 \| 57426 \| 0.3778 \| \| 0.3702 \| 1127.0 \| 57477 \| 0.3772 \| \| 0.3717 \| 1128.0 \| 57528 \| 0.3766 \| \| 0.3703 \| 1129.0 \| 57579 \| 0.3757 \| \| 0.3695 \| 1130.0 \| 57630 \| 0.3808 \| \| 0.3729 \| 1131.0 \| 57681 \| 0.3721 \| \| 0.3657 \| 1132.0 \| 57732 \| 0.3784 \| \| 0.3676 \| 1133.0 \| 57783 \| 0.3793 \| \| 0.3684 \| 1134.0 \| 57834 \| 0.3797 \| \| 0.3703 \| 1135.0 \| 57885 \| 0.3771 \| \| 0.3705 \| 1136.0 \| 57936 \| 0.3752 \| \| 0.3691 \| 1137.0 \| 57987 \| 0.3773 \| \| 0.3673 \| 1138.0 \| 58038 \| 0.3766 \| \| 0.3715 \| 1139.0 \| 58089 \| 0.3779 \| \| 0.37 \| 1140.0 \| 58140 \| 0.3750 \| \| 0.3709 \| 1141.0 \| 58191 \| 0.3786 \| \| 0.3696 \| 1142.0 \| 58242 \| 0.3776 \| \| 0.3752 \| 1143.0 \| 58293 \| 0.3758 \| \| 0.3675 \| 1144.0 \| 58344 \| 0.3762 \| \| 0.3681 \| 1145.0 \| 58395 \| 0.3741 \| \| 0.3684 \| 1146.0 \| 58446 \| 0.3794 \| \| 0.3663 \| 1147.0 \| 58497 \| 0.3720 \| \| 0.3712 \| 1148.0 \| 58548 \| 0.3742 \| \| 0.3672 \| 1149.0 \| 58599 \| 0.3786 \| \| 0.369 \| 1150.0 \| 58650 \| 0.3737 \| \| 0.3648 \| 1151.0 \| 58701 \| 0.3767 \| \| 0.3704 \| 1152.0 \| 58752 \| 0.3740 \| \| 0.3695 \| 1153.0 \| 58803 \| 0.3781 \| \| 0.3707 \| 1154.0 \| 58854 \| 0.3753 \| \| 0.3661 \| 1155.0 \| 58905 \| 0.3774 \| \| 0.367 \| 1156.0 \| 58956 \| 0.3763 \| \| 0.3657 \| 1157.0 \| 59007 \| 0.3767 \| \| 0.3638 \| 1158.0 \| 59058 \| 0.3738 \| \| 0.3728 \| 1159.0 \| 59109 \| 0.3732 \| \| 0.3748 \| 1160.0 \| 59160 \| 0.3787 \| \| 0.3753 \| 1161.0 \| 59211 \| 0.3743 \| \| 0.3663 \| 1162.0 \| 59262 \| 0.3758 \| \| 0.3694 \| 1163.0 \| 59313 \| 0.3772 \| \| 0.3657 \| 1164.0 \| 59364 \| 0.3763 \| \| 0.3643 \| 1165.0 \| 59415 \| 0.3770 \| \| 0.3679 \| 1166.0 \| 59466 \| 0.3772 \| \| 0.37 \| 1167.0 \| 59517 \| 0.3724 \| \| 0.3693 \| 1168.0 \| 59568 \| 0.3752 \| \| 0.3705 \| 1169.0 \| 59619 \| 0.3732 \| \| 0.3671 \| 1170.0 \| 59670 \| 0.3767 \| \| 0.3729 \| 1171.0 \| 59721 \| 0.3723 \| \| 0.3701 \| 1172.0 \| 59772 \| 0.3768 \| \| 0.3717 \| 1173.0 \| 59823 \| 0.3782 \| \| 0.3716 \| 1174.0 \| 59874 \| 0.3721 \| \| 0.3723 \| 1175.0 \| 59925 \| 0.3712 \| \| 0.3674 \| 1176.0 \| 59976 \| 0.3746 \| \| 0.365 \| 1177.0 \| 60027 \| 0.3768 \| \| 0.3725 \| 1178.0 \| 60078 \| 0.3760 \| \| 0.3679 \| 1179.0 \| 60129 \| 0.3742 \| \| 0.3707 \| 1180.0 \| 60180 \| 0.3753 \| \| 0.3698 \| 1181.0 \| 60231 \| 0.3730 \| \| 0.3697 \| 1182.0 \| 60282 \| 0.3748 \| \| 0.368 \| 1183.0 \| 60333 \| 0.3722 \| \| 0.3689 \| 1184.0 \| 60384 \| 0.3724 \| \| 0.3667 \| 1185.0 \| 60435 \| 0.3731 \| \| 0.3708 \| 1186.0 \| 60486 \| 0.3785 \| \| 0.3684 \| 1187.0 \| 60537 \| 0.3755 \| \| 0.3701 \| 1188.0 \| 60588 \| 0.3774 \| \| 0.3685 \| 1189.0 \| 60639 \| 0.3733 \| \| 0.37 \| 1190.0 \| 60690 \| 0.3773 \| \| 0.372 \| 1191.0 \| 60741 \| 0.3761 \| \| 0.3677 \| 1192.0 \| 60792 \| 0.3733 \| \| 0.367 \| 1193.0 \| 60843 \| 0.3770 \| \| 0.3641 \| 1194.0 \| 60894 \| 0.3731 \| \| 0.3679 \| 1195.0 \| 60945 \| 0.3739 \| \| 0.3709 \| 1196.0 \| 60996 \| 0.3731 \| \| 0.3668 \| 1197.0 \| 61047 \| 0.3784 \| \| 0.3678 \| 1198.0 \| 61098 \| 0.3754 \| \| 0.3642 \| 1199.0 \| 61149 \| 0.3795 \| \| 0.3717 \| 1200.0 \| 61200 \| 0.3766 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.13.2
azamat/mapper	azamat	2023-02-14T19:30:12Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-02-14T16:19:26Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2471 with parameters: ``` {'batch_size': 512, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters: ``` {'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True} ``` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
shaiman12/flan-t5-base-samsum	shaiman12	2023-02-14T19:29:12Z	5	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-02-14T16:47:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: flan-t5-base-samsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: test args: samsum metrics: - name: Rouge1 type: rouge value: 47.5929 --- <!-- 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. --> # flan-t5-base-samsum This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.3776 - Rouge1: 47.5929 - Rouge2: 23.8272 - Rougel: 40.1493 - Rougelsum: 43.7798 - Gen Len: 17.2503 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.4416 \| 1.0 \| 1842 \| 1.3837 \| 46.6013 \| 23.125 \| 39.4894 \| 42.9943 \| 17.0684 \| \| 1.3581 \| 2.0 \| 3684 \| 1.3730 \| 47.3142 \| 23.5981 \| 39.5786 \| 43.447 \| 17.3675 \| \| 1.2781 \| 3.0 \| 5526 \| 1.3739 \| 47.5321 \| 23.8035 \| 40.0555 \| 43.7595 \| 17.2271 \| \| 1.2368 \| 4.0 \| 7368 \| 1.3767 \| 47.0944 \| 23.2414 \| 39.6673 \| 43.2155 \| 17.2405 \| \| 1.1953 \| 5.0 \| 9210 \| 1.3776 \| 47.5929 \| 23.8272 \| 40.1493 \| 43.7798 \| 17.2503 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.12.1 - Datasets 2.9.0 - Tokenizers 0.13.2
pete88b/q-Taxi-v3-0.0.1	pete88b	2023-02-14T19:24:03Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T19:24:01Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-0.0.1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="pete88b/q-Taxi-v3-0.0.1", 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"]) ```
civility-lab/roberta-base-namecalling	civility-lab	2023-02-14T19:18:42Z	14,385	2	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "incivility", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-13T17:54:29Z	--- language: - en license: apache-2.0 library_name: transformers tags: - incivility metrics: - f1 widget: - text: "Be careful around those DemocRats." example_title: "Namecall" - text: "Be careful around those Democrats." example_title: "No Namecall" --- # Model Card for roberta-base-namecalling This is a [roBERTa-base](https://huggingface.co/roberta-base) model fine-tuned on ~12K social media posts annotated for the presence or absence of namecalling. # How to Get Started with the Model You can use this model directly with a pipeline for text classification: ```python >>> import transformers >>> model_name = "civility-lab/roberta-base-namecalling" >>> classifier = transformers.TextClassificationPipeline( ... tokenizer=transformers.AutoTokenizer.from_pretrained(model_name), ... model=transformers.AutoModelForSequenceClassification.from_pretrained(model_name)) >>> classifier("Be careful around those Democrats.") [{'label': 'not-namecalling', 'score': 0.9995089769363403}] >>> classifier("Be careful around those DemocRats.") [{'label': 'namecalling', 'score': 0.996940016746521}] ``` # Model Details This is a 2023 update of the model built by [Ozler et al. (2020)](https://aclanthology.org/2020.alw-1.4/) incorporating data from [Rains et al. (2021)](https://doi.org/10.1093/hcr/hqab009) and using a more recent version of the transformers library. - Developed by: [Steven Bethard](https://bethard.github.io/), [Kate Kenski](https://comm.arizona.edu/user/kate-kenski), [Steve Rains](https://comm.arizona.edu/user/steve-rains), [Yotam Shmargad](https://www.yotamshmargad.com/), [Kevin Coe](https://faculty.utah.edu/u0915886-Kevin_Coe/) - Language: en - License: apache-2.0 - Parent Model: roberta-base - Resources for more information: - [GitHub Repo](https://github.com/clulab/incivility) - Kadir Bulut Ozler; Kate Kenski; Steve Rains; Yotam Shmargad; Kevin Coe; and Steven Bethard. [Fine-tuning for multi-domain and multi-label uncivil language detection](https://aclanthology.org/2020.alw-1.4/). In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 28–33, Online, November 2020. Association for Computational Linguistics - Stephen A Rains; Yotam Shmargad; Kevin Coe; Kate Kenski; and Steven Bethard. [Assessing the Russian Troll Efforts to Sow Discord on Twitter during the 2016 U.S. Election](https://doi.org/10.1093/hcr/hqab009). Human Communication Research, 47(4): 477-486. 08 2021. - Stephen A Rains; Jake Harwood; Yotam Shmargad; Kate Kenski; Kevin Coe; and Steven Bethard. [Engagement with partisan Russian troll tweets during the 2016 U.S. presidential election: a social identity perspective](https://doi.org/10.1093/joc/jqac037). Journal of Communication, 73(1): 38-48. 02 2023. # Uses The model is intended to be used for text classification, taking as input social media posts and predicting as output whether the post contains namecalling. It is not intended to generate namecalling, and it should not be used as part of any incivility generation model. # Training Details The model was trained on data from four sources: comments on the Arizona Daily Star website from 2011, Russian troll Tweets from 2012-2018, Tucson politician Tweets from 2018, and US presidential primary Tweets from 2019. Each dataset was annotated for the presence of namecalling following the approach of [Coe et al. (2014)](https://doi.org/10.1111/jcom.12104) and split into training, development, and test partitions. The [roberta-base](https://huggingface.co/roberta-base) model was fine-tuned on the combined training partitions from all four datasets, with texts tokenized using the standard [roberta-base](https://huggingface.co/roberta-base) tokenizer. # Evaluation The model was evaluated on the test partition of each of the datasets. It achieves the following F1 scores: - 0.58 F1 on Arizona Daily Star comments - 0.71 F1 on Russian troll Tweets - 0.71 F1 on Tucson politician Tweets - 0.81 F1 on US presidential primary Tweets # Limitations and Biases The human coders and their trainers were mostly [Western, educated, industrialized, rich and democratic (WEIRD)](https://www.nature.com/articles/466029a), which may have shaped how they evaluated incivility. The trained models will reflect such biases. # Environmental Impact - Hardware Type: Tesla V100S-PCIE-32GB - Hours used: 22 - HPC Provider: <https://hpc.arizona.edu/> - Carbon Emitted: 2.85 kg CO2 (estimated by [ML CO2 Impact](https://mlco2.github.io/impact#compute)) # Citation ```bibtex @inproceedings{ozler-etal-2020-fine, title = "Fine-tuning for multi-domain and multi-label uncivil language detection", author = "Ozler, Kadir Bulut and Kenski, Kate and Rains, Steve and Shmargad, Yotam and Coe, Kevin and Bethard, Steven", booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.alw-1.4", doi = "10.18653/v1/2020.alw-1.4", pages = "28--33", } ```
kaloyloyloyloy/wav2vec2-base-finetuned-ks	kaloyloyloyloy	2023-02-14T19:14:46Z	163	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:superb", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-02-14T15:47:52Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - superb 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 superb dataset. It achieves the following results on the evaluation set: - Loss: 1.5492 - Accuracy: 0.6209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4528 \| 1.0 \| 399 \| 1.5492 \| 0.6209 \| \| 1.5591 \| 2.0 \| 798 \| 1.5411 \| 0.6209 \| \| 1.4814 \| 3.0 \| 1197 \| 1.5397 \| 0.6209 \| \| 1.4528 \| 4.0 \| 1596 \| 1.5412 \| 0.6209 \| \| 1.4829 \| 5.0 \| 1995 \| 1.5395 \| 0.6209 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2
Ahmed-ibn-Harun/es_pipeline	Ahmed-ibn-Harun	2023-02-14T18:53:10Z	1	0	spacy	[ "spacy", "token-classification", "es", "model-index", "region:us" ]	token-classification	2023-02-14T18:52:47Z	--- tags: - spacy - token-classification language: - es model-index: - name: es_pipeline results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9937984496 - name: NER Recall type: recall value: 0.9953416149 - name: NER F Score type: f_score value: 0.9945694337 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `es_pipeline` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.5.0,<3.6.0` \| \| Default Pipeline \| `tok2vec`, `ner` \| \| Components \| `tok2vec`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (3 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `NOUN`, `PNOUN`, `VERB` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 99.46 \| \| `ENTS_P` \| 99.38 \| \| `ENTS_R` \| 99.53 \| \| `TOK2VEC_LOSS` \| 8110.20 \| \| `NER_LOSS` \| 35115.85 \|
dwchris/dqn-SpaceInvadersNoFrameskip-v4	dwchris	2023-02-14T18:48:16Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T18:47:42Z	--- 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: 14.50 +/- 12.34 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 dwchris -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 dwchris -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 dwchris ``` ## 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', 15000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
badalsahani/text-classification-multi	badalsahani	2023-02-14T17:59:32Z	10	7	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "en", "dataset:badalsahani/autotrain-data-text-classification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-14T17:32:19Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - badalsahani/autotrain-data-text-classification co2_eq_emissions: emissions: 7.761992510873142 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3486594647 - CO2 Emissions (in grams): 7.7620 ## Validation Metrics - Loss: 0.008 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000 ## Usage You can use cURL to access this model: ```curl $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/badalsahani/text-classification-multi ``` Or Python API: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("badalsahani/text-classification-multi", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("badalsahani/text-classification-multi", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
darkwolfsam/dqn-SpaceInvadersNoFrameskip-v4	darkwolfsam	2023-02-14T17:44:13Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-03T21:20:18Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 229.50 +/- 55.79 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 darkwolfsam -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 darkwolfsam -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 darkwolfsam ``` ## 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', 150000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Zekunli/t5-base-da-multiwoz2.1_500	Zekunli	2023-02-14T17:42:14Z	106	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-02-14T17:25:23Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-da-multiwoz2.1_500 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-da-multiwoz2.1_500 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4253 - Accuracy: 22.6359 - Num: 3689 - Gen Len: 15.4806 ## Model description More information needed ## Intended uses & 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: 24 - eval_batch_size: 48 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Num \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----:\|:-------:\| \| 0.5137 \| 2.78 \| 400 \| 0.4454 \| 21.1276 \| 3689 \| 15.569 \| \| 0.4673 \| 5.56 \| 800 \| 0.4329 \| 21.7406 \| 3689 \| 15.4573 \| \| 0.4591 \| 8.33 \| 1200 \| 0.4253 \| 22.6359 \| 3689 \| 15.4806 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1
Zekunli/t5-base-extraction-cnndm_10000-all	Zekunli	2023-02-14T17:38:04Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-02-14T17:01:07Z	--- tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-extraction-cnndm_10000-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. --> # t5-base-extraction-cnndm_10000-all This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8182 - Rouge1: 33.8286 - Rouge2: 14.4919 - Rougel: 28.8935 - Rougelsum: 28.9581 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.9662 \| 0.48 \| 200 \| 1.9092 \| 33.2564 \| 14.236 \| 28.2044 \| 28.3269 \| 18.992 \| \| 1.9495 \| 0.96 \| 400 \| 1.8775 \| 33.7516 \| 14.2246 \| 28.9019 \| 28.9507 \| 19.0 \| \| 1.9062 \| 1.44 \| 600 \| 1.8580 \| 33.7533 \| 14.2196 \| 28.3873 \| 28.4658 \| 19.0 \| \| 1.8713 \| 1.92 \| 800 \| 1.8496 \| 33.6921 \| 14.4532 \| 28.5695 \| 28.6573 \| 19.0 \| \| 1.85 \| 2.4 \| 1000 \| 1.8327 \| 34.1551 \| 14.7671 \| 28.9492 \| 28.9885 \| 19.0 \| \| 1.8232 \| 2.88 \| 1200 \| 1.8182 \| 33.8286 \| 14.4919 \| 28.8935 \| 28.9581 \| 19.0 \| \| 1.8004 \| 3.36 \| 1400 \| 1.8299 \| 34.5099 \| 14.8659 \| 29.1119 \| 29.1544 \| 19.0 \| \| 1.7832 \| 3.84 \| 1600 \| 1.8252 \| 34.5877 \| 15.1259 \| 29.3368 \| 29.3638 \| 19.0 \| \| 1.7677 \| 4.32 \| 1800 \| 1.8226 \| 34.4487 \| 15.0361 \| 29.2962 \| 29.3431 \| 19.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1
albburtsev/faucet-v2-1	albburtsev	2023-02-14T16:51:22Z	29	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-02-14T16:40:51Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### faucet-v2.1 Dreambooth model trained by albburtsev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
drnkwtr/Malware_Spectrogram_Prediction	drnkwtr	2023-02-14T16:38:21Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-02-14T15:15:53Z	--- license: openrail --- This model is trained on spectrograms of malwares. It is supposed to make a prediction. POST /image - accepts an image in binary format, either as an octet-stream file, or as a multipart/form-data with files in the imageData parameter. The server loads the image using the Python Imaging Library, runs it through the predict_image function from the predict module (not shown), and returns the results as a JSON object. POST /url - accepts a JSON object containing a URL to an image. The server loads the image using the predict_url function from the predict module, and returns the results as a JSON object.
aioe/anysium_anypas	aioe	2023-02-14T16:11:45Z	0	7	null	[ "text-to-image", "art", "stable diffusion", "ja", "en", "license:other", "region:us" ]	text-to-image	2023-02-14T10:21:57Z	--- license: other language: - ja - en tags: - text-to-image - art - stable diffusion --- # 【概要(Outline)】コンセプトは<strong>「儚さ」</strong>です。 <br> 1つはElysium特有の線の細さを活かしたマージモデル、もう1つはpastelmix特有の色味を活かしたマージモデルとなっています。 <br> <br> The concept is <strong>"like disappearing."</strong> <br> One is the marge model by the feature of thin outline of Elysium, and another is the marge model by the feature of unique colors of pastelmix. <br> <br> # 【モデル紹介とマージ素材(Models introduction and merged materials)】 <strong>■anysium-v1.0</strong> <br> ・anything-v4.5 <br> ・Elysium_Anime_V3 <br> →線の細さが特徴的です。 <br> (The feature is thin outline.) <br> <br> <strong>■anypas-v1.0</strong> <br> ・anything-v4.5 <br> ・pastelmix <br> →pastelmix風の色味が特徴的です。 <br> (The feature is like colors of pastelmix.) <br> <br> # 【推奨設定(Recommended settings)】 <strong>■anysium-v1.0</strong> <br> ・Steps:20 <br> ・CFG Scale:5.5 <br> ・Clip Skip:2 <br> ・Negative:(worst quality, low quality:1.2), <br> <br> <strong>■anypas-v1.0</strong> <br> ・Steps:30 <br> ・CFG Scale:10 <br> ・Clip Skip:2 <br> ・Negative:(worst quality, low quality, poorly eyes:1.2), <br> <br> # 【作例(Examples)】 Positive:one girl, <br> <br> <strong>■anysium-v1.0</strong> <img src="https://imgur.com/B4fPJul.png" width="1152" height="768"> <br> <strong>■anypas-v1.0</strong> <img src="https://imgur.com/AdkCtC0.png" width="1152" height="768"> <br> <br> Positive:(one little and cute girl:1.2), (loli), (solo:1.3), (masterpiece), (best quality), the girl wearing (white headdress), (frilled white capelet:1.2), (short sleeves), (short white gloves), (intricate and gorgeous frilled white dress with gold decoration), the girl has (long blonde hair), [[[blue eyes]]], the background is large western style garden, (many colorful flowers), trees, birds, <br> <br> <strong>■anysium-v1.0</strong> <img src="https://imgur.com/f903ASm.jpg" width="1152" height="768"> <br> <strong>■anypas-v1.0</strong> <img src="https://imgur.com/hkeyhX2.jpg" width="1152" height="768">
pyflynn/dqn-SpaceInvadersNoFrameskip-v4	pyflynn	2023-02-14T16:09:20Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T16:08:37Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 608.50 +/- 150.13 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 pyflynn -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 pyflynn -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 pyflynn ``` ## 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', 5e-05), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
khiyza/PPO-LunarLander-v2	khiyza	2023-02-14T15:59:31Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T15:59:07Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.80 +/- 24.27 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 ... ```
bofenghuang/whisper-large-v2-cv11-french	bofenghuang	2023-02-14T15:17:59Z	7	5	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "whisper-event", "fr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-05T16:50:41Z	--- license: apache-2.0 language: fr library_name: transformers thumbnail: null tags: - automatic-speech-recognition - hf-asr-leaderboard - whisper-event datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Fine-tuned whisper-large-v2 model for ASR in French results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 8.05 - name: WER (Beam 5) type: wer value: 7.67 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Multilingual LibriSpeech (MLS) type: facebook/multilingual_librispeech config: french split: test args: french metrics: - name: WER (Greedy) type: wer value: 5.56 - name: WER (Beam 5) type: wer value: 5.28 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: VoxPopuli type: facebook/voxpopuli config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 11.50 - name: WER (Beam 5) type: wer value: 10.69 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Fleurs type: google/fleurs config: fr_fr split: test args: fr_fr metrics: - name: WER (Greedy) type: wer value: 5.42 - name: WER (Beam 5) type: wer value: 5.05 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: African Accented French type: gigant/african_accented_french config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 6.47 - name: WER (Beam 5) type: wer value: 5.95 --- <style> img { display: inline; } </style> ![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey) ![Model size](https://img.shields.io/badge/Params-1550M-lightgrey) ![Language](https://img.shields.io/badge/Language-French-lightgrey) # Fine-tuned whisper-large-v2 model for ASR in French This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2), trained on the mozilla-foundation/common_voice_11_0 fr dataset. When using the model make sure that your speech input is also sampled at 16Khz. This model also predicts casing and punctuation. ## Performance Below are the WERs of the pre-trained models on the [Common Voice 9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli) and [Fleurs](https://huggingface.co/datasets/google/fleurs). These results are reported in the original [paper](https://cdn.openai.com/papers/whisper.pdf). \| Model \| Common Voice 9.0 \| MLS \| VoxPopuli \| Fleurs \| \| --- \| :---: \| :---: \| :---: \| :---: \| \| [openai/whisper-small](https://huggingface.co/openai/whisper-small) \| 22.7 \| 16.2 \| 15.7 \| 15.0 \| \| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) \| 16.0 \| 8.9 \| 12.2 \| 8.7 \| \| [openai/whisper-large](https://huggingface.co/openai/whisper-large) \| 14.7 \| 8.9 \| 11.0 \| 7.7 \| \| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) \| 13.9 \| 7.3 \| 11.4 \| 8.3 \| Below are the WERs of the fine-tuned models on the [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), and [Fleurs](https://huggingface.co/datasets/google/fleurs). Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of apostrophe. The results in the table are reported as `WER (greedy search) / WER (beam search with beam width 5)`. \| Model \| Common Voice 11.0 \| MLS \| VoxPopuli \| Fleurs \| \| --- \| :---: \| :---: \| :---: \| :---: \| \| [bofenghuang/whisper-small-cv11-french](https://huggingface.co/bofenghuang/whisper-small-cv11-french) \| 11.76 / 10.99 \| 9.65 / 8.91 \| 14.45 / 13.66 \| 10.76 / 9.83 \| \| [bofenghuang/whisper-medium-cv11-french](https://huggingface.co/bofenghuang/whisper-medium-cv11-french) \| 9.03 / 8.54 \| 6.34 / 5.86 \| 11.64 / 11.35 \| 7.13 / 6.85 \| \| [bofenghuang/whisper-medium-french](https://huggingface.co/bofenghuang/whisper-medium-french) \| 9.03 / 8.73 \| 4.60 / 4.44 \| 9.53 / 9.46 \| 6.33 / 5.94 \| \| [bofenghuang/whisper-large-v2-cv11-french](https://huggingface.co/bofenghuang/whisper-large-v2-cv11-french) \| 8.05 / 7.67 \| 5.56 / 5.28 \| 11.50 / 10.69 \| 5.42 / 5.05 \| \| [bofenghuang/whisper-large-v2-french](https://huggingface.co/bofenghuang/whisper-large-v2-french) \| 8.15 / 7.83 \| 4.20 / 4.03 \| 9.10 / 8.66 \| 5.22 / 4.98 \| ## Usage Inference with 🤗 Pipeline ```python import torch from datasets import load_dataset from transformers import pipeline device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load pipeline pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-cv11-french", device=device) # NB: set forced_decoder_ids for generation utils pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", task="transcribe") # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = test_segment["audio"] # Run generated_sentences = pipe(waveform, max_new_tokens=225)["text"] # greedy # generated_sentences = pipe(waveform, max_new_tokens=225, generate_kwargs={"num_beams": 5})["text"] # beam search # Normalise predicted sentences if necessary ``` Inference with 🤗 low-level APIs ```python import torch import torchaudio from datasets import load_dataset from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load model model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-cv11-french").to(device) processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-cv11-french", language="french", task="transcribe") # NB: set forced_decoder_ids for generation utils model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="fr", task="transcribe") # 16_000 model_sample_rate = processor.feature_extractor.sampling_rate # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = torch.from_numpy(test_segment["audio"]["array"]) sample_rate = test_segment["audio"]["sampling_rate"] # Resample if sample_rate != model_sample_rate: resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate) waveform = resampler(waveform) # Get feat inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt") input_features = inputs.input_features input_features = input_features.to(device) # Generate generated_ids = model.generate(inputs=input_features, max_new_tokens=225) # greedy # generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5) # beam search # Detokenize generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # Normalise predicted sentences if necessary ```
mocker/KaBoom	mocker	2023-02-14T15:09:06Z	0	128	null	[ "art", "en", "license:creativeml-openrail-m", "region:us" ]	null	2023-02-06T23:47:49Z	--- license: creativeml-openrail-m language: - en tags: - art --- # In short, - FaceBomb : Covers from anime to 2.5D style. Suited for general use. - recipe : 0.5((0.5(AbyssOrangeMix2_hard) + 0.5(pastelmix-better-vae-fp32)) + 0.5(CounterfeitV25_25)) + 0.5(dalcefoV3Painting_dalcefoV3Painting) - ColorBomb : FaceBomb + vivid color and lighting. A bit picky about prompts. - recipe : dalcefoV3Painting_dalcefoV3Painting + 0.5(ultracolorV4_ultracolorV4 - CounterfeitV25_25) - HyperBomb : Strong anime style w/ highly saturated color. - recipe : 0.5((0.5(AbyssOrangeMix2_hard) + 0.5(pastelmix-better-vae-fp32)) + 0.5(CounterfeitV25_25)) + 0.5(dalcefoV3Painting_dalcefoV3Painting) + 0.3(0.8(pastelMixStylizedAnime_pastelMixPrunedFP16) + 0.2(CounterfeitV25_25) - f222) # Recommended Setting ## VAE - If the color appears dull or washed out, try applying VAE. I used `kl-f8-anime2` - https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt ## Sampling method and Hires.fix 1. `DPM++ SDE Karras: 24~32 steps` / `R-ESRGAN 4x+ Anime6B: 2x, 14 steps` / `Denoising strength:0.45 ~ 0.55` 2. `DDIM: 24~32 steps` / `Latent: 2x 14 steps` / `Denoising Strength:0.45 ~ 0.7` - First option yields better result in general. Recommended. - Second option was 1.5 ~ 2 times faster on my system but the output was questionable. Especially for ColorBomb. ## FaceBomb - Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), masterpieceportrait, realistic, 3d face, lustrous skin, ` - Negative : `(worst quality, low quality:1.4), watermark, logo,` ## ColorBomb - Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), (ultra-detailed, high-resolution: 1.2), beautiful girl, { color } { color } theme, ` - e.g. black gold theme - Negative : `(worst quality, low quality:1.4), watermark, logo,` ## HyperBomb - Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2),` - Negative : `(worst quality, low quality:1.4), watermark, logo,` # Example - More pictures in folder. - Below are the ideal/intended outputs. ![Alt text](sample/00184-20230207043613___Custom_FaceBombMix-fp16-no-ema_aad629159b.png) ### FaceBomb (masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), masterpieceportrait, realistic, 3d face, glowing eyes, shiny hair, lustrous skin, solo, embarassed Negative prompt: (worst quality, low quality:1.4), watermark, logo, Steps: 32, Sampler: DPM++ SDE Karras, CFG scale: 9, Seed: 3624413002, Size: 512x768, Model hash: aad629159b, Model: __Custom_FaceBombMix-fp16-no-ema, Denoising strength: 0.5, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: R-ESRGAN 4x+ Anime6B --- ![Alt text](sample/00217-20230207050723___Custom_ColorBomb-fp16-no-ema_627f50eea8.png) ### ColorBomb ((masterpiece, best quality, ultra-detailed, high-resolution)), solo, beautiful girl, gleaming eye, perfect eye, age 15, black white gold theme, Negative prompt: (worst quality, low quality:1.4), (depth of field, blurry:1.2), (greyscale, monochrome:1.1), 3D face, cropped, lowres, text, jpeg artifacts, signature, watermark, username, blurry, artist name, trademark, watermark, title, (tan, muscular, sd character:1.1), multiple view, Reference sheet, non-linear background, blurred background, bad anatomy, cropped hands, extra digit, fewer digit, Steps: 24, Sampler: DDIM, CFG scale: 7, Seed: 3050494714, Size: 512x768, Model hash: 627f50eea8, Model: __Custom_ColorBomb-fp16-no-ema, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: Latent --- ![Alt text](sample/00212-20230207050130___Custom_HyperBombMix-fp16-no-ema_16c6ca45b1.png) ### HyperBomb (masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), Negative prompt: (worst quality, low quality:1.4), watermark, logo, Steps: 32, Sampler: DDIM, CFG scale: 9, Seed: 2411870881, Size: 768x512, Model hash: 16c6ca45b1, Model: __Custom_HyperBombMix-fp16-no-ema, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: Latent
AdamOswald1/Tester	AdamOswald1	2023-02-14T15:02:27Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-11-09T18:10:39Z	--- title: Finetuned Diffusion emoji: 🪄🖼️ colorFrom: red colorTo: pink sdk: gradio sdk_version: 3.18.0 app_file: app.py pinned: true license: mit --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
mafaisalsust/xlm-roberta-large-finetuned-ac	mafaisalsust	2023-02-14T14:58:33Z	80	0	transformers	[ "transformers", "tf", "xlm-roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-02-14T14:43:51Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: mafaisalsust/xlm-roberta-large-finetuned-ac 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. --> # mafaisalsust/xlm-roberta-large-finetuned-ac This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 11.0965 - Validation Loss: 4.5876 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 11.0965 \| 4.5876 \| 0 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.8.0 - Datasets 2.9.0 - Tokenizers 0.13.2
chradden/q-FrozenLake-v1-4x4-noSlippery	chradden	2023-02-14T14:56:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-02-13T16:07:06Z	--- 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="chradden/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"]) ```
YoriV/a2c-AntBulletEnv-v0	YoriV	2023-02-14T14:44:39Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T14:43:28Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1159.33 +/- 399.23 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
nhiro3303/a2c-PandaReachDense-v2	nhiro3303	2023-02-14T14:41:25Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T14:39:48Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.43 +/- 0.65 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
bencevans/ddpm-ena24-gen-512-100	bencevans	2023-02-14T14:37:54Z	12	0	diffusers	[ "diffusers", "diffusers:DDPMPipeline", "region:us" ]	null	2023-02-03T09:24:53Z	--- library_name: diffusers --- # Model Card for ddpm-ena24-gen-512-100 This model generates Camera Trap Imagery. It's been trained on the ENA24-Detection Dataset for 100 epochs.
frangiral/ppo-Pyramids1	frangiral	2023-02-14T14:29:26Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-02-14T14:29:04Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: frangiral/ppo-Pyramids1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mmiteva/qa_model_customs	mmiteva	2023-02-14T14:24:06Z	63	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-02-13T19:44:16Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mmiteva/qa_model-customs 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. --> # mmiteva/qa_model-customs 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: 0.3517 - Train End Logits Accuracy: 0.8772 - Train Start Logits Accuracy: 0.8735 - Validation Loss: 0.8793 - Validation End Logits Accuracy: 0.7642 - Validation Start Logits Accuracy: 0.7586 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 32050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.3795 \| 0.6168 \| 0.6015 \| 0.9590 \| 0.7074 \| 0.6950 \| 0 \| \| 0.8193 \| 0.7377 \| 0.7260 \| 0.8504 \| 0.7313 \| 0.7260 \| 1 \| \| 0.5982 \| 0.8004 \| 0.7932 \| 0.8225 \| 0.7505 \| 0.7440 \| 2 \| \| 0.4467 \| 0.8462 \| 0.8405 \| 0.8469 \| 0.7633 \| 0.7584 \| 3 \| \| 0.3517 \| 0.8772 \| 0.8735 \| 0.8793 \| 0.7642 \| 0.7586 \| 4 \| ### Framework versions - Transformers 4.25.1 - TensorFlow 2.10.1 - Datasets 2.7.1 - Tokenizers 0.12.1
dwarfbum/myfavloras	dwarfbum	2023-02-14T14:14:04Z	0	2	null	[ "nagatoro", "hayase nagatoro", "makima", "nazuna nanakusa", "lora", "region:us" ]	null	2023-02-06T15:09:03Z	--- tags: - nagatoro - hayase nagatoro - makima - nazuna nanakusa - lora --- https://civitai.com/models/6060/nagatoro-hayase-ti NOT LORA. THATS TI https://civitai.com/models/5662/nazuna-nanakusa-call-of-the-night-lora https://civitai.com/models/5373/makima-chainsaw-man-lora
frangiral/ppo-SnowballTarget	frangiral	2023-02-14T14:03:38Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-02-14T14:03:31Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: frangiral/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
XperienciaVirtual/bdbybt-puig	XperienciaVirtual	2023-02-14T14:03:29Z	4	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-02-14T14:02:07Z	--- license: creativeml-openrail-m tags: - text-to-image widget: - text: bdbybt --- ### bdbybt_puig Dreambooth model trained by jaimexv with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: bdbybt (use that on your prompt) ![bdbybt 0](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%281%29.jpg)![bdbybt 1](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%282%29.jpg)![bdbybt 2](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%283%29.jpg)![bdbybt 3](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%284%29.jpg)![bdbybt 4](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%285%29.jpg)![bdbybt 5](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%286%29.jpg)![bdbybt 6](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%287%29.jpg)![bdbybt 7](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%288%29.jpg)
ybelkada/flan-t5-large-financial-phrasebank-lora	ybelkada	2023-02-14T13:47:51Z	0	4	null	[ "text2text-generation", "en", "sp", "ja", "pe", "hi", "fr", "ch", "be", "gu", "ge", "te", "it", "ar", "po", "ta", "ma", "or", "pa", "ur", "ga", "he", "ko", "ca", "th", "du", "in", "vi", "bu", "fi", "ce", "la", "tu", "ru", "cr", "sw", "yo", "ku", "cz", "so", "si", "ka", "zh", "ig", "xh", "ro", "ha", "es", "sl", "li", "gr", "ne", "as", "no", "dataset:svakulenk0/qrecc", "dataset:taskmaster2", "dataset:djaym7/wiki_dialog", "dataset:deepmind/code_contests", "dataset:lambada", "dataset:gsm8k", "dataset:aqua_rat", "dataset:esnli", "dataset:quasc", "dataset:qed", "dataset:financial_phrasebank", "license:apache-2.0", "region:us" ]	text2text-generation	2023-02-14T13:39:05Z	--- language: - en - sp - ja - pe - hi - fr - ch - be - gu - ge - te - it - ar - po - ta - ma - ma - or - pa - po - ur - ga - he - ko - ca - th - du - in - vi - bu - fi - ce - la - tu - ru - cr - sw - yo - ku - bu - ma - cz - fi - so - ta - sw - si - ka - zh - ig - xh - ro - ha - es - sl - li - gr - ne - as - no widget: - text: "Translate to German: My name is Arthur" example_title: "Translation" - text: "Please answer to the following question. Who is going to be the next Ballon d'or?" example_title: "Question Answering" - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." example_title: "Logical reasoning" - text: "Please answer the following question. What is the boiling point of Nitrogen?" example_title: "Scientific knowledge" - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" example_title: "Yes/no question" - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" example_title: "Reasoning task" - text: "Q: ( False or not False or False ) is? A: Let's think step by step" example_title: "Boolean Expressions" - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" example_title: "Math reasoning" - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" example_title: "Premise and hypothesis" tags: - text2text-generation datasets: - svakulenk0/qrecc - taskmaster2 - djaym7/wiki_dialog - deepmind/code_contests - lambada - gsm8k - aqua_rat - esnli - quasc - qed - financial_phrasebank license: apache-2.0 --- # Model Card for LoRA-FLAN-T5 large ![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png) This repository contains the LoRA (Low Rank Adapters) of `flan-t5-large` that has been fine-tuned on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset. ## Usage Use this adapter with `peft` library ```python # pip install peft transformers import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForSeq2SeqLM, AutoTokenizer peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForSeq2SeqLM.from_pretrained( config.base_model_name_or_path, torch_dtype='auto', device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id) ``` Enjoy!
huggingtweets/antoniobanderas-oquimbarreiros-snoopdogg	huggingtweets	2023-02-14T13:38:01Z	107	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-02-14T13:37:53Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1618012757621673992/kPppseWI_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/564137495244570624/CrWEr-Xk_400x400.jpeg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1564375315526275072/7Xnua7BM_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Quim Barreiros & Antonio Banderas & Snoop Dogg</div> <div style="text-align: center; font-size: 14px;">@antoniobanderas-oquimbarreiros-snoopdogg</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Quim Barreiros & Antonio Banderas & Snoop Dogg. \| Data \| Quim Barreiros \| Antonio Banderas \| Snoop Dogg \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 2275 \| 3248 \| 3221 \| \| Retweets \| 55 \| 593 \| 1035 \| \| Short tweets \| 860 \| 1006 \| 414 \| \| Tweets kept \| 1360 \| 1649 \| 1772 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9sxu3b40/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @antoniobanderas-oquimbarreiros-snoopdogg's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/d57ngqld) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/d57ngqld/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/antoniobanderas-oquimbarreiros-snoopdogg') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
NathanaelM/poca-SoccerTwos	NathanaelM	2023-02-14T13:34:23Z	36	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-02-14T13:27:47Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: NathanaelM/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Maksim-t/Tiktok_Memes	Maksim-t	2023-02-14T13:27:37Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-02-14T13:26:29Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ## Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] # Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ## Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ## Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] # Training Details ## Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ## Training Procedure [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] # Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ## Testing Data, Factors & Metrics ### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] ### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] ### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] # Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] # Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed]
jamesthong/ppo-Huggy	jamesthong	2023-02-14T13:16:52Z	14	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-02-14T13:16:45Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: mikato/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
alfredplpl/x2-latent-upscaler-for-anime	alfredplpl	2023-02-14T12:51:41Z	19	1	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "license:openrail++", "diffusers:StableDiffusionLatentUpscalePipeline", "region:us" ]	text-to-image	2023-02-14T12:42:02Z	--- license: openrail++ tags: - stable-diffusion - text-to-image inference: false --- # Stable Diffusion x2 latent upscaler model card This model card focuses on the latent diffusion-based upscaler developed by [Katherine Crowson](https://github.com/crowsonkb/k-diffusion) in collaboration with [Stability AI](https://stability.ai/). This model was trained on a high-resolution subset of the LAION-2B dataset. It is a diffusion model that operates in the same latent space as the Stable Diffusion model, which is decoded into a full-resolution image. To use it with Stable Diffusion, You can take the generated latent from Stable Diffusion and pass it into the upscaler before decoding with your standard VAE. Or you can take any image, encode it into the latent space, use the upscaler, and decode it. Note: This upscaling model is designed explicitely for Stable Diffusion as it can upscale Stable Diffusion's latent denoised image embeddings. This allows for very fast text-to-image + upscaling pipelines as all intermeditate states can be kept on GPU. More for information, see example below. This model works on all [Stable Diffusion checkpoints](https://huggingface.co/models?other=stable-diffusion) \| ![upscaler.jpg](https://pbs.twimg.com/media/FhK0YjAVUAUtBbx?format=jpg&name=4096x4096) \| \|:--:\| Image by Tanishq Abraham from [Stability AI](https://stability.ai/) originating from [this tweet](https://twitter.com/StabilityAI/status/1590531958815064065)\| Original output image \| 2x upscaled output image :-------------------------:\|:-------------------------: ![](https://pbs.twimg.com/media/Fg8UijAaEAAqfvS?format=png&name=small) \| ![](https://pbs.twimg.com/media/Fg8UjCmaMAAAUdS?format=jpg&name=medium) - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/sd-x2-latent-upscaler#examples) ## Model Details - Developed by: Katherine Crowson - Model type: Diffusion-based latent upscaler - Language(s): English - License: [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run latent upscaler on top of any `StableDiffusionUpscalePipeline` checkpoint to enhance its output image resolution by a factor of 2. ```bash pip install git+https://github.com/huggingface/diffusers.git pip install transformers accelerate scipy safetensors ``` ```python from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline import torch pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16) pipeline.to("cuda") upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16) upscaler.to("cuda") prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic" generator = torch.manual_seed(33) # we stay in latent space! Let's make sure that Stable Diffusion returns the image # in latent space low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images upscaled_image = upscaler( prompt=prompt, image=low_res_latents, num_inference_steps=20, guidance_scale=0, generator=generator, ).images[0] # Let's save the upscaled image under "upscaled_astronaut.png" upscaled_image.save("astronaut_1024.png") # as a comparison: Let's also save the low-res image with torch.no_grad(): image = pipeline.decode_latents(low_res_latents) image = pipeline.numpy_to_pil(image)[0] image.save("astronaut_512.png") ``` Result: 512-res Astronaut ![ow_res](./astronaut_512.png) 1024-res Astronaut ![upscaled](./astronaut_1024.png) Notes: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
juliawabant/ppo-LunarLander-v2	juliawabant	2023-02-14T12:46:09Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-10T15:37:38Z	--- 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: 219.19 +/- 89.86 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 ... ```
Ili1991/q-FrozenLake-v1-4x4-noSlippery	Ili1991	2023-02-14T12:39:10Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T12:39:07Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Ili1991/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"]) ```
vinayak361/token_fine_tunned_flipkart_2_gl9	vinayak361	2023-02-14T12:31:32Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-02-14T10:44:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: token_fine_tunned_flipkart_2_gl9 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. --> # token_fine_tunned_flipkart_2_gl9 This model is a fine-tuned version of [vinayak361/token_fine_tunned_flipkart_2_gl7](https://huggingface.co/vinayak361/token_fine_tunned_flipkart_2_gl7) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2452 - Precision: 0.8593 - Recall: 0.8767 - F1: 0.8679 - Accuracy: 0.9105 ## Model description More information needed ## Intended uses & 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-06 - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 451 \| 0.3331 \| 0.8070 \| 0.8310 \| 0.8188 \| 0.8774 \| \| 0.4065 \| 2.0 \| 902 \| 0.2927 \| 0.8319 \| 0.8526 \| 0.8421 \| 0.8940 \| \| 0.3251 \| 3.0 \| 1353 \| 0.2737 \| 0.8428 \| 0.8633 \| 0.8529 \| 0.9021 \| \| 0.2825 \| 4.0 \| 1804 \| 0.2650 \| 0.8484 \| 0.8651 \| 0.8567 \| 0.9046 \| \| 0.2568 \| 5.0 \| 2255 \| 0.2586 \| 0.8543 \| 0.8749 \| 0.8645 \| 0.9085 \| \| 0.2419 \| 6.0 \| 2706 \| 0.2511 \| 0.8552 \| 0.8754 \| 0.8652 \| 0.9083 \| \| 0.2351 \| 7.0 \| 3157 \| 0.2481 \| 0.8564 \| 0.8746 \| 0.8654 \| 0.9102 \| \| 0.2226 \| 8.0 \| 3608 \| 0.2455 \| 0.8551 \| 0.8746 \| 0.8647 \| 0.9089 \| \| 0.222 \| 9.0 \| 4059 \| 0.2458 \| 0.8597 \| 0.8769 \| 0.8682 \| 0.9106 \| \| 0.2207 \| 10.0 \| 4510 \| 0.2452 \| 0.8593 \| 0.8767 \| 0.8679 \| 0.9105 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
justlotw/ppo-Huggy	justlotw	2023-02-14T12:26:13Z	12	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-02-14T12:26:06Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: justlotw/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-og_dataset_5e	Gokulapriyan	2023-02-14T12:25:55Z	41	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-02-13T10:10:40Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-og_dataset_5e results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.970523929063082 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-og_dataset_5e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0788 - Accuracy: 0.9705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4626 \| 1.0 \| 546 \| 0.3468 \| 0.8578 \| \| 0.2915 \| 2.0 \| 1092 \| 0.1998 \| 0.9200 \| \| 0.2333 \| 3.0 \| 1638 \| 0.1155 \| 0.9566 \| \| 0.2019 \| 4.0 \| 2184 \| 0.0977 \| 0.9634 \| \| 0.1713 \| 5.0 \| 2730 \| 0.0788 \| 0.9705 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
Maki7/cnew	Maki7	2023-02-14T12:16:57Z	0	1	null	[ "arxiv:2302.05543", "region:us" ]	null	2023-02-14T12:14:50Z	# ControlNet Official implementation of [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543). ControlNet is a neural network structure to control diffusion models by adding extra conditions. ![img](github_page/he.png) It copys the weights of neural network blocks into a "locked" copy and a "trainable" copy. The "trainable" one learns your condition. The "locked" one preserves your model. Thanks to this, training with small dataset of image pairs will not destroy the production-ready diffusion models. The "zero convolution" is 1×1 convolution with both weight and bias initialized as zeros. Before training, all zero convolutions output zeros, and ControlNet will not cause any distortion. No layer is trained from scratch. You are still fine-tuning. Your original model is safe. This allows training on small-scale or even personal devices. This is also friendly to merge/replacement/offsetting of models/weights/blocks/layers. ### FAQ Q: But wait, if the weight of a conv layer is zero, the gradient will also be zero, and the network will not learn anything. Why "zero convolution" works? A: This is not true. [See an explanation here](docs/faq.md). # Stable Diffusion + ControlNet By repeating the above simple structure 14 times, we can control stable diffusion in this way: ![img](github_page/sd.png) Note that the way we connect layers is computational efficient. The original SD encoder does not need to store gradients (the locked original SD Encoder Block 1234 and Middle). The required GPU memory is not much larger than original SD, although many layers are added. Great! # Production-Ready Pretrained Models First create a new conda environment conda env create -f environment.yaml conda activate control All models and detectors can be downloaded from [our Hugging Face page](https://huggingface.co/lllyasviel/ControlNet). Make sure that SD models are put in "ControlNet/models" and detectors are put in "ControlNet/annotator/ckpts". Make sure that you download all necessary pretrained weights and detector models from that Hugging Face page, including HED edge detection model, Midas depth estimation model, Openpose, and so on. We provide 9 Gradio apps with these models. All test images can be found at the folder "test_imgs". ### News 2023/02/12 - Now you can play with any community model by [Transferring the ControlNet](https://github.com/lllyasviel/ControlNet/discussions/12). 2023/02/11 - [Low VRAM mode](docs/low_vram.md) is added. Please use this mode if you are using 8GB GPU(s) or if you want larger batch size. ## ControlNet with Canny Edge Stable Diffusion 1.5 + ControlNet (using simple Canny edge detection) python gradio_canny2image.py The Gradio app also allows you to change the Canny edge thresholds. Just try it for more details. Prompt: "bird" ![p](github_page/p1.png) Prompt: "cute dog" ![p](github_page/p2.png) ## ControlNet with M-LSD Lines Stable Diffusion 1.5 + ControlNet (using simple M-LSD straight line detection) python gradio_hough2image.py The Gradio app also allows you to change the M-LSD thresholds. Just try it for more details. Prompt: "room" ![p](github_page/p3.png) Prompt: "building" ![p](github_page/p4.png) ## ControlNet with HED Boundary Stable Diffusion 1.5 + ControlNet (using soft HED Boundary) python gradio_hed2image.py The soft HED Boundary will preserve many details in input images, making this app suitable for recoloring and stylizing. Just try it for more details. Prompt: "oil painting of handsome old man, masterpiece" ![p](github_page/p5.png) Prompt: "Cyberpunk robot" ![p](github_page/p6.png) ## ControlNet with User Scribbles Stable Diffusion 1.5 + ControlNet (using Scribbles) python gradio_scribble2image.py Note that the UI is based on Gradio, and Gradio is somewhat difficult to customize. Right now you need to draw scribbles outside the UI (using your favorite drawing software, for example, MS Paint) and then import the scribble image to Gradio. Prompt: "turtle" ![p](github_page/p7.png) Prompt: "hot air balloon" ![p](github_page/p8.png) ### Interactive Interface We actually provide an interactive interface python gradio_scribble2image_interactive.py However, because gradio is very [buggy](https://github.com/gradio-app/gradio/issues/3166) and difficult to customize, right now, user need to first set canvas width and heights and then click "Open drawing canvas" to get a drawing area. Please do not upload image to that drawing canvas. Also, the drawing area is very small; it should be bigger. But I failed to find out how to make it larger. Again, gradio is really buggy. The below dog sketch is drawn by me. Perhaps we should draw a better dog for showcase. Prompt: "dog in a room" ![p](github_page/p20.png) ## ControlNet with Fake Scribbles Stable Diffusion 1.5 + ControlNet (using fake scribbles) python gradio_fake_scribble2image.py Sometimes we are lazy, and we do not want to draw scribbles. This script use the exactly same scribble-based model but use a simple algorithm to synthesize scribbles from input images. Prompt: "bag" ![p](github_page/p9.png) Prompt: "shose" (Note that "shose" is a typo; it should be "shoes". But it still seems to work.) ![p](github_page/p10.png) ## ControlNet with Human Pose Stable Diffusion 1.5 + ControlNet (using human pose) python gradio_pose2image.py Apparently, this model deserves a better UI to directly manipulate pose skeleton. However, again, Gradio is somewhat difficult to customize. Right now you need to input an image and then the Openpose will detect the pose for you. Prompt: "Chief in the kitchen" ![p](github_page/p11.png) Prompt: "An astronaut on the moon" ![p](github_page/p12.png) ## ControlNet with Semantic Segmentation Stable Diffusion 1.5 + ControlNet (using semantic segmentation) python gradio_seg2image.py This model use ADE20K's segmentation protocol. Again, this model deserves a better UI to directly draw the segmentations. However, again, Gradio is somewhat difficult to customize. Right now you need to input an image and then a model called Uniformer will detect the segmentations for you. Just try it for more details. Prompt: "House" ![p](github_page/p13.png) Prompt: "River" ![p](github_page/p14.png) ## ControlNet with Depth Stable Diffusion 1.5 + ControlNet (using depth map) python gradio_depth2image.py Great! Now SD 1.5 also have a depth control. FINALLY. So many possibilities (considering SD1.5 has much more community models than SD2). Note that different from Stability's model, the ControlNet receive the full 512×512 depth map, rather than 64×64 depth. Note that Stability's SD2 depth model use 64*64 depth maps. This means that the ControlNet will preserve more details in the depth map. This is always a strength because if users do not want to preserve more details, they can simply use another SD to post-process an i2i. But if they want to preserve more details, ControlNet becomes their only choice. Again, SD2 uses 64×64 depth, we use 512×512. Prompt: "Stormtrooper's lecture" ![p](github_page/p15.png) ## ControlNet with Normal Map Stable Diffusion 1.5 + ControlNet (using normal map) python gradio_normal2image.py This model use normal map. Rightnow in the APP, the normal is computed from the midas depth map and a user threshold (to determine how many area is background with identity normal face to viewer, tune the "Normal background threshold" in the gradio app to get a feeling). Prompt: "Cute toy" ![p](github_page/p17.png) Prompt: "Plaster statue of Abraham Lincoln" ![p](github_page/p18.png) Compared to depth model, this model seems to be a bit better at preserving the geometry. This is intuitive: minor details are not salient in depth maps, but are salient in normal maps. Below is the depth result with same inputs. You can see that the hairstyle of the man in the input image is modified by depth model, but preserved by the normal model. Prompt: "Plaster statue of Abraham Lincoln" ![p](github_page/p19.png) ## ControlNet with Anime Line Drawing We also trained a relatively simple ControlNet for anime line drawings. This tool may be useful for artistic creations. (Although the image details in the results is a bit modified, since it still diffuse latent images.) This model is not available right now. We need to evaluate the potential risks before releasing this model. Nevertheless, you may be interested in [transferring the ControlNet to any community model](https://github.com/lllyasviel/ControlNet/discussions/12). ![p](github_page/p21.png) # Annotate Your Own Data We provide simple python scripts to process images. [See a gradio example here](docs/annotator.md). # Train with Your Own Data Training a ControlNet is as easy as (or even easier than) training a simple pix2pix. [See the steps here](docs/train.md). # Citation @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } [Arxiv Link](https://arxiv.org/abs/2302.05543)
saiful-sit/whisper-small-hi	saiful-sit	2023-02-14T11:59:11Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-02-14T09:46:38Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Saiful 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. --> # Whisper Small Hi - Saiful This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2908 - eval_wer: 38.0005 - eval_runtime: 1507.0416 - eval_samples_per_second: 1.92 - eval_steps_per_second: 0.24 - epoch: 2.44 - step: 1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
Manzer/army_men	Manzer	2023-02-14T11:58:32Z	0	0	keras	[ "keras", "en", "license:afl-3.0", "region:us" ]	null	2023-02-14T10:53:08Z	--- license: afl-3.0 language: - en library_name: keras ---
FabioDataGeek/CartPole-v1	FabioDataGeek	2023-02-14T11:32:59Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T11:32:49Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
z4x/poca-SoccerTwos-v3	z4x	2023-02-14T11:27:15Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-02-14T11:32:41Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: z4x/poca-SoccerTwos-v3 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
iotengtr/bert-base-uncased-with-mrpc-trained	iotengtr	2023-02-14T11:27:04Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-14T08:54:49Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue model-index: - name: bert-base-uncased-with-mrpc-trained results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-with-mrpc-trained This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 459 \| 0.3987 \| \| 0.5157 \| 2.0 \| 918 \| 0.4586 \| \| 0.3096 \| 3.0 \| 1377 \| 0.6346 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
selvino/CartPole-v4	selvino	2023-02-14T11:19:08Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T11:18:58Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
meganstodel/pixelcopter2	meganstodel	2023-02-14T11:04:06Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T11:03:57Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 12.60 +/- 8.97 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
g8a9/roberta-tiny-10M	g8a9	2023-02-14T11:01:05Z	48	1	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-01-31T16:41:29Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-tiny-10M results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-tiny-10M This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7391 - Accuracy: 0.5148 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 7.8031 \| 1.04 \| 50 \| 7.3560 \| 0.0606 \| \| 7.1948 \| 2.08 \| 100 \| 6.7374 \| 0.1182 \| \| 6.8927 \| 3.12 \| 150 \| 6.5022 \| 0.1415 \| \| 6.7339 \| 4.16 \| 200 \| 6.4005 \| 0.1483 \| \| 6.6609 \| 5.21 \| 250 \| 6.3535 \| 0.1510 \| \| 6.1972 \| 6.25 \| 300 \| 6.3324 \| 0.1519 \| \| 6.1685 \| 7.29 \| 350 \| 6.3029 \| 0.1528 \| \| 6.1302 \| 8.33 \| 400 \| 6.2828 \| 0.1521 \| \| 6.093 \| 9.37 \| 450 \| 6.2568 \| 0.1536 \| \| 6.0543 \| 10.41 \| 500 \| 6.2430 \| 0.1544 \| \| 6.0479 \| 11.45 \| 550 \| 6.2346 \| 0.1541 \| \| 6.0372 \| 12.49 \| 600 \| 6.2232 \| 0.1546 \| \| 6.0127 \| 13.53 \| 650 \| 6.2139 \| 0.1541 \| \| 5.968 \| 14.58 \| 700 \| 6.2053 \| 0.1547 \| \| 5.9635 \| 15.62 \| 750 \| 6.1996 \| 0.1549 \| \| 5.9479 \| 16.66 \| 800 \| 6.1953 \| 0.1548 \| \| 5.9371 \| 17.7 \| 850 \| 6.1887 \| 0.1545 \| \| 5.9046 \| 18.74 \| 900 \| 6.1613 \| 0.1545 \| \| 5.8368 \| 19.78 \| 950 \| 6.0952 \| 0.1557 \| \| 5.7914 \| 20.82 \| 1000 \| 6.0330 \| 0.1569 \| \| 5.7026 \| 21.86 \| 1050 \| 5.9430 \| 0.1612 \| \| 5.491 \| 22.9 \| 1100 \| 5.6100 \| 0.1974 \| \| 4.9289 \| 23.95 \| 1150 \| 4.9607 \| 0.2702 \| \| 4.5214 \| 24.99 \| 1200 \| 4.5795 \| 0.3051 \| \| 4.5663 \| 26.04 \| 1250 \| 4.3454 \| 0.3265 \| \| 4.3717 \| 27.08 \| 1300 \| 4.1738 \| 0.3412 \| \| 4.1483 \| 28.12 \| 1350 \| 4.0336 \| 0.3555 \| \| 3.9988 \| 29.16 \| 1400 \| 3.9180 \| 0.3677 \| \| 3.8695 \| 30.21 \| 1450 \| 3.8108 \| 0.3782 \| \| 3.5017 \| 31.25 \| 1500 \| 3.7240 \| 0.3879 \| \| 3.4311 \| 32.29 \| 1550 \| 3.6426 \| 0.3974 \| \| 3.3517 \| 33.33 \| 1600 \| 3.5615 \| 0.4068 \| \| 3.2856 \| 34.37 \| 1650 \| 3.4915 \| 0.4156 \| \| 3.227 \| 35.41 \| 1700 \| 3.4179 \| 0.4255 \| \| 3.1675 \| 36.45 \| 1750 \| 3.3636 \| 0.4325 \| \| 3.0908 \| 37.49 \| 1800 \| 3.3083 \| 0.4394 \| \| 3.0561 \| 38.53 \| 1850 \| 3.2572 \| 0.4473 \| \| 3.0139 \| 39.58 \| 1900 \| 3.2159 \| 0.4525 \| \| 2.9837 \| 40.62 \| 1950 \| 3.1789 \| 0.4575 \| \| 2.9387 \| 41.66 \| 2000 \| 3.1431 \| 0.4618 \| \| 2.9034 \| 42.7 \| 2050 \| 3.1163 \| 0.4654 \| \| 2.8822 \| 43.74 \| 2100 \| 3.0842 \| 0.4694 \| \| 2.836 \| 44.78 \| 2150 \| 3.0583 \| 0.4727 \| \| 2.8129 \| 45.82 \| 2200 \| 3.0359 \| 0.4760 \| \| 2.7733 \| 46.86 \| 2250 \| 3.0173 \| 0.4776 \| \| 2.7589 \| 47.9 \| 2300 \| 2.9978 \| 0.4812 \| \| 2.7378 \| 48.95 \| 2350 \| 2.9788 \| 0.4831 \| \| 2.7138 \| 49.99 \| 2400 \| 2.9674 \| 0.4844 \| \| 2.8692 \| 51.04 \| 2450 \| 2.9476 \| 0.4874 \| \| 2.8462 \| 52.08 \| 2500 \| 2.9342 \| 0.4893 \| \| 2.8312 \| 53.12 \| 2550 \| 2.9269 \| 0.4900 \| \| 2.7834 \| 54.16 \| 2600 \| 2.9111 \| 0.4917 \| \| 2.7822 \| 55.21 \| 2650 \| 2.8987 \| 0.4934 \| \| 2.584 \| 56.25 \| 2700 \| 2.8844 \| 0.4949 \| \| 2.5668 \| 57.29 \| 2750 \| 2.8808 \| 0.4965 \| \| 2.5536 \| 58.33 \| 2800 \| 2.8640 \| 0.4982 \| \| 2.5403 \| 59.37 \| 2850 \| 2.8606 \| 0.4982 \| \| 2.5294 \| 60.41 \| 2900 \| 2.8441 \| 0.5008 \| \| 2.513 \| 61.45 \| 2950 \| 2.8402 \| 0.5013 \| \| 2.5105 \| 62.49 \| 3000 \| 2.8316 \| 0.5022 \| \| 2.4897 \| 63.53 \| 3050 \| 2.8237 \| 0.5027 \| \| 2.4974 \| 64.58 \| 3100 \| 2.8187 \| 0.5040 \| \| 2.4799 \| 65.62 \| 3150 \| 2.8129 \| 0.5044 \| \| 2.4741 \| 66.66 \| 3200 \| 2.8056 \| 0.5057 \| \| 2.4582 \| 67.7 \| 3250 \| 2.8025 \| 0.5061 \| \| 2.4389 \| 68.74 \| 3300 \| 2.7913 \| 0.5076 \| \| 2.4539 \| 69.78 \| 3350 \| 2.7881 \| 0.5072 \| \| 2.4252 \| 70.82 \| 3400 \| 2.7884 \| 0.5082 \| \| 2.4287 \| 71.86 \| 3450 \| 2.7784 \| 0.5093 \| \| 2.4131 \| 72.9 \| 3500 \| 2.7782 \| 0.5099 \| \| 2.4016 \| 73.95 \| 3550 \| 2.7724 \| 0.5098 \| \| 2.3998 \| 74.99 \| 3600 \| 2.7659 \| 0.5111 \| \| 2.5475 \| 76.04 \| 3650 \| 2.7650 \| 0.5108 \| \| 2.5443 \| 77.08 \| 3700 \| 2.7620 \| 0.5117 \| \| 2.5381 \| 78.12 \| 3750 \| 2.7631 \| 0.5115 \| \| 2.5269 \| 79.16 \| 3800 \| 2.7578 \| 0.5122 \| \| 2.5288 \| 80.21 \| 3850 \| 2.7540 \| 0.5124 \| \| 2.3669 \| 81.25 \| 3900 \| 2.7529 \| 0.5125 \| \| 2.3631 \| 82.29 \| 3950 \| 2.7498 \| 0.5132 \| \| 2.3499 \| 83.33 \| 4000 \| 2.7454 \| 0.5136 \| \| 2.3726 \| 84.37 \| 4050 \| 2.7446 \| 0.5141 \| \| 2.3411 \| 85.41 \| 4100 \| 2.7403 \| 0.5144 \| \| 2.3321 \| 86.45 \| 4150 \| 2.7372 \| 0.5146 \| \| 2.3456 \| 87.49 \| 4200 \| 2.7389 \| 0.5146 \| \| 2.3372 \| 88.53 \| 4250 \| 2.7384 \| 0.5151 \| \| 2.343 \| 89.58 \| 4300 \| 2.7398 \| 0.5144 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.6.1 - Tokenizers 0.12.1
0RisingStar0/HighRiseMixV1	0RisingStar0	2023-02-14T10:42:49Z	4	19	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-02-07T17:45:17Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers --- <p align="center"><img src="https://huggingface.co/0RisingStar0/HighRiseMixV1/resolve/main/00401-2269441947-(masterpiece%2C%20excellent%20quality%2C%20high%20quality%2C%20highres%20_%201.5)%2C%20(1girl%2C%20solo)%2C%20solo%20focus%2C%20sky%2C%20city%2C%20skyscrapers%2C%20pavement%2C%20tree.png"> <img src="https://huggingface.co/0RisingStar0/HighRiseMixV1/resolve/main/13.png"></p> <b>V2 is out! : https://huggingface.co/0RisingStar0/HighRiseMixV2</b> <center><b>HighRiseMixV1</b></center> U-Net mixed model <b>specialized for city and skyscrapers background.</b> <b>FP16 Pruned version</b>(No EMA). (Quality change may occur in very small details on buildings' textures) <b>Recommended prompts : </b> (masterpiece, best quality, excellent quality), ((1girl, solo)), sky, city, (skyscrapers), trees, pavement, lens flare EasyNegative, moss, phone, man, pedestrians, extras, border, outside border, white border (EasyNegative is a negative embedding : https://huggingface.co/datasets/gsdf/EasyNegative) <b>Recommended settings : </b> Sampler : DPM++ 2M Karras OR DPM++ SDE Karras Sampling steps : 25 ~ 30 Resolution : 512x768 OR 768x512 CFG Scale : 9 <b> Upscale is a must-do!! </b> Otherwise, you won't get great results. Upscaler : Latent (nearest) Hires steps : 0 Denoise : 0.6 Upscale 2x <b> Mixed models : </b> AbyssOrangeMix2_NSFW, AnythingV4.5, BasilMixFixed, CounterfeitV2.5, EerieOrangeMix2, PowercolorV2 (Thanks to everyone who made above models!) This is my first mixed model being uploaded to public site, so feel free to give feedbacks as you wish, I'll try and work around with it.
asuzuki/a2c-PandaReachDense-v2	asuzuki	2023-02-14T10:19:46Z	3	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T09:49:42Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.74 +/- 0.27 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python #install !apt install python-opengl !apt install ffmpeg !apt install xvfb !pip3 install pyvirtualdisplay !pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit6/requirements-unit6.txt # Virtual display from pyvirtualdisplay import Display virtual_display = Display(visible=0, size=(1400, 900)) virtual_display.start() #imports import pybullet_envs import panda_gym import gym import os from huggingface_sb3 import load_from_hub, package_to_hub from stable_baselines3 import A2C from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize from stable_baselines3.common.env_util import make_vec_env from huggingface_hub import notebook_login #Define the environment called "PandaReachDense-v2" env_id = "PandaReachDense-v2" #Make a vectorized environment env = make_vec_env(env_id, n_envs=4) #Add a wrapper to normalize the observations and rewards. Check the documentation env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10) #Create the A2C Model (don't forget verbose=1 to print the training logs). model = A2C(policy = "MultiInputPolicy", env = env, gae_lambda = 0.9, gamma = 0.95, learning_rate = 0.001, max_grad_norm = 0.5, n_steps = 8, vf_coef = 0.4, ent_coef = 0.0, seed=11, policy_kwargs=dict( log_std_init=-2, ortho_init=False), normalize_advantage=False, use_rms_prop= True, use_sde= True, verbose=1) #Train it for 1M Timesteps model.learn(1_500_000) #Save the model and VecNormalize statistics when saving the agent model.save(f"a2c-{env_id}") env.save(f"vec_normalize_{env_id}.pkl") #Evaluate your agent eval_env = DummyVecEnv([lambda: gym.make(env_id)]) eval_env = VecNormalize.load(f"vec_normalize_{env_id}.pkl", eval_env) # do not update them at test time eval_env.training = False # reward normalization is not needed at test time eval_env.norm_reward = False # Load the model model = A2C.load(f"a2c-{env_id}") #Evaluate model mean_reward, std_reward = evaluate_policy(model, eval_env) print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}") ... ```
Guigadal/layoutxlm-finetuned-xfund-es	Guigadal	2023-02-14T09:53:57Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "dataset:xfun", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-02-09T09:14:02Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - xfun model-index: - name: layoutxlm-finetuned-xfund-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. --> # layoutxlm-finetuned-xfund-es -> This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun dataset. -> Este modelo es una versión de [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) con ajuste fino sobre xfun-es dataset (español). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2500 ### Training results Train output: - global_step=2500 - training_loss=0.3006648193359375 - train_runtime: 3385.7522 - train_samples_per_second: 1.477 - train_steps_per_second: 0.738 - total_flos: 2688500014387200.0 - train_loss: 0.3006648193359375 - epoch: 20.49 ### Framework versions - Transformers 4.26.0 - Pytorch 1.10.0+cu111 - Datasets 2.9.0 - Tokenizers 0.13.2
EdenYav/dqn-SpaceInvadersNoFrameskip-v4	EdenYav	2023-02-14T09:27:34Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-08T11:42:18Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 427.00 +/- 94.58 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 EdenYav -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 EdenYav -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 EdenYav ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 150000), ('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', 0.0005), ('learning_starts', 100000), ('n_timesteps', 500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
fermaat/poca-SoccerTwos	fermaat	2023-02-14T09:22:24Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-02-14T09:22:19Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: fermaat/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
giggling-squid/q-Taxi-v3-5x5	giggling-squid	2023-02-14T09:21:09Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T08:51:27Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-5x5 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="giggling-squid/q-Taxi-v3-5x5", 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"]) ```
ysugawa/distilbert-base-uncased-finetuned-squad-d5716d28	ysugawa	2023-02-14T09:04:31Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	question-answering	2023-02-14T09:02:05Z	--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results \| \| Exact Match \| F1 \| \|------------------\|-------------\|------\| \| DistilBERT paper \| 79.1 \| 86.9 \| \| Ours \| 78.4 \| 86.5 \| The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
TracyWang/MAUD_KWM_AWS_Roberta-base	TracyWang	2023-02-14T08:37:02Z	9	1	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "legal", "text-classification", "en", "arxiv:2301.00876", "license:mit", "endpoints_compatible", "region:us" ]	text-classification	2023-02-08T03:08:17Z	--- license: mit language: - en pipeline_tag: text-classification tags: - legal --- Dataset and Training Script offered by the Atticus Project MAUD. Trained on AWS Sagemaker with 4 A10 GPUs. Model owned by King & Wood Mallesons Law Firm AI LAB. Project Member: - Wuyue(Tracy) Wang @ King & Wood Mallesons - Anbei Zhao @ Amazon Web Services - Xiaodong Guo @ Amazon Web Services - Xiuyu Wu @ Peking University Reference: ``` @misc{wang2023maud, title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding}, author={Steven H. Wang and Antoine Scardigli and Leonard Tang and Wei Chen and Dimitry Levkin and Anya Chen and Spencer Ball and Thomas Woodside and Oliver Zhang and Dan Hendrycks}, year={2023}, eprint={2301.00876}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
k-ush/xlm-roberta-base-ance-warmup	k-ush	2023-02-14T08:30:05Z	108	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "en", "autotrain_compatible", "region:us" ]	fill-mask	2023-02-13T16:57:17Z	--- language: - en inference: false --- # XLM RoBERTa Base ANCE-Warmuped This is a XLM RoBERTa Base model trained with ANCE warmup script. RobertaForSequenceClassification is replaced to XLMRobertaForSequenceClassification in warmup script. trained 60k steps. train args is below: ``` text data_dir: ../data/raw_data/ train_model_type: rdot_nll model_name_or_path: xlm-roberta-base task_name: msmarco output_dir: config_name: tokenizer_name: cache_dir: max_seq_length: 128 do_train: True do_eval: False evaluate_during_training: True do_lower_case: False log_dir: ../logs/ eval_type: full optimizer: lamb scheduler: linear per_gpu_train_batch_size: 32 per_gpu_eval_batch_size: 32 gradient_accumulation_steps: 1 learning_rate: 0.0002 weight_decay: 0.0 adam_epsilon: 1e-08 max_grad_norm: 1.0 num_train_epochs: 2.0 max_steps: -1 warmup_steps: 1000 logging_steps: 1000 logging_steps_per_eval: 20 save_steps: 30000 eval_all_checkpoints: False no_cuda: False overwrite_output_dir: True overwrite_cache: False seed: 42 fp16: True fp16_opt_level: O1 expected_train_size: 35000000 load_optimizer_scheduler: False local_rank: 0 server_ip: server_port: n_gpu: 1 device: cuda:0 output_mode: classification num_labels: 2 train_batch_size: 32 ``` # Eval Result ``` text Reranking/Full ranking mrr: 0.27380855732933/0.24284821712830248 {"learning_rate": 0.00019460324719871943, "loss": 0.0895877162806064, "step": 60000} ``` # Usage ``` python3 from transformers import XLMRobertaForSequenceClassification, XLMRobertaTokenizer repo = "k-ush/xlm-roberta-base-ance-warmup" model = XLMRobertaForSequenceClassification.from_pretrained(repo) tokenizer = XLMRobertaTokenizer.from_pretrained(repo) ```
mqy/mt5-small-finetuned-14feb-1	mqy	2023-02-14T08:26:00Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-02-14T02:02:27Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-14feb-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-14feb-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4516 - Rouge1: 20.33 - Rouge2: 6.2 - Rougel: 19.9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000275 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| 4.0401 \| 1.0 \| 388 \| 2.5481 \| 16.31 \| 4.7 \| 16.1 \| \| 2.9776 \| 2.0 \| 776 \| 2.4442 \| 17.25 \| 4.93 \| 16.93 \| \| 2.7362 \| 3.0 \| 1164 \| 2.4181 \| 19.73 \| 5.74 \| 19.21 \| \| 2.5767 \| 4.0 \| 1552 \| 2.4071 \| 19.37 \| 5.62 \| 18.89 \| \| 2.4466 \| 5.0 \| 1940 \| 2.3560 \| 18.98 \| 5.94 \| 18.55 \| \| 2.3402 \| 6.0 \| 2328 \| 2.3923 \| 20.45 \| 5.5 \| 20.03 \| \| 2.2385 \| 7.0 \| 2716 \| 2.3639 \| 20.03 \| 5.96 \| 19.76 \| \| 2.1663 \| 8.0 \| 3104 \| 2.3431 \| 19.17 \| 5.34 \| 18.84 \| \| 2.0849 \| 9.0 \| 3492 \| 2.4008 \| 19.97 \| 5.58 \| 19.67 \| \| 2.0203 \| 10.0 \| 3880 \| 2.3948 \| 19.67 \| 5.75 \| 19.26 \| \| 1.9653 \| 11.0 \| 4268 \| 2.3915 \| 20.06 \| 6.07 \| 19.61 \| \| 1.9067 \| 12.0 \| 4656 \| 2.4025 \| 20.83 \| 6.46 \| 20.41 \| \| 1.8592 \| 13.0 \| 5044 \| 2.4194 \| 19.97 \| 6.4 \| 19.69 \| \| 1.8158 \| 14.0 \| 5432 \| 2.4156 \| 19.87 \| 6.16 \| 19.38 \| \| 1.7679 \| 15.0 \| 5820 \| 2.4053 \| 19.9 \| 5.99 \| 19.52 \| \| 1.748 \| 16.0 \| 6208 \| 2.4156 \| 19.68 \| 5.81 \| 19.28 \| \| 1.7198 \| 17.0 \| 6596 \| 2.4306 \| 20.0 \| 6.26 \| 19.63 \| \| 1.6959 \| 18.0 \| 6984 \| 2.4499 \| 19.1 \| 6.19 \| 18.82 \| \| 1.6769 \| 19.0 \| 7372 \| 2.4536 \| 20.62 \| 6.3 \| 20.15 \| \| 1.6682 \| 20.0 \| 7760 \| 2.4516 \| 20.33 \| 6.2 \| 19.9 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
amrisaurus/pretrained-m-bert-90	amrisaurus	2023-02-14T07:57:21Z	46	0	transformers	[ "transformers", "tf", "bert", "pretraining", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]	null	2023-02-14T07:55:57Z	--- tags: - generated_from_keras_callback model-index: - name: pretrained-m-bert-90 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # pretrained-m-bert-90 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.7094 - Validation Loss: 14.5332 - Epoch: 89 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - 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': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 10.2413 \| 10.9668 \| 0 \| \| 7.5814 \| 10.9638 \| 1 \| \| 7.0095 \| 11.3733 \| 2 \| \| 6.4352 \| 11.5989 \| 3 \| \| 6.7137 \| 11.4072 \| 4 \| \| 6.4383 \| 11.8287 \| 5 \| \| 6.2223 \| 12.0344 \| 6 \| \| 6.1759 \| 11.6900 \| 7 \| \| 6.0764 \| 11.7144 \| 8 \| \| 5.8802 \| 12.1089 \| 9 \| \| 6.0159 \| 12.3456 \| 10 \| \| 5.9254 \| 12.7065 \| 11 \| \| 5.6652 \| nan \| 12 \| \| 5.8185 \| 12.8155 \| 13 \| \| 5.9185 \| 12.7047 \| 14 \| \| 5.8418 \| 12.7175 \| 15 \| \| 5.9122 \| 12.5688 \| 16 \| \| 5.9698 \| 12.5251 \| 17 \| \| 5.8286 \| 12.7015 \| 18 \| \| 5.8807 \| 13.2514 \| 19 \| \| 5.8330 \| 12.8541 \| 20 \| \| 5.6456 \| 13.4088 \| 21 \| \| 5.7257 \| 13.5517 \| 22 \| \| 5.8854 \| 12.8775 \| 23 \| \| 5.6770 \| 13.6499 \| 24 \| \| 5.6026 \| 13.9732 \| 25 \| \| 5.6651 \| 13.0827 \| 26 \| \| 5.8888 \| 13.1292 \| 27 \| \| 5.8123 \| 12.8970 \| 28 \| \| 5.7525 \| 13.3724 \| 29 \| \| 5.9020 \| 13.5507 \| 30 \| \| 5.8642 \| 13.3284 \| 31 \| \| 5.9329 \| 13.7350 \| 32 \| \| 5.7728 \| 13.3011 \| 33 \| \| 5.8297 \| 13.6108 \| 34 \| \| 5.8118 \| 13.3331 \| 35 \| \| 5.7382 \| 13.7047 \| 36 \| \| 5.8061 \| 13.8107 \| 37 \| \| 5.8423 \| 13.4207 \| 38 \| \| 5.8442 \| 13.6832 \| 39 \| \| 5.7680 \| 14.1248 \| 40 \| \| 5.7668 \| 13.6626 \| 41 \| \| 5.7826 \| 13.6470 \| 42 \| \| 5.7692 \| 13.9430 \| 43 \| \| 5.5109 \| 14.0924 \| 44 \| \| 5.7394 \| 14.0253 \| 45 \| \| 5.8013 \| 13.5926 \| 46 \| \| 5.7222 \| 13.9732 \| 47 \| \| 5.7023 \| 14.0204 \| 48 \| \| 5.8250 \| 13.9655 \| 49 \| \| 5.6064 \| 14.0406 \| 50 \| \| 5.7319 \| 14.1826 \| 51 \| \| 5.6849 \| 13.9114 \| 52 \| \| 5.8167 \| 13.9917 \| 53 \| \| 5.7573 \| 14.1509 \| 54 \| \| 5.6921 \| 14.3722 \| 55 \| \| 5.7190 \| 14.4919 \| 56 \| \| 5.8501 \| 13.6970 \| 57 \| \| 5.7627 \| 14.1393 \| 58 \| \| 5.8031 \| 14.1246 \| 59 \| \| 5.7207 \| 14.3084 \| 60 \| \| 5.7979 \| 13.9398 \| 61 \| \| 5.7068 \| 14.2865 \| 62 \| \| 5.7547 \| 14.2590 \| 63 \| \| 5.8349 \| 14.1481 \| 64 \| \| 5.7924 \| 14.0461 \| 65 \| \| 5.8127 \| 14.1274 \| 66 \| \| 5.7590 \| 14.3578 \| 67 \| \| 5.8297 \| 14.2429 \| 68 \| \| 5.7822 \| 14.2742 \| 69 \| \| 5.7708 \| 14.3720 \| 70 \| \| 5.6521 \| 14.8640 \| 71 \| \| 5.7253 \| 14.4404 \| 72 \| \| 5.8076 \| 14.1843 \| 73 \| \| 5.7746 \| 14.4657 \| 74 \| \| 5.8592 \| 14.2965 \| 75 \| \| 5.6643 \| 14.0996 \| 76 \| \| 5.7849 \| 14.3531 \| 77 \| \| 5.7418 \| 14.4266 \| 78 \| \| 5.7030 \| 14.5584 \| 79 \| \| 5.8298 \| 14.1390 \| 80 \| \| 5.9061 \| 13.9172 \| 81 \| \| 5.6570 \| 14.6991 \| 82 \| \| 5.7040 \| 14.7839 \| 83 \| \| 5.8064 \| 14.2581 \| 84 \| \| 5.6855 \| 14.4449 \| 85 \| \| 5.7803 \| 14.7469 \| 86 \| \| 5.7495 \| 14.4704 \| 87 \| \| 5.7539 \| 14.5520 \| 88 \| \| 5.7094 \| 14.5332 \| 89 \| ### Framework versions - Transformers 4.27.0.dev0 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2
akghxhs55/poca-SoccerTwos-4	akghxhs55	2023-02-14T07:54:19Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-02-14T07:54:12Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: akghxhs55/poca-SoccerTwos-4 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
nolanaatama/crns7thhvnmx	nolanaatama	2023-02-14T07:53:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-02-14T07:45:39Z	--- license: creativeml-openrail-m ---
Zekunli/flan-t5-large-extraction-cnndm_10000-all	Zekunli	2023-02-14T07:52:30Z	4	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	2023-02-14T06:39:35Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-large-extraction-cnndm_10000-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. --> # flan-t5-large-extraction-cnndm_10000-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7044 - Rouge1: 34.8618 - Rouge2: 15.5978 - Rougel: 29.7948 - Rougelsum: 29.7581 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.1668 \| 0.16 \| 200 \| 1.8280 \| 33.7941 \| 14.3114 \| 28.7743 \| 28.7968 \| 19.0 \| \| 1.9736 \| 0.32 \| 400 \| 1.7818 \| 34.8351 \| 15.5548 \| 29.8974 \| 29.8557 \| 18.99 \| \| 1.904 \| 0.48 \| 600 \| 1.7513 \| 35.465 \| 15.8566 \| 30.7139 \| 30.6596 \| 18.986 \| \| 1.8938 \| 0.64 \| 800 \| 1.7440 \| 34.6193 \| 15.5473 \| 30.0661 \| 30.0019 \| 18.99 \| \| 1.8471 \| 0.8 \| 1000 \| 1.7366 \| 34.553 \| 15.2214 \| 29.8807 \| 29.8419 \| 18.99 \| \| 1.8621 \| 0.96 \| 1200 \| 1.7486 \| 34.9309 \| 15.1932 \| 29.8973 \| 29.8774 \| 18.99 \| \| 1.8082 \| 1.12 \| 1400 \| 1.7311 \| 35.3395 \| 16.0976 \| 30.2748 \| 30.293 \| 18.99 \| \| 1.7448 \| 1.28 \| 1600 \| 1.7155 \| 35.1387 \| 15.7462 \| 29.924 \| 29.9287 \| 18.99 \| \| 1.7655 \| 1.44 \| 1800 \| 1.7239 \| 35.3603 \| 15.6355 \| 30.3944 \| 30.3766 \| 19.0 \| \| 1.7283 \| 1.6 \| 2000 \| 1.7132 \| 34.7368 \| 15.4073 \| 29.9027 \| 29.8971 \| 19.0 \| \| 1.7463 \| 1.76 \| 2200 \| 1.7171 \| 35.0545 \| 15.726 \| 30.0364 \| 30.0056 \| 19.0 \| \| 1.7462 \| 1.92 \| 2400 \| 1.7044 \| 34.8618 \| 15.5978 \| 29.7948 \| 29.7581 \| 19.0 \| \| 1.719 \| 2.08 \| 2600 \| 1.7285 \| 34.9598 \| 15.5237 \| 29.5593 \| 29.5803 \| 19.0 \| \| 1.6828 \| 2.24 \| 2800 \| 1.7179 \| 35.0944 \| 15.7333 \| 29.8381 \| 29.7784 \| 19.0 \| \| 1.7 \| 2.4 \| 3000 \| 1.7047 \| 35.1766 \| 15.7758 \| 29.818 \| 29.7859 \| 19.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1
sinny/baseline	sinny	2023-02-14T07:45:17Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-02-14T07:45:11Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: sinny/baseline 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
joelniklaus/legal-swedish-roberta-base	joelniklaus	2023-02-14T06:08:34Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-02-06T02:38:57Z	--- tags: - generated_from_trainer model-index: - name: legal-swedish-roberta-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # legal-swedish-roberta-base This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6149 ## Model description More information needed ## Intended uses & 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 - distributed_type: tpu - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 200000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.9458 \| 2.04 \| 50000 \| 0.7559 \| \| 0.8037 \| 4.08 \| 100000 \| 0.6746 \| \| 0.8366 \| 7.01 \| 150000 \| 0.6285 \| \| 0.7924 \| 9.05 \| 200000 \| 0.6149 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.9.0 - Tokenizers 0.12.0
Ransaka/a2c-AntBulletEnv-v0	Ransaka	2023-02-14T05:56:44Z	1	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T05:55:33Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1950.91 +/- 129.99 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
PecanPi/pixelcopter-v1	PecanPi	2023-02-14T05:51:41Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-02-13T03:45:46Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.90 +/- 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
LarryAIDraw/azuraFireEmblemFates_v10	LarryAIDraw	2023-02-14T05:31:03Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-02-14T05:29:06Z	--- license: creativeml-openrail-m ---
LarryAIDraw/kaguyaSamaLoveIsWar_hayasakaAiV1	LarryAIDraw	2023-02-14T05:30:28Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-02-14T05:23:37Z	--- license: creativeml-openrail-m --- https://civitai.com/models/8613/kaguya-samalove-is-war-hayasaka-ai
TheRains/whisper-small-id	TheRains	2023-02-14T04:46:04Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "id", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-16T08:30:02Z	--- language: - id license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Id - TheRains 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. --> # Whisper Small Id - TheRains This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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-06 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
UchihaMadara/model1-thesis-5	UchihaMadara	2023-02-14T04:42:32Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-02-14T04:18:52Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: model1-thesis-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. --> # model1-thesis-5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6273 - Precision: 0.4620 - Recall: 0.6348 - F1: 0.5348 - Accuracy: 0.8196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 29 \| 0.5895 \| 0.3871 \| 0.6261 \| 0.4784 \| 0.8086 \| \| No log \| 2.0 \| 58 \| 0.5814 \| 0.4424 \| 0.6348 \| 0.5214 \| 0.8118 \| \| No log \| 3.0 \| 87 \| 0.5734 \| 0.4360 \| 0.6522 \| 0.5226 \| 0.8332 \| \| No log \| 4.0 \| 116 \| 0.6326 \| 0.4808 \| 0.6522 \| 0.5535 \| 0.8170 \| \| No log \| 5.0 \| 145 \| 0.6273 \| 0.4620 \| 0.6348 \| 0.5348 \| 0.8196 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
bakuaifuji/test	bakuaifuji	2023-02-14T04:41:49Z	0	0	null	[ "music", "license:apache-2.0", "region:us" ]	null	2023-02-10T05:40:41Z	--- license: apache-2.0 tags: - music ---
enankobh1/whisper-small-ASR-EN	enankobh1	2023-02-14T04:35:06Z	78	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-02-13T10:13:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-ASR-EN 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. --> # whisper-small-ASR-EN This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7014 - Wer: 20.6492 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0389 \| 5.95 \| 1000 \| 0.4354 \| 24.2479 \| \| 0.0015 \| 11.9 \| 2000 \| 0.6301 \| 21.1699 \| \| 0.0003 \| 17.86 \| 3000 \| 0.6822 \| 20.5739 \| \| 0.0002 \| 23.81 \| 4000 \| 0.7014 \| 20.6492 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
nhiro3303/ppo-SnowballTarget	nhiro3303	2023-02-14T04:23:07Z	14	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-02-14T04:23:02Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: nhiro3303/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dfm794/poca-SoccerTwos-2x-2-r-l	dfm794	2023-02-14T04:22:23Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-02-14T04:22:16Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: dfm794/poca-SoccerTwos-2x-2-r-l 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yhchoi/klue-emotional-finetuned-emotion	yhchoi	2023-02-14T04:21:14Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-14T03:44:34Z	--- tags: - generated_from_trainer model-index: - name: klue-emotional-finetuned-emotion 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. --> # klue-emotional-finetuned-emotion This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.8.1+cu111 - Datasets 2.9.0 - Tokenizers 0.13.2
jhn9803/Contract-new-tokenizer-roberta-large	jhn9803	2023-02-14T04:19:19Z	4	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-14T03:36:26Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Contract-new-tokenizer-roberta-large 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. --> # Contract-new-tokenizer-roberta-large This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6076 - Accuracy: 0.7042 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 249 \| 0.6072 \| 0.7042 \| \| No log \| 2.0 \| 498 \| 0.6138 \| 0.7042 \| \| No log \| 3.0 \| 747 \| 0.6076 \| 0.7042 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
printr/gptx-rl-371k	printr	2023-02-14T03:55:52Z	0	0	null	[ "license:cc-by-2.0", "region:us" ]	null	2023-02-13T22:40:20Z	--- license: cc-by-2.0 --- [Github for Inference and Training](https://github.com/Quicksticks-oss/GPTX-RL) ### Features - Just like GPT this model generates text based on an input prompt. - This project uses Reinforcement Learning (RL) for training and inference. - All models can be found on hugging face. --- ### Training - First step is to run ``generate_vocab.py`` on whatever text data you would like to train on for example ``python3 generate_vocab.py -i train.txt`` - Next step is to run ``train.py`` - Now all you have to do is wait! --- ### Inference - Run ``inference.py`` with ``-i`` as an argumentand with models path. ### Logging - Output plot graphs will be avalible in the next version.
mjschock/a2c-AntBulletEnv-v0	mjschock	2023-02-14T03:54:20Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T03:53:05Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1358.66 +/- 213.32 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jhn9803/Contract-base-tokenizer-roberta-large	jhn9803	2023-02-14T03:28:12Z	7	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-13T13:20:38Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Contract-base-tokenizer-roberta-large 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. --> # Contract-base-tokenizer-roberta-large This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0431 - Accuracy: 0.9930 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 249 \| 0.0580 \| 0.9869 \| \| No log \| 2.0 \| 498 \| 0.0380 \| 0.9920 \| \| No log \| 3.0 \| 747 \| 0.0431 \| 0.9930 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
cwwojin/stt_kr_conformer_ctc_medium	cwwojin	2023-02-14T03:23:47Z	1	1	nemo	[ "nemo", "ksponspeech", "automatic-speech-recognition", "ko", "license:mit", "model-index", "region:us" ]	automatic-speech-recognition	2023-02-12T13:10:42Z	--- license: mit language: - ko metrics: - cer pipeline_tag: automatic-speech-recognition tags: - ksponspeech model-index: - name: cwwojin/stt_kr_conformer_ctc_medium results: - task: type: automatic-speech-recognition # Required. Example: automatic-speech-recognition dataset: type: Murple/ksponspeech # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: KsponSpeech-eval (Korean) # Required. A pretty name for the dataset. Example: Common Voice (French) split: test # Optional. Example: test metrics: - type: cer # Required. Example: wer. Use metric id from https://hf.co/metrics value: 11.902 # Required. Example: 20.90 name: Test CER(%) # Optional. Example: Test WER --- # stt_kr_conformer_ctc_medium - Fine-tuned from "stt_en_conformer_ctc_medium" https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_medium - Trained on KsponSpeech, provided by https://aihub.or.kr/ ## Preprocessing - Files converted from .pcm -> .wav - Text - Korean phonetic transcription - SentencePiece tokenizer (Byte-pair encoding), vocab-size = 5,000 ## Evaluation - "KsponSpeech_eval_clean", "KsponSpeech_eval_other"
letingliu/my_awesome_model	letingliu	2023-02-14T03:21:41Z	5	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-13T01:03:05Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: letingliu/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. --> # letingliu/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: 0.5129 - Validation Loss: 0.5051 - Train Accuracy: 0.9231 - Epoch: 7 ## Model description More information needed ## 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': 30, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6853 \| 0.6586 \| 0.7788 \| 0 \| \| 0.6489 \| 0.6197 \| 0.7788 \| 1 \| \| 0.6090 \| 0.5693 \| 0.8942 \| 2 \| \| 0.5617 \| 0.5245 \| 0.8942 \| 3 \| \| 0.5235 \| 0.5051 \| 0.9231 \| 4 \| \| 0.5116 \| 0.5051 \| 0.9231 \| 5 \| \| 0.5112 \| 0.5051 \| 0.9231 \| 6 \| \| 0.5129 \| 0.5051 \| 0.9231 \| 7 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2
amrutha3899/my_awesome_qa_model2	amrutha3899	2023-02-14T02:49:16Z	107	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]	question-answering	2023-02-14T01:44:19Z	--- tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_model2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_qa_model2 This model was trained from scratch on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
svenkate/ppo-LunarLander-v2	svenkate	2023-02-14T02:44:08Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T02:43:44Z	--- 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: 268.79 +/- 25.14 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 ... ```
Zekunli/flan-t5-large-extraction-cnndm_20000-all	Zekunli	2023-02-14T02:35:46Z	3	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	2023-02-14T01:29:24Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-large-extraction-cnndm_20000-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. --> # flan-t5-large-extraction-cnndm_20000-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6652 - Rouge1: 35.487 - Rouge2: 15.6713 - Rougel: 29.9519 - Rougelsum: 29.9368 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.1295 \| 0.08 \| 200 \| 1.8266 \| 34.0465 \| 14.7511 \| 29.3395 \| 29.3437 \| 19.0 \| \| 1.9354 \| 0.16 \| 400 \| 1.7732 \| 34.7923 \| 15.3094 \| 29.8484 \| 29.8757 \| 18.99 \| \| 1.854 \| 0.24 \| 600 \| 1.7367 \| 34.8358 \| 15.1969 \| 29.9971 \| 30.0064 \| 18.986 \| \| 1.833 \| 0.32 \| 800 \| 1.7120 \| 34.7854 \| 15.5144 \| 29.8141 \| 29.7863 \| 18.982 \| \| 1.8217 \| 0.4 \| 1000 \| 1.7256 \| 34.7274 \| 15.2763 \| 30.0298 \| 30.0871 \| 19.0 \| \| 1.8309 \| 0.48 \| 1200 \| 1.7089 \| 35.4328 \| 15.7724 \| 30.0655 \| 30.0199 \| 19.0 \| \| 1.825 \| 0.56 \| 1400 \| 1.6947 \| 35.4116 \| 15.6911 \| 30.1438 \| 30.1764 \| 19.0 \| \| 1.7914 \| 0.64 \| 1600 \| 1.7119 \| 35.5918 \| 16.3762 \| 30.3234 \| 30.2807 \| 19.0 \| \| 1.7889 \| 0.72 \| 1800 \| 1.6810 \| 35.6413 \| 15.8936 \| 30.2848 \| 30.2291 \| 19.0 \| \| 1.7576 \| 0.8 \| 2000 \| 1.6826 \| 35.9424 \| 15.6803 \| 30.5998 \| 30.5571 \| 19.0 \| \| 1.7763 \| 0.88 \| 2200 \| 1.6748 \| 35.7543 \| 15.984 \| 30.7197 \| 30.721 \| 18.998 \| \| 1.7604 \| 0.96 \| 2400 \| 1.6652 \| 35.487 \| 15.6713 \| 29.9519 \| 29.9368 \| 19.0 \| \| 1.7138 \| 1.04 \| 2600 \| 1.6860 \| 36.0333 \| 16.4065 \| 30.7249 \| 30.7168 \| 19.0 \| \| 1.6951 \| 1.12 \| 2800 \| 1.6792 \| 35.3149 \| 15.7178 \| 30.1555 \| 30.1517 \| 18.998 \| \| 1.6752 \| 1.2 \| 3000 \| 1.6832 \| 34.7566 \| 15.4179 \| 29.7687 \| 29.8259 \| 19.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1
alc15492/msemj30	alc15492	2023-02-14T01:23:58Z	9	0	diffusers	[ "diffusers", "text-to-image", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-02-14T00:55:50Z	--- license: openrail library_name: diffusers pipeline_tag: text-to-image ---
muchad/idt5-qa-qg	muchad	2023-02-14T01:20:53Z	128	9	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "question-generation", "multitask-model", "idt5", "id", "dataset:SQuADv2.0", "arxiv:2302.00856", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-12T16:32:11Z	--- language: id datasets: - SQuADv2.0 tags: - question-generation - multitask-model - idt5 widget: - text: "generate question: <hl> Dua orang <hl> pengembara berjalan di sepanjang jalan yang berdebu dan tandus di hari yang sangat panas. Tidak lama kemudian, mereka menemukan sebuah pohon besar. </s>" - text: "question: Siapa pemimpin Kerajaan Tarumanegara? context: Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M. </s>" license: apache-2.0 --- # idT5 for Indonesian Question Generation and Question Answering [idT5](https://huggingface.co/muchad/idt5-base) (Indonesian version of [mT5](https://huggingface.co/google/mt5-base)) is fine-tuned on 30% of [translated SQuAD v2.0](https://github.com/Wikidepia/indonesian_datasets/tree/master/question-answering/squad) for Question Generation and Question Answering tasks. ## Live Demo * Question Generation: [ai.muchad.com/qg](https://ai.muchad.com/qg/) * Question Answering: [t.me/caritahubot](https://t.me/caritahubot) ## Requirements ``` !pip install transformers==4.4.2 !pip install sentencepiece==0.1.95 !git clone https://github.com/muchad/qaqg.git %cd qaqg/ ``` ## Usage 🚀 #### Question Generation [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Generation.ipynb) ``` from pipeline_qg import pipeline #pipeline_qg.py script in the cloned repo qg = pipeline() #sample qg("Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M.") #output => [{'answer': 'Raja Purnawarman','question': 'Siapa yang memerintah Kerajaan Tarumanegara?'}, {'answer': '395 M','question': 'Kapan Raja Purnawarman memerintah Kerajaan Tarumanegara?'}] ``` #### Question Answering [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Answering.ipynb) ``` from pipeline_qa import pipeline #pipeline_qa.py script in the cloned repo qa = pipeline() #sample qa({"context":"Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M.","question":"Siapa pemimpin Kerajaan Tarumanegara?"}) #output => Raja Purnawarman ``` #### Citation Paper: [idT5: Indonesian Version of Multilingual T5 Transformer](https://arxiv.org/abs/2302.00856) ``` @misc{https://doi.org/10.48550/arxiv.2302.00856, doi = {10.48550/ARXIV.2302.00856}, url = {https://arxiv.org/abs/2302.00856}, author = {Fuadi, Mukhlish and Wibawa, Adhi Dharma and Sumpeno, Surya}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7}, title = {idT5: Indonesian Version of Multilingual T5 Transformer}, publisher = {arXiv}, year = {2023} } ```
deprem-ml/deprem_bert_128k	deprem-ml	2023-02-14T01:15:10Z	8	3	transformers	[ "transformers", "pytorch", "bert", "text-classification", "deprem-clf-v1", "tr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-13T17:56:09Z	--- license: apache-2.0 language: - tr metrics: - accuracy - recall - f1 tags: - deprem-clf-v1 library_name: transformers pipeline_tag: text-classification model-index: - name: deprem_v12 results: - task: type: text-classification dataset: type: deprem_private_dataset_v1_2 name: deprem_private_dataset_v1_2 metrics: - type: recall value: 0.82 verified: false - type: f1 value: 0.76 verified: false widget: - text: >- acil acil acil antakyadan istanbula gitmek için antakya expoya ulaşmaya çalışan 19 kişilik bir aile için şehir içi ulaşım desteği istiyoruz. dışardalar üşüyorlar.iletebileceğiniz numaraları bekliyorum example_title: Örnek --- ## Eval Results ``` precision recall f1-score support Alakasiz 0.87 0.91 0.89 734 Barinma 0.79 0.89 0.84 207 Elektronik 0.69 0.83 0.75 130 Giysi 0.71 0.81 0.76 94 Kurtarma 0.82 0.85 0.83 362 Lojistik 0.57 0.67 0.62 112 Saglik 0.68 0.85 0.75 108 Su 0.56 0.76 0.64 78 Yagma 0.60 0.77 0.68 31 Yemek 0.71 0.89 0.79 117 micro avg 0.77 0.86 0.81 1973 macro avg 0.70 0.82 0.76 1973 weighted avg 0.78 0.86 0.82 1973 samples avg 0.83 0.88 0.84 1973 ``` ## Training Params: ```python {'per_device_train_batch_size': 32, 'per_device_eval_batch_size': 32, 'learning_rate': 5.8679699888213376e-05, 'weight_decay': 0.03530961718117487, 'num_train_epochs': 4, 'lr_scheduler_type': 'cosine', 'warmup_steps': 40, 'seed': 42, 'fp16': True, 'load_best_model_at_end': True, 'metric_for_best_model': 'macro f1', 'greater_is_better': True } ``` ## Threshold: - Best Threshold: 0.40 ## Class Loss Weights - Same as Anıl's approach: ```python [1.0, 1.5167249178108022, 1.7547338578655642, 1.9610520059358458, 1.8684086209021484, 1.8019018017117145, 2.110648663094536, 3.081208739200435, 1.7994815143101963] ```
zb/ppo-LunarLander-v2	zb	2023-02-14T00:47:03Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T00:46:35Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 233.87 +/- 16.42 name: mean_reward verified: false --- # MlpPolicy Agent playing LunarLander-v2 This is a trained model of a MlpPolicy 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 ... ```
NathanS-HuggingFace/LunarLander	NathanS-HuggingFace	2023-02-14T00:29:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-12T16:53:23Z	--- 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: 308.50 +/- 11.39 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 ... ```
nikgeo/ppo-LunarLander-v2	nikgeo	2023-02-14T00:13:50Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-13T23:14:35Z	--- 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: 251.12 +/- 21.78 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
cleandata/ppo-LunarLander-v2	cleandata	2023-02-14T00:09:17Z	6	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-14T00:08:45Z	--- 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: 244.85 +/- 17.73 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 ... ```
nossal/ppo-LunarLander-v2	nossal	2023-02-13T23:15:23Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-02-13T23:14:49Z	--- 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: 239.91 +/- 21.04 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 ... ```
lilouuch/Goodreads_Books_Reviews_Roberta_53	lilouuch	2023-02-13T23:13:42Z	162	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-13T11:28:16Z	--- license: mit tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: Goodreads_Books_Reviews_Roberta_53 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. --> # Goodreads_Books_Reviews_Roberta_53 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8038 - F1: 0.6238 - Accuracy: 0.6614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:--------:\| \| 0.8378 \| 1.0 \| 25313 \| 0.8197 \| 0.6092 \| 0.6538 \| \| 0.7683 \| 2.0 \| 50626 \| 0.8038 \| 0.6238 \| 0.6614 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
GrimReaperSam/q-Taxi-v3	GrimReaperSam	2023-02-13T23:08:58Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-02-13T23:08:51Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.78 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="GrimReaperSam/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

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

tvarella

2023-02-14T21:10:59Z

0

null

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

reinforcement-learning

2023-02-14T21:08:24Z

--- 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="tvarella/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"]) ```

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

enlacinglines

2023-02-14T21:08:21Z

0

null

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

reinforcement-learning

2023-02-14T21:08:18Z

--- 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="enlacinglines/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"]) ```

dor88/Reinforce-pixelcopter

dor88

2023-02-14T20:00:08Z

0

null

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

reinforcement-learning

2023-02-14T13:33:37Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 58.50 +/- 53.06 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

Deisler/poca-SoccerTwos

Deisler

2023-02-14T19:50:10Z

47

0

ml-agents

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

reinforcement-learning

2023-02-09T22:49:24Z

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

fpsandnoob/mae-vit-base-patch32-224-ct

fpsandnoob

2023-02-14T19:43:11Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit_mae", "pretraining", "masked-auto-encoding", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2023-02-14T12:05:25Z

--- license: apache-2.0 tags: - masked-auto-encoding - generated_from_trainer datasets: - imagefolder model-index: - name: mae-vit-base-patch32-224-ct results: [] ---  # mae-vit-base-patch32-224-ct This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3803 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - train_batch_size: 256 - eval_batch_size: 256 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1200.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 1.1257 | 1.0 | 51 | 1.1119 | | 1.0507 | 2.0 | 102 | 1.0434 | | 1.0046 | 3.0 | 153 | 0.9988 | | 0.9761 | 4.0 | 204 | 0.9725 | | 0.9572 | 5.0 | 255 | 0.9529 | | 0.9357 | 6.0 | 306 | 0.9304 | | 0.9128 | 7.0 | 357 | 0.9100 | | 0.9037 | 8.0 | 408 | 0.9004 | | 0.8984 | 9.0 | 459 | 0.8941 | | 0.8904 | 10.0 | 510 | 0.8896 | | 0.8846 | 11.0 | 561 | 0.8802 | | 0.8748 | 12.0 | 612 | 0.8775 | | 0.8692 | 13.0 | 663 | 0.8685 | | 0.8656 | 14.0 | 714 | 0.8665 | | 0.8634 | 15.0 | 765 | 0.8607 | | 0.8565 | 16.0 | 816 | 0.8561 | | 0.8555 | 17.0 | 867 | 0.8548 | | 0.8521 | 18.0 | 918 | 0.8464 | | 0.8478 | 19.0 | 969 | 0.8449 | | 0.847 | 20.0 | 1020 | 0.8455 | | 0.842 | 21.0 | 1071 | 0.8378 | | 0.8385 | 22.0 | 1122 | 0.8358 | | 0.8319 | 23.0 | 1173 | 0.8332 | | 0.8267 | 24.0 | 1224 | 0.8347 | | 0.8266 | 25.0 | 1275 | 0.8247 | | 0.8242 | 26.0 | 1326 | 0.8242 | | 0.8215 | 27.0 | 1377 | 0.8192 | | 0.8171 | 28.0 | 1428 | 0.8213 | | 0.8176 | 29.0 | 1479 | 0.8160 | | 0.8122 | 30.0 | 1530 | 0.8128 | | 0.8107 | 31.0 | 1581 | 0.8036 | | 0.8069 | 32.0 | 1632 | 0.8069 | | 0.8081 | 33.0 | 1683 | 0.8023 | | 0.8043 | 34.0 | 1734 | 0.8048 | | 0.8071 | 35.0 | 1785 | 0.8082 | | 0.8017 | 36.0 | 1836 | 0.7971 | | 0.7965 | 37.0 | 1887 | 0.7953 | | 0.7953 | 38.0 | 1938 | 0.8112 | | 0.7979 | 39.0 | 1989 | 0.7955 | | 0.7887 | 40.0 | 2040 | 0.7966 | | 0.7866 | 41.0 | 2091 | 0.7879 | | 0.7862 | 42.0 | 2142 | 0.7828 | | 0.7836 | 43.0 | 2193 | 0.7865 | | 0.7851 | 44.0 | 2244 | 0.7830 | | 0.7813 | 45.0 | 2295 | 0.7840 | | 0.78 | 46.0 | 2346 | 0.7749 | | 0.779 | 47.0 | 2397 | 0.7825 | | 0.7762 | 48.0 | 2448 | 0.7712 | | 0.7676 | 49.0 | 2499 | 0.7675 | | 0.7638 | 50.0 | 2550 | 0.7645 | | 0.7826 | 51.0 | 2601 | 0.7879 | | 0.7728 | 52.0 | 2652 | 0.7730 | | 0.7629 | 53.0 | 2703 | 0.7606 | | 0.7819 | 54.0 | 2754 | 0.7718 | | 0.7802 | 55.0 | 2805 | 0.7809 | | 0.7632 | 56.0 | 2856 | 0.7577 | | 0.7567 | 57.0 | 2907 | 0.7654 | | 0.7564 | 58.0 | 2958 | 0.7574 | | 0.7535 | 59.0 | 3009 | 0.7555 | | 0.75 | 60.0 | 3060 | 0.7484 | | 0.7512 | 61.0 | 3111 | 0.7487 | | 0.7493 | 62.0 | 3162 | 0.7462 | | 0.742 | 63.0 | 3213 | 0.7450 | | 0.7469 | 64.0 | 3264 | 0.7464 | | 0.7449 | 65.0 | 3315 | 0.7393 | | 0.7321 | 66.0 | 3366 | 0.7425 | | 0.7411 | 67.0 | 3417 | 0.7391 | | 0.7394 | 68.0 | 3468 | 0.7413 | | 0.7301 | 69.0 | 3519 | 0.7344 | | 0.7208 | 70.0 | 3570 | 0.7256 | | 0.7211 | 71.0 | 3621 | 0.7225 | | 0.7273 | 72.0 | 3672 | 0.7264 | | 0.7267 | 73.0 | 3723 | 0.7221 | | 0.7222 | 74.0 | 3774 | 0.7256 | | 0.7175 | 75.0 | 3825 | 0.7202 | | 0.7174 | 76.0 | 3876 | 0.7149 | | 0.7143 | 77.0 | 3927 | 0.7127 | | 0.7106 | 78.0 | 3978 | 0.7061 | | 0.7188 | 79.0 | 4029 | 0.7153 | | 0.7103 | 80.0 | 4080 | 0.7086 | | 0.7055 | 81.0 | 4131 | 0.7098 | | 0.7026 | 82.0 | 4182 | 0.7075 | | 0.7191 | 83.0 | 4233 | 0.7127 | | 0.7027 | 84.0 | 4284 | 0.7172 | | 0.6981 | 85.0 | 4335 | 0.7070 | | 0.7064 | 86.0 | 4386 | 0.7029 | | 0.6943 | 87.0 | 4437 | 0.7046 | | 0.7025 | 88.0 | 4488 | 0.7036 | | 0.6959 | 89.0 | 4539 | 0.7094 | | 0.6988 | 90.0 | 4590 | 0.6917 | | 0.6912 | 91.0 | 4641 | 0.6926 | | 0.689 | 92.0 | 4692 | 0.6881 | | 0.687 | 93.0 | 4743 | 0.6866 | | 0.6867 | 94.0 | 4794 | 0.6873 | | 0.6832 | 95.0 | 4845 | 0.6820 | | 0.6863 | 96.0 | 4896 | 0.6809 | | 0.6908 | 97.0 | 4947 | 0.6792 | | 0.6891 | 98.0 | 4998 | 0.6796 | | 0.6803 | 99.0 | 5049 | 0.6793 | | 0.6755 | 100.0 | 5100 | 0.6738 | | 0.6735 | 101.0 | 5151 | 0.6750 | | 0.6727 | 102.0 | 5202 | 0.6729 | | 0.6695 | 103.0 | 5253 | 0.6734 | | 0.6678 | 104.0 | 5304 | 0.6702 | | 0.671 | 105.0 | 5355 | 0.6720 | | 0.6654 | 106.0 | 5406 | 0.6686 | | 0.669 | 107.0 | 5457 | 0.6683 | | 0.6628 | 108.0 | 5508 | 0.6639 | | 0.6655 | 109.0 | 5559 | 0.6663 | | 0.6637 | 110.0 | 5610 | 0.6651 | | 0.6643 | 111.0 | 5661 | 0.6639 | | 0.6607 | 112.0 | 5712 | 0.6561 | | 0.6598 | 113.0 | 5763 | 0.6591 | | 0.6589 | 114.0 | 5814 | 0.6610 | | 0.6566 | 115.0 | 5865 | 0.6566 | | 0.6706 | 116.0 | 5916 | 0.6749 | | 0.6688 | 117.0 | 5967 | 0.6670 | | 0.6657 | 118.0 | 6018 | 0.6599 | | 0.6611 | 119.0 | 6069 | 0.6567 | | 0.6528 | 120.0 | 6120 | 0.6591 | | 0.652 | 121.0 | 6171 | 0.6566 | | 0.6488 | 122.0 | 6222 | 0.6528 | | 0.6538 | 123.0 | 6273 | 0.6558 | | 0.6457 | 124.0 | 6324 | 0.6509 | | 0.643 | 125.0 | 6375 | 0.6462 | | 0.6433 | 126.0 | 6426 | 0.6459 | | 0.6451 | 127.0 | 6477 | 0.6454 | | 0.6413 | 128.0 | 6528 | 0.6441 | | 0.6407 | 129.0 | 6579 | 0.6409 | | 0.6381 | 130.0 | 6630 | 0.6422 | | 0.6408 | 131.0 | 6681 | 0.6432 | | 0.6404 | 132.0 | 6732 | 0.6408 | | 0.6412 | 133.0 | 6783 | 0.6354 | | 0.6348 | 134.0 | 6834 | 0.6350 | | 0.6307 | 135.0 | 6885 | 0.6389 | | 0.639 | 136.0 | 6936 | 0.6417 | | 0.6319 | 137.0 | 6987 | 0.6353 | | 0.6306 | 138.0 | 7038 | 0.6385 | | 0.6307 | 139.0 | 7089 | 0.6412 | | 0.6343 | 140.0 | 7140 | 0.6308 | | 0.6289 | 141.0 | 7191 | 0.6337 | | 0.6298 | 142.0 | 7242 | 0.6342 | | 0.6284 | 143.0 | 7293 | 0.6287 | | 0.624 | 144.0 | 7344 | 0.6305 | | 0.6266 | 145.0 | 7395 | 0.6338 | | 0.6253 | 146.0 | 7446 | 0.6281 | | 0.6204 | 147.0 | 7497 | 0.6241 | | 0.6232 | 148.0 | 7548 | 0.6222 | | 0.6213 | 149.0 | 7599 | 0.6201 | | 0.6225 | 150.0 | 7650 | 0.6237 | | 0.6228 | 151.0 | 7701 | 0.6193 | | 0.6191 | 152.0 | 7752 | 0.6200 | | 0.6198 | 153.0 | 7803 | 0.6229 | | 0.6183 | 154.0 | 7854 | 0.6213 | | 0.6181 | 155.0 | 7905 | 0.6213 | | 0.6168 | 156.0 | 7956 | 0.6164 | | 0.6156 | 157.0 | 8007 | 0.6160 | | 0.6125 | 158.0 | 8058 | 0.6153 | | 0.6126 | 159.0 | 8109 | 0.6151 | | 0.6115 | 160.0 | 8160 | 0.6163 | | 0.611 | 161.0 | 8211 | 0.6167 | | 0.6099 | 162.0 | 8262 | 0.6083 | | 0.6089 | 163.0 | 8313 | 0.6104 | | 0.6091 | 164.0 | 8364 | 0.6140 | | 0.6105 | 165.0 | 8415 | 0.6122 | | 0.61 | 166.0 | 8466 | 0.6106 | | 0.6104 | 167.0 | 8517 | 0.6062 | | 0.6067 | 168.0 | 8568 | 0.6095 | | 0.6056 | 169.0 | 8619 | 0.6067 | | 0.607 | 170.0 | 8670 | 0.6091 | | 0.6032 | 171.0 | 8721 | 0.6041 | | 0.6038 | 172.0 | 8772 | 0.6104 | | 0.605 | 173.0 | 8823 | 0.6068 | | 0.6036 | 174.0 | 8874 | 0.6005 | | 0.6035 | 175.0 | 8925 | 0.6055 | | 0.6026 | 176.0 | 8976 | 0.6014 | | 0.6012 | 177.0 | 9027 | 0.6029 | | 0.5945 | 178.0 | 9078 | 0.5967 | | 0.6011 | 179.0 | 9129 | 0.5921 | | 0.5929 | 180.0 | 9180 | 0.5991 | | 0.5981 | 181.0 | 9231 | 0.5954 | | 0.6011 | 182.0 | 9282 | 0.6007 | | 0.5977 | 183.0 | 9333 | 0.6013 | | 0.5947 | 184.0 | 9384 | 0.6023 | | 0.59 | 185.0 | 9435 | 0.5968 | | 0.5924 | 186.0 | 9486 | 0.5987 | | 0.5906 | 187.0 | 9537 | 0.5915 | | 0.5928 | 188.0 | 9588 | 0.5877 | | 0.5849 | 189.0 | 9639 | 0.5911 | | 0.5913 | 190.0 | 9690 | 0.5954 | | 0.5863 | 191.0 | 9741 | 0.5906 | | 0.588 | 192.0 | 9792 | 0.5942 | | 0.5906 | 193.0 | 9843 | 0.5924 | | 0.5927 | 194.0 | 9894 | 0.5911 | | 0.5857 | 195.0 | 9945 | 0.5852 | | 0.5859 | 196.0 | 9996 | 0.5910 | | 0.5775 | 197.0 | 10047 | 0.5853 | | 0.586 | 198.0 | 10098 | 0.5877 | | 0.5853 | 199.0 | 10149 | 0.5848 | | 0.5824 | 200.0 | 10200 | 0.5854 | | 0.5797 | 201.0 | 10251 | 0.5834 | | 0.5857 | 202.0 | 10302 | 0.5792 | | 0.5863 | 203.0 | 10353 | 0.5824 | | 0.5826 | 204.0 | 10404 | 0.5838 | | 0.579 | 205.0 | 10455 | 0.5808 | | 0.5758 | 206.0 | 10506 | 0.5810 | | 0.5798 | 207.0 | 10557 | 0.5782 | | 0.576 | 208.0 | 10608 | 0.5818 | | 0.5717 | 209.0 | 10659 | 0.5826 | | 0.5774 | 210.0 | 10710 | 0.5800 | | 0.5724 | 211.0 | 10761 | 0.5813 | | 0.5706 | 212.0 | 10812 | 0.5755 | | 0.5737 | 213.0 | 10863 | 0.5788 | | 0.5791 | 214.0 | 10914 | 0.5769 | | 0.5712 | 215.0 | 10965 | 0.5767 | | 0.567 | 216.0 | 11016 | 0.5790 | | 0.5671 | 217.0 | 11067 | 0.5734 | | 0.5733 | 218.0 | 11118 | 0.5722 | | 0.5673 | 219.0 | 11169 | 0.5806 | | 0.5713 | 220.0 | 11220 | 0.5764 | | 0.5669 | 221.0 | 11271 | 0.5694 | | 0.5669 | 222.0 | 11322 | 0.5749 | | 0.5665 | 223.0 | 11373 | 0.5732 | | 0.5676 | 224.0 | 11424 | 0.5676 | | 0.5621 | 225.0 | 11475 | 0.5677 | | 0.5623 | 226.0 | 11526 | 0.5715 | | 0.5695 | 227.0 | 11577 | 0.5676 | | 0.5657 | 228.0 | 11628 | 0.5667 | | 0.565 | 229.0 | 11679 | 0.5644 | | 0.5617 | 230.0 | 11730 | 0.5650 | | 0.5587 | 231.0 | 11781 | 0.5637 | | 0.5591 | 232.0 | 11832 | 0.5652 | | 0.5607 | 233.0 | 11883 | 0.5648 | | 0.559 | 234.0 | 11934 | 0.5681 | | 0.5601 | 235.0 | 11985 | 0.5637 | | 0.5605 | 236.0 | 12036 | 0.5697 | | 0.5555 | 237.0 | 12087 | 0.5593 | | 0.5602 | 238.0 | 12138 | 0.5683 | | 0.5647 | 239.0 | 12189 | 0.5629 | | 0.5575 | 240.0 | 12240 | 0.5611 | | 0.5577 | 241.0 | 12291 | 0.5588 | | 0.5514 | 242.0 | 12342 | 0.5584 | | 0.5581 | 243.0 | 12393 | 0.5566 | | 0.555 | 244.0 | 12444 | 0.5563 | | 0.5571 | 245.0 | 12495 | 0.5541 | | 0.5549 | 246.0 | 12546 | 0.5541 | | 0.5521 | 247.0 | 12597 | 0.5521 | | 0.55 | 248.0 | 12648 | 0.5567 | | 0.5518 | 249.0 | 12699 | 0.5559 | | 0.5522 | 250.0 | 12750 | 0.5536 | | 0.5481 | 251.0 | 12801 | 0.5504 | | 0.5516 | 252.0 | 12852 | 0.5563 | | 0.5524 | 253.0 | 12903 | 0.5503 | | 0.5582 | 254.0 | 12954 | 0.5519 | | 0.5514 | 255.0 | 13005 | 0.5504 | | 0.5498 | 256.0 | 13056 | 0.5520 | | 0.5481 | 257.0 | 13107 | 0.5540 | | 0.551 | 258.0 | 13158 | 0.5503 | | 0.5495 | 259.0 | 13209 | 0.5491 | | 0.5483 | 260.0 | 13260 | 0.5461 | | 0.5468 | 261.0 | 13311 | 0.5586 | | 0.5454 | 262.0 | 13362 | 0.5495 | | 0.5447 | 263.0 | 13413 | 0.5455 | | 0.5475 | 264.0 | 13464 | 0.5511 | | 0.5439 | 265.0 | 13515 | 0.5453 | | 0.542 | 266.0 | 13566 | 0.5477 | | 0.5437 | 267.0 | 13617 | 0.5502 | | 0.5452 | 268.0 | 13668 | 0.5432 | | 0.5397 | 269.0 | 13719 | 0.5443 | | 0.5424 | 270.0 | 13770 | 0.5410 | | 0.5391 | 271.0 | 13821 | 0.5420 | | 0.5368 | 272.0 | 13872 | 0.5402 | | 0.5387 | 273.0 | 13923 | 0.5401 | | 0.5362 | 274.0 | 13974 | 0.5414 | | 0.5374 | 275.0 | 14025 | 0.5418 | | 0.5375 | 276.0 | 14076 | 0.5415 | | 0.5427 | 277.0 | 14127 | 0.5436 | | 0.5382 | 278.0 | 14178 | 0.5366 | | 0.5341 | 279.0 | 14229 | 0.5411 | | 0.5348 | 280.0 | 14280 | 0.5377 | | 0.5339 | 281.0 | 14331 | 0.5393 | | 0.5359 | 282.0 | 14382 | 0.5359 | | 0.536 | 283.0 | 14433 | 0.5368 | | 0.5362 | 284.0 | 14484 | 0.5384 | | 0.532 | 285.0 | 14535 | 0.5346 | | 0.5298 | 286.0 | 14586 | 0.5376 | | 0.5352 | 287.0 | 14637 | 0.5373 | | 0.5344 | 288.0 | 14688 | 0.5359 | | 0.5399 | 289.0 | 14739 | 0.5427 | | 0.5329 | 290.0 | 14790 | 0.5349 | | 0.531 | 291.0 | 14841 | 0.5321 | | 0.5317 | 292.0 | 14892 | 0.5361 | | 0.5303 | 293.0 | 14943 | 0.5296 | | 0.5291 | 294.0 | 14994 | 0.5312 | | 0.5335 | 295.0 | 15045 | 0.5244 | | 0.5309 | 296.0 | 15096 | 0.5252 | | 0.5251 | 297.0 | 15147 | 0.5310 | | 0.5266 | 298.0 | 15198 | 0.5301 | | 0.5279 | 299.0 | 15249 | 0.5308 | | 0.5261 | 300.0 | 15300 | 0.5250 | | 0.5214 | 301.0 | 15351 | 0.5252 | | 0.5269 | 302.0 | 15402 | 0.5306 | | 0.5229 | 303.0 | 15453 | 0.5264 | | 0.5234 | 304.0 | 15504 | 0.5263 | | 0.5271 | 305.0 | 15555 | 0.5280 | | 0.525 | 306.0 | 15606 | 0.5233 | | 0.5216 | 307.0 | 15657 | 0.5211 | | 0.5247 | 308.0 | 15708 | 0.5246 | | 0.5203 | 309.0 | 15759 | 0.5279 | | 0.5201 | 310.0 | 15810 | 0.5246 | | 0.5254 | 311.0 | 15861 | 0.5306 | | 0.5166 | 312.0 | 15912 | 0.5224 | | 0.525 | 313.0 | 15963 | 0.5192 | | 0.5224 | 314.0 | 16014 | 0.5247 | | 0.5195 | 315.0 | 16065 | 0.5230 | | 0.5189 | 316.0 | 16116 | 0.5239 | | 0.5226 | 317.0 | 16167 | 0.5180 | | 0.5166 | 318.0 | 16218 | 0.5197 | | 0.5159 | 319.0 | 16269 | 0.5156 | | 0.5156 | 320.0 | 16320 | 0.5204 | | 0.5179 | 321.0 | 16371 | 0.5215 | | 0.5194 | 322.0 | 16422 | 0.5211 | | 0.519 | 323.0 | 16473 | 0.5212 | | 0.5112 | 324.0 | 16524 | 0.5175 | | 0.5163 | 325.0 | 16575 | 0.5225 | | 0.5165 | 326.0 | 16626 | 0.5172 | | 0.5104 | 327.0 | 16677 | 0.5200 | | 0.51 | 328.0 | 16728 | 0.5156 | | 0.5129 | 329.0 | 16779 | 0.5160 | | 0.5084 | 330.0 | 16830 | 0.5207 | | 0.5159 | 331.0 | 16881 | 0.5147 | | 0.5126 | 332.0 | 16932 | 0.5159 | | 0.5132 | 333.0 | 16983 | 0.5156 | | 0.5092 | 334.0 | 17034 | 0.5151 | | 0.5116 | 335.0 | 17085 | 0.5147 | | 0.5113 | 336.0 | 17136 | 0.5121 | | 0.5076 | 337.0 | 17187 | 0.5101 | | 0.5106 | 338.0 | 17238 | 0.5111 | | 0.5117 | 339.0 | 17289 | 0.5094 | | 0.5086 | 340.0 | 17340 | 0.5132 | | 0.5034 | 341.0 | 17391 | 0.5162 | | 0.5061 | 342.0 | 17442 | 0.5142 | | 0.5101 | 343.0 | 17493 | 0.5136 | | 0.5042 | 344.0 | 17544 | 0.5135 | | 0.5091 | 345.0 | 17595 | 0.5083 | | 0.5095 | 346.0 | 17646 | 0.5112 | | 0.5058 | 347.0 | 17697 | 0.5121 | | 0.504 | 348.0 | 17748 | 0.5082 | | 0.5016 | 349.0 | 17799 | 0.5075 | | 0.5042 | 350.0 | 17850 | 0.5090 | | 0.5036 | 351.0 | 17901 | 0.5089 | | 0.5045 | 352.0 | 17952 | 0.5095 | | 0.5067 | 353.0 | 18003 | 0.5087 | | 0.5026 | 354.0 | 18054 | 0.5064 | | 0.5001 | 355.0 | 18105 | 0.5055 | | 0.5036 | 356.0 | 18156 | 0.5057 | | 0.5012 | 357.0 | 18207 | 0.5083 | | 0.5031 | 358.0 | 18258 | 0.5110 | | 0.5021 | 359.0 | 18309 | 0.5128 | | 0.4973 | 360.0 | 18360 | 0.5014 | | 0.4988 | 361.0 | 18411 | 0.5028 | | 0.5013 | 362.0 | 18462 | 0.5035 | | 0.5001 | 363.0 | 18513 | 0.5040 | | 0.4972 | 364.0 | 18564 | 0.5056 | | 0.4994 | 365.0 | 18615 | 0.5070 | | 0.5005 | 366.0 | 18666 | 0.5070 | | 0.4993 | 367.0 | 18717 | 0.5053 | | 0.4975 | 368.0 | 18768 | 0.5036 | | 0.4967 | 369.0 | 18819 | 0.5026 | | 0.4968 | 370.0 | 18870 | 0.5011 | | 0.498 | 371.0 | 18921 | 0.4990 | | 0.5022 | 372.0 | 18972 | 0.5032 | | 0.4959 | 373.0 | 19023 | 0.4972 | | 0.4921 | 374.0 | 19074 | 0.4967 | | 0.4936 | 375.0 | 19125 | 0.4967 | | 0.496 | 376.0 | 19176 | 0.5000 | | 0.4941 | 377.0 | 19227 | 0.4980 | | 0.4937 | 378.0 | 19278 | 0.4975 | | 0.4979 | 379.0 | 19329 | 0.4975 | | 0.4996 | 380.0 | 19380 | 0.4932 | | 0.4961 | 381.0 | 19431 | 0.4983 | | 0.4903 | 382.0 | 19482 | 0.4974 | | 0.4899 | 383.0 | 19533 | 0.4953 | | 0.4924 | 384.0 | 19584 | 0.4953 | | 0.4895 | 385.0 | 19635 | 0.4964 | | 0.4965 | 386.0 | 19686 | 0.5006 | | 0.4896 | 387.0 | 19737 | 0.4938 | | 0.497 | 388.0 | 19788 | 0.4956 | | 0.4924 | 389.0 | 19839 | 0.4960 | | 0.4904 | 390.0 | 19890 | 0.4972 | | 0.5 | 391.0 | 19941 | 0.4958 | | 0.4961 | 392.0 | 19992 | 0.4906 | | 0.491 | 393.0 | 20043 | 0.4918 | | 0.4878 | 394.0 | 20094 | 0.4954 | | 0.4881 | 395.0 | 20145 | 0.4916 | | 0.49 | 396.0 | 20196 | 0.4946 | | 0.4881 | 397.0 | 20247 | 0.4924 | | 0.4871 | 398.0 | 20298 | 0.4959 | | 0.492 | 399.0 | 20349 | 0.4867 | | 0.4883 | 400.0 | 20400 | 0.4891 | | 0.4864 | 401.0 | 20451 | 0.4946 | | 0.4898 | 402.0 | 20502 | 0.4922 | | 0.4841 | 403.0 | 20553 | 0.4902 | | 0.4879 | 404.0 | 20604 | 0.4921 | | 0.4801 | 405.0 | 20655 | 0.4914 | | 0.4877 | 406.0 | 20706 | 0.4882 | | 0.4858 | 407.0 | 20757 | 0.4882 | | 0.4856 | 408.0 | 20808 | 0.4872 | | 0.4825 | 409.0 | 20859 | 0.4871 | | 0.4865 | 410.0 | 20910 | 0.4853 | | 0.4834 | 411.0 | 20961 | 0.4908 | | 0.4815 | 412.0 | 21012 | 0.4847 | | 0.4828 | 413.0 | 21063 | 0.4919 | | 0.487 | 414.0 | 21114 | 0.4899 | | 0.4842 | 415.0 | 21165 | 0.4876 | | 0.4902 | 416.0 | 21216 | 0.4873 | | 0.4809 | 417.0 | 21267 | 0.4913 | | 0.4825 | 418.0 | 21318 | 0.4832 | | 0.4797 | 419.0 | 21369 | 0.4872 | | 0.4852 | 420.0 | 21420 | 0.4868 | | 0.4879 | 421.0 | 21471 | 0.4833 | | 0.4823 | 422.0 | 21522 | 0.4824 | | 0.4729 | 423.0 | 21573 | 0.4793 | | 0.4825 | 424.0 | 21624 | 0.4812 | | 0.4739 | 425.0 | 21675 | 0.4831 | | 0.4767 | 426.0 | 21726 | 0.4848 | | 0.4806 | 427.0 | 21777 | 0.4858 | | 0.4736 | 428.0 | 21828 | 0.4831 | | 0.4857 | 429.0 | 21879 | 0.4785 | | 0.4819 | 430.0 | 21930 | 0.4805 | | 0.4767 | 431.0 | 21981 | 0.4845 | | 0.4765 | 432.0 | 22032 | 0.4803 | | 0.4785 | 433.0 | 22083 | 0.4826 | | 0.4758 | 434.0 | 22134 | 0.4814 | | 0.4677 | 435.0 | 22185 | 0.4815 | | 0.4735 | 436.0 | 22236 | 0.4811 | | 0.4764 | 437.0 | 22287 | 0.4749 | | 0.4743 | 438.0 | 22338 | 0.4846 | | 0.4736 | 439.0 | 22389 | 0.4825 | | 0.4732 | 440.0 | 22440 | 0.4783 | | 0.4706 | 441.0 | 22491 | 0.4810 | | 0.4735 | 442.0 | 22542 | 0.4780 | | 0.4796 | 443.0 | 22593 | 0.4881 | | 0.4724 | 444.0 | 22644 | 0.4785 | | 0.4701 | 445.0 | 22695 | 0.4753 | | 0.4764 | 446.0 | 22746 | 0.4787 | | 0.4729 | 447.0 | 22797 | 0.4824 | | 0.4726 | 448.0 | 22848 | 0.4742 | | 0.4736 | 449.0 | 22899 | 0.4775 | | 0.4764 | 450.0 | 22950 | 0.4755 | | 0.4701 | 451.0 | 23001 | 0.4755 | | 0.4746 | 452.0 | 23052 | 0.4750 | | 0.4727 | 453.0 | 23103 | 0.4731 | | 0.4691 | 454.0 | 23154 | 0.4686 | | 0.4673 | 455.0 | 23205 | 0.4761 | | 0.4726 | 456.0 | 23256 | 0.4763 | | 0.4726 | 457.0 | 23307 | 0.4807 | | 0.4696 | 458.0 | 23358 | 0.4738 | | 0.4689 | 459.0 | 23409 | 0.4727 | | 0.4702 | 460.0 | 23460 | 0.4793 | | 0.4692 | 461.0 | 23511 | 0.4696 | | 0.4694 | 462.0 | 23562 | 0.4713 | | 0.4628 | 463.0 | 23613 | 0.4747 | | 0.4677 | 464.0 | 23664 | 0.4787 | | 0.4673 | 465.0 | 23715 | 0.4682 | | 0.4709 | 466.0 | 23766 | 0.4692 | | 0.463 | 467.0 | 23817 | 0.4676 | | 0.4654 | 468.0 | 23868 | 0.4696 | | 0.4648 | 469.0 | 23919 | 0.4675 | | 0.4642 | 470.0 | 23970 | 0.4700 | | 0.4687 | 471.0 | 24021 | 0.4691 | | 0.469 | 472.0 | 24072 | 0.4749 | | 0.4692 | 473.0 | 24123 | 0.4672 | | 0.4635 | 474.0 | 24174 | 0.4707 | | 0.4635 | 475.0 | 24225 | 0.4696 | | 0.4655 | 476.0 | 24276 | 0.4652 | | 0.4633 | 477.0 | 24327 | 0.4702 | | 0.4622 | 478.0 | 24378 | 0.4637 | | 0.4571 | 479.0 | 24429 | 0.4678 | | 0.4645 | 480.0 | 24480 | 0.4635 | | 0.4654 | 481.0 | 24531 | 0.4655 | | 0.4588 | 482.0 | 24582 | 0.4688 | | 0.4608 | 483.0 | 24633 | 0.4639 | | 0.4606 | 484.0 | 24684 | 0.4654 | | 0.4624 | 485.0 | 24735 | 0.4661 | | 0.4612 | 486.0 | 24786 | 0.4669 | | 0.46 | 487.0 | 24837 | 0.4653 | | 0.4623 | 488.0 | 24888 | 0.4688 | | 0.4648 | 489.0 | 24939 | 0.4648 | | 0.4602 | 490.0 | 24990 | 0.4620 | | 0.4587 | 491.0 | 25041 | 0.4652 | | 0.4627 | 492.0 | 25092 | 0.4694 | | 0.4638 | 493.0 | 25143 | 0.4620 | | 0.4565 | 494.0 | 25194 | 0.4653 | | 0.4588 | 495.0 | 25245 | 0.4598 | | 0.4568 | 496.0 | 25296 | 0.4617 | | 0.4524 | 497.0 | 25347 | 0.4631 | | 0.4635 | 498.0 | 25398 | 0.4640 | | 0.4534 | 499.0 | 25449 | 0.4643 | | 0.4599 | 500.0 | 25500 | 0.4663 | | 0.4549 | 501.0 | 25551 | 0.4588 | | 0.4595 | 502.0 | 25602 | 0.4661 | | 0.46 | 503.0 | 25653 | 0.4626 | | 0.4504 | 504.0 | 25704 | 0.4591 | | 0.459 | 505.0 | 25755 | 0.4623 | | 0.4582 | 506.0 | 25806 | 0.4617 | | 0.4532 | 507.0 | 25857 | 0.4580 | | 0.4555 | 508.0 | 25908 | 0.4615 | | 0.4571 | 509.0 | 25959 | 0.4617 | | 0.4561 | 510.0 | 26010 | 0.4579 | | 0.4541 | 511.0 | 26061 | 0.4601 | | 0.4534 | 512.0 | 26112 | 0.4627 | | 0.4569 | 513.0 | 26163 | 0.4615 | | 0.4583 | 514.0 | 26214 | 0.4527 | | 0.4498 | 515.0 | 26265 | 0.4587 | | 0.4511 | 516.0 | 26316 | 0.4552 | | 0.4535 | 517.0 | 26367 | 0.4579 | | 0.4551 | 518.0 | 26418 | 0.4543 | | 0.4581 | 519.0 | 26469 | 0.4597 | | 0.4573 | 520.0 | 26520 | 0.4540 | | 0.4495 | 521.0 | 26571 | 0.4578 | | 0.4532 | 522.0 | 26622 | 0.4605 | | 0.4474 | 523.0 | 26673 | 0.4579 | | 0.4504 | 524.0 | 26724 | 0.4563 | | 0.4529 | 525.0 | 26775 | 0.4583 | | 0.4475 | 526.0 | 26826 | 0.4616 | | 0.4457 | 527.0 | 26877 | 0.4558 | | 0.4532 | 528.0 | 26928 | 0.4584 | | 0.4566 | 529.0 | 26979 | 0.4573 | | 0.4546 | 530.0 | 27030 | 0.4563 | | 0.4479 | 531.0 | 27081 | 0.4628 | | 0.4485 | 532.0 | 27132 | 0.4547 | | 0.4491 | 533.0 | 27183 | 0.4539 | | 0.4522 | 534.0 | 27234 | 0.4536 | | 0.4477 | 535.0 | 27285 | 0.4561 | | 0.45 | 536.0 | 27336 | 0.4530 | | 0.4522 | 537.0 | 27387 | 0.4525 | | 0.4475 | 538.0 | 27438 | 0.4554 | | 0.4475 | 539.0 | 27489 | 0.4486 | | 0.4512 | 540.0 | 27540 | 0.4584 | | 0.445 | 541.0 | 27591 | 0.4543 | | 0.4478 | 542.0 | 27642 | 0.4507 | | 0.4472 | 543.0 | 27693 | 0.4520 | | 0.448 | 544.0 | 27744 | 0.4507 | | 0.4447 | 545.0 | 27795 | 0.4514 | | 0.4485 | 546.0 | 27846 | 0.4553 | | 0.4482 | 547.0 | 27897 | 0.4532 | | 0.4448 | 548.0 | 27948 | 0.4533 | | 0.4467 | 549.0 | 27999 | 0.4511 | | 0.4473 | 550.0 | 28050 | 0.4531 | | 0.4423 | 551.0 | 28101 | 0.4462 | | 0.4473 | 552.0 | 28152 | 0.4538 | | 0.4463 | 553.0 | 28203 | 0.4472 | | 0.4459 | 554.0 | 28254 | 0.4486 | | 0.4432 | 555.0 | 28305 | 0.4470 | | 0.4448 | 556.0 | 28356 | 0.4522 | | 0.4406 | 557.0 | 28407 | 0.4528 | | 0.4433 | 558.0 | 28458 | 0.4502 | | 0.4447 | 559.0 | 28509 | 0.4471 | | 0.4438 | 560.0 | 28560 | 0.4500 | | 0.4433 | 561.0 | 28611 | 0.4471 | | 0.4412 | 562.0 | 28662 | 0.4491 | | 0.4357 | 563.0 | 28713 | 0.4474 | | 0.4424 | 564.0 | 28764 | 0.4481 | | 0.4412 | 565.0 | 28815 | 0.4480 | | 0.4483 | 566.0 | 28866 | 0.4453 | | 0.4397 | 567.0 | 28917 | 0.4435 | | 0.4377 | 568.0 | 28968 | 0.4460 | | 0.4424 | 569.0 | 29019 | 0.4475 | | 0.4412 | 570.0 | 29070 | 0.4445 | | 0.4435 | 571.0 | 29121 | 0.4418 | | 0.4398 | 572.0 | 29172 | 0.4434 | | 0.4427 | 573.0 | 29223 | 0.4417 | | 0.4409 | 574.0 | 29274 | 0.4410 | | 0.4425 | 575.0 | 29325 | 0.4434 | | 0.4402 | 576.0 | 29376 | 0.4489 | | 0.4394 | 577.0 | 29427 | 0.4435 | | 0.4379 | 578.0 | 29478 | 0.4447 | | 0.4391 | 579.0 | 29529 | 0.4471 | | 0.4404 | 580.0 | 29580 | 0.4435 | | 0.4399 | 581.0 | 29631 | 0.4411 | | 0.4353 | 582.0 | 29682 | 0.4416 | | 0.4417 | 583.0 | 29733 | 0.4417 | | 0.4389 | 584.0 | 29784 | 0.4399 | | 0.4378 | 585.0 | 29835 | 0.4432 | | 0.439 | 586.0 | 29886 | 0.4427 | | 0.431 | 587.0 | 29937 | 0.4403 | | 0.4348 | 588.0 | 29988 | 0.4409 | | 0.4363 | 589.0 | 30039 | 0.4425 | | 0.4399 | 590.0 | 30090 | 0.4394 | | 0.4342 | 591.0 | 30141 | 0.4412 | | 0.4342 | 592.0 | 30192 | 0.4399 | | 0.4348 | 593.0 | 30243 | 0.4420 | | 0.4326 | 594.0 | 30294 | 0.4446 | | 0.4333 | 595.0 | 30345 | 0.4430 | | 0.4336 | 596.0 | 30396 | 0.4397 | | 0.4314 | 597.0 | 30447 | 0.4418 | | 0.4371 | 598.0 | 30498 | 0.4411 | | 0.4333 | 599.0 | 30549 | 0.4385 | | 0.4337 | 600.0 | 30600 | 0.4394 | | 0.4371 | 601.0 | 30651 | 0.4407 | | 0.4294 | 602.0 | 30702 | 0.4395 | | 0.4323 | 603.0 | 30753 | 0.4404 | | 0.4303 | 604.0 | 30804 | 0.4422 | | 0.4325 | 605.0 | 30855 | 0.4376 | | 0.44 | 606.0 | 30906 | 0.4399 | | 0.4343 | 607.0 | 30957 | 0.4403 | | 0.4313 | 608.0 | 31008 | 0.4397 | | 0.4338 | 609.0 | 31059 | 0.4379 | | 0.4299 | 610.0 | 31110 | 0.4349 | | 0.4325 | 611.0 | 31161 | 0.4370 | | 0.429 | 612.0 | 31212 | 0.4371 | | 0.4291 | 613.0 | 31263 | 0.4299 | | 0.4349 | 614.0 | 31314 | 0.4364 | | 0.4308 | 615.0 | 31365 | 0.4336 | | 0.4305 | 616.0 | 31416 | 0.4343 | | 0.4267 | 617.0 | 31467 | 0.4391 | | 0.4329 | 618.0 | 31518 | 0.4365 | | 0.4269 | 619.0 | 31569 | 0.4333 | | 0.4251 | 620.0 | 31620 | 0.4343 | | 0.427 | 621.0 | 31671 | 0.4344 | | 0.4327 | 622.0 | 31722 | 0.4345 | | 0.4263 | 623.0 | 31773 | 0.4370 | | 0.4288 | 624.0 | 31824 | 0.4323 | | 0.4316 | 625.0 | 31875 | 0.4325 | | 0.431 | 626.0 | 31926 | 0.4328 | | 0.4316 | 627.0 | 31977 | 0.4316 | | 0.4325 | 628.0 | 32028 | 0.4311 | | 0.4287 | 629.0 | 32079 | 0.4323 | | 0.4267 | 630.0 | 32130 | 0.4302 | | 0.426 | 631.0 | 32181 | 0.4342 | | 0.4259 | 632.0 | 32232 | 0.4324 | | 0.427 | 633.0 | 32283 | 0.4315 | | 0.4268 | 634.0 | 32334 | 0.4300 | | 0.4251 | 635.0 | 32385 | 0.4385 | | 0.4291 | 636.0 | 32436 | 0.4358 | | 0.4273 | 637.0 | 32487 | 0.4342 | | 0.4238 | 638.0 | 32538 | 0.4311 | | 0.4262 | 639.0 | 32589 | 0.4327 | | 0.4251 | 640.0 | 32640 | 0.4329 | | 0.4276 | 641.0 | 32691 | 0.4344 | | 0.4274 | 642.0 | 32742 | 0.4304 | | 0.4269 | 643.0 | 32793 | 0.4263 | | 0.4217 | 644.0 | 32844 | 0.4305 | | 0.4204 | 645.0 | 32895 | 0.4314 | | 0.4268 | 646.0 | 32946 | 0.4284 | | 0.4227 | 647.0 | 32997 | 0.4281 | | 0.4236 | 648.0 | 33048 | 0.4320 | | 0.4245 | 649.0 | 33099 | 0.4295 | | 0.4229 | 650.0 | 33150 | 0.4262 | | 0.423 | 651.0 | 33201 | 0.4239 | | 0.4209 | 652.0 | 33252 | 0.4294 | | 0.4209 | 653.0 | 33303 | 0.4315 | | 0.425 | 654.0 | 33354 | 0.4299 | | 0.418 | 655.0 | 33405 | 0.4282 | | 0.423 | 656.0 | 33456 | 0.4264 | | 0.4267 | 657.0 | 33507 | 0.4296 | | 0.4226 | 658.0 | 33558 | 0.4269 | | 0.4213 | 659.0 | 33609 | 0.4296 | | 0.4192 | 660.0 | 33660 | 0.4259 | | 0.4234 | 661.0 | 33711 | 0.4243 | | 0.4205 | 662.0 | 33762 | 0.4256 | | 0.4185 | 663.0 | 33813 | 0.4251 | | 0.4212 | 664.0 | 33864 | 0.4231 | | 0.4228 | 665.0 | 33915 | 0.4250 | | 0.421 | 666.0 | 33966 | 0.4284 | | 0.4226 | 667.0 | 34017 | 0.4243 | | 0.4201 | 668.0 | 34068 | 0.4279 | | 0.4213 | 669.0 | 34119 | 0.4210 | | 0.4237 | 670.0 | 34170 | 0.4264 | | 0.4228 | 671.0 | 34221 | 0.4237 | | 0.4181 | 672.0 | 34272 | 0.4245 | | 0.4242 | 673.0 | 34323 | 0.4244 | | 0.4178 | 674.0 | 34374 | 0.4250 | | 0.4184 | 675.0 | 34425 | 0.4274 | | 0.4163 | 676.0 | 34476 | 0.4221 | | 0.4288 | 677.0 | 34527 | 0.4245 | | 0.4205 | 678.0 | 34578 | 0.4258 | | 0.4167 | 679.0 | 34629 | 0.4243 | | 0.4172 | 680.0 | 34680 | 0.4241 | | 0.4212 | 681.0 | 34731 | 0.4216 | | 0.4164 | 682.0 | 34782 | 0.4214 | | 0.4171 | 683.0 | 34833 | 0.4230 | | 0.4166 | 684.0 | 34884 | 0.4261 | | 0.4172 | 685.0 | 34935 | 0.4224 | | 0.4188 | 686.0 | 34986 | 0.4209 | | 0.4187 | 687.0 | 35037 | 0.4168 | | 0.4174 | 688.0 | 35088 | 0.4201 | | 0.4184 | 689.0 | 35139 | 0.4177 | | 0.4126 | 690.0 | 35190 | 0.4192 | | 0.4168 | 691.0 | 35241 | 0.4171 | | 0.4152 | 692.0 | 35292 | 0.4202 | | 0.4137 | 693.0 | 35343 | 0.4210 | | 0.4139 | 694.0 | 35394 | 0.4143 | | 0.418 | 695.0 | 35445 | 0.4250 | | 0.4116 | 696.0 | 35496 | 0.4237 | | 0.4113 | 697.0 | 35547 | 0.4172 | | 0.4131 | 698.0 | 35598 | 0.4219 | | 0.4148 | 699.0 | 35649 | 0.4179 | | 0.4117 | 700.0 | 35700 | 0.4264 | | 0.4115 | 701.0 | 35751 | 0.4244 | | 0.4149 | 702.0 | 35802 | 0.4223 | | 0.4129 | 703.0 | 35853 | 0.4190 | | 0.4134 | 704.0 | 35904 | 0.4197 | | 0.4155 | 705.0 | 35955 | 0.4203 | | 0.4112 | 706.0 | 36006 | 0.4206 | | 0.4113 | 707.0 | 36057 | 0.4176 | | 0.4117 | 708.0 | 36108 | 0.4202 | | 0.4128 | 709.0 | 36159 | 0.4186 | | 0.4111 | 710.0 | 36210 | 0.4196 | | 0.4168 | 711.0 | 36261 | 0.4225 | | 0.408 | 712.0 | 36312 | 0.4146 | | 0.4117 | 713.0 | 36363 | 0.4185 | | 0.4089 | 714.0 | 36414 | 0.4214 | | 0.408 | 715.0 | 36465 | 0.4196 | | 0.4126 | 716.0 | 36516 | 0.4175 | | 0.4106 | 717.0 | 36567 | 0.4145 | | 0.4112 | 718.0 | 36618 | 0.4160 | | 0.4064 | 719.0 | 36669 | 0.4175 | | 0.41 | 720.0 | 36720 | 0.4181 | | 0.4046 | 721.0 | 36771 | 0.4159 | | 0.4141 | 722.0 | 36822 | 0.4119 | | 0.414 | 723.0 | 36873 | 0.4167 | | 0.4118 | 724.0 | 36924 | 0.4166 | | 0.4106 | 725.0 | 36975 | 0.4157 | | 0.4079 | 726.0 | 37026 | 0.4176 | | 0.4114 | 727.0 | 37077 | 0.4108 | | 0.4117 | 728.0 | 37128 | 0.4135 | | 0.4155 | 729.0 | 37179 | 0.4171 | | 0.4117 | 730.0 | 37230 | 0.4147 | | 0.4092 | 731.0 | 37281 | 0.4094 | | 0.4091 | 732.0 | 37332 | 0.4133 | | 0.4081 | 733.0 | 37383 | 0.4142 | | 0.4084 | 734.0 | 37434 | 0.4170 | | 0.4082 | 735.0 | 37485 | 0.4158 | | 0.4097 | 736.0 | 37536 | 0.4118 | | 0.4082 | 737.0 | 37587 | 0.4105 | | 0.4043 | 738.0 | 37638 | 0.4162 | | 0.4011 | 739.0 | 37689 | 0.4122 | | 0.4082 | 740.0 | 37740 | 0.4158 | | 0.4098 | 741.0 | 37791 | 0.4153 | | 0.4082 | 742.0 | 37842 | 0.4107 | | 0.4073 | 743.0 | 37893 | 0.4117 | | 0.403 | 744.0 | 37944 | 0.4163 | | 0.4024 | 745.0 | 37995 | 0.4080 | | 0.4098 | 746.0 | 38046 | 0.4082 | | 0.4072 | 747.0 | 38097 | 0.4111 | | 0.4065 | 748.0 | 38148 | 0.4119 | | 0.404 | 749.0 | 38199 | 0.4087 | | 0.4024 | 750.0 | 38250 | 0.4093 | | 0.4054 | 751.0 | 38301 | 0.4111 | | 0.403 | 752.0 | 38352 | 0.4093 | | 0.4042 | 753.0 | 38403 | 0.4117 | | 0.4025 | 754.0 | 38454 | 0.4088 | | 0.4025 | 755.0 | 38505 | 0.4102 | | 0.4056 | 756.0 | 38556 | 0.4135 | | 0.4025 | 757.0 | 38607 | 0.4125 | | 0.4035 | 758.0 | 38658 | 0.4110 | | 0.4026 | 759.0 | 38709 | 0.4127 | | 0.4028 | 760.0 | 38760 | 0.4107 | | 0.4007 | 761.0 | 38811 | 0.4079 | | 0.4043 | 762.0 | 38862 | 0.4106 | | 0.3979 | 763.0 | 38913 | 0.4084 | | 0.4071 | 764.0 | 38964 | 0.4093 | | 0.4097 | 765.0 | 39015 | 0.4130 | | 0.4052 | 766.0 | 39066 | 0.4118 | | 0.4063 | 767.0 | 39117 | 0.4055 | | 0.4051 | 768.0 | 39168 | 0.4056 | | 0.403 | 769.0 | 39219 | 0.4054 | | 0.4061 | 770.0 | 39270 | 0.4102 | | 0.3989 | 771.0 | 39321 | 0.4141 | | 0.4022 | 772.0 | 39372 | 0.4050 | | 0.4018 | 773.0 | 39423 | 0.4098 | | 0.3993 | 774.0 | 39474 | 0.4090 | | 0.3984 | 775.0 | 39525 | 0.4074 | | 0.4034 | 776.0 | 39576 | 0.4068 | | 0.4036 | 777.0 | 39627 | 0.4043 | | 0.4027 | 778.0 | 39678 | 0.4056 | | 0.3999 | 779.0 | 39729 | 0.4104 | | 0.401 | 780.0 | 39780 | 0.4033 | | 0.4058 | 781.0 | 39831 | 0.4058 | | 0.3977 | 782.0 | 39882 | 0.4094 | | 0.402 | 783.0 | 39933 | 0.4057 | | 0.3972 | 784.0 | 39984 | 0.4044 | | 0.3997 | 785.0 | 40035 | 0.4075 | | 0.4003 | 786.0 | 40086 | 0.4074 | | 0.3973 | 787.0 | 40137 | 0.4045 | | 0.3989 | 788.0 | 40188 | 0.4078 | | 0.4029 | 789.0 | 40239 | 0.4092 | | 0.4011 | 790.0 | 40290 | 0.4051 | | 0.3975 | 791.0 | 40341 | 0.4008 | | 0.3952 | 792.0 | 40392 | 0.4049 | | 0.4032 | 793.0 | 40443 | 0.4054 | | 0.4027 | 794.0 | 40494 | 0.4034 | | 0.397 | 795.0 | 40545 | 0.4042 | | 0.3941 | 796.0 | 40596 | 0.4030 | | 0.3929 | 797.0 | 40647 | 0.4031 | | 0.4016 | 798.0 | 40698 | 0.4003 | | 0.3926 | 799.0 | 40749 | 0.4026 | | 0.3985 | 800.0 | 40800 | 0.4046 | | 0.3978 | 801.0 | 40851 | 0.4002 | | 0.3972 | 802.0 | 40902 | 0.4058 | | 0.3993 | 803.0 | 40953 | 0.4026 | | 0.3935 | 804.0 | 41004 | 0.4049 | | 0.3973 | 805.0 | 41055 | 0.3989 | | 0.4002 | 806.0 | 41106 | 0.4003 | | 0.3918 | 807.0 | 41157 | 0.4006 | | 0.4001 | 808.0 | 41208 | 0.3997 | | 0.397 | 809.0 | 41259 | 0.4018 | | 0.3984 | 810.0 | 41310 | 0.4030 | | 0.3925 | 811.0 | 41361 | 0.4074 | | 0.398 | 812.0 | 41412 | 0.4032 | | 0.4 | 813.0 | 41463 | 0.3987 | | 0.3943 | 814.0 | 41514 | 0.4015 | | 0.3973 | 815.0 | 41565 | 0.3962 | | 0.3922 | 816.0 | 41616 | 0.4032 | | 0.3902 | 817.0 | 41667 | 0.3993 | | 0.3942 | 818.0 | 41718 | 0.4018 | | 0.3994 | 819.0 | 41769 | 0.4031 | | 0.3959 | 820.0 | 41820 | 0.4008 | | 0.3911 | 821.0 | 41871 | 0.4036 | | 0.3941 | 822.0 | 41922 | 0.3997 | | 0.3936 | 823.0 | 41973 | 0.3971 | | 0.397 | 824.0 | 42024 | 0.4011 | | 0.3974 | 825.0 | 42075 | 0.3964 | | 0.3921 | 826.0 | 42126 | 0.4010 | | 0.3961 | 827.0 | 42177 | 0.4019 | | 0.3912 | 828.0 | 42228 | 0.4004 | | 0.3939 | 829.0 | 42279 | 0.3980 | | 0.3917 | 830.0 | 42330 | 0.4027 | | 0.3977 | 831.0 | 42381 | 0.4005 | | 0.3881 | 832.0 | 42432 | 0.3983 | | 0.3939 | 833.0 | 42483 | 0.4026 | | 0.393 | 834.0 | 42534 | 0.3991 | | 0.3928 | 835.0 | 42585 | 0.3980 | | 0.394 | 836.0 | 42636 | 0.3953 | | 0.3908 | 837.0 | 42687 | 0.4002 | | 0.3926 | 838.0 | 42738 | 0.4015 | | 0.3947 | 839.0 | 42789 | 0.3991 | | 0.3965 | 840.0 | 42840 | 0.3969 | | 0.3934 | 841.0 | 42891 | 0.4002 | | 0.3916 | 842.0 | 42942 | 0.3969 | | 0.3887 | 843.0 | 42993 | 0.3941 | | 0.3938 | 844.0 | 43044 | 0.3972 | | 0.3928 | 845.0 | 43095 | 0.4015 | | 0.3948 | 846.0 | 43146 | 0.3976 | | 0.3925 | 847.0 | 43197 | 0.3953 | | 0.3876 | 848.0 | 43248 | 0.3958 | | 0.3857 | 849.0 | 43299 | 0.3967 | | 0.389 | 850.0 | 43350 | 0.3975 | | 0.3905 | 851.0 | 43401 | 0.3916 | | 0.389 | 852.0 | 43452 | 0.3987 | | 0.3872 | 853.0 | 43503 | 0.3965 | | 0.3902 | 854.0 | 43554 | 0.3963 | | 0.3883 | 855.0 | 43605 | 0.3941 | | 0.393 | 856.0 | 43656 | 0.3945 | | 0.3908 | 857.0 | 43707 | 0.3987 | | 0.3891 | 858.0 | 43758 | 0.3970 | | 0.39 | 859.0 | 43809 | 0.3934 | | 0.3894 | 860.0 | 43860 | 0.3981 | | 0.3859 | 861.0 | 43911 | 0.3940 | | 0.3896 | 862.0 | 43962 | 0.3956 | | 0.3897 | 863.0 | 44013 | 0.3952 | | 0.385 | 864.0 | 44064 | 0.3941 | | 0.3876 | 865.0 | 44115 | 0.3937 | | 0.3889 | 866.0 | 44166 | 0.3975 | | 0.3926 | 867.0 | 44217 | 0.3953 | | 0.3895 | 868.0 | 44268 | 0.3918 | | 0.3926 | 869.0 | 44319 | 0.3926 | | 0.3861 | 870.0 | 44370 | 0.3933 | | 0.3881 | 871.0 | 44421 | 0.3941 | | 0.3863 | 872.0 | 44472 | 0.3939 | | 0.3863 | 873.0 | 44523 | 0.3913 | | 0.386 | 874.0 | 44574 | 0.3919 | | 0.382 | 875.0 | 44625 | 0.3879 | | 0.384 | 876.0 | 44676 | 0.3938 | | 0.3898 | 877.0 | 44727 | 0.3949 | | 0.3913 | 878.0 | 44778 | 0.3947 | | 0.3859 | 879.0 | 44829 | 0.3952 | | 0.385 | 880.0 | 44880 | 0.3950 | | 0.3872 | 881.0 | 44931 | 0.3877 | | 0.383 | 882.0 | 44982 | 0.3905 | | 0.387 | 883.0 | 45033 | 0.3939 | | 0.3834 | 884.0 | 45084 | 0.3947 | | 0.3866 | 885.0 | 45135 | 0.3935 | | 0.3834 | 886.0 | 45186 | 0.3925 | | 0.3848 | 887.0 | 45237 | 0.3903 | | 0.3896 | 888.0 | 45288 | 0.3918 | | 0.3863 | 889.0 | 45339 | 0.3880 | | 0.384 | 890.0 | 45390 | 0.3884 | | 0.3844 | 891.0 | 45441 | 0.3907 | | 0.3863 | 892.0 | 45492 | 0.3954 | | 0.3872 | 893.0 | 45543 | 0.3919 | | 0.3869 | 894.0 | 45594 | 0.3928 | | 0.3801 | 895.0 | 45645 | 0.3941 | | 0.3832 | 896.0 | 45696 | 0.3930 | | 0.3886 | 897.0 | 45747 | 0.3933 | | 0.3871 | 898.0 | 45798 | 0.3917 | | 0.3892 | 899.0 | 45849 | 0.3927 | | 0.3864 | 900.0 | 45900 | 0.3934 | | 0.3827 | 901.0 | 45951 | 0.3916 | | 0.3838 | 902.0 | 46002 | 0.3932 | | 0.3859 | 903.0 | 46053 | 0.3901 | | 0.382 | 904.0 | 46104 | 0.3918 | | 0.3824 | 905.0 | 46155 | 0.3939 | | 0.3799 | 906.0 | 46206 | 0.3907 | | 0.3851 | 907.0 | 46257 | 0.3891 | | 0.3854 | 908.0 | 46308 | 0.3885 | | 0.3855 | 909.0 | 46359 | 0.3912 | | 0.3855 | 910.0 | 46410 | 0.3912 | | 0.3799 | 911.0 | 46461 | 0.3882 | | 0.387 | 912.0 | 46512 | 0.3894 | | 0.3792 | 913.0 | 46563 | 0.3887 | | 0.3831 | 914.0 | 46614 | 0.3875 | | 0.3821 | 915.0 | 46665 | 0.3863 | | 0.3853 | 916.0 | 46716 | 0.3884 | | 0.381 | 917.0 | 46767 | 0.3873 | | 0.3847 | 918.0 | 46818 | 0.3850 | | 0.3813 | 919.0 | 46869 | 0.3875 | | 0.3853 | 920.0 | 46920 | 0.3860 | | 0.3849 | 921.0 | 46971 | 0.3880 | | 0.3771 | 922.0 | 47022 | 0.3891 | | 0.3815 | 923.0 | 47073 | 0.3887 | | 0.3827 | 924.0 | 47124 | 0.3902 | | 0.3828 | 925.0 | 47175 | 0.3900 | | 0.3861 | 926.0 | 47226 | 0.3915 | | 0.383 | 927.0 | 47277 | 0.3911 | | 0.3785 | 928.0 | 47328 | 0.3837 | | 0.3825 | 929.0 | 47379 | 0.3879 | | 0.3793 | 930.0 | 47430 | 0.3921 | | 0.3836 | 931.0 | 47481 | 0.3893 | | 0.3858 | 932.0 | 47532 | 0.3874 | | 0.387 | 933.0 | 47583 | 0.3881 | | 0.3855 | 934.0 | 47634 | 0.3863 | | 0.3813 | 935.0 | 47685 | 0.3833 | | 0.3787 | 936.0 | 47736 | 0.3876 | | 0.3834 | 937.0 | 47787 | 0.3870 | | 0.3807 | 938.0 | 47838 | 0.3839 | | 0.3788 | 939.0 | 47889 | 0.3863 | | 0.3788 | 940.0 | 47940 | 0.3847 | | 0.3819 | 941.0 | 47991 | 0.3876 | | 0.3814 | 942.0 | 48042 | 0.3845 | | 0.3817 | 943.0 | 48093 | 0.3830 | | 0.3838 | 944.0 | 48144 | 0.3880 | | 0.3787 | 945.0 | 48195 | 0.3880 | | 0.3812 | 946.0 | 48246 | 0.3884 | | 0.3806 | 947.0 | 48297 | 0.3891 | | 0.3816 | 948.0 | 48348 | 0.3855 | | 0.3813 | 949.0 | 48399 | 0.3847 | | 0.3811 | 950.0 | 48450 | 0.3847 | | 0.3776 | 951.0 | 48501 | 0.3831 | | 0.3794 | 952.0 | 48552 | 0.3867 | | 0.3782 | 953.0 | 48603 | 0.3812 | | 0.3834 | 954.0 | 48654 | 0.3852 | | 0.3785 | 955.0 | 48705 | 0.3830 | | 0.3789 | 956.0 | 48756 | 0.3852 | | 0.3801 | 957.0 | 48807 | 0.3882 | | 0.3771 | 958.0 | 48858 | 0.3842 | | 0.3808 | 959.0 | 48909 | 0.3840 | | 0.3762 | 960.0 | 48960 | 0.3849 | | 0.3777 | 961.0 | 49011 | 0.3842 | | 0.3781 | 962.0 | 49062 | 0.3874 | | 0.3781 | 963.0 | 49113 | 0.3838 | | 0.376 | 964.0 | 49164 | 0.3863 | | 0.3777 | 965.0 | 49215 | 0.3827 | | 0.3808 | 966.0 | 49266 | 0.3853 | | 0.3835 | 967.0 | 49317 | 0.3869 | | 0.3801 | 968.0 | 49368 | 0.3859 | | 0.3839 | 969.0 | 49419 | 0.3841 | | 0.3768 | 970.0 | 49470 | 0.3849 | | 0.3797 | 971.0 | 49521 | 0.3844 | | 0.3763 | 972.0 | 49572 | 0.3855 | | 0.3788 | 973.0 | 49623 | 0.3832 | | 0.374 | 974.0 | 49674 | 0.3858 | | 0.3785 | 975.0 | 49725 | 0.3805 | | 0.3752 | 976.0 | 49776 | 0.3855 | | 0.3752 | 977.0 | 49827 | 0.3827 | | 0.3779 | 978.0 | 49878 | 0.3826 | | 0.3769 | 979.0 | 49929 | 0.3824 | | 0.3778 | 980.0 | 49980 | 0.3848 | | 0.3749 | 981.0 | 50031 | 0.3831 | | 0.3756 | 982.0 | 50082 | 0.3879 | | 0.3739 | 983.0 | 50133 | 0.3830 | | 0.3769 | 984.0 | 50184 | 0.3845 | | 0.3737 | 985.0 | 50235 | 0.3894 | | 0.3769 | 986.0 | 50286 | 0.3815 | | 0.373 | 987.0 | 50337 | 0.3797 | | 0.374 | 988.0 | 50388 | 0.3827 | | 0.3778 | 989.0 | 50439 | 0.3844 | | 0.3773 | 990.0 | 50490 | 0.3846 | | 0.3759 | 991.0 | 50541 | 0.3826 | | 0.3752 | 992.0 | 50592 | 0.3843 | | 0.3747 | 993.0 | 50643 | 0.3817 | | 0.3781 | 994.0 | 50694 | 0.3784 | | 0.3751 | 995.0 | 50745 | 0.3832 | | 0.3758 | 996.0 | 50796 | 0.3800 | | 0.3718 | 997.0 | 50847 | 0.3837 | | 0.3745 | 998.0 | 50898 | 0.3823 | | 0.3757 | 999.0 | 50949 | 0.3798 | | 0.3786 | 1000.0 | 51000 | 0.3794 | | 0.3738 | 1001.0 | 51051 | 0.3781 | | 0.3779 | 1002.0 | 51102 | 0.3851 | | 0.3735 | 1003.0 | 51153 | 0.3844 | | 0.3753 | 1004.0 | 51204 | 0.3841 | | 0.3701 | 1005.0 | 51255 | 0.3805 | | 0.3738 | 1006.0 | 51306 | 0.3826 | | 0.3729 | 1007.0 | 51357 | 0.3793 | | 0.3765 | 1008.0 | 51408 | 0.3825 | | 0.3725 | 1009.0 | 51459 | 0.3817 | | 0.3766 | 1010.0 | 51510 | 0.3813 | | 0.3736 | 1011.0 | 51561 | 0.3834 | | 0.3747 | 1012.0 | 51612 | 0.3800 | | 0.3726 | 1013.0 | 51663 | 0.3817 | | 0.3819 | 1014.0 | 51714 | 0.3840 | | 0.3799 | 1015.0 | 51765 | 0.3834 | | 0.3754 | 1016.0 | 51816 | 0.3818 | | 0.3762 | 1017.0 | 51867 | 0.3769 | | 0.3718 | 1018.0 | 51918 | 0.3794 | | 0.3785 | 1019.0 | 51969 | 0.3825 | | 0.3754 | 1020.0 | 52020 | 0.3827 | | 0.374 | 1021.0 | 52071 | 0.3818 | | 0.3785 | 1022.0 | 52122 | 0.3780 | | 0.3735 | 1023.0 | 52173 | 0.3815 | | 0.3726 | 1024.0 | 52224 | 0.3794 | | 0.3798 | 1025.0 | 52275 | 0.3787 | | 0.3714 | 1026.0 | 52326 | 0.3810 | | 0.3776 | 1027.0 | 52377 | 0.3787 | | 0.3688 | 1028.0 | 52428 | 0.3771 | | 0.375 | 1029.0 | 52479 | 0.3776 | | 0.372 | 1030.0 | 52530 | 0.3795 | | 0.3736 | 1031.0 | 52581 | 0.3781 | | 0.3713 | 1032.0 | 52632 | 0.3815 | | 0.3772 | 1033.0 | 52683 | 0.3802 | | 0.375 | 1034.0 | 52734 | 0.3788 | | 0.3725 | 1035.0 | 52785 | 0.3819 | | 0.3696 | 1036.0 | 52836 | 0.3836 | | 0.3741 | 1037.0 | 52887 | 0.3814 | | 0.3734 | 1038.0 | 52938 | 0.3799 | | 0.3759 | 1039.0 | 52989 | 0.3789 | | 0.3726 | 1040.0 | 53040 | 0.3802 | | 0.3693 | 1041.0 | 53091 | 0.3769 | | 0.3705 | 1042.0 | 53142 | 0.3812 | | 0.3691 | 1043.0 | 53193 | 0.3806 | | 0.3736 | 1044.0 | 53244 | 0.3796 | | 0.3707 | 1045.0 | 53295 | 0.3784 | | 0.3735 | 1046.0 | 53346 | 0.3752 | | 0.3773 | 1047.0 | 53397 | 0.3801 | | 0.3714 | 1048.0 | 53448 | 0.3800 | | 0.3747 | 1049.0 | 53499 | 0.3787 | | 0.3735 | 1050.0 | 53550 | 0.3775 | | 0.3727 | 1051.0 | 53601 | 0.3771 | | 0.3736 | 1052.0 | 53652 | 0.3833 | | 0.3676 | 1053.0 | 53703 | 0.3796 | | 0.3688 | 1054.0 | 53754 | 0.3758 | | 0.369 | 1055.0 | 53805 | 0.3775 | | 0.3696 | 1056.0 | 53856 | 0.3811 | | 0.3707 | 1057.0 | 53907 | 0.3776 | | 0.3765 | 1058.0 | 53958 | 0.3804 | | 0.3697 | 1059.0 | 54009 | 0.3813 | | 0.3718 | 1060.0 | 54060 | 0.3722 | | 0.3699 | 1061.0 | 54111 | 0.3771 | | 0.3725 | 1062.0 | 54162 | 0.3780 | | 0.3705 | 1063.0 | 54213 | 0.3767 | | 0.3698 | 1064.0 | 54264 | 0.3783 | | 0.374 | 1065.0 | 54315 | 0.3775 | | 0.3665 | 1066.0 | 54366 | 0.3813 | | 0.3695 | 1067.0 | 54417 | 0.3801 | | 0.3705 | 1068.0 | 54468 | 0.3805 | | 0.3709 | 1069.0 | 54519 | 0.3780 | | 0.3762 | 1070.0 | 54570 | 0.3758 | | 0.3718 | 1071.0 | 54621 | 0.3801 | | 0.3736 | 1072.0 | 54672 | 0.3769 | | 0.3702 | 1073.0 | 54723 | 0.3763 | | 0.3716 | 1074.0 | 54774 | 0.3791 | | 0.3684 | 1075.0 | 54825 | 0.3745 | | 0.3682 | 1076.0 | 54876 | 0.3796 | | 0.3699 | 1077.0 | 54927 | 0.3784 | | 0.3745 | 1078.0 | 54978 | 0.3794 | | 0.3721 | 1079.0 | 55029 | 0.3780 | | 0.3758 | 1080.0 | 55080 | 0.3792 | | 0.3742 | 1081.0 | 55131 | 0.3781 | | 0.3693 | 1082.0 | 55182 | 0.3819 | | 0.3676 | 1083.0 | 55233 | 0.3746 | | 0.3684 | 1084.0 | 55284 | 0.3812 | | 0.3727 | 1085.0 | 55335 | 0.3745 | | 0.3689 | 1086.0 | 55386 | 0.3743 | | 0.3704 | 1087.0 | 55437 | 0.3785 | | 0.3664 | 1088.0 | 55488 | 0.3774 | | 0.3704 | 1089.0 | 55539 | 0.3757 | | 0.3702 | 1090.0 | 55590 | 0.3790 | | 0.3747 | 1091.0 | 55641 | 0.3798 | | 0.3704 | 1092.0 | 55692 | 0.3756 | | 0.3749 | 1093.0 | 55743 | 0.3783 | | 0.3686 | 1094.0 | 55794 | 0.3759 | | 0.369 | 1095.0 | 55845 | 0.3762 | | 0.3671 | 1096.0 | 55896 | 0.3783 | | 0.3686 | 1097.0 | 55947 | 0.3780 | | 0.3693 | 1098.0 | 55998 | 0.3778 | | 0.3728 | 1099.0 | 56049 | 0.3759 | | 0.3715 | 1100.0 | 56100 | 0.3777 | | 0.3712 | 1101.0 | 56151 | 0.3775 | | 0.3695 | 1102.0 | 56202 | 0.3767 | | 0.3715 | 1103.0 | 56253 | 0.3762 | | 0.3728 | 1104.0 | 56304 | 0.3775 | | 0.368 | 1105.0 | 56355 | 0.3783 | | 0.3705 | 1106.0 | 56406 | 0.3797 | | 0.3705 | 1107.0 | 56457 | 0.3771 | | 0.3734 | 1108.0 | 56508 | 0.3754 | | 0.3701 | 1109.0 | 56559 | 0.3793 | | 0.3707 | 1110.0 | 56610 | 0.3729 | | 0.3677 | 1111.0 | 56661 | 0.3763 | | 0.3734 | 1112.0 | 56712 | 0.3813 | | 0.3714 | 1113.0 | 56763 | 0.3772 | | 0.3654 | 1114.0 | 56814 | 0.3765 | | 0.3692 | 1115.0 | 56865 | 0.3757 | | 0.3721 | 1116.0 | 56916 | 0.3749 | | 0.3741 | 1117.0 | 56967 | 0.3769 | | 0.3649 | 1118.0 | 57018 | 0.3806 | | 0.3709 | 1119.0 | 57069 | 0.3720 | | 0.3721 | 1120.0 | 57120 | 0.3794 | | 0.3701 | 1121.0 | 57171 | 0.3748 | | 0.3674 | 1122.0 | 57222 | 0.3787 | | 0.3669 | 1123.0 | 57273 | 0.3736 | | 0.3726 | 1124.0 | 57324 | 0.3789 | | 0.3672 | 1125.0 | 57375 | 0.3774 | | 0.3674 | 1126.0 | 57426 | 0.3778 | | 0.3702 | 1127.0 | 57477 | 0.3772 | | 0.3717 | 1128.0 | 57528 | 0.3766 | | 0.3703 | 1129.0 | 57579 | 0.3757 | | 0.3695 | 1130.0 | 57630 | 0.3808 | | 0.3729 | 1131.0 | 57681 | 0.3721 | | 0.3657 | 1132.0 | 57732 | 0.3784 | | 0.3676 | 1133.0 | 57783 | 0.3793 | | 0.3684 | 1134.0 | 57834 | 0.3797 | | 0.3703 | 1135.0 | 57885 | 0.3771 | | 0.3705 | 1136.0 | 57936 | 0.3752 | | 0.3691 | 1137.0 | 57987 | 0.3773 | | 0.3673 | 1138.0 | 58038 | 0.3766 | | 0.3715 | 1139.0 | 58089 | 0.3779 | | 0.37 | 1140.0 | 58140 | 0.3750 | | 0.3709 | 1141.0 | 58191 | 0.3786 | | 0.3696 | 1142.0 | 58242 | 0.3776 | | 0.3752 | 1143.0 | 58293 | 0.3758 | | 0.3675 | 1144.0 | 58344 | 0.3762 | | 0.3681 | 1145.0 | 58395 | 0.3741 | | 0.3684 | 1146.0 | 58446 | 0.3794 | | 0.3663 | 1147.0 | 58497 | 0.3720 | | 0.3712 | 1148.0 | 58548 | 0.3742 | | 0.3672 | 1149.0 | 58599 | 0.3786 | | 0.369 | 1150.0 | 58650 | 0.3737 | | 0.3648 | 1151.0 | 58701 | 0.3767 | | 0.3704 | 1152.0 | 58752 | 0.3740 | | 0.3695 | 1153.0 | 58803 | 0.3781 | | 0.3707 | 1154.0 | 58854 | 0.3753 | | 0.3661 | 1155.0 | 58905 | 0.3774 | | 0.367 | 1156.0 | 58956 | 0.3763 | | 0.3657 | 1157.0 | 59007 | 0.3767 | | 0.3638 | 1158.0 | 59058 | 0.3738 | | 0.3728 | 1159.0 | 59109 | 0.3732 | | 0.3748 | 1160.0 | 59160 | 0.3787 | | 0.3753 | 1161.0 | 59211 | 0.3743 | | 0.3663 | 1162.0 | 59262 | 0.3758 | | 0.3694 | 1163.0 | 59313 | 0.3772 | | 0.3657 | 1164.0 | 59364 | 0.3763 | | 0.3643 | 1165.0 | 59415 | 0.3770 | | 0.3679 | 1166.0 | 59466 | 0.3772 | | 0.37 | 1167.0 | 59517 | 0.3724 | | 0.3693 | 1168.0 | 59568 | 0.3752 | | 0.3705 | 1169.0 | 59619 | 0.3732 | | 0.3671 | 1170.0 | 59670 | 0.3767 | | 0.3729 | 1171.0 | 59721 | 0.3723 | | 0.3701 | 1172.0 | 59772 | 0.3768 | | 0.3717 | 1173.0 | 59823 | 0.3782 | | 0.3716 | 1174.0 | 59874 | 0.3721 | | 0.3723 | 1175.0 | 59925 | 0.3712 | | 0.3674 | 1176.0 | 59976 | 0.3746 | | 0.365 | 1177.0 | 60027 | 0.3768 | | 0.3725 | 1178.0 | 60078 | 0.3760 | | 0.3679 | 1179.0 | 60129 | 0.3742 | | 0.3707 | 1180.0 | 60180 | 0.3753 | | 0.3698 | 1181.0 | 60231 | 0.3730 | | 0.3697 | 1182.0 | 60282 | 0.3748 | | 0.368 | 1183.0 | 60333 | 0.3722 | | 0.3689 | 1184.0 | 60384 | 0.3724 | | 0.3667 | 1185.0 | 60435 | 0.3731 | | 0.3708 | 1186.0 | 60486 | 0.3785 | | 0.3684 | 1187.0 | 60537 | 0.3755 | | 0.3701 | 1188.0 | 60588 | 0.3774 | | 0.3685 | 1189.0 | 60639 | 0.3733 | | 0.37 | 1190.0 | 60690 | 0.3773 | | 0.372 | 1191.0 | 60741 | 0.3761 | | 0.3677 | 1192.0 | 60792 | 0.3733 | | 0.367 | 1193.0 | 60843 | 0.3770 | | 0.3641 | 1194.0 | 60894 | 0.3731 | | 0.3679 | 1195.0 | 60945 | 0.3739 | | 0.3709 | 1196.0 | 60996 | 0.3731 | | 0.3668 | 1197.0 | 61047 | 0.3784 | | 0.3678 | 1198.0 | 61098 | 0.3754 | | 0.3642 | 1199.0 | 61149 | 0.3795 | | 0.3717 | 1200.0 | 61200 | 0.3766 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.13.2

azamat/mapper

azamat

2023-02-14T19:30:12Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-02-14T16:19:26Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 2471 with parameters: ``` {'batch_size': 512, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters: ``` {'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True} ``` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

shaiman12/flan-t5-base-samsum

shaiman12

2023-02-14T19:29:12Z

5

0

transformers

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

text2text-generation

2023-02-14T16:47:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: flan-t5-base-samsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: test args: samsum metrics: - name: Rouge1 type: rouge value: 47.5929 ---  # flan-t5-base-samsum This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.3776 - Rouge1: 47.5929 - Rouge2: 23.8272 - Rougel: 40.1493 - Rougelsum: 43.7798 - Gen Len: 17.2503 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.4416 | 1.0 | 1842 | 1.3837 | 46.6013 | 23.125 | 39.4894 | 42.9943 | 17.0684 | | 1.3581 | 2.0 | 3684 | 1.3730 | 47.3142 | 23.5981 | 39.5786 | 43.447 | 17.3675 | | 1.2781 | 3.0 | 5526 | 1.3739 | 47.5321 | 23.8035 | 40.0555 | 43.7595 | 17.2271 | | 1.2368 | 4.0 | 7368 | 1.3767 | 47.0944 | 23.2414 | 39.6673 | 43.2155 | 17.2405 | | 1.1953 | 5.0 | 9210 | 1.3776 | 47.5929 | 23.8272 | 40.1493 | 43.7798 | 17.2503 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.12.1 - Datasets 2.9.0 - Tokenizers 0.13.2

pete88b/q-Taxi-v3-0.0.1

pete88b

2023-02-14T19:24:03Z

0

null

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

reinforcement-learning

2023-02-14T19:24:01Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-0.0.1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="pete88b/q-Taxi-v3-0.0.1", 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"]) ```

civility-lab/roberta-base-namecalling

civility-lab

2023-02-14T19:18:42Z

14,385

2

transformers

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

text-classification

2023-02-13T17:54:29Z

--- language: - en license: apache-2.0 library_name: transformers tags: - incivility metrics: - f1 widget: - text: "Be careful around those DemocRats." example_title: "Namecall" - text: "Be careful around those Democrats." example_title: "No Namecall" --- # Model Card for roberta-base-namecalling This is a [roBERTa-base](https://huggingface.co/roberta-base) model fine-tuned on ~12K social media posts annotated for the presence or absence of namecalling. # How to Get Started with the Model You can use this model directly with a pipeline for text classification: ```python >>> import transformers >>> model_name = "civility-lab/roberta-base-namecalling" >>> classifier = transformers.TextClassificationPipeline( ... tokenizer=transformers.AutoTokenizer.from_pretrained(model_name), ... model=transformers.AutoModelForSequenceClassification.from_pretrained(model_name)) >>> classifier("Be careful around those Democrats.") [{'label': 'not-namecalling', 'score': 0.9995089769363403}] >>> classifier("Be careful around those DemocRats.") [{'label': 'namecalling', 'score': 0.996940016746521}] ``` # Model Details This is a 2023 update of the model built by [Ozler et al. (2020)](https://aclanthology.org/2020.alw-1.4/) incorporating data from [Rains et al. (2021)](https://doi.org/10.1093/hcr/hqab009) and using a more recent version of the transformers library. - **Developed by:** [Steven Bethard](https://bethard.github.io/), [Kate Kenski](https://comm.arizona.edu/user/kate-kenski), [Steve Rains](https://comm.arizona.edu/user/steve-rains), [Yotam Shmargad](https://www.yotamshmargad.com/), [Kevin Coe](https://faculty.utah.edu/u0915886-Kevin_Coe/) - **Language:** en - **License:** apache-2.0 - **Parent Model:** roberta-base - **Resources for more information:** - [GitHub Repo](https://github.com/clulab/incivility) - Kadir Bulut Ozler; Kate Kenski; Steve Rains; Yotam Shmargad; Kevin Coe; and Steven Bethard. [Fine-tuning for multi-domain and multi-label uncivil language detection](https://aclanthology.org/2020.alw-1.4/). In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 28–33, Online, November 2020. Association for Computational Linguistics - Stephen A Rains; Yotam Shmargad; Kevin Coe; Kate Kenski; and Steven Bethard. [Assessing the Russian Troll Efforts to Sow Discord on Twitter during the 2016 U.S. Election](https://doi.org/10.1093/hcr/hqab009). Human Communication Research, 47(4): 477-486. 08 2021. - Stephen A Rains; Jake Harwood; Yotam Shmargad; Kate Kenski; Kevin Coe; and Steven Bethard. [Engagement with partisan Russian troll tweets during the 2016 U.S. presidential election: a social identity perspective](https://doi.org/10.1093/joc/jqac037). Journal of Communication, 73(1): 38-48. 02 2023. # Uses The model is intended to be used for text classification, taking as input social media posts and predicting as output whether the post contains namecalling. It is not intended to generate namecalling, and it should not be used as part of any incivility generation model. # Training Details The model was trained on data from four sources: comments on the Arizona Daily Star website from 2011, Russian troll Tweets from 2012-2018, Tucson politician Tweets from 2018, and US presidential primary Tweets from 2019. Each dataset was annotated for the presence of namecalling following the approach of [Coe et al. (2014)](https://doi.org/10.1111/jcom.12104) and split into training, development, and test partitions. The [roberta-base](https://huggingface.co/roberta-base) model was fine-tuned on the combined training partitions from all four datasets, with texts tokenized using the standard [roberta-base](https://huggingface.co/roberta-base) tokenizer. # Evaluation The model was evaluated on the test partition of each of the datasets. It achieves the following F1 scores: - 0.58 F1 on Arizona Daily Star comments - 0.71 F1 on Russian troll Tweets - 0.71 F1 on Tucson politician Tweets - 0.81 F1 on US presidential primary Tweets # Limitations and Biases The human coders and their trainers were mostly [Western, educated, industrialized, rich and democratic (WEIRD)](https://www.nature.com/articles/466029a), which may have shaped how they evaluated incivility. The trained models will reflect such biases. # Environmental Impact - **Hardware Type:** Tesla V100S-PCIE-32GB - **Hours used:** 22 - **HPC Provider:** <https://hpc.arizona.edu/> - **Carbon Emitted:** 2.85 kg CO2 (estimated by [ML CO2 Impact](https://mlco2.github.io/impact#compute)) # Citation ```bibtex @inproceedings{ozler-etal-2020-fine, title = "Fine-tuning for multi-domain and multi-label uncivil language detection", author = "Ozler, Kadir Bulut and Kenski, Kate and Rains, Steve and Shmargad, Yotam and Coe, Kevin and Bethard, Steven", booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.alw-1.4", doi = "10.18653/v1/2020.alw-1.4", pages = "28--33", } ```

kaloyloyloyloy/wav2vec2-base-finetuned-ks

kaloyloyloyloy

2023-02-14T19:14:46Z

163

0

transformers

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

audio-classification

2023-02-14T15:47:52Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - superb 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 superb dataset. It achieves the following results on the evaluation set: - Loss: 1.5492 - Accuracy: 0.6209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4528 | 1.0 | 399 | 1.5492 | 0.6209 | | 1.5591 | 2.0 | 798 | 1.5411 | 0.6209 | | 1.4814 | 3.0 | 1197 | 1.5397 | 0.6209 | | 1.4528 | 4.0 | 1596 | 1.5412 | 0.6209 | | 1.4829 | 5.0 | 1995 | 1.5395 | 0.6209 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

Ahmed-ibn-Harun/es_pipeline

Ahmed-ibn-Harun

2023-02-14T18:53:10Z

1

0

spacy

[ "spacy", "token-classification", "es", "model-index", "region:us" ]

token-classification

2023-02-14T18:52:47Z

--- tags: - spacy - token-classification language: - es model-index: - name: es_pipeline results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9937984496 - name: NER Recall type: recall value: 0.9953416149 - name: NER F Score type: f_score value: 0.9945694337 --- | Feature | Description | | --- | --- | | **Name** | `es_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `ner` | | **Components** | `tok2vec`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (3 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `NOUN`, `PNOUN`, `VERB` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 99.46 | | `ENTS_P` | 99.38 | | `ENTS_R` | 99.53 | | `TOK2VEC_LOSS` | 8110.20 | | `NER_LOSS` | 35115.85 |

dwchris/dqn-SpaceInvadersNoFrameskip-v4

dwchris

2023-02-14T18:48:16Z

0

stable-baselines3

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

reinforcement-learning

2023-02-14T18:47:42Z

--- 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: 14.50 +/- 12.34 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 dwchris -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 dwchris -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 dwchris ``` ## 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', 15000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

badalsahani/text-classification-multi

badalsahani

2023-02-14T17:59:32Z

10

7

transformers

[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "en", "dataset:badalsahani/autotrain-data-text-classification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-02-14T17:32:19Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - badalsahani/autotrain-data-text-classification co2_eq_emissions: emissions: 7.761992510873142 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3486594647 - CO2 Emissions (in grams): 7.7620 ## Validation Metrics - Loss: 0.008 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000 ## Usage You can use cURL to access this model: ```curl $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/badalsahani/text-classification-multi ``` Or Python API: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("badalsahani/text-classification-multi", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("badalsahani/text-classification-multi", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

darkwolfsam/dqn-SpaceInvadersNoFrameskip-v4

darkwolfsam

2023-02-14T17:44:13Z

0

stable-baselines3

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

reinforcement-learning

2023-02-03T21:20:18Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 229.50 +/- 55.79 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 darkwolfsam -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 darkwolfsam -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 darkwolfsam ``` ## 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', 150000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

Zekunli/t5-base-da-multiwoz2.1_500

Zekunli

2023-02-14T17:42:14Z

106

0

transformers

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

text2text-generation

2023-02-14T17:25:23Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: t5-base-da-multiwoz2.1_500 results: [] ---  # t5-base-da-multiwoz2.1_500 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4253 - Accuracy: 22.6359 - Num: 3689 - Gen Len: 15.4806 ## Model description More information needed ## Intended uses & 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: 24 - eval_batch_size: 48 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:| | 0.5137 | 2.78 | 400 | 0.4454 | 21.1276 | 3689 | 15.569 | | 0.4673 | 5.56 | 800 | 0.4329 | 21.7406 | 3689 | 15.4573 | | 0.4591 | 8.33 | 1200 | 0.4253 | 22.6359 | 3689 | 15.4806 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1

Zekunli/t5-base-extraction-cnndm_10000-all

Zekunli

2023-02-14T17:38:04Z

3

0

transformers

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

text2text-generation

2023-02-14T17:01:07Z

--- tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-extraction-cnndm_10000-all results: [] ---  # t5-base-extraction-cnndm_10000-all This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8182 - Rouge1: 33.8286 - Rouge2: 14.4919 - Rougel: 28.8935 - Rougelsum: 28.9581 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9662 | 0.48 | 200 | 1.9092 | 33.2564 | 14.236 | 28.2044 | 28.3269 | 18.992 | | 1.9495 | 0.96 | 400 | 1.8775 | 33.7516 | 14.2246 | 28.9019 | 28.9507 | 19.0 | | 1.9062 | 1.44 | 600 | 1.8580 | 33.7533 | 14.2196 | 28.3873 | 28.4658 | 19.0 | | 1.8713 | 1.92 | 800 | 1.8496 | 33.6921 | 14.4532 | 28.5695 | 28.6573 | 19.0 | | 1.85 | 2.4 | 1000 | 1.8327 | 34.1551 | 14.7671 | 28.9492 | 28.9885 | 19.0 | | 1.8232 | 2.88 | 1200 | 1.8182 | 33.8286 | 14.4919 | 28.8935 | 28.9581 | 19.0 | | 1.8004 | 3.36 | 1400 | 1.8299 | 34.5099 | 14.8659 | 29.1119 | 29.1544 | 19.0 | | 1.7832 | 3.84 | 1600 | 1.8252 | 34.5877 | 15.1259 | 29.3368 | 29.3638 | 19.0 | | 1.7677 | 4.32 | 1800 | 1.8226 | 34.4487 | 15.0361 | 29.2962 | 29.3431 | 19.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1

albburtsev/faucet-v2-1

albburtsev

2023-02-14T16:51:22Z

29

1

diffusers

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

text-to-image

2023-02-14T16:40:51Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### faucet-v2.1 Dreambooth model trained by albburtsev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

drnkwtr/Malware_Spectrogram_Prediction

drnkwtr

2023-02-14T16:38:21Z

0

null

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

null

2023-02-14T15:15:53Z

--- license: openrail --- This model is trained on spectrograms of malwares. It is supposed to make a prediction. POST /image - accepts an image in binary format, either as an octet-stream file, or as a multipart/form-data with files in the imageData parameter. The server loads the image using the Python Imaging Library, runs it through the predict_image function from the predict module (not shown), and returns the results as a JSON object. POST /url - accepts a JSON object containing a URL to an image. The server loads the image using the predict_url function from the predict module, and returns the results as a JSON object.

aioe/anysium_anypas

aioe

2023-02-14T16:11:45Z

0

7

null

[ "text-to-image", "art", "stable diffusion", "ja", "en", "license:other", "region:us" ]

text-to-image

2023-02-14T10:21:57Z

--- license: other language: - ja - en tags: - text-to-image - art - stable diffusion --- # 【概要(Outline)】コンセプトは<strong>「儚さ」</strong>です。 <br> 1つはElysium特有の線の細さを活かしたマージモデル、もう1つはpastelmix特有の色味を活かしたマージモデルとなっています。 <br> <br> The concept is <strong>"like disappearing."</strong> <br> One is the marge model by the feature of thin outline of Elysium, and another is the marge model by the feature of unique colors of pastelmix. <br> <br> # 【モデル紹介とマージ素材(Models introduction and merged materials)】 <strong>*■anysium-v1.0*</strong> <br> ・anything-v4.5 <br> ・Elysium_Anime_V3 <br> →線の細さが特徴的です。 <br> (The feature is thin outline.) <br> <br> <strong>*■anypas-v1.0*</strong> <br> ・anything-v4.5 <br> ・pastelmix <br> →pastelmix風の色味が特徴的です。 <br> (The feature is like colors of pastelmix.) <br> <br> # 【推奨設定(Recommended settings)】 <strong>*■anysium-v1.0*</strong> <br> ・Steps:20 <br> ・CFG Scale:5.5 <br> ・Clip Skip:2 <br> ・Negative:(worst quality, low quality:1.2), <br> <br> <strong>*■anypas-v1.0*</strong> <br> ・Steps:30 <br> ・CFG Scale:10 <br> ・Clip Skip:2 <br> ・Negative:(worst quality, low quality, poorly eyes:1.2), <br> <br> # 【作例(Examples)】 Positive:one girl, <br> <br> <strong>*■anysium-v1.0*</strong> <img src="https://imgur.com/B4fPJul.png" width="1152" height="768"> <br> <strong>*■anypas-v1.0*</strong> <img src="https://imgur.com/AdkCtC0.png" width="1152" height="768"> <br> <br> Positive:(one little and cute girl:1.2), (loli), (solo:1.3), (masterpiece), (best quality), the girl wearing (white headdress), (frilled white capelet:1.2), (short sleeves), (short white gloves), (intricate and gorgeous frilled white dress with gold decoration), the girl has (long blonde hair), [[[blue eyes]]], the background is large western style garden, (many colorful flowers), trees, birds, <br> <br> <strong>*■anysium-v1.0*</strong> <img src="https://imgur.com/f903ASm.jpg" width="1152" height="768"> <br> <strong>*■anypas-v1.0*</strong> <img src="https://imgur.com/hkeyhX2.jpg" width="1152" height="768">

pyflynn/dqn-SpaceInvadersNoFrameskip-v4

pyflynn

2023-02-14T16:09:20Z

0

stable-baselines3

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

reinforcement-learning

2023-02-14T16:08:37Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 608.50 +/- 150.13 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 pyflynn -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 pyflynn -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 pyflynn ``` ## 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', 5e-05), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

khiyza/PPO-LunarLander-v2

khiyza

2023-02-14T15:59:31Z

0

stable-baselines3

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

reinforcement-learning

2023-02-14T15:59:07Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.80 +/- 24.27 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 ... ```

bofenghuang/whisper-large-v2-cv11-french

bofenghuang

2023-02-14T15:17:59Z

7

5

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "whisper-event", "fr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-01-05T16:50:41Z

--- license: apache-2.0 language: fr library_name: transformers thumbnail: null tags: - automatic-speech-recognition - hf-asr-leaderboard - whisper-event datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Fine-tuned whisper-large-v2 model for ASR in French results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 8.05 - name: WER (Beam 5) type: wer value: 7.67 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Multilingual LibriSpeech (MLS) type: facebook/multilingual_librispeech config: french split: test args: french metrics: - name: WER (Greedy) type: wer value: 5.56 - name: WER (Beam 5) type: wer value: 5.28 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: VoxPopuli type: facebook/voxpopuli config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 11.50 - name: WER (Beam 5) type: wer value: 10.69 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Fleurs type: google/fleurs config: fr_fr split: test args: fr_fr metrics: - name: WER (Greedy) type: wer value: 5.42 - name: WER (Beam 5) type: wer value: 5.05 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: African Accented French type: gigant/african_accented_french config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 6.47 - name: WER (Beam 5) type: wer value: 5.95 --- <style> img { display: inline; } </style> ![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey) ![Model size](https://img.shields.io/badge/Params-1550M-lightgrey) ![Language](https://img.shields.io/badge/Language-French-lightgrey) # Fine-tuned whisper-large-v2 model for ASR in French This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2), trained on the mozilla-foundation/common_voice_11_0 fr dataset. When using the model make sure that your speech input is also sampled at 16Khz. **This model also predicts casing and punctuation.** ## Performance *Below are the WERs of the pre-trained models on the [Common Voice 9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli) and [Fleurs](https://huggingface.co/datasets/google/fleurs). These results are reported in the original [paper](https://cdn.openai.com/papers/whisper.pdf).* | Model | Common Voice 9.0 | MLS | VoxPopuli | Fleurs | | --- | :---: | :---: | :---: | :---: | | [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 22.7 | 16.2 | 15.7 | 15.0 | | [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 16.0 | 8.9 | 12.2 | 8.7 | | [openai/whisper-large](https://huggingface.co/openai/whisper-large) | 14.7 | 8.9 | **11.0** | **7.7** | | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | **13.9** | **7.3** | 11.4 | 8.3 | *Below are the WERs of the fine-tuned models on the [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), and [Fleurs](https://huggingface.co/datasets/google/fleurs). Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of apostrophe. The results in the table are reported as `WER (greedy search) / WER (beam search with beam width 5)`.* | Model | Common Voice 11.0 | MLS | VoxPopuli | Fleurs | | --- | :---: | :---: | :---: | :---: | | [bofenghuang/whisper-small-cv11-french](https://huggingface.co/bofenghuang/whisper-small-cv11-french) | 11.76 / 10.99 | 9.65 / 8.91 | 14.45 / 13.66 | 10.76 / 9.83 | | [bofenghuang/whisper-medium-cv11-french](https://huggingface.co/bofenghuang/whisper-medium-cv11-french) | 9.03 / 8.54 | 6.34 / 5.86 | 11.64 / 11.35 | 7.13 / 6.85 | | [bofenghuang/whisper-medium-french](https://huggingface.co/bofenghuang/whisper-medium-french) | 9.03 / 8.73 | 4.60 / 4.44 | 9.53 / 9.46 | 6.33 / 5.94 | | [bofenghuang/whisper-large-v2-cv11-french](https://huggingface.co/bofenghuang/whisper-large-v2-cv11-french) | **8.05** / **7.67** | 5.56 / 5.28 | 11.50 / 10.69 | 5.42 / 5.05 | | [bofenghuang/whisper-large-v2-french](https://huggingface.co/bofenghuang/whisper-large-v2-french) | 8.15 / 7.83 | **4.20** / **4.03** | **9.10** / **8.66** | **5.22** / **4.98** | ## Usage Inference with 🤗 Pipeline ```python import torch from datasets import load_dataset from transformers import pipeline device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load pipeline pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-cv11-french", device=device) # NB: set forced_decoder_ids for generation utils pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", task="transcribe") # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = test_segment["audio"] # Run generated_sentences = pipe(waveform, max_new_tokens=225)["text"] # greedy # generated_sentences = pipe(waveform, max_new_tokens=225, generate_kwargs={"num_beams": 5})["text"] # beam search # Normalise predicted sentences if necessary ``` Inference with 🤗 low-level APIs ```python import torch import torchaudio from datasets import load_dataset from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load model model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-cv11-french").to(device) processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-cv11-french", language="french", task="transcribe") # NB: set forced_decoder_ids for generation utils model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="fr", task="transcribe") # 16_000 model_sample_rate = processor.feature_extractor.sampling_rate # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = torch.from_numpy(test_segment["audio"]["array"]) sample_rate = test_segment["audio"]["sampling_rate"] # Resample if sample_rate != model_sample_rate: resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate) waveform = resampler(waveform) # Get feat inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt") input_features = inputs.input_features input_features = input_features.to(device) # Generate generated_ids = model.generate(inputs=input_features, max_new_tokens=225) # greedy # generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5) # beam search # Detokenize generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # Normalise predicted sentences if necessary ```

mocker/KaBoom

mocker

2023-02-14T15:09:06Z

0

128

null

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

null

2023-02-06T23:47:49Z

--- license: creativeml-openrail-m language: - en tags: - art --- # In short, - FaceBomb : Covers from anime to 2.5D style. Suited for general use. - recipe : 0.5((0.5(AbyssOrangeMix2_hard) + 0.5(pastelmix-better-vae-fp32)) + 0.5(CounterfeitV25_25)) + 0.5(dalcefoV3Painting_dalcefoV3Painting) - ColorBomb : FaceBomb + vivid color and lighting. A bit picky about prompts. - recipe : dalcefoV3Painting_dalcefoV3Painting + 0.5(ultracolorV4_ultracolorV4 - CounterfeitV25_25) - HyperBomb : Strong anime style w/ highly saturated color. - recipe : 0.5((0.5(AbyssOrangeMix2_hard) + 0.5(pastelmix-better-vae-fp32)) + 0.5(CounterfeitV25_25)) + 0.5(dalcefoV3Painting_dalcefoV3Painting) + 0.3(0.8(pastelMixStylizedAnime_pastelMixPrunedFP16) + 0.2(CounterfeitV25_25) - f222) # Recommended Setting ## VAE - If the color appears dull or washed out, try applying VAE. I used `kl-f8-anime2` - https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt ## Sampling method and Hires.fix 1. `DPM++ SDE Karras: 24~32 steps` / `R-ESRGAN 4x+ Anime6B: 2x, 14 steps` / `Denoising strength:0.45 ~ 0.55` 2. `DDIM: 24~32 steps` / `Latent: 2x 14 steps` / `Denoising Strength:0.45 ~ 0.7` - First option yields better result in general. Recommended. - Second option was 1.5 ~ 2 times faster on my system but the output was questionable. Especially for ColorBomb. ## FaceBomb - Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), masterpiece*portrait, realistic, 3d face, lustrous skin, ` - Negative : `(worst quality, low quality:1.4), watermark, logo,` ## ColorBomb - Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), (ultra-detailed, high-resolution: 1.2), beautiful girl, { color } { color } theme, ` - e.g. black gold theme - Negative : `(worst quality, low quality:1.4), watermark, logo,` ## HyperBomb - Positive : `(masterpiece, sidelighting, finely detailed beautiful eyes: 1.2),` - Negative : `(worst quality, low quality:1.4), watermark, logo,` # Example - More pictures in folder. - Below are the ideal/intended outputs. ![Alt text](sample/00184-20230207043613___Custom_FaceBombMix-fp16-no-ema_aad629159b.png) ### FaceBomb (masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), masterpiece*portrait, realistic, 3d face, glowing eyes, shiny hair, lustrous skin, solo, embarassed Negative prompt: (worst quality, low quality:1.4), watermark, logo, Steps: 32, Sampler: DPM++ SDE Karras, CFG scale: 9, Seed: 3624413002, Size: 512x768, Model hash: aad629159b, Model: __Custom_FaceBombMix-fp16-no-ema, Denoising strength: 0.5, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: R-ESRGAN 4x+ Anime6B --- ![Alt text](sample/00217-20230207050723___Custom_ColorBomb-fp16-no-ema_627f50eea8.png) ### ColorBomb ((masterpiece, best quality, ultra-detailed, high-resolution)), solo, beautiful girl, gleaming eye, perfect eye, age 15, black white gold theme, Negative prompt: (worst quality, low quality:1.4), (depth of field, blurry:1.2), (greyscale, monochrome:1.1), 3D face, cropped, lowres, text, jpeg artifacts, signature, watermark, username, blurry, artist name, trademark, watermark, title, (tan, muscular, sd character:1.1), multiple view, Reference sheet, non-linear background, blurred background, bad anatomy, cropped hands, extra digit, fewer digit, Steps: 24, Sampler: DDIM, CFG scale: 7, Seed: 3050494714, Size: 512x768, Model hash: 627f50eea8, Model: __Custom_ColorBomb-fp16-no-ema, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: Latent --- ![Alt text](sample/00212-20230207050130___Custom_HyperBombMix-fp16-no-ema_16c6ca45b1.png) ### HyperBomb (masterpiece, sidelighting, finely detailed beautiful eyes: 1.2), Negative prompt: (worst quality, low quality:1.4), watermark, logo, Steps: 32, Sampler: DDIM, CFG scale: 9, Seed: 2411870881, Size: 768x512, Model hash: 16c6ca45b1, Model: __Custom_HyperBombMix-fp16-no-ema, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 14, Hires upscaler: Latent

AdamOswald1/Tester

AdamOswald1

2023-02-14T15:02:27Z

0

null

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

null

2022-11-09T18:10:39Z

--- title: Finetuned Diffusion emoji: 🪄🖼️ colorFrom: red colorTo: pink sdk: gradio sdk_version: 3.18.0 app_file: app.py pinned: true license: mit --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

mafaisalsust/xlm-roberta-large-finetuned-ac

mafaisalsust

2023-02-14T14:58:33Z

80

0

transformers

[ "transformers", "tf", "xlm-roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-02-14T14:43:51Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: mafaisalsust/xlm-roberta-large-finetuned-ac results: [] ---  # mafaisalsust/xlm-roberta-large-finetuned-ac This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 11.0965 - Validation Loss: 4.5876 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, '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 | |:----------:|:---------------:|:-----:| | 11.0965 | 4.5876 | 0 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.8.0 - Datasets 2.9.0 - Tokenizers 0.13.2

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

chradden

2023-02-14T14:56:08Z

0

null

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

reinforcement-learning

2023-02-13T16:07:06Z

--- 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="chradden/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"]) ```

YoriV/a2c-AntBulletEnv-v0

YoriV

2023-02-14T14:44:39Z

0

stable-baselines3

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

reinforcement-learning

2023-02-14T14:43:28Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1159.33 +/- 399.23 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

nhiro3303/a2c-PandaReachDense-v2

nhiro3303

2023-02-14T14:41:25Z

0

stable-baselines3

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

reinforcement-learning

2023-02-14T14:39:48Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.43 +/- 0.65 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

bencevans/ddpm-ena24-gen-512-100

bencevans

2023-02-14T14:37:54Z

12

0

diffusers

[ "diffusers", "diffusers:DDPMPipeline", "region:us" ]

null

2023-02-03T09:24:53Z

--- library_name: diffusers --- # Model Card for ddpm-ena24-gen-512-100 This model generates Camera Trap Imagery. It's been trained on the ENA24-Detection Dataset for 100 epochs.

frangiral/ppo-Pyramids1

frangiral

2023-02-14T14:29:26Z

3

0

ml-agents

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

reinforcement-learning

2023-02-14T14:29:04Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: frangiral/ppo-Pyramids1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

mmiteva/qa_model_customs

mmiteva

2023-02-14T14:24:06Z

63

0

transformers

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

question-answering

2023-02-13T19:44:16Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mmiteva/qa_model-customs results: [] ---  # mmiteva/qa_model-customs 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: 0.3517 - Train End Logits Accuracy: 0.8772 - Train Start Logits Accuracy: 0.8735 - Validation Loss: 0.8793 - Validation End Logits Accuracy: 0.7642 - Validation Start Logits Accuracy: 0.7586 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 32050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.3795 | 0.6168 | 0.6015 | 0.9590 | 0.7074 | 0.6950 | 0 | | 0.8193 | 0.7377 | 0.7260 | 0.8504 | 0.7313 | 0.7260 | 1 | | 0.5982 | 0.8004 | 0.7932 | 0.8225 | 0.7505 | 0.7440 | 2 | | 0.4467 | 0.8462 | 0.8405 | 0.8469 | 0.7633 | 0.7584 | 3 | | 0.3517 | 0.8772 | 0.8735 | 0.8793 | 0.7642 | 0.7586 | 4 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.10.1 - Datasets 2.7.1 - Tokenizers 0.12.1

dwarfbum/myfavloras

dwarfbum

2023-02-14T14:14:04Z

0

2

null

[ "nagatoro", "hayase nagatoro", "makima", "nazuna nanakusa", "lora", "region:us" ]

null

2023-02-06T15:09:03Z

--- tags: - nagatoro - hayase nagatoro - makima - nazuna nanakusa - lora --- https://civitai.com/models/6060/nagatoro-hayase-ti NOT LORA. THATS TI https://civitai.com/models/5662/nazuna-nanakusa-call-of-the-night-lora https://civitai.com/models/5373/makima-chainsaw-man-lora

frangiral/ppo-SnowballTarget

frangiral

2023-02-14T14:03:38Z

10

0

ml-agents

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

reinforcement-learning

2023-02-14T14:03:31Z

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

XperienciaVirtual/bdbybt-puig

XperienciaVirtual

2023-02-14T14:03:29Z

4

0

diffusers

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

text-to-image

2023-02-14T14:02:07Z

--- license: creativeml-openrail-m tags: - text-to-image widget: - text: bdbybt --- ### bdbybt_puig Dreambooth model trained by jaimexv with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: bdbybt (use that on your prompt) ![bdbybt 0](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%281%29.jpg)![bdbybt 1](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%282%29.jpg)![bdbybt 2](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%283%29.jpg)![bdbybt 3](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%284%29.jpg)![bdbybt 4](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%285%29.jpg)![bdbybt 5](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%286%29.jpg)![bdbybt 6](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%287%29.jpg)![bdbybt 7](https://huggingface.co/jaimexv/bdbybt-puig/resolve/main/concept_images/bdbybt_%288%29.jpg)

ybelkada/flan-t5-large-financial-phrasebank-lora

ybelkada

2023-02-14T13:47:51Z

0

4

null

[ "text2text-generation", "en", "sp", "ja", "pe", "hi", "fr", "ch", "be", "gu", "ge", "te", "it", "ar", "po", "ta", "ma", "or", "pa", "ur", "ga", "he", "ko", "ca", "th", "du", "in", "vi", "bu", "fi", "ce", "la", "tu", "ru", "cr", "sw", "yo", "ku", "cz", "so", "si", "ka", "zh", "ig", "xh", "ro", "ha", "es", "sl", "li", "gr", "ne", "as", "no", "dataset:svakulenk0/qrecc", "dataset:taskmaster2", "dataset:djaym7/wiki_dialog", "dataset:deepmind/code_contests", "dataset:lambada", "dataset:gsm8k", "dataset:aqua_rat", "dataset:esnli", "dataset:quasc", "dataset:qed", "dataset:financial_phrasebank", "license:apache-2.0", "region:us" ]

text2text-generation

2023-02-14T13:39:05Z

--- language: - en - sp - ja - pe - hi - fr - ch - be - gu - ge - te - it - ar - po - ta - ma - ma - or - pa - po - ur - ga - he - ko - ca - th - du - in - vi - bu - fi - ce - la - tu - ru - cr - sw - yo - ku - bu - ma - cz - fi - so - ta - sw - si - ka - zh - ig - xh - ro - ha - es - sl - li - gr - ne - as - no widget: - text: "Translate to German: My name is Arthur" example_title: "Translation" - text: "Please answer to the following question. Who is going to be the next Ballon d'or?" example_title: "Question Answering" - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." example_title: "Logical reasoning" - text: "Please answer the following question. What is the boiling point of Nitrogen?" example_title: "Scientific knowledge" - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" example_title: "Yes/no question" - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" example_title: "Reasoning task" - text: "Q: ( False or not False or False ) is? A: Let's think step by step" example_title: "Boolean Expressions" - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" example_title: "Math reasoning" - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" example_title: "Premise and hypothesis" tags: - text2text-generation datasets: - svakulenk0/qrecc - taskmaster2 - djaym7/wiki_dialog - deepmind/code_contests - lambada - gsm8k - aqua_rat - esnli - quasc - qed - financial_phrasebank license: apache-2.0 --- # Model Card for LoRA-FLAN-T5 large ![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png) This repository contains the LoRA (Low Rank Adapters) of `flan-t5-large` that has been fine-tuned on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset. ## Usage Use this adapter with `peft` library ```python # pip install peft transformers import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForSeq2SeqLM, AutoTokenizer peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForSeq2SeqLM.from_pretrained( config.base_model_name_or_path, torch_dtype='auto', device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id) ``` Enjoy!

huggingtweets/antoniobanderas-oquimbarreiros-snoopdogg

huggingtweets

2023-02-14T13:38:01Z

107

0

transformers

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

text-generation

2023-02-14T13:37:53Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1618012757621673992/kPppseWI_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/564137495244570624/CrWEr-Xk_400x400.jpeg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1564375315526275072/7Xnua7BM_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Quim Barreiros & Antonio Banderas & Snoop Dogg</div> <div style="text-align: center; font-size: 14px;">@antoniobanderas-oquimbarreiros-snoopdogg</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Quim Barreiros & Antonio Banderas & Snoop Dogg. | Data | Quim Barreiros | Antonio Banderas | Snoop Dogg | | --- | --- | --- | --- | | Tweets downloaded | 2275 | 3248 | 3221 | | Retweets | 55 | 593 | 1035 | | Short tweets | 860 | 1006 | 414 | | Tweets kept | 1360 | 1649 | 1772 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9sxu3b40/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @antoniobanderas-oquimbarreiros-snoopdogg's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/d57ngqld) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/d57ngqld/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/antoniobanderas-oquimbarreiros-snoopdogg') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

NathanaelM/poca-SoccerTwos

NathanaelM

2023-02-14T13:34:23Z

36

0

ml-agents

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

reinforcement-learning

2023-02-14T13:27:47Z

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

Maksim-t/Tiktok_Memes

Maksim-t

2023-02-14T13:27:37Z

0

null

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

null

2023-02-14T13:26:29Z

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

jamesthong/ppo-Huggy

jamesthong

2023-02-14T13:16:52Z

14

0

ml-agents

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

reinforcement-learning

2023-02-14T13:16:45Z

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

alfredplpl/x2-latent-upscaler-for-anime

alfredplpl

2023-02-14T12:51:41Z

19

1

diffusers

[ "diffusers", "stable-diffusion", "text-to-image", "license:openrail++", "diffusers:StableDiffusionLatentUpscalePipeline", "region:us" ]

text-to-image

2023-02-14T12:42:02Z

--- license: openrail++ tags: - stable-diffusion - text-to-image inference: false --- # Stable Diffusion x2 latent upscaler model card This model card focuses on the latent diffusion-based upscaler developed by [Katherine Crowson](https://github.com/crowsonkb/k-diffusion) in collaboration with [Stability AI](https://stability.ai/). This model was trained on a high-resolution subset of the LAION-2B dataset. It is a diffusion model that operates in the same latent space as the Stable Diffusion model, which is decoded into a full-resolution image. To use it with Stable Diffusion, You can take the generated latent from Stable Diffusion and pass it into the upscaler before decoding with your standard VAE. Or you can take any image, encode it into the latent space, use the upscaler, and decode it. **Note**: This upscaling model is designed explicitely for **Stable Diffusion** as it can upscale Stable Diffusion's latent denoised image embeddings. This allows for very fast text-to-image + upscaling pipelines as all intermeditate states can be kept on GPU. More for information, see example below. This model works on all [Stable Diffusion checkpoints](https://huggingface.co/models?other=stable-diffusion) | ![upscaler.jpg](https://pbs.twimg.com/media/FhK0YjAVUAUtBbx?format=jpg&name=4096x4096) | |:--:| Image by Tanishq Abraham from [Stability AI](https://stability.ai/) originating from [this tweet](https://twitter.com/StabilityAI/status/1590531958815064065)| Original output image | 2x upscaled output image :-------------------------:|:-------------------------: ![](https://pbs.twimg.com/media/Fg8UijAaEAAqfvS?format=png&name=small) | ![](https://pbs.twimg.com/media/Fg8UjCmaMAAAUdS?format=jpg&name=medium) - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/sd-x2-latent-upscaler#examples) ## Model Details - **Developed by:** Katherine Crowson - **Model type:** Diffusion-based latent upscaler - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run latent upscaler on top of any `StableDiffusionUpscalePipeline` checkpoint to enhance its output image resolution by a factor of 2. ```bash pip install git+https://github.com/huggingface/diffusers.git pip install transformers accelerate scipy safetensors ``` ```python from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline import torch pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16) pipeline.to("cuda") upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16) upscaler.to("cuda") prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic" generator = torch.manual_seed(33) # we stay in latent space! Let's make sure that Stable Diffusion returns the image # in latent space low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images upscaled_image = upscaler( prompt=prompt, image=low_res_latents, num_inference_steps=20, guidance_scale=0, generator=generator, ).images[0] # Let's save the upscaled image under "upscaled_astronaut.png" upscaled_image.save("astronaut_1024.png") # as a comparison: Let's also save the low-res image with torch.no_grad(): image = pipeline.decode_latents(low_res_latents) image = pipeline.numpy_to_pil(image)[0] image.save("astronaut_512.png") ``` **Result**: *512-res Astronaut* ![ow_res](./astronaut_512.png) *1024-res Astronaut* ![upscaled](./astronaut_1024.png) **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.

juliawabant/ppo-LunarLander-v2

juliawabant

2023-02-14T12:46:09Z

0

stable-baselines3

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

reinforcement-learning

2023-02-10T15:37:38Z

--- 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: 219.19 +/- 89.86 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 ... ```

Ili1991/q-FrozenLake-v1-4x4-noSlippery

Ili1991

2023-02-14T12:39:10Z

0

null

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

reinforcement-learning

2023-02-14T12:39:07Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Ili1991/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"]) ```

vinayak361/token_fine_tunned_flipkart_2_gl9

vinayak361

2023-02-14T12:31:32Z

7

0

transformers

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

token-classification

2023-02-14T10:44:04Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: token_fine_tunned_flipkart_2_gl9 results: [] ---  # token_fine_tunned_flipkart_2_gl9 This model is a fine-tuned version of [vinayak361/token_fine_tunned_flipkart_2_gl7](https://huggingface.co/vinayak361/token_fine_tunned_flipkart_2_gl7) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2452 - Precision: 0.8593 - Recall: 0.8767 - F1: 0.8679 - Accuracy: 0.9105 ## Model description More information needed ## Intended uses & 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-06 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 451 | 0.3331 | 0.8070 | 0.8310 | 0.8188 | 0.8774 | | 0.4065 | 2.0 | 902 | 0.2927 | 0.8319 | 0.8526 | 0.8421 | 0.8940 | | 0.3251 | 3.0 | 1353 | 0.2737 | 0.8428 | 0.8633 | 0.8529 | 0.9021 | | 0.2825 | 4.0 | 1804 | 0.2650 | 0.8484 | 0.8651 | 0.8567 | 0.9046 | | 0.2568 | 5.0 | 2255 | 0.2586 | 0.8543 | 0.8749 | 0.8645 | 0.9085 | | 0.2419 | 6.0 | 2706 | 0.2511 | 0.8552 | 0.8754 | 0.8652 | 0.9083 | | 0.2351 | 7.0 | 3157 | 0.2481 | 0.8564 | 0.8746 | 0.8654 | 0.9102 | | 0.2226 | 8.0 | 3608 | 0.2455 | 0.8551 | 0.8746 | 0.8647 | 0.9089 | | 0.222 | 9.0 | 4059 | 0.2458 | 0.8597 | 0.8769 | 0.8682 | 0.9106 | | 0.2207 | 10.0 | 4510 | 0.2452 | 0.8593 | 0.8767 | 0.8679 | 0.9105 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1

justlotw/ppo-Huggy

justlotw

2023-02-14T12:26:13Z

12

0

ml-agents

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

reinforcement-learning

2023-02-14T12:26:06Z

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

Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-og_dataset_5e

Gokulapriyan

2023-02-14T12:25:55Z

41

0

transformers

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

image-classification

2023-02-13T10:10:40Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-og_dataset_5e results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.970523929063082 ---  # swin-tiny-patch4-window7-224-finetuned-og_dataset_5e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0788 - Accuracy: 0.9705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4626 | 1.0 | 546 | 0.3468 | 0.8578 | | 0.2915 | 2.0 | 1092 | 0.1998 | 0.9200 | | 0.2333 | 3.0 | 1638 | 0.1155 | 0.9566 | | 0.2019 | 4.0 | 2184 | 0.0977 | 0.9634 | | 0.1713 | 5.0 | 2730 | 0.0788 | 0.9705 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Maki7/cnew

Maki7

2023-02-14T12:16:57Z

0

1

null

[ "arxiv:2302.05543", "region:us" ]

null

2023-02-14T12:14:50Z

# ControlNet Official implementation of [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543). ControlNet is a neural network structure to control diffusion models by adding extra conditions. ![img](github_page/he.png) It copys the weights of neural network blocks into a "locked" copy and a "trainable" copy. The "trainable" one learns your condition. The "locked" one preserves your model. Thanks to this, training with small dataset of image pairs will not destroy the production-ready diffusion models. The "zero convolution" is 1×1 convolution with both weight and bias initialized as zeros. Before training, all zero convolutions output zeros, and ControlNet will not cause any distortion. No layer is trained from scratch. You are still fine-tuning. Your original model is safe. This allows training on small-scale or even personal devices. This is also friendly to merge/replacement/offsetting of models/weights/blocks/layers. ### FAQ **Q:** But wait, if the weight of a conv layer is zero, the gradient will also be zero, and the network will not learn anything. Why "zero convolution" works? **A:** This is not true. [See an explanation here](docs/faq.md). # Stable Diffusion + ControlNet By repeating the above simple structure 14 times, we can control stable diffusion in this way: ![img](github_page/sd.png) Note that the way we connect layers is computational efficient. The original SD encoder does not need to store gradients (the locked original SD Encoder Block 1234 and Middle). The required GPU memory is not much larger than original SD, although many layers are added. Great! # Production-Ready Pretrained Models First create a new conda environment conda env create -f environment.yaml conda activate control All models and detectors can be downloaded from [our Hugging Face page](https://huggingface.co/lllyasviel/ControlNet). Make sure that SD models are put in "ControlNet/models" and detectors are put in "ControlNet/annotator/ckpts". Make sure that you download all necessary pretrained weights and detector models from that Hugging Face page, including HED edge detection model, Midas depth estimation model, Openpose, and so on. We provide 9 Gradio apps with these models. All test images can be found at the folder "test_imgs". ### News 2023/02/12 - Now you can play with any community model by [Transferring the ControlNet](https://github.com/lllyasviel/ControlNet/discussions/12). 2023/02/11 - [Low VRAM mode](docs/low_vram.md) is added. Please use this mode if you are using 8GB GPU(s) or if you want larger batch size. ## ControlNet with Canny Edge Stable Diffusion 1.5 + ControlNet (using simple Canny edge detection) python gradio_canny2image.py The Gradio app also allows you to change the Canny edge thresholds. Just try it for more details. Prompt: "bird" ![p](github_page/p1.png) Prompt: "cute dog" ![p](github_page/p2.png) ## ControlNet with M-LSD Lines Stable Diffusion 1.5 + ControlNet (using simple M-LSD straight line detection) python gradio_hough2image.py The Gradio app also allows you to change the M-LSD thresholds. Just try it for more details. Prompt: "room" ![p](github_page/p3.png) Prompt: "building" ![p](github_page/p4.png) ## ControlNet with HED Boundary Stable Diffusion 1.5 + ControlNet (using soft HED Boundary) python gradio_hed2image.py The soft HED Boundary will preserve many details in input images, making this app suitable for recoloring and stylizing. Just try it for more details. Prompt: "oil painting of handsome old man, masterpiece" ![p](github_page/p5.png) Prompt: "Cyberpunk robot" ![p](github_page/p6.png) ## ControlNet with User Scribbles Stable Diffusion 1.5 + ControlNet (using Scribbles) python gradio_scribble2image.py Note that the UI is based on Gradio, and Gradio is somewhat difficult to customize. Right now you need to draw scribbles outside the UI (using your favorite drawing software, for example, MS Paint) and then import the scribble image to Gradio. Prompt: "turtle" ![p](github_page/p7.png) Prompt: "hot air balloon" ![p](github_page/p8.png) ### Interactive Interface We actually provide an interactive interface python gradio_scribble2image_interactive.py However, because gradio is very [buggy](https://github.com/gradio-app/gradio/issues/3166) and difficult to customize, right now, user need to first set canvas width and heights and then click "Open drawing canvas" to get a drawing area. Please do not upload image to that drawing canvas. Also, the drawing area is very small; it should be bigger. But I failed to find out how to make it larger. Again, gradio is really buggy. The below dog sketch is drawn by me. Perhaps we should draw a better dog for showcase. Prompt: "dog in a room" ![p](github_page/p20.png) ## ControlNet with Fake Scribbles Stable Diffusion 1.5 + ControlNet (using fake scribbles) python gradio_fake_scribble2image.py Sometimes we are lazy, and we do not want to draw scribbles. This script use the exactly same scribble-based model but use a simple algorithm to synthesize scribbles from input images. Prompt: "bag" ![p](github_page/p9.png) Prompt: "shose" (Note that "shose" is a typo; it should be "shoes". But it still seems to work.) ![p](github_page/p10.png) ## ControlNet with Human Pose Stable Diffusion 1.5 + ControlNet (using human pose) python gradio_pose2image.py Apparently, this model deserves a better UI to directly manipulate pose skeleton. However, again, Gradio is somewhat difficult to customize. Right now you need to input an image and then the Openpose will detect the pose for you. Prompt: "Chief in the kitchen" ![p](github_page/p11.png) Prompt: "An astronaut on the moon" ![p](github_page/p12.png) ## ControlNet with Semantic Segmentation Stable Diffusion 1.5 + ControlNet (using semantic segmentation) python gradio_seg2image.py This model use ADE20K's segmentation protocol. Again, this model deserves a better UI to directly draw the segmentations. However, again, Gradio is somewhat difficult to customize. Right now you need to input an image and then a model called Uniformer will detect the segmentations for you. Just try it for more details. Prompt: "House" ![p](github_page/p13.png) Prompt: "River" ![p](github_page/p14.png) ## ControlNet with Depth Stable Diffusion 1.5 + ControlNet (using depth map) python gradio_depth2image.py Great! Now SD 1.5 also have a depth control. FINALLY. So many possibilities (considering SD1.5 has much more community models than SD2). Note that different from Stability's model, the ControlNet receive the full 512×512 depth map, rather than 64×64 depth. Note that Stability's SD2 depth model use 64*64 depth maps. This means that the ControlNet will preserve more details in the depth map. This is always a strength because if users do not want to preserve more details, they can simply use another SD to post-process an i2i. But if they want to preserve more details, ControlNet becomes their only choice. Again, SD2 uses 64×64 depth, we use 512×512. Prompt: "Stormtrooper's lecture" ![p](github_page/p15.png) ## ControlNet with Normal Map Stable Diffusion 1.5 + ControlNet (using normal map) python gradio_normal2image.py This model use normal map. Rightnow in the APP, the normal is computed from the midas depth map and a user threshold (to determine how many area is background with identity normal face to viewer, tune the "Normal background threshold" in the gradio app to get a feeling). Prompt: "Cute toy" ![p](github_page/p17.png) Prompt: "Plaster statue of Abraham Lincoln" ![p](github_page/p18.png) Compared to depth model, this model seems to be a bit better at preserving the geometry. This is intuitive: minor details are not salient in depth maps, but are salient in normal maps. Below is the depth result with same inputs. You can see that the hairstyle of the man in the input image is modified by depth model, but preserved by the normal model. Prompt: "Plaster statue of Abraham Lincoln" ![p](github_page/p19.png) ## ControlNet with Anime Line Drawing We also trained a relatively simple ControlNet for anime line drawings. This tool may be useful for artistic creations. (Although the image details in the results is a bit modified, since it still diffuse latent images.) This model is not available right now. We need to evaluate the potential risks before releasing this model. Nevertheless, you may be interested in [transferring the ControlNet to any community model](https://github.com/lllyasviel/ControlNet/discussions/12). ![p](github_page/p21.png) # Annotate Your Own Data We provide simple python scripts to process images. [See a gradio example here](docs/annotator.md). # Train with Your Own Data Training a ControlNet is as easy as (or even easier than) training a simple pix2pix. [See the steps here](docs/train.md). # Citation @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } [Arxiv Link](https://arxiv.org/abs/2302.05543)

saiful-sit/whisper-small-hi

saiful-sit

2023-02-14T11:59:11Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-02-14T09:46:38Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Saiful results: [] ---  # Whisper Small Hi - Saiful This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2908 - eval_wer: 38.0005 - eval_runtime: 1507.0416 - eval_samples_per_second: 1.92 - eval_steps_per_second: 0.24 - epoch: 2.44 - step: 1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Manzer/army_men

Manzer

2023-02-14T11:58:32Z

0

keras

[ "keras", "en", "license:afl-3.0", "region:us" ]

null

2023-02-14T10:53:08Z

--- license: afl-3.0 language: - en library_name: keras ---

FabioDataGeek/CartPole-v1

FabioDataGeek

2023-02-14T11:32:59Z

0

null

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

reinforcement-learning

2023-02-14T11:32:49Z

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

z4x/poca-SoccerTwos-v3

z4x

2023-02-14T11:27:15Z

8

0

ml-agents

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

reinforcement-learning

2023-02-14T11:32:41Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: z4x/poca-SoccerTwos-v3 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

iotengtr/bert-base-uncased-with-mrpc-trained

iotengtr

2023-02-14T11:27:04Z

107

0

transformers

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

text-classification

2023-02-14T08:54:49Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue model-index: - name: bert-base-uncased-with-mrpc-trained results: [] ---  # bert-base-uncased-with-mrpc-trained This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 459 | 0.3987 | | 0.5157 | 2.0 | 918 | 0.4586 | | 0.3096 | 3.0 | 1377 | 0.6346 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

selvino/CartPole-v4

selvino

2023-02-14T11:19:08Z

0

null

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

reinforcement-learning

2023-02-14T11:18:58Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

meganstodel/pixelcopter2

meganstodel

2023-02-14T11:04:06Z

0

null

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

reinforcement-learning

2023-02-14T11:03:57Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 12.60 +/- 8.97 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

g8a9/roberta-tiny-10M

g8a9

2023-02-14T11:01:05Z

48

1

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-01-31T16:41:29Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-tiny-10M results: [] ---  # roberta-tiny-10M This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7391 - Accuracy: 0.5148 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 7.8031 | 1.04 | 50 | 7.3560 | 0.0606 | | 7.1948 | 2.08 | 100 | 6.7374 | 0.1182 | | 6.8927 | 3.12 | 150 | 6.5022 | 0.1415 | | 6.7339 | 4.16 | 200 | 6.4005 | 0.1483 | | 6.6609 | 5.21 | 250 | 6.3535 | 0.1510 | | 6.1972 | 6.25 | 300 | 6.3324 | 0.1519 | | 6.1685 | 7.29 | 350 | 6.3029 | 0.1528 | | 6.1302 | 8.33 | 400 | 6.2828 | 0.1521 | | 6.093 | 9.37 | 450 | 6.2568 | 0.1536 | | 6.0543 | 10.41 | 500 | 6.2430 | 0.1544 | | 6.0479 | 11.45 | 550 | 6.2346 | 0.1541 | | 6.0372 | 12.49 | 600 | 6.2232 | 0.1546 | | 6.0127 | 13.53 | 650 | 6.2139 | 0.1541 | | 5.968 | 14.58 | 700 | 6.2053 | 0.1547 | | 5.9635 | 15.62 | 750 | 6.1996 | 0.1549 | | 5.9479 | 16.66 | 800 | 6.1953 | 0.1548 | | 5.9371 | 17.7 | 850 | 6.1887 | 0.1545 | | 5.9046 | 18.74 | 900 | 6.1613 | 0.1545 | | 5.8368 | 19.78 | 950 | 6.0952 | 0.1557 | | 5.7914 | 20.82 | 1000 | 6.0330 | 0.1569 | | 5.7026 | 21.86 | 1050 | 5.9430 | 0.1612 | | 5.491 | 22.9 | 1100 | 5.6100 | 0.1974 | | 4.9289 | 23.95 | 1150 | 4.9607 | 0.2702 | | 4.5214 | 24.99 | 1200 | 4.5795 | 0.3051 | | 4.5663 | 26.04 | 1250 | 4.3454 | 0.3265 | | 4.3717 | 27.08 | 1300 | 4.1738 | 0.3412 | | 4.1483 | 28.12 | 1350 | 4.0336 | 0.3555 | | 3.9988 | 29.16 | 1400 | 3.9180 | 0.3677 | | 3.8695 | 30.21 | 1450 | 3.8108 | 0.3782 | | 3.5017 | 31.25 | 1500 | 3.7240 | 0.3879 | | 3.4311 | 32.29 | 1550 | 3.6426 | 0.3974 | | 3.3517 | 33.33 | 1600 | 3.5615 | 0.4068 | | 3.2856 | 34.37 | 1650 | 3.4915 | 0.4156 | | 3.227 | 35.41 | 1700 | 3.4179 | 0.4255 | | 3.1675 | 36.45 | 1750 | 3.3636 | 0.4325 | | 3.0908 | 37.49 | 1800 | 3.3083 | 0.4394 | | 3.0561 | 38.53 | 1850 | 3.2572 | 0.4473 | | 3.0139 | 39.58 | 1900 | 3.2159 | 0.4525 | | 2.9837 | 40.62 | 1950 | 3.1789 | 0.4575 | | 2.9387 | 41.66 | 2000 | 3.1431 | 0.4618 | | 2.9034 | 42.7 | 2050 | 3.1163 | 0.4654 | | 2.8822 | 43.74 | 2100 | 3.0842 | 0.4694 | | 2.836 | 44.78 | 2150 | 3.0583 | 0.4727 | | 2.8129 | 45.82 | 2200 | 3.0359 | 0.4760 | | 2.7733 | 46.86 | 2250 | 3.0173 | 0.4776 | | 2.7589 | 47.9 | 2300 | 2.9978 | 0.4812 | | 2.7378 | 48.95 | 2350 | 2.9788 | 0.4831 | | 2.7138 | 49.99 | 2400 | 2.9674 | 0.4844 | | 2.8692 | 51.04 | 2450 | 2.9476 | 0.4874 | | 2.8462 | 52.08 | 2500 | 2.9342 | 0.4893 | | 2.8312 | 53.12 | 2550 | 2.9269 | 0.4900 | | 2.7834 | 54.16 | 2600 | 2.9111 | 0.4917 | | 2.7822 | 55.21 | 2650 | 2.8987 | 0.4934 | | 2.584 | 56.25 | 2700 | 2.8844 | 0.4949 | | 2.5668 | 57.29 | 2750 | 2.8808 | 0.4965 | | 2.5536 | 58.33 | 2800 | 2.8640 | 0.4982 | | 2.5403 | 59.37 | 2850 | 2.8606 | 0.4982 | | 2.5294 | 60.41 | 2900 | 2.8441 | 0.5008 | | 2.513 | 61.45 | 2950 | 2.8402 | 0.5013 | | 2.5105 | 62.49 | 3000 | 2.8316 | 0.5022 | | 2.4897 | 63.53 | 3050 | 2.8237 | 0.5027 | | 2.4974 | 64.58 | 3100 | 2.8187 | 0.5040 | | 2.4799 | 65.62 | 3150 | 2.8129 | 0.5044 | | 2.4741 | 66.66 | 3200 | 2.8056 | 0.5057 | | 2.4582 | 67.7 | 3250 | 2.8025 | 0.5061 | | 2.4389 | 68.74 | 3300 | 2.7913 | 0.5076 | | 2.4539 | 69.78 | 3350 | 2.7881 | 0.5072 | | 2.4252 | 70.82 | 3400 | 2.7884 | 0.5082 | | 2.4287 | 71.86 | 3450 | 2.7784 | 0.5093 | | 2.4131 | 72.9 | 3500 | 2.7782 | 0.5099 | | 2.4016 | 73.95 | 3550 | 2.7724 | 0.5098 | | 2.3998 | 74.99 | 3600 | 2.7659 | 0.5111 | | 2.5475 | 76.04 | 3650 | 2.7650 | 0.5108 | | 2.5443 | 77.08 | 3700 | 2.7620 | 0.5117 | | 2.5381 | 78.12 | 3750 | 2.7631 | 0.5115 | | 2.5269 | 79.16 | 3800 | 2.7578 | 0.5122 | | 2.5288 | 80.21 | 3850 | 2.7540 | 0.5124 | | 2.3669 | 81.25 | 3900 | 2.7529 | 0.5125 | | 2.3631 | 82.29 | 3950 | 2.7498 | 0.5132 | | 2.3499 | 83.33 | 4000 | 2.7454 | 0.5136 | | 2.3726 | 84.37 | 4050 | 2.7446 | 0.5141 | | 2.3411 | 85.41 | 4100 | 2.7403 | 0.5144 | | 2.3321 | 86.45 | 4150 | 2.7372 | 0.5146 | | 2.3456 | 87.49 | 4200 | 2.7389 | 0.5146 | | 2.3372 | 88.53 | 4250 | 2.7384 | 0.5151 | | 2.343 | 89.58 | 4300 | 2.7398 | 0.5144 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.6.1 - Tokenizers 0.12.1

0RisingStar0/HighRiseMixV1

0RisingStar0

2023-02-14T10:42:49Z

4

19

diffusers

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

text-to-image

2023-02-07T17:45:17Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers --- <p align="center"><img src="https://huggingface.co/0RisingStar0/HighRiseMixV1/resolve/main/00401-2269441947-(masterpiece%2C%20excellent%20quality%2C%20high%20quality%2C%20highres%20_%201.5)%2C%20(1girl%2C%20solo)%2C%20solo%20focus%2C%20sky%2C%20city%2C%20skyscrapers%2C%20pavement%2C%20tree.png"> <img src="https://huggingface.co/0RisingStar0/HighRiseMixV1/resolve/main/13.png"></p> <b>V2 is out! : https://huggingface.co/0RisingStar0/HighRiseMixV2</b> <center><b>HighRiseMixV1</b></center> U-Net mixed model <b>specialized for city and skyscrapers background.</b> <b>FP16 Pruned version</b>(No EMA). (Quality change may occur in very small details on buildings' textures) <b>Recommended prompts : </b> (masterpiece, best quality, excellent quality), ((1girl, solo)), sky, city, (skyscrapers), trees, pavement, lens flare EasyNegative, moss, phone, man, pedestrians, extras, border, outside border, white border (EasyNegative is a negative embedding : https://huggingface.co/datasets/gsdf/EasyNegative) <b>Recommended settings : </b> Sampler : DPM++ 2M Karras OR DPM++ SDE Karras Sampling steps : 25 ~ 30 Resolution : 512x768 OR 768x512 CFG Scale : 9 <b> Upscale is a must-do!! </b> Otherwise, you won't get great results. Upscaler : Latent (nearest) Hires steps : 0 Denoise : 0.6 Upscale 2x <b> Mixed models : </b> AbyssOrangeMix2_NSFW, AnythingV4.5, BasilMixFixed, CounterfeitV2.5, EerieOrangeMix2, PowercolorV2 (Thanks to everyone who made above models!) This is my first mixed model being uploaded to public site, so feel free to give feedbacks as you wish, I'll try and work around with it.

asuzuki/a2c-PandaReachDense-v2

asuzuki

2023-02-14T10:19:46Z

3

0

stable-baselines3

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

reinforcement-learning

2023-02-14T09:49:42Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.74 +/- 0.27 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python #install !apt install python-opengl !apt install ffmpeg !apt install xvfb !pip3 install pyvirtualdisplay !pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit6/requirements-unit6.txt # Virtual display from pyvirtualdisplay import Display virtual_display = Display(visible=0, size=(1400, 900)) virtual_display.start() #imports import pybullet_envs import panda_gym import gym import os from huggingface_sb3 import load_from_hub, package_to_hub from stable_baselines3 import A2C from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize from stable_baselines3.common.env_util import make_vec_env from huggingface_hub import notebook_login #Define the environment called "PandaReachDense-v2" env_id = "PandaReachDense-v2" #Make a vectorized environment env = make_vec_env(env_id, n_envs=4) #Add a wrapper to normalize the observations and rewards. Check the documentation env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10) #Create the A2C Model (don't forget verbose=1 to print the training logs). model = A2C(policy = "MultiInputPolicy", env = env, gae_lambda = 0.9, gamma = 0.95, learning_rate = 0.001, max_grad_norm = 0.5, n_steps = 8, vf_coef = 0.4, ent_coef = 0.0, seed=11, policy_kwargs=dict( log_std_init=-2, ortho_init=False), normalize_advantage=False, use_rms_prop= True, use_sde= True, verbose=1) #Train it for 1M Timesteps model.learn(1_500_000) #Save the model and VecNormalize statistics when saving the agent model.save(f"a2c-{env_id}") env.save(f"vec_normalize_{env_id}.pkl") #Evaluate your agent eval_env = DummyVecEnv([lambda: gym.make(env_id)]) eval_env = VecNormalize.load(f"vec_normalize_{env_id}.pkl", eval_env) # do not update them at test time eval_env.training = False # reward normalization is not needed at test time eval_env.norm_reward = False # Load the model model = A2C.load(f"a2c-{env_id}") #Evaluate model mean_reward, std_reward = evaluate_policy(model, eval_env) print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}") ... ```

Guigadal/layoutxlm-finetuned-xfund-es

Guigadal

2023-02-14T09:53:57Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "dataset:xfun", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-02-09T09:14:02Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - xfun model-index: - name: layoutxlm-finetuned-xfund-es results: [] ---  # layoutxlm-finetuned-xfund-es -> This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun dataset. -> Este modelo es una versión de [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) con ajuste fino sobre xfun-es dataset (español). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2500 ### Training results Train output: - global_step=2500 - training_loss=0.3006648193359375 - train_runtime: 3385.7522 - train_samples_per_second: 1.477 - train_steps_per_second: 0.738 - total_flos: 2688500014387200.0 - train_loss: 0.3006648193359375 - epoch: 20.49 ### Framework versions - Transformers 4.26.0 - Pytorch 1.10.0+cu111 - Datasets 2.9.0 - Tokenizers 0.13.2

EdenYav/dqn-SpaceInvadersNoFrameskip-v4

EdenYav

2023-02-14T09:27:34Z

4

0

stable-baselines3

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

reinforcement-learning

2023-02-08T11:42:18Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 427.00 +/- 94.58 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 EdenYav -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 EdenYav -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 EdenYav ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 150000), ('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', 0.0005), ('learning_starts', 100000), ('n_timesteps', 500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

fermaat/poca-SoccerTwos

fermaat

2023-02-14T09:22:24Z

7

0

ml-agents

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

reinforcement-learning

2023-02-14T09:22:19Z

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

giggling-squid/q-Taxi-v3-5x5

giggling-squid

2023-02-14T09:21:09Z

0

null

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

reinforcement-learning

2023-02-14T08:51:27Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-5x5 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="giggling-squid/q-Taxi-v3-5x5", 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"]) ```

ysugawa/distilbert-base-uncased-finetuned-squad-d5716d28

ysugawa

2023-02-14T09:04:31Z

4

0

transformers

[ "transformers", "pytorch", "distilbert", "fill-mask", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

question-answering

2023-02-14T09:02:05Z

--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results | | Exact Match | F1 | |------------------|-------------|------| | DistilBERT paper | 79.1 | 86.9 | | Ours | 78.4 | 86.5 | The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

TracyWang/MAUD_KWM_AWS_Roberta-base

TracyWang

2023-02-14T08:37:02Z

9

1

transformers

[ "transformers", "pytorch", "roberta", "question-answering", "legal", "text-classification", "en", "arxiv:2301.00876", "license:mit", "endpoints_compatible", "region:us" ]

text-classification

2023-02-08T03:08:17Z

--- license: mit language: - en pipeline_tag: text-classification tags: - legal --- Dataset and Training Script offered by the Atticus Project MAUD. Trained on AWS Sagemaker with 4 A10 GPUs. Model owned by King & Wood Mallesons Law Firm AI LAB. Project Member: - Wuyue(Tracy) Wang @ King & Wood Mallesons - Anbei Zhao @ Amazon Web Services - Xiaodong Guo @ Amazon Web Services - Xiuyu Wu @ Peking University Reference: ``` @misc{wang2023maud, title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding}, author={Steven H. Wang and Antoine Scardigli and Leonard Tang and Wei Chen and Dimitry Levkin and Anya Chen and Spencer Ball and Thomas Woodside and Oliver Zhang and Dan Hendrycks}, year={2023}, eprint={2301.00876}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

k-ush/xlm-roberta-base-ance-warmup

k-ush

2023-02-14T08:30:05Z

108

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "en", "autotrain_compatible", "region:us" ]

fill-mask

2023-02-13T16:57:17Z

--- language: - en inference: false --- # XLM RoBERTa Base ANCE-Warmuped This is a XLM RoBERTa Base model trained with ANCE warmup script. RobertaForSequenceClassification is replaced to XLMRobertaForSequenceClassification in warmup script. trained 60k steps. train args is below: ``` text data_dir: ../data/raw_data/ train_model_type: rdot_nll model_name_or_path: xlm-roberta-base task_name: msmarco output_dir: config_name: tokenizer_name: cache_dir: max_seq_length: 128 do_train: True do_eval: False evaluate_during_training: True do_lower_case: False log_dir: ../logs/ eval_type: full optimizer: lamb scheduler: linear per_gpu_train_batch_size: 32 per_gpu_eval_batch_size: 32 gradient_accumulation_steps: 1 learning_rate: 0.0002 weight_decay: 0.0 adam_epsilon: 1e-08 max_grad_norm: 1.0 num_train_epochs: 2.0 max_steps: -1 warmup_steps: 1000 logging_steps: 1000 logging_steps_per_eval: 20 save_steps: 30000 eval_all_checkpoints: False no_cuda: False overwrite_output_dir: True overwrite_cache: False seed: 42 fp16: True fp16_opt_level: O1 expected_train_size: 35000000 load_optimizer_scheduler: False local_rank: 0 server_ip: server_port: n_gpu: 1 device: cuda:0 output_mode: classification num_labels: 2 train_batch_size: 32 ``` # Eval Result ``` text Reranking/Full ranking mrr: 0.27380855732933/0.24284821712830248 {"learning_rate": 0.00019460324719871943, "loss": 0.0895877162806064, "step": 60000} ``` # Usage ``` python3 from transformers import XLMRobertaForSequenceClassification, XLMRobertaTokenizer repo = "k-ush/xlm-roberta-base-ance-warmup" model = XLMRobertaForSequenceClassification.from_pretrained(repo) tokenizer = XLMRobertaTokenizer.from_pretrained(repo) ```

mqy/mt5-small-finetuned-14feb-1

mqy

2023-02-14T08:26:00Z

10

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-02-14T02:02:27Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-14feb-1 results: [] ---  # mt5-small-finetuned-14feb-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4516 - Rouge1: 20.33 - Rouge2: 6.2 - Rougel: 19.9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000275 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 4.0401 | 1.0 | 388 | 2.5481 | 16.31 | 4.7 | 16.1 | | 2.9776 | 2.0 | 776 | 2.4442 | 17.25 | 4.93 | 16.93 | | 2.7362 | 3.0 | 1164 | 2.4181 | 19.73 | 5.74 | 19.21 | | 2.5767 | 4.0 | 1552 | 2.4071 | 19.37 | 5.62 | 18.89 | | 2.4466 | 5.0 | 1940 | 2.3560 | 18.98 | 5.94 | 18.55 | | 2.3402 | 6.0 | 2328 | 2.3923 | 20.45 | 5.5 | 20.03 | | 2.2385 | 7.0 | 2716 | 2.3639 | 20.03 | 5.96 | 19.76 | | 2.1663 | 8.0 | 3104 | 2.3431 | 19.17 | 5.34 | 18.84 | | 2.0849 | 9.0 | 3492 | 2.4008 | 19.97 | 5.58 | 19.67 | | 2.0203 | 10.0 | 3880 | 2.3948 | 19.67 | 5.75 | 19.26 | | 1.9653 | 11.0 | 4268 | 2.3915 | 20.06 | 6.07 | 19.61 | | 1.9067 | 12.0 | 4656 | 2.4025 | 20.83 | 6.46 | 20.41 | | 1.8592 | 13.0 | 5044 | 2.4194 | 19.97 | 6.4 | 19.69 | | 1.8158 | 14.0 | 5432 | 2.4156 | 19.87 | 6.16 | 19.38 | | 1.7679 | 15.0 | 5820 | 2.4053 | 19.9 | 5.99 | 19.52 | | 1.748 | 16.0 | 6208 | 2.4156 | 19.68 | 5.81 | 19.28 | | 1.7198 | 17.0 | 6596 | 2.4306 | 20.0 | 6.26 | 19.63 | | 1.6959 | 18.0 | 6984 | 2.4499 | 19.1 | 6.19 | 18.82 | | 1.6769 | 19.0 | 7372 | 2.4536 | 20.62 | 6.3 | 20.15 | | 1.6682 | 20.0 | 7760 | 2.4516 | 20.33 | 6.2 | 19.9 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

amrisaurus/pretrained-m-bert-90

amrisaurus

2023-02-14T07:57:21Z

46

0

transformers

[ "transformers", "tf", "bert", "pretraining", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]

null

2023-02-14T07:55:57Z

--- tags: - generated_from_keras_callback model-index: - name: pretrained-m-bert-90 results: [] ---  # pretrained-m-bert-90 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.7094 - Validation Loss: 14.5332 - Epoch: 89 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - 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': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2413 | 10.9668 | 0 | | 7.5814 | 10.9638 | 1 | | 7.0095 | 11.3733 | 2 | | 6.4352 | 11.5989 | 3 | | 6.7137 | 11.4072 | 4 | | 6.4383 | 11.8287 | 5 | | 6.2223 | 12.0344 | 6 | | 6.1759 | 11.6900 | 7 | | 6.0764 | 11.7144 | 8 | | 5.8802 | 12.1089 | 9 | | 6.0159 | 12.3456 | 10 | | 5.9254 | 12.7065 | 11 | | 5.6652 | nan | 12 | | 5.8185 | 12.8155 | 13 | | 5.9185 | 12.7047 | 14 | | 5.8418 | 12.7175 | 15 | | 5.9122 | 12.5688 | 16 | | 5.9698 | 12.5251 | 17 | | 5.8286 | 12.7015 | 18 | | 5.8807 | 13.2514 | 19 | | 5.8330 | 12.8541 | 20 | | 5.6456 | 13.4088 | 21 | | 5.7257 | 13.5517 | 22 | | 5.8854 | 12.8775 | 23 | | 5.6770 | 13.6499 | 24 | | 5.6026 | 13.9732 | 25 | | 5.6651 | 13.0827 | 26 | | 5.8888 | 13.1292 | 27 | | 5.8123 | 12.8970 | 28 | | 5.7525 | 13.3724 | 29 | | 5.9020 | 13.5507 | 30 | | 5.8642 | 13.3284 | 31 | | 5.9329 | 13.7350 | 32 | | 5.7728 | 13.3011 | 33 | | 5.8297 | 13.6108 | 34 | | 5.8118 | 13.3331 | 35 | | 5.7382 | 13.7047 | 36 | | 5.8061 | 13.8107 | 37 | | 5.8423 | 13.4207 | 38 | | 5.8442 | 13.6832 | 39 | | 5.7680 | 14.1248 | 40 | | 5.7668 | 13.6626 | 41 | | 5.7826 | 13.6470 | 42 | | 5.7692 | 13.9430 | 43 | | 5.5109 | 14.0924 | 44 | | 5.7394 | 14.0253 | 45 | | 5.8013 | 13.5926 | 46 | | 5.7222 | 13.9732 | 47 | | 5.7023 | 14.0204 | 48 | | 5.8250 | 13.9655 | 49 | | 5.6064 | 14.0406 | 50 | | 5.7319 | 14.1826 | 51 | | 5.6849 | 13.9114 | 52 | | 5.8167 | 13.9917 | 53 | | 5.7573 | 14.1509 | 54 | | 5.6921 | 14.3722 | 55 | | 5.7190 | 14.4919 | 56 | | 5.8501 | 13.6970 | 57 | | 5.7627 | 14.1393 | 58 | | 5.8031 | 14.1246 | 59 | | 5.7207 | 14.3084 | 60 | | 5.7979 | 13.9398 | 61 | | 5.7068 | 14.2865 | 62 | | 5.7547 | 14.2590 | 63 | | 5.8349 | 14.1481 | 64 | | 5.7924 | 14.0461 | 65 | | 5.8127 | 14.1274 | 66 | | 5.7590 | 14.3578 | 67 | | 5.8297 | 14.2429 | 68 | | 5.7822 | 14.2742 | 69 | | 5.7708 | 14.3720 | 70 | | 5.6521 | 14.8640 | 71 | | 5.7253 | 14.4404 | 72 | | 5.8076 | 14.1843 | 73 | | 5.7746 | 14.4657 | 74 | | 5.8592 | 14.2965 | 75 | | 5.6643 | 14.0996 | 76 | | 5.7849 | 14.3531 | 77 | | 5.7418 | 14.4266 | 78 | | 5.7030 | 14.5584 | 79 | | 5.8298 | 14.1390 | 80 | | 5.9061 | 13.9172 | 81 | | 5.6570 | 14.6991 | 82 | | 5.7040 | 14.7839 | 83 | | 5.8064 | 14.2581 | 84 | | 5.6855 | 14.4449 | 85 | | 5.7803 | 14.7469 | 86 | | 5.7495 | 14.4704 | 87 | | 5.7539 | 14.5520 | 88 | | 5.7094 | 14.5332 | 89 | ### Framework versions - Transformers 4.27.0.dev0 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2

akghxhs55/poca-SoccerTwos-4

akghxhs55

2023-02-14T07:54:19Z

6

0

ml-agents

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

reinforcement-learning

2023-02-14T07:54:12Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: akghxhs55/poca-SoccerTwos-4 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

nolanaatama/crns7thhvnmx

nolanaatama

2023-02-14T07:53:35Z

0

null

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

null

2023-02-14T07:45:39Z

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

Zekunli/flan-t5-large-extraction-cnndm_10000-all

Zekunli

2023-02-14T07:52:30Z

4

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

2023-02-14T06:39:35Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-large-extraction-cnndm_10000-all results: [] ---  # flan-t5-large-extraction-cnndm_10000-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7044 - Rouge1: 34.8618 - Rouge2: 15.5978 - Rougel: 29.7948 - Rougelsum: 29.7581 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1668 | 0.16 | 200 | 1.8280 | 33.7941 | 14.3114 | 28.7743 | 28.7968 | 19.0 | | 1.9736 | 0.32 | 400 | 1.7818 | 34.8351 | 15.5548 | 29.8974 | 29.8557 | 18.99 | | 1.904 | 0.48 | 600 | 1.7513 | 35.465 | 15.8566 | 30.7139 | 30.6596 | 18.986 | | 1.8938 | 0.64 | 800 | 1.7440 | 34.6193 | 15.5473 | 30.0661 | 30.0019 | 18.99 | | 1.8471 | 0.8 | 1000 | 1.7366 | 34.553 | 15.2214 | 29.8807 | 29.8419 | 18.99 | | 1.8621 | 0.96 | 1200 | 1.7486 | 34.9309 | 15.1932 | 29.8973 | 29.8774 | 18.99 | | 1.8082 | 1.12 | 1400 | 1.7311 | 35.3395 | 16.0976 | 30.2748 | 30.293 | 18.99 | | 1.7448 | 1.28 | 1600 | 1.7155 | 35.1387 | 15.7462 | 29.924 | 29.9287 | 18.99 | | 1.7655 | 1.44 | 1800 | 1.7239 | 35.3603 | 15.6355 | 30.3944 | 30.3766 | 19.0 | | 1.7283 | 1.6 | 2000 | 1.7132 | 34.7368 | 15.4073 | 29.9027 | 29.8971 | 19.0 | | 1.7463 | 1.76 | 2200 | 1.7171 | 35.0545 | 15.726 | 30.0364 | 30.0056 | 19.0 | | 1.7462 | 1.92 | 2400 | 1.7044 | 34.8618 | 15.5978 | 29.7948 | 29.7581 | 19.0 | | 1.719 | 2.08 | 2600 | 1.7285 | 34.9598 | 15.5237 | 29.5593 | 29.5803 | 19.0 | | 1.6828 | 2.24 | 2800 | 1.7179 | 35.0944 | 15.7333 | 29.8381 | 29.7784 | 19.0 | | 1.7 | 2.4 | 3000 | 1.7047 | 35.1766 | 15.7758 | 29.818 | 29.7859 | 19.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1

sinny/baseline

sinny

2023-02-14T07:45:17Z

4

0

ml-agents

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

reinforcement-learning

2023-02-14T07:45:11Z

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

joelniklaus/legal-swedish-roberta-base

joelniklaus

2023-02-14T06:08:34Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-02-06T02:38:57Z

--- tags: - generated_from_trainer model-index: - name: legal-swedish-roberta-base results: [] ---  # legal-swedish-roberta-base This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6149 ## Model description More information needed ## Intended uses & 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 - distributed_type: tpu - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 200000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.9458 | 2.04 | 50000 | 0.7559 | | 0.8037 | 4.08 | 100000 | 0.6746 | | 0.8366 | 7.01 | 150000 | 0.6285 | | 0.7924 | 9.05 | 200000 | 0.6149 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.9.0 - Tokenizers 0.12.0

Ransaka/a2c-AntBulletEnv-v0

Ransaka

2023-02-14T05:56:44Z

1

0

stable-baselines3

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

reinforcement-learning

2023-02-14T05:55:33Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1950.91 +/- 129.99 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

PecanPi/pixelcopter-v1

PecanPi

2023-02-14T05:51:41Z

0

null

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

reinforcement-learning

2023-02-13T03:45:46Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.90 +/- 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

LarryAIDraw/azuraFireEmblemFates_v10

LarryAIDraw

2023-02-14T05:31:03Z

0

null

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

null

2023-02-14T05:29:06Z

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

LarryAIDraw/kaguyaSamaLoveIsWar_hayasakaAiV1

LarryAIDraw

2023-02-14T05:30:28Z

0

null

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

null

2023-02-14T05:23:37Z

--- license: creativeml-openrail-m --- https://civitai.com/models/8613/kaguya-samalove-is-war-hayasaka-ai

TheRains/whisper-small-id

TheRains

2023-02-14T04:46:04Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "id", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-01-16T08:30:02Z

--- language: - id license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Id - TheRains results: [] ---  # Whisper Small Id - TheRains This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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-06 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

UchihaMadara/model1-thesis-5

UchihaMadara

2023-02-14T04:42:32Z

107

0

transformers

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

token-classification

2023-02-14T04:18:52Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: model1-thesis-5 results: [] ---  # model1-thesis-5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6273 - Precision: 0.4620 - Recall: 0.6348 - F1: 0.5348 - Accuracy: 0.8196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 29 | 0.5895 | 0.3871 | 0.6261 | 0.4784 | 0.8086 | | No log | 2.0 | 58 | 0.5814 | 0.4424 | 0.6348 | 0.5214 | 0.8118 | | No log | 3.0 | 87 | 0.5734 | 0.4360 | 0.6522 | 0.5226 | 0.8332 | | No log | 4.0 | 116 | 0.6326 | 0.4808 | 0.6522 | 0.5535 | 0.8170 | | No log | 5.0 | 145 | 0.6273 | 0.4620 | 0.6348 | 0.5348 | 0.8196 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

bakuaifuji/test

bakuaifuji

2023-02-14T04:41:49Z

0

null

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

null

2023-02-10T05:40:41Z

--- license: apache-2.0 tags: - music ---

enankobh1/whisper-small-ASR-EN

enankobh1

2023-02-14T04:35:06Z

78

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-02-13T10:13:04Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-ASR-EN results: [] ---  # whisper-small-ASR-EN This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7014 - Wer: 20.6492 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0389 | 5.95 | 1000 | 0.4354 | 24.2479 | | 0.0015 | 11.9 | 2000 | 0.6301 | 21.1699 | | 0.0003 | 17.86 | 3000 | 0.6822 | 20.5739 | | 0.0002 | 23.81 | 4000 | 0.7014 | 20.6492 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

nhiro3303/ppo-SnowballTarget

nhiro3303

2023-02-14T04:23:07Z

14

0

ml-agents

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

reinforcement-learning

2023-02-14T04:23:02Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: nhiro3303/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

dfm794/poca-SoccerTwos-2x-2-r-l

dfm794

2023-02-14T04:22:23Z

9

0

ml-agents

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

reinforcement-learning

2023-02-14T04:22:16Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: dfm794/poca-SoccerTwos-2x-2-r-l 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

yhchoi/klue-emotional-finetuned-emotion

yhchoi

2023-02-14T04:21:14Z

5

0

transformers

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

text-classification

2023-02-14T03:44:34Z

--- tags: - generated_from_trainer model-index: - name: klue-emotional-finetuned-emotion results: [] ---  # klue-emotional-finetuned-emotion This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.8.1+cu111 - Datasets 2.9.0 - Tokenizers 0.13.2

jhn9803/Contract-new-tokenizer-roberta-large

jhn9803

2023-02-14T04:19:19Z

4

1

transformers

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

text-classification

2023-02-14T03:36:26Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Contract-new-tokenizer-roberta-large results: [] ---  # Contract-new-tokenizer-roberta-large This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6076 - Accuracy: 0.7042 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 249 | 0.6072 | 0.7042 | | No log | 2.0 | 498 | 0.6138 | 0.7042 | | No log | 3.0 | 747 | 0.6076 | 0.7042 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

printr/gptx-rl-371k

printr

2023-02-14T03:55:52Z

0

null

[ "license:cc-by-2.0", "region:us" ]

null

2023-02-13T22:40:20Z

--- license: cc-by-2.0 --- [Github for Inference and Training](https://github.com/Quicksticks-oss/GPTX-RL) ### Features - Just like GPT this model generates text based on an input prompt. - This project uses Reinforcement Learning (RL) for training and inference. - All models can be found on hugging face. --- ### Training - First step is to run ``generate_vocab.py`` on whatever text data you would like to train on for example ``python3 generate_vocab.py -i train.txt`` - Next step is to run ``train.py`` - Now all you have to do is wait! --- ### Inference - Run ``inference.py`` with ``-i`` as an argumentand with models path. ### Logging - Output plot graphs will be avalible in the next version.

mjschock/a2c-AntBulletEnv-v0

mjschock

2023-02-14T03:54:20Z

0

stable-baselines3

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

reinforcement-learning

2023-02-14T03:53:05Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1358.66 +/- 213.32 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jhn9803/Contract-base-tokenizer-roberta-large

jhn9803

2023-02-14T03:28:12Z

7

0

transformers

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

text-classification

2023-02-13T13:20:38Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: Contract-base-tokenizer-roberta-large results: [] ---  # Contract-base-tokenizer-roberta-large This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0431 - Accuracy: 0.9930 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 249 | 0.0580 | 0.9869 | | No log | 2.0 | 498 | 0.0380 | 0.9920 | | No log | 3.0 | 747 | 0.0431 | 0.9930 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

cwwojin/stt_kr_conformer_ctc_medium

cwwojin

2023-02-14T03:23:47Z

1

nemo

[ "nemo", "ksponspeech", "automatic-speech-recognition", "ko", "license:mit", "model-index", "region:us" ]

automatic-speech-recognition

2023-02-12T13:10:42Z

--- license: mit language: - ko metrics: - cer pipeline_tag: automatic-speech-recognition tags: - ksponspeech model-index: - name: cwwojin/stt_kr_conformer_ctc_medium results: - task: type: automatic-speech-recognition # Required. Example: automatic-speech-recognition dataset: type: Murple/ksponspeech # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: KsponSpeech-eval (Korean) # Required. A pretty name for the dataset. Example: Common Voice (French) split: test # Optional. Example: test metrics: - type: cer # Required. Example: wer. Use metric id from https://hf.co/metrics value: 11.902 # Required. Example: 20.90 name: Test CER(%) # Optional. Example: Test WER --- # stt_kr_conformer_ctc_medium - Fine-tuned from "stt_en_conformer_ctc_medium" https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_medium - Trained on KsponSpeech, provided by https://aihub.or.kr/ ## Preprocessing - Files converted from .pcm -> .wav - Text - Korean phonetic transcription - SentencePiece tokenizer (Byte-pair encoding), vocab-size = 5,000 ## Evaluation - "KsponSpeech_eval_clean", "KsponSpeech_eval_other"

letingliu/my_awesome_model

letingliu

2023-02-14T03:21:41Z

5

0

transformers

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

text-classification

2022-12-13T01:03:05Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: letingliu/my_awesome_model results: [] ---  # letingliu/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: 0.5129 - Validation Loss: 0.5051 - Train Accuracy: 0.9231 - Epoch: 7 ## Model description More information needed ## 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': 30, '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 | |:----------:|:---------------:|:--------------:|:-----:| | 0.6853 | 0.6586 | 0.7788 | 0 | | 0.6489 | 0.6197 | 0.7788 | 1 | | 0.6090 | 0.5693 | 0.8942 | 2 | | 0.5617 | 0.5245 | 0.8942 | 3 | | 0.5235 | 0.5051 | 0.9231 | 4 | | 0.5116 | 0.5051 | 0.9231 | 5 | | 0.5112 | 0.5051 | 0.9231 | 6 | | 0.5129 | 0.5051 | 0.9231 | 7 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2

amrutha3899/my_awesome_qa_model2

amrutha3899

2023-02-14T02:49:16Z

107

0

transformers

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

question-answering

2023-02-14T01:44:19Z

--- tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_model2 results: [] ---  # my_awesome_qa_model2 This model was trained from scratch on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

svenkate/ppo-LunarLander-v2

svenkate

2023-02-14T02:44:08Z

4

0

stable-baselines3

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

reinforcement-learning

2023-02-14T02:43:44Z

--- 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: 268.79 +/- 25.14 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 ... ```

Zekunli/flan-t5-large-extraction-cnndm_20000-all

Zekunli

2023-02-14T02:35:46Z

3

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

2023-02-14T01:29:24Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-large-extraction-cnndm_20000-all results: [] ---  # flan-t5-large-extraction-cnndm_20000-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6652 - Rouge1: 35.487 - Rouge2: 15.6713 - Rougel: 29.9519 - Rougelsum: 29.9368 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1295 | 0.08 | 200 | 1.8266 | 34.0465 | 14.7511 | 29.3395 | 29.3437 | 19.0 | | 1.9354 | 0.16 | 400 | 1.7732 | 34.7923 | 15.3094 | 29.8484 | 29.8757 | 18.99 | | 1.854 | 0.24 | 600 | 1.7367 | 34.8358 | 15.1969 | 29.9971 | 30.0064 | 18.986 | | 1.833 | 0.32 | 800 | 1.7120 | 34.7854 | 15.5144 | 29.8141 | 29.7863 | 18.982 | | 1.8217 | 0.4 | 1000 | 1.7256 | 34.7274 | 15.2763 | 30.0298 | 30.0871 | 19.0 | | 1.8309 | 0.48 | 1200 | 1.7089 | 35.4328 | 15.7724 | 30.0655 | 30.0199 | 19.0 | | 1.825 | 0.56 | 1400 | 1.6947 | 35.4116 | 15.6911 | 30.1438 | 30.1764 | 19.0 | | 1.7914 | 0.64 | 1600 | 1.7119 | 35.5918 | 16.3762 | 30.3234 | 30.2807 | 19.0 | | 1.7889 | 0.72 | 1800 | 1.6810 | 35.6413 | 15.8936 | 30.2848 | 30.2291 | 19.0 | | 1.7576 | 0.8 | 2000 | 1.6826 | 35.9424 | 15.6803 | 30.5998 | 30.5571 | 19.0 | | 1.7763 | 0.88 | 2200 | 1.6748 | 35.7543 | 15.984 | 30.7197 | 30.721 | 18.998 | | 1.7604 | 0.96 | 2400 | 1.6652 | 35.487 | 15.6713 | 29.9519 | 29.9368 | 19.0 | | 1.7138 | 1.04 | 2600 | 1.6860 | 36.0333 | 16.4065 | 30.7249 | 30.7168 | 19.0 | | 1.6951 | 1.12 | 2800 | 1.6792 | 35.3149 | 15.7178 | 30.1555 | 30.1517 | 18.998 | | 1.6752 | 1.2 | 3000 | 1.6832 | 34.7566 | 15.4179 | 29.7687 | 29.8259 | 19.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1

alc15492/msemj30

alc15492

2023-02-14T01:23:58Z

9

0

diffusers

[ "diffusers", "text-to-image", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-02-14T00:55:50Z

--- license: openrail library_name: diffusers pipeline_tag: text-to-image ---

muchad/idt5-qa-qg

muchad

2023-02-14T01:20:53Z

128

9

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "question-generation", "multitask-model", "idt5", "id", "dataset:SQuADv2.0", "arxiv:2302.00856", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-12T16:32:11Z

--- language: id datasets: - SQuADv2.0 tags: - question-generation - multitask-model - idt5 widget: - text: "generate question: <hl> Dua orang <hl> pengembara berjalan di sepanjang jalan yang berdebu dan tandus di hari yang sangat panas. Tidak lama kemudian, mereka menemukan sebuah pohon besar. </s>" - text: "question: Siapa pemimpin Kerajaan Tarumanegara? context: Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M. </s>" license: apache-2.0 --- # idT5 for Indonesian Question Generation and Question Answering [idT5](https://huggingface.co/muchad/idt5-base) (Indonesian version of [mT5](https://huggingface.co/google/mt5-base)) is fine-tuned on 30% of [translated SQuAD v2.0](https://github.com/Wikidepia/indonesian_datasets/tree/master/question-answering/squad) for **Question Generation** and **Question Answering** tasks. ## Live Demo * **Question Generation:** [ai.muchad.com/qg](https://ai.muchad.com/qg/) * **Question Answering:** [t.me/caritahubot](https://t.me/caritahubot) ## Requirements ``` !pip install transformers==4.4.2 !pip install sentencepiece==0.1.95 !git clone https://github.com/muchad/qaqg.git %cd qaqg/ ``` ## Usage 🚀 #### Question Generation [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Generation.ipynb) ``` from pipeline_qg import pipeline #pipeline_qg.py script in the cloned repo qg = pipeline() #sample qg("Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M.") #output => [{'answer': 'Raja Purnawarman','question': 'Siapa yang memerintah Kerajaan Tarumanegara?'}, {'answer': '395 M','question': 'Kapan Raja Purnawarman memerintah Kerajaan Tarumanegara?'}] ``` #### Question Answering [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/muchad/qaqg/blob/main/idT5_Question_Answering.ipynb) ``` from pipeline_qa import pipeline #pipeline_qa.py script in the cloned repo qa = pipeline() #sample qa({"context":"Raja Purnawarman mulai memerintah Kerajaan Tarumanegara pada tahun 395 M.","question":"Siapa pemimpin Kerajaan Tarumanegara?"}) #output => Raja Purnawarman ``` #### Citation Paper: [idT5: Indonesian Version of Multilingual T5 Transformer](https://arxiv.org/abs/2302.00856) ``` @misc{https://doi.org/10.48550/arxiv.2302.00856, doi = {10.48550/ARXIV.2302.00856}, url = {https://arxiv.org/abs/2302.00856}, author = {Fuadi, Mukhlish and Wibawa, Adhi Dharma and Sumpeno, Surya}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7}, title = {idT5: Indonesian Version of Multilingual T5 Transformer}, publisher = {arXiv}, year = {2023} } ```

deprem-ml/deprem_bert_128k

deprem-ml

2023-02-14T01:15:10Z

8

3

transformers

[ "transformers", "pytorch", "bert", "text-classification", "deprem-clf-v1", "tr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-02-13T17:56:09Z

--- license: apache-2.0 language: - tr metrics: - accuracy - recall - f1 tags: - deprem-clf-v1 library_name: transformers pipeline_tag: text-classification model-index: - name: deprem_v12 results: - task: type: text-classification dataset: type: deprem_private_dataset_v1_2 name: deprem_private_dataset_v1_2 metrics: - type: recall value: 0.82 verified: false - type: f1 value: 0.76 verified: false widget: - text: >- acil acil acil antakyadan istanbula gitmek için antakya expoya ulaşmaya çalışan 19 kişilik bir aile için şehir içi ulaşım desteği istiyoruz. dışardalar üşüyorlar.iletebileceğiniz numaraları bekliyorum example_title: Örnek --- ## Eval Results ``` precision recall f1-score support Alakasiz 0.87 0.91 0.89 734 Barinma 0.79 0.89 0.84 207 Elektronik 0.69 0.83 0.75 130 Giysi 0.71 0.81 0.76 94 Kurtarma 0.82 0.85 0.83 362 Lojistik 0.57 0.67 0.62 112 Saglik 0.68 0.85 0.75 108 Su 0.56 0.76 0.64 78 Yagma 0.60 0.77 0.68 31 Yemek 0.71 0.89 0.79 117 micro avg 0.77 0.86 0.81 1973 macro avg 0.70 0.82 0.76 1973 weighted avg 0.78 0.86 0.82 1973 samples avg 0.83 0.88 0.84 1973 ``` ## Training Params: ```python {'per_device_train_batch_size': 32, 'per_device_eval_batch_size': 32, 'learning_rate': 5.8679699888213376e-05, 'weight_decay': 0.03530961718117487, 'num_train_epochs': 4, 'lr_scheduler_type': 'cosine', 'warmup_steps': 40, 'seed': 42, 'fp16': True, 'load_best_model_at_end': True, 'metric_for_best_model': 'macro f1', 'greater_is_better': True } ``` ## Threshold: - **Best Threshold:** 0.40 ## Class Loss Weights - Same as Anıl's approach: ```python [1.0, 1.5167249178108022, 1.7547338578655642, 1.9610520059358458, 1.8684086209021484, 1.8019018017117145, 2.110648663094536, 3.081208739200435, 1.7994815143101963] ```

zb/ppo-LunarLander-v2

zb

2023-02-14T00:47:03Z

0

stable-baselines3

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

reinforcement-learning

2023-02-14T00:46:35Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 233.87 +/- 16.42 name: mean_reward verified: false --- # **MlpPolicy** Agent playing **LunarLander-v2** This is a trained model of a **MlpPolicy** 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 ... ```

NathanS-HuggingFace/LunarLander

NathanS-HuggingFace

2023-02-14T00:29:30Z

0

stable-baselines3

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

reinforcement-learning

2023-02-12T16:53:23Z

--- 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: 308.50 +/- 11.39 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 ... ```

nikgeo/ppo-LunarLander-v2

nikgeo

2023-02-14T00:13:50Z

0

stable-baselines3

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

reinforcement-learning

2023-02-13T23:14:35Z

--- 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: 251.12 +/- 21.78 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

cleandata/ppo-LunarLander-v2

cleandata

2023-02-14T00:09:17Z

6

0

stable-baselines3

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

reinforcement-learning

2023-02-14T00:08:45Z

--- 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: 244.85 +/- 17.73 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 ... ```

nossal/ppo-LunarLander-v2

nossal

2023-02-13T23:15:23Z

0

stable-baselines3

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

reinforcement-learning

2023-02-13T23:14:49Z

--- 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: 239.91 +/- 21.04 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 ... ```

lilouuch/Goodreads_Books_Reviews_Roberta_53

lilouuch

2023-02-13T23:13:42Z

162

0

transformers

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

text-classification

2023-02-13T11:28:16Z

--- license: mit tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: Goodreads_Books_Reviews_Roberta_53 results: [] ---  # Goodreads_Books_Reviews_Roberta_53 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8038 - F1: 0.6238 - Accuracy: 0.6614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:| | 0.8378 | 1.0 | 25313 | 0.8197 | 0.6092 | 0.6538 | | 0.7683 | 2.0 | 50626 | 0.8038 | 0.6238 | 0.6614 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

GrimReaperSam/q-Taxi-v3

GrimReaperSam

2023-02-13T23:08:58Z

0

null

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

reinforcement-learning

2023-02-13T23:08:51Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.78 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="GrimReaperSam/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```