librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-02 18:52:31	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 533 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-02 18:52:05	card stringlengths 11 1.01M
carolinetfls/plant-seedlings-model-beit-free-0-6	carolinetfls	2023-04-26T23:00:49Z	19	0	transformers	[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-04-26T20:11:12Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: plant-seedlings-model-beit-free-0-6 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: 0.7475442043222004 --- <!-- 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. --> # plant-seedlings-model-beit-free-0-6 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7557 - Accuracy: 0.7475 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 2.4892 \| 0.2 \| 100 \| 2.4909 \| 0.0751 \| \| 2.4906 \| 0.39 \| 200 \| 2.4886 \| 0.0756 \| \| 2.3925 \| 0.59 \| 300 \| 2.3344 \| 0.1537 \| \| 2.31 \| 0.79 \| 400 \| 2.3306 \| 0.1464 \| \| 2.2355 \| 0.98 \| 500 \| 2.2335 \| 0.1778 \| \| 2.2642 \| 1.18 \| 600 \| 2.1889 \| 0.1807 \| \| 2.0806 \| 1.38 \| 700 \| 2.3229 \| 0.1680 \| \| 2.1013 \| 1.57 \| 800 \| 2.1519 \| 0.2004 \| \| 2.0094 \| 1.77 \| 900 \| 2.0611 \| 0.2146 \| \| 2.0387 \| 1.96 \| 1000 \| 2.0413 \| 0.2210 \| \| 2.0032 \| 2.16 \| 1100 \| 1.9758 \| 0.2618 \| \| 1.986 \| 2.36 \| 1200 \| 1.9238 \| 0.2638 \| \| 2.0885 \| 2.55 \| 1300 \| 1.8944 \| 0.2942 \| \| 1.8808 \| 2.75 \| 1400 \| 1.9330 \| 0.2868 \| \| 1.915 \| 2.95 \| 1500 \| 1.8919 \| 0.2814 \| \| 1.958 \| 3.14 \| 1600 \| 1.8762 \| 0.3114 \| \| 1.9001 \| 3.34 \| 1700 \| 1.8389 \| 0.3232 \| \| 1.8572 \| 3.54 \| 1800 \| 1.7978 \| 0.3487 \| \| 1.9969 \| 3.73 \| 1900 \| 1.9371 \| 0.3089 \| \| 1.9186 \| 3.93 \| 2000 \| 1.8055 \| 0.3502 \| \| 1.7591 \| 4.13 \| 2100 \| 1.7695 \| 0.3428 \| \| 1.8368 \| 4.32 \| 2200 \| 1.7498 \| 0.3502 \| \| 1.9842 \| 4.52 \| 2300 \| 1.8049 \| 0.3193 \| \| 1.7606 \| 4.72 \| 2400 \| 1.6730 \| 0.3954 \| \| 1.7787 \| 4.91 \| 2500 \| 1.7104 \| 0.3777 \| \| 1.6377 \| 5.11 \| 2600 \| 1.6647 \| 0.3870 \| \| 1.8834 \| 5.3 \| 2700 \| 1.6325 \| 0.3973 \| \| 1.6149 \| 5.5 \| 2800 \| 1.6722 \| 0.3787 \| \| 1.7038 \| 5.7 \| 2900 \| 1.6425 \| 0.3973 \| \| 1.682 \| 5.89 \| 3000 \| 1.5927 \| 0.4180 \| \| 1.6326 \| 6.09 \| 3100 \| 1.4982 \| 0.4622 \| \| 1.5687 \| 6.29 \| 3200 \| 1.4440 \| 0.4774 \| \| 1.3637 \| 6.48 \| 3300 \| 1.4477 \| 0.4877 \| \| 1.4079 \| 6.68 \| 3400 \| 1.3827 \| 0.5020 \| \| 1.3721 \| 6.88 \| 3500 \| 1.4069 \| 0.5010 \| \| 1.5675 \| 7.07 \| 3600 \| 1.3595 \| 0.5083 \| \| 1.5725 \| 7.27 \| 3700 \| 1.3790 \| 0.4956 \| \| 1.4522 \| 7.47 \| 3800 \| 1.3116 \| 0.5378 \| \| 1.4692 \| 7.66 \| 3900 \| 1.3729 \| 0.4980 \| \| 1.5073 \| 7.86 \| 4000 \| 1.3799 \| 0.5216 \| \| 1.2529 \| 8.06 \| 4100 \| 1.2706 \| 0.5486 \| \| 1.3727 \| 8.25 \| 4200 \| 1.2519 \| 0.5535 \| \| 1.2451 \| 8.45 \| 4300 \| 1.2595 \| 0.5648 \| \| 1.339 \| 8.64 \| 4400 \| 1.3614 \| 0.5172 \| \| 1.2858 \| 8.84 \| 4500 \| 1.3028 \| 0.5393 \| \| 1.1039 \| 9.04 \| 4600 \| 1.2309 \| 0.5771 \| \| 1.0351 \| 9.23 \| 4700 \| 1.2678 \| 0.5609 \| \| 1.1125 \| 9.43 \| 4800 \| 1.2786 \| 0.5624 \| \| 1.1667 \| 9.63 \| 4900 \| 1.2131 \| 0.5840 \| \| 1.1386 \| 9.82 \| 5000 \| 1.1359 \| 0.6154 \| \| 1.1888 \| 10.02 \| 5100 \| 1.1309 \| 0.6041 \| \| 1.1777 \| 10.22 \| 5200 \| 1.1288 \| 0.6287 \| \| 1.3693 \| 10.41 \| 5300 \| 1.3827 \| 0.5182 \| \| 1.1016 \| 10.61 \| 5400 \| 1.2255 \| 0.5594 \| \| 1.1527 \| 10.81 \| 5500 \| 1.0772 \| 0.6434 \| \| 1.1039 \| 11.0 \| 5600 \| 1.1032 \| 0.6100 \| \| 1.2502 \| 11.2 \| 5700 \| 1.1230 \| 0.6169 \| \| 1.0818 \| 11.39 \| 5800 \| 1.0750 \| 0.6302 \| \| 1.0872 \| 11.59 \| 5900 \| 1.0397 \| 0.6331 \| \| 1.0425 \| 11.79 \| 6000 \| 1.0231 \| 0.6483 \| \| 1.0791 \| 11.98 \| 6100 \| 1.0250 \| 0.6636 \| \| 0.9736 \| 12.18 \| 6200 \| 1.0879 \| 0.6267 \| \| 0.9788 \| 12.38 \| 6300 \| 1.1334 \| 0.5968 \| \| 0.8982 \| 12.57 \| 6400 \| 0.9934 \| 0.6528 \| \| 1.077 \| 12.77 \| 6500 \| 0.9698 \| 0.6812 \| \| 1.0347 \| 12.97 \| 6600 \| 1.0265 \| 0.6513 \| \| 0.9159 \| 13.16 \| 6700 \| 0.9442 \| 0.6788 \| \| 1.1187 \| 13.36 \| 6800 \| 0.9738 \| 0.6685 \| \| 0.9624 \| 13.56 \| 6900 \| 1.0008 \| 0.6699 \| \| 0.922 \| 13.75 \| 7000 \| 0.9502 \| 0.6906 \| \| 0.9317 \| 13.95 \| 7100 \| 0.9687 \| 0.6758 \| \| 0.9979 \| 14.15 \| 7200 \| 0.9869 \| 0.6768 \| \| 0.8362 \| 14.34 \| 7300 \| 0.9220 \| 0.6994 \| \| 0.8449 \| 14.54 \| 7400 \| 0.9181 \| 0.6861 \| \| 0.9678 \| 14.73 \| 7500 \| 0.9789 \| 0.6729 \| \| 0.9119 \| 14.93 \| 7600 \| 0.8879 \| 0.7009 \| \| 0.9517 \| 15.13 \| 7700 \| 0.8816 \| 0.6994 \| \| 0.9688 \| 15.32 \| 7800 \| 0.8803 \| 0.7117 \| \| 0.8625 \| 15.52 \| 7900 \| 0.8782 \| 0.7038 \| \| 0.9121 \| 15.72 \| 8000 \| 0.8225 \| 0.7191 \| \| 0.9035 \| 15.91 \| 8100 \| 0.8649 \| 0.7087 \| \| 0.8762 \| 16.11 \| 8200 \| 0.8427 \| 0.7102 \| \| 0.7708 \| 16.31 \| 8300 \| 0.8685 \| 0.7117 \| \| 0.8893 \| 16.5 \| 8400 \| 0.8178 \| 0.7264 \| \| 0.9584 \| 16.7 \| 8500 \| 0.8709 \| 0.7092 \| \| 0.757 \| 16.9 \| 8600 \| 0.8244 \| 0.7254 \| \| 0.8184 \| 17.09 \| 8700 \| 0.8128 \| 0.7240 \| \| 0.8858 \| 17.29 \| 8800 \| 0.8360 \| 0.7156 \| \| 0.7116 \| 17.49 \| 8900 \| 0.7952 \| 0.7279 \| \| 0.9579 \| 17.68 \| 9000 \| 0.8263 \| 0.7274 \| \| 0.7037 \| 17.88 \| 9100 \| 0.7884 \| 0.7348 \| \| 1.0359 \| 18.07 \| 9200 \| 0.8118 \| 0.7402 \| \| 1.067 \| 18.27 \| 9300 \| 0.8203 \| 0.7186 \| \| 0.8503 \| 18.47 \| 9400 \| 0.7918 \| 0.7362 \| \| 0.8552 \| 18.66 \| 9500 \| 0.7972 \| 0.7382 \| \| 0.7498 \| 18.86 \| 9600 \| 0.8038 \| 0.7343 \| \| 0.8542 \| 19.06 \| 9700 \| 0.7799 \| 0.7333 \| \| 0.9539 \| 19.25 \| 9800 \| 0.7795 \| 0.7333 \| \| 0.7369 \| 19.45 \| 9900 \| 0.8103 \| 0.7269 \| \| 0.6637 \| 19.65 \| 10000 \| 0.7597 \| 0.7441 \| \| 0.6712 \| 19.84 \| 10100 \| 0.7557 \| 0.7475 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Sergendel/a2c-AntBulletEnv-v0	Sergendel	2023-04-26T22:48:01Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T18:09:29Z	--- 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: 1226.61 +/- 481.06 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 ... ```
kerdel/GenerAd-AI	kerdel	2023-04-26T22:23:59Z	36	0	adapter-transformers	[ "adapter-transformers", "text-generation", "dataset:kerdel/generative_ai_sample", "license:bigscience-openrail-m", "region:us" ]	text-generation	2023-04-26T22:18:12Z	--- license: bigscience-openrail-m datasets: - kerdel/generative_ai_sample library_name: adapter-transformers pipeline_tag: text-generation ---
myklicious/Reinforce-CartPole-V1	myklicious	2023-04-26T21:49:52Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T21:49:40Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-V1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
FacehugmanIII/4x_foolhardy_Remacri	FacehugmanIII	2023-04-26T21:49:37Z	0	90	null	[ "art", "license:unknown", "region:us" ]	null	2023-04-26T21:39:16Z	--- license: unknown tags: - art --- Using the Remacri upscaler in Automatic1111: Get the '4x_foolhardy_Remacri.pth' file linked in this post Copy it to: \stable-diffusion-webui\models\ESRGAN Restart WebUI 4x_foolhardy_Remacri is now available in the Extras tab and for the SD Upscale script I didn't create this upscaler, I simply downloaded it from a random link on reddit and uploaded here as I couldn't find it anywhere else.
Dsfajardob/rl_course_vizdoom_health_gathering_supreme	Dsfajardob	2023-04-26T21:45:35Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T20:57:34Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.04 +/- 5.36 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Dsfajardob/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
jkorstad/a2c-PandaReachDense-v2	jkorstad	2023-04-26T21:18:32Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T05:03:19Z	--- 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: -2.03 +/- 1.11 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 ... ```
jmartin233/bloom-1b7-lora-reading-comprehension	jmartin233	2023-04-26T20:50:10Z	0	0	null	[ "text-generation", "en", "dataset:jmartin233/reading_comprehension_exercise_dataset_v2", "license:bigscience-openrail-m", "region:us" ]	text-generation	2023-04-26T19:25:42Z	--- license: bigscience-openrail-m datasets: - jmartin233/reading_comprehension_exercise_dataset_v2 language: - en pipeline_tag: text-generation --- # Model Card for Model ID The model generates short reading comprehension exercises for English teachers to use. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Finetuned using this process: https://github.com/FourthBrain/Building-Generative-AI-Apps/blob/main/GenerAd-AI/notebooks/%F0%9F%92%AE%20GenerAd%20AI%F0%9F%92%AE%20Fine%20tuning%20BLOOM.ipynb ## Uses English teachers can use the model to generate short texts that use specified types of grammar, and are written a specified level (beginner, intermediate or advanced.)
amitrajitbh1/distilroberta-base-finetuned-teen-2	amitrajitbh1	2023-04-26T20:49:20Z	125	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-04-26T20:14:09Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-teen-2 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. --> # distilroberta-base-finetuned-teen-2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0436 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.5736 \| 1.0 \| 157 \| 3.3554 \| \| 3.1559 \| 2.0 \| 314 \| 3.1532 \| \| 3.0252 \| 3.0 \| 471 \| 3.0850 \| \| 2.858 \| 4.0 \| 628 \| 2.9401 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
JuanDa14Sa/Taxi-v3-QLearn	JuanDa14Sa	2023-04-26T20:42:28Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T20:30:22Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-QLearn 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="JuanDa14Sa/Taxi-v3-QLearn", 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"]) ```
akadhim-ai/sd_aiconos-model-v1-2_400	akadhim-ai	2023-04-26T20:37:30Z	31	0	diffusers	[ "diffusers", "tensorboard", "art", "text-to-image", "en", "dataset:Ali-fb/ios_icons_2", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-04-26T14:02:51Z	--- license: openrail datasets: - Ali-fb/ios_icons_2 language: - en metrics: - accuracy library_name: diffusers pipeline_tag: text-to-image tags: - art ---
Pedrampedram/MarketMail-AI	Pedrampedram	2023-04-26T20:27:00Z	0	0	null	[ "marketing", "question-answering", "en", "dataset:Pedrampedram/MarketMail-AI-Dataset", "license:openrail", "region:us" ]	question-answering	2023-04-26T20:00:26Z	--- license: openrail datasets: - Pedrampedram/MarketMail-AI-Dataset language: - en pipeline_tag: question-answering tags: - marketing ---
khadija267/distilbert-base-uncased-distilled-clinc	khadija267	2023-04-26T20:25:28Z	110	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-26T00:36:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.947741935483871 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2830 - Accuracy: 0.9477 ## Model description More information needed ## Intended uses & 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.8723 \| 1.0 \| 318 \| 2.8941 \| 0.7461 \| \| 2.2155 \| 2.0 \| 636 \| 1.4516 \| 0.8613 \| \| 1.0985 \| 3.0 \| 954 \| 0.7466 \| 0.9152 \| \| 0.5635 \| 4.0 \| 1272 \| 0.4707 \| 0.9358 \| \| 0.3294 \| 5.0 \| 1590 \| 0.3628 \| 0.9429 \| \| 0.221 \| 6.0 \| 1908 \| 0.3173 \| 0.9439 \| \| 0.1671 \| 7.0 \| 2226 \| 0.2968 \| 0.9477 \| \| 0.14 \| 8.0 \| 2544 \| 0.2876 \| 0.9484 \| \| 0.1263 \| 9.0 \| 2862 \| 0.2838 \| 0.9471 \| \| 0.1189 \| 10.0 \| 3180 \| 0.2830 \| 0.9477 \| ### Framework versions - Transformers 4.11.3 - Pytorch 2.0.0+cu118 - Datasets 1.16.1 - Tokenizers 0.10.3
Dsfajardob/ppo-LunarLander-v2	Dsfajardob	2023-04-26T20:19:44Z	1	0	stable-baselines3	[ "stable-baselines3", "tensorboard", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-28T20:15:40Z	--- 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: 269.59 +/- 20.52 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 ... ```
minoosh/ast-finetuned-audioset-10-10-0.4593-finetuned-ie	minoosh	2023-04-26T20:09:47Z	7	0	transformers	[ "transformers", "pytorch", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	audio-classification	2023-04-26T09:55:48Z	--- license: bsd-3-clause tags: - generated_from_trainer model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-ie 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. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-ie This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 3.0698 - eval_accuracy: 0.6076 - eval_runtime: 163.7462 - eval_samples_per_second: 7.579 - eval_steps_per_second: 0.953 - epoch: 18.08 - step: 1844 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Framework versions - Transformers 4.27.4 - Pytorch 1.13.0 - Datasets 2.11.0 - Tokenizers 0.13.2
khadija267/distilbert-base-uncased-finetuned-clinc	khadija267	2023-04-26T20:02:43Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-25T23:46:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9161290322580645 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7754 - Accuracy: 0.9161 ## Model description More information needed ## Intended uses & 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2893 \| 1.0 \| 318 \| 3.2831 \| 0.7397 \| \| 2.6289 \| 2.0 \| 636 \| 1.8731 \| 0.8345 \| \| 1.5481 \| 3.0 \| 954 \| 1.1580 \| 0.89 \| \| 1.0137 \| 4.0 \| 1272 \| 0.8584 \| 0.9077 \| \| 0.7969 \| 5.0 \| 1590 \| 0.7754 \| 0.9161 \| ### Framework versions - Transformers 4.11.3 - Pytorch 2.0.0+cu118 - Datasets 1.16.1 - Tokenizers 0.10.3
yerx/videomae-base-finetuned-basketball-subset-20epochs	yerx	2023-04-26T20:01:14Z	63	0	transformers	[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-04-26T18:56:04Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-basketball-subset-20epochs 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. --> # videomae-base-finetuned-basketball-subset-20epochs This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8785 - Accuracy: 0.1972 ## Model description More information needed ## Intended uses & 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: 1 - eval_batch_size: 1 - 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: 4060 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2525 \| 0.05 \| 200 \| 0.7720 \| 0.52 \| \| 0.8649 \| 1.05 \| 400 \| 0.7721 \| 0.48 \| \| 1.0703 \| 2.05 \| 600 \| 1.3605 \| 0.52 \| \| 0.606 \| 3.05 \| 800 \| 1.0668 \| 0.6 \| \| 2.0221 \| 4.05 \| 1000 \| 1.1741 \| 0.56 \| \| 1.2916 \| 5.05 \| 1200 \| 1.4747 \| 0.52 \| \| 1.4861 \| 6.05 \| 1400 \| 1.1454 \| 0.6 \| \| 1.3012 \| 7.05 \| 1600 \| 1.6105 \| 0.56 \| \| 1.3327 \| 8.05 \| 1800 \| 1.2343 \| 0.52 \| \| 2.077 \| 9.05 \| 2000 \| 1.3243 \| 0.6 \| \| 1.2349 \| 10.05 \| 2200 \| 1.2044 \| 0.6 \| \| 1.005 \| 11.05 \| 2400 \| 1.6417 \| 0.52 \| \| 1.1622 \| 12.05 \| 2600 \| 1.3058 \| 0.56 \| \| 0.8031 \| 13.05 \| 2800 \| 0.6776 \| 0.48 \| \| 0.8588 \| 14.05 \| 3000 \| 1.1644 \| 0.64 \| \| 0.8451 \| 15.05 \| 3200 \| 0.8491 \| 0.64 \| \| 1.1336 \| 16.05 \| 3400 \| 1.0237 \| 0.6 \| \| 1.5719 \| 17.05 \| 3600 \| 1.0391 \| 0.64 \| \| 0.4892 \| 18.05 \| 3800 \| 0.9995 \| 0.64 \| \| 1.2092 \| 19.05 \| 4000 \| 0.9802 \| 0.56 \| \| 0.9784 \| 20.01 \| 4060 \| 0.9771 \| 0.56 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Dsfajardob/ppo-LunarLander-v2-U8	Dsfajardob	2023-04-26T19:56:33Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T19:56:25Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -218.11 +/- 142.77 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Dsfajardob/ppo-LunarLander-v2-U8' 'batch_size': 512 'minibatch_size': 128} ```
Anyayolp/t5-end2end-questions-generation	Anyayolp	2023-04-26T19:56:27Z	161	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_modified_for_t5_qg", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-26T14:25:47Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_modified_for_t5_qg model-index: - name: t5-end2end-questions-generation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. It achieves the following results on the evaluation set: - Loss: 1.5674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5884 \| 0.34 \| 100 \| 1.9159 \| \| 1.9705 \| 0.68 \| 200 \| 1.7310 \| \| 1.8439 \| 1.02 \| 300 \| 1.6672 \| \| 1.7426 \| 1.35 \| 400 \| 1.6382 \| \| 1.7147 \| 1.69 \| 500 \| 1.6199 \| \| 1.6908 \| 2.03 \| 600 \| 1.6053 \| \| 1.6315 \| 2.37 \| 700 \| 1.5967 \| \| 1.627 \| 2.71 \| 800 \| 1.5939 \| \| 1.6122 \| 3.05 \| 900 \| 1.5877 \| \| 1.5706 \| 3.39 \| 1000 \| 1.5861 \| \| 1.5708 \| 3.73 \| 1100 \| 1.5742 \| \| 1.5534 \| 4.06 \| 1200 \| 1.5798 \| \| 1.5351 \| 4.4 \| 1300 \| 1.5738 \| \| 1.5226 \| 4.74 \| 1400 \| 1.5757 \| \| 1.5187 \| 5.08 \| 1500 \| 1.5727 \| \| 1.4963 \| 5.42 \| 1600 \| 1.5710 \| \| 1.4841 \| 5.76 \| 1700 \| 1.5668 \| \| 1.5025 \| 6.1 \| 1800 \| 1.5688 \| \| 1.4778 \| 6.44 \| 1900 \| 1.5717 \| \| 1.4769 \| 6.77 \| 2000 \| 1.5674 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy	mrm8488	2023-04-26T19:55:30Z	651	6	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "medical", "colon", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification - pytorch - medical - colon metrics: - accuracy: 0.93 --- # Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification ## Demo ### Drag the following images to the widget to test the model - ![](https://i.imgur.com/2ykziCJ_d.webp?maxwidth=224&fidelity=grand) - ![](https://i.imgur.com/NfWPHkj_d.webp?maxwidth=224&fidelity=grand) - ![](https://i.imgur.com/C3RexVQ_d.webp?maxwidth=224&fidelity=grand) - ![](https://i.imgur.com/qcCYpN9_d.webp?maxwidth=224&fidelity=grand) ## Training You can find the code [here](https://github.com/qanastek/HugsVision/blob/main/recipes/kvasir_v2/binary_classification/Kvasir_v2_Image_Classifier.ipynb) ## Metrics ``` precision recall f1-score support dyed-lifted-polyps 0.95 0.93 0.94 60 dyed-resection-margins 0.97 0.95 0.96 64 esophagitis 0.93 0.79 0.85 67 normal-cecum 1.00 0.98 0.99 54 normal-pylorus 0.95 1.00 0.97 57 normal-z-line 0.82 0.93 0.87 67 polyps 0.92 0.92 0.92 52 ulcerative-colitis 0.93 0.95 0.94 59 accuracy 0.93 480 macro avg 0.93 0.93 0.93 480 weighted avg 0.93 0.93 0.93 480 ``` ## How to use ```py from transformers import ViTFeatureExtractor, ViTForImageClassification from hugsvision.inference.VisionClassifierInference import VisionClassifierInference path = "mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy" classifier = VisionClassifierInference( feature_extractor = ViTFeatureExtractor.from_pretrained(path), model = ViTForImageClassification.from_pretrained(path), ) img = "Your image path" label = classifier.predict(img_path=img) print("Predicted class:", label) ``` > Disclaimer: This model was trained for research only > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) \| [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
gmongaras/gpt-anime-sub-1.3B	gmongaras	2023-04-26T19:53:42Z	142	5	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neo", "text-generation", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-02-01T05:26:48Z	--- license: openrail --- This model is fintuned using the following model: https://huggingface.co/EleutherAI/gpt-neo-1.3B The data was scraped from: https://www.kitsunekko.net/dirlist.php?dir=subtitles%2F To load, use: model = pipeline('text-generation',model="gmongaras/gpt-anime-sub-1.3B", tokenizer="EleutherAI/gpt-neo-1.3B")
Callidior/bert2bert-base-arxiv-titlegen	Callidior	2023-04-26T19:42:59Z	163	13	transformers	[ "transformers", "pytorch", "safetensors", "encoder-decoder", "text2text-generation", "summarization", "en", "dataset:arxiv_dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:04Z	--- language: - en tags: - summarization license: apache-2.0 datasets: - arxiv_dataset metrics: - rouge widget: - text: "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data." --- # Paper Title Generator Generates titles for computer science papers given an abstract. The model is a BERT2BERT Encoder-Decoder using the official `bert-base-uncased` checkpoint as initialization for the encoder and decoder. It was fine-tuned on 318,500 computer science papers posted on arXiv.org between 2007 and 2022 and achieved a 26.3% Rouge2 F1-Score on held-out validation data. Live Demo: [https://paper-titles.ey.r.appspot.com/](https://paper-titles.ey.r.appspot.com/)
PanEa/dolly-v2-gptj-enhanced-auto-gptq	PanEa	2023-04-26T19:16:11Z	6	1	transformers	[ "transformers", "gptj", "text-generation", "en", "dataset:vicgalle/alpaca-gpt4", "dataset:databricks/databricks-dolly-15k", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-04-26T08:08:50Z	--- license: afl-3.0 datasets: - vicgalle/alpaca-gpt4 - databricks/databricks-dolly-15k language: - en metrics: - perplexity pipeline_tag: text-generation ---
JgnMama/Erniee	JgnMama	2023-04-26T19:15:08Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T19:12:31Z	--- license: creativeml-openrail-m ---
Pennyyyyy/t5-end2end-questions-generation	Pennyyyyy	2023-04-26T19:10:40Z	162	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_modified_for_t5_qg", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-26T13:24:34Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_modified_for_t5_qg model-index: - name: t5-end2end-questions-generation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. It achieves the following results on the evaluation set: - Loss: 1.5674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5884 \| 0.34 \| 100 \| 1.9159 \| \| 1.9705 \| 0.68 \| 200 \| 1.7310 \| \| 1.8439 \| 1.02 \| 300 \| 1.6672 \| \| 1.7426 \| 1.35 \| 400 \| 1.6382 \| \| 1.7147 \| 1.69 \| 500 \| 1.6199 \| \| 1.6908 \| 2.03 \| 600 \| 1.6053 \| \| 1.6315 \| 2.37 \| 700 \| 1.5967 \| \| 1.627 \| 2.71 \| 800 \| 1.5939 \| \| 1.6122 \| 3.05 \| 900 \| 1.5877 \| \| 1.5706 \| 3.39 \| 1000 \| 1.5861 \| \| 1.5708 \| 3.73 \| 1100 \| 1.5742 \| \| 1.5534 \| 4.06 \| 1200 \| 1.5798 \| \| 1.5351 \| 4.4 \| 1300 \| 1.5738 \| \| 1.5226 \| 4.74 \| 1400 \| 1.5757 \| \| 1.5187 \| 5.08 \| 1500 \| 1.5727 \| \| 1.4963 \| 5.42 \| 1600 \| 1.5710 \| \| 1.4841 \| 5.76 \| 1700 \| 1.5668 \| \| 1.5025 \| 6.1 \| 1800 \| 1.5688 \| \| 1.4778 \| 6.44 \| 1900 \| 1.5717 \| \| 1.4769 \| 6.77 \| 2000 \| 1.5674 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Sergendel/a2c-PandaReachDense-v2	Sergendel	2023-04-26T19:08:07Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T19:05:30Z	--- 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: -1.47 +/- 0.30 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 ... ```
anilkumar2444/a2c-AntBulletEnv-v0	anilkumar2444	2023-04-26T19:06:19Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T19:05:13Z	--- 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: 838.75 +/- 152.64 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 ... ```
sdesai/narrativa-finetuned-wmt22-en-pt-br-brwac	sdesai	2023-04-26T18:58:42Z	159	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-26T18:43:19Z	--- tags: - generated_from_trainer model-index: - name: narrativa-finetuned-wmt22-en-pt-br-brwac 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. --> # narrativa-finetuned-wmt22-en-pt-br-brwac This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-en-pt-translation](https://huggingface.co/Narrativa/mbart-large-50-finetuned-opus-en-pt-translation) on an unknown dataset. Also added brwac. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results - epoch = 3.0 - eval_bleu = 63.9813 - eval_gen_len = 12.3215 - eval_loss = 0.4894 - eval_runtime = 0:00:20.01 - eval_samples = 190 - eval_samples_per_second = 9.492 - eval_steps_per_second = 2.398 ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu116 - Datasets 2.4.0 - Tokenizers 0.12.1
ericrincon/Qtable_taxi	ericrincon	2023-04-26T18:52:52Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T18:52:50Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Qtable_taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.72 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="ericrincon/Qtable_taxi", 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"]) ```
ericrincon/Frozen_Lake_4x4	ericrincon	2023-04-26T18:50:48Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T18:50:46Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: Frozen_Lake_4x4 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="ericrincon/Frozen_Lake_4x4", 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"]) ```
email81227/poca-SoccerTwos	email81227	2023-04-26T18:38:17Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-04-26T18:07:55Z	--- 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: email81227/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LecJackS/distilbert-base-uncased-finetuned-emotion	LecJackS	2023-04-26T18:36:36Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-26T18:23:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: split metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9244610483889744 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2193 - Accuracy: 0.9245 - F1: 0.9245 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8598 \| 1.0 \| 250 \| 0.3274 \| 0.9005 \| 0.8966 \| \| 0.2584 \| 2.0 \| 500 \| 0.2193 \| 0.9245 \| 0.9245 \| ### Framework versions - Transformers 4.13.0 - Pytorch 2.0.0+cu118 - Datasets 2.8.0 - Tokenizers 0.10.3
Asif782/lora	Asif782	2023-04-26T18:36:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T18:11:22Z	--- license: creativeml-openrail-m ---
KigenCHESS/eng-sw_translation	KigenCHESS	2023-04-26T18:33:41Z	61	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-26T18:30:31Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: KigenCHESS/eng-sw_translation 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. --> # KigenCHESS/eng-sw_translation This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-sw](https://huggingface.co/Helsinki-NLP/opus-mt-en-sw) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5372 - Validation Loss: 0.6656 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 424, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.9069 \| 0.7022 \| 0 \| \| 0.5372 \| 0.6656 \| 1 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.2
Raiden-1001/poca-SoccerTwos	Raiden-1001	2023-04-26T18:27:48Z	89	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-03-23T05:59:23Z	--- 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: Raiden-1001/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dkerja/songhyekyo	dkerja	2023-04-26T18:14:39Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T18:13:08Z	--- license: creativeml-openrail-m ---
Pranjalya/a2c-AntBulletEnv-v0	Pranjalya	2023-04-26T18:09:15Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T18:08:06Z	--- 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: 1829.43 +/- 423.83 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 ... ```
freya48/nekoda	freya48	2023-04-26T18:08:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T18:03:06Z	--- license: creativeml-openrail-m ---
kaisar-barlybay-sse/qard-bert-base-multilingual-uncased_6	kaisar-barlybay-sse	2023-04-26T17:52:59Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2023-04-26T14:51:34Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: qard-bert-base-multilingual-uncased_6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # qard-bert-base-multilingual-uncased_6 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3863 - Accuracy: 0.3074 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.399 \| 1.0 \| 501 \| 1.3862 \| 0.3373 \| \| 1.3921 \| 2.0 \| 1002 \| 1.3862 \| 0.3613 \| \| 1.3905 \| 3.0 \| 1503 \| 1.3863 \| 0.3273 \| \| 1.3903 \| 4.0 \| 2004 \| 1.3863 \| 0.2455 \| \| 1.3904 \| 5.0 \| 2505 \| 1.3863 \| 0.2834 \| \| 1.3898 \| 6.0 \| 3006 \| 1.3863 \| 0.3074 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
kaisar-barlybay-sse/qard-distilbert-base-multilingual-cased_6	kaisar-barlybay-sse	2023-04-26T17:33:35Z	101	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2023-04-26T14:19:40Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: qard-distilbert-base-multilingual-cased_6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # qard-distilbert-base-multilingual-cased_6 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3254 - Accuracy: 0.4331 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3738 \| 1.0 \| 501 \| 1.3361 \| 0.4152 \| \| 1.2866 \| 2.0 \| 1002 \| 1.3259 \| 0.4212 \| \| 1.0942 \| 3.0 \| 1503 \| 1.3760 \| 0.4391 \| \| 0.8393 \| 4.0 \| 2004 \| 1.6132 \| 0.4291 \| \| 0.6062 \| 5.0 \| 2505 \| 1.8334 \| 0.4391 \| \| 0.4319 \| 6.0 \| 3006 \| 2.3254 \| 0.4331 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
YchKhan/EloQuence	YchKhan	2023-04-26T17:30:58Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-04-26T17:22:36Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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shahukareem/bulhaa-cat	shahukareem	2023-04-26T17:10:07Z	31	0	diffusers	[ "diffusers", "pytorch", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth-hackathon", "animal", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-19T15:15:29Z	--- license: creativeml-openrail-m tags: - pytorch - diffusers - stable-diffusion - text-to-image - diffusion-models-class - dreambooth-hackathon - animal widget: - text: A cute and adorable photo of a cat --- # DreamBooth model for the naseemee concept trained by shahukareem on the shahukareem/cat dataset. This is a Stable Diffusion model fine-tuned on the bulhaa concept with DreamBooth. It can be used by modifying the `instance_prompt`: a photo of bulhaa cat This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is a Stable Diffusion model fine-tuned on `cat` images for the animal theme. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('shahukareem/bulhaa-cat') image = pipeline().images[0] image ```
ericrincon/LunarLander-v2	ericrincon	2023-04-26T16:51:34Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T16:44:07Z	--- 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: 265.64 +/- 23.25 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 ... ```
sophiebottani/distilbert_squad_newsqa	sophiebottani	2023-04-26T16:22:54Z	11	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:newsqa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-04-26T07:12:32Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert_squad_newsqa results: [] datasets: - newsqa --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_squad_newsqa This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the NewsQA dataset. It achieves the following results on the evaluation set: - Loss: 1.6247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.7181 \| 1.0 \| 6730 \| 1.6477 \| \| 1.4932 \| 2.0 \| 13460 \| 1.6274 \| \| 1.4426 \| 3.0 \| 20190 \| 1.6247 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1+cu102 - Datasets 2.9.0 - Tokenizers 0.13.2
kaisar-barlybay-sse/qard-bert-base-multilingual-cased_6	kaisar-barlybay-sse	2023-04-26T15:57:26Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2023-04-26T11:43:17Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: qard-bert-base-multilingual-cased_6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # qard-bert-base-multilingual-cased_6 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3526 - Accuracy: 0.3453 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3904 \| 1.0 \| 501 \| 1.3831 \| 0.3473 \| \| 1.3997 \| 2.0 \| 1002 \| 1.3863 \| 0.3234 \| \| 1.3937 \| 3.0 \| 1503 \| 1.3865 \| 0.2395 \| \| 1.3912 \| 4.0 \| 2004 \| 1.3862 \| 0.3253 \| \| 1.3919 \| 5.0 \| 2505 \| 1.3861 \| 0.3633 \| \| 1.3713 \| 6.0 \| 3006 \| 1.3526 \| 0.3453 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Sergendel/ppo-SnowballTarget	Sergendel	2023-04-26T15:33:35Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-04-26T15:33:30Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: Sergendel/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
bobLi/autotrain-burp-52899124622	bobLi	2023-04-26T15:30:07Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "en", "dataset:bobLi/autotrain-data-burp", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-26T15:28:52Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - bobLi/autotrain-data-burp co2_eq_emissions: emissions: 0.004479786338858913 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 52899124622 - CO2 Emissions (in grams): 0.0045 ## Validation Metrics - Loss: 0.000 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000 ## Usage You can use cURL to access this model: ``` $ 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/bobLi/autotrain-burp-52899124622 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("bobLi/autotrain-burp-52899124622", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("bobLi/autotrain-burp-52899124622", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
GANYANG/gpt-2	GANYANG	2023-04-26T15:24:20Z	0	0	null	[ "pytorch", "tensorboard", "generated_from_trainer", "license:mit", "region:us" ]	null	2023-04-12T03:39:20Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt-2 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. --> # gpt-2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.837 \| 0.51 \| 200 \| 1.7257 \| \| 1.7602 \| 1.03 \| 400 \| 1.6777 \| \| 1.7341 \| 1.54 \| 600 \| 1.6604 \| ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.0 - Datasets 2.10.1 - Tokenizers 0.13.3
Supparesk/t5-end2end-questions-generation	Supparesk	2023-04-26T15:09:36Z	163	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_modified_for_t5_qg", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-24T17:56:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_modified_for_t5_qg model-index: - name: t5-end2end-questions-generation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. It achieves the following results on the evaluation set: - Loss: 1.5791 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5888 \| 0.34 \| 100 \| 1.9194 \| \| 1.9722 \| 0.68 \| 200 \| 1.7316 \| \| 1.8479 \| 1.02 \| 300 \| 1.6689 \| \| 1.7478 \| 1.35 \| 400 \| 1.6409 \| \| 1.7204 \| 1.69 \| 500 \| 1.6268 \| \| 1.6986 \| 2.03 \| 600 \| 1.6105 \| \| 1.6437 \| 2.37 \| 700 \| 1.6007 \| \| 1.639 \| 2.71 \| 800 \| 1.5952 \| \| 1.6261 \| 3.05 \| 900 \| 1.5909 \| \| 1.5915 \| 3.39 \| 1000 \| 1.5861 \| \| 1.5917 \| 3.73 \| 1100 \| 1.5829 \| \| 1.5772 \| 4.06 \| 1200 \| 1.5788 \| \| 1.5697 \| 4.4 \| 1300 \| 1.5800 \| \| 1.557 \| 4.74 \| 1400 \| 1.5791 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
gaussalgo/MiniLM-L6-v2-Canard-Fullwiki	gaussalgo	2023-04-26T15:03:24Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-04-26T15:02:58Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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) ``` ## 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 1988 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ckallur/ppo-LunarLander-v2	ckallur	2023-04-26T14:59:53Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T14:59:23Z	--- 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: 258.44 +/- 19.98 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 ... ```
lorenzoncina/whisper-medium-ru	lorenzoncina	2023-04-26T14:59:33Z	34	7	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ru", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-04-07T15:58:48Z	--- language: - ru license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Medium Russian results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 ru type: mozilla-foundation/common_voice_11_0 config: ru split: test args: ru metrics: - type: wer value: 7.562437929892964 name: Wer - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: ru_ru split: test metrics: - type: wer value: 10.92 name: WER --- <!-- 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 Medium Russian This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 ru dataset. It achieves the following results on the evaluation set: - Loss: 0.2253 - Wer: 7.5624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.1578 \| 0.1 \| 1000 \| 0.1662 \| 8.8290 \| \| 0.045 \| 1.08 \| 2000 \| 0.1748 \| 8.9148 \| \| 0.0176 \| 2.06 \| 3000 \| 0.1889 \| 8.7848 \| \| 0.0104 \| 3.04 \| 4000 \| 0.1922 \| 8.4354 \| \| 0.0051 \| 4.02 \| 5000 \| 0.2034 \| 8.1865 \| \| 0.0047 \| 4.12 \| 6000 \| 0.2012 \| 8.0455 \| \| 0.0018 \| 5.1 \| 7000 \| 0.2117 \| 7.6237 \| \| 0.0004 \| 6.08 \| 8000 \| 0.2177 \| 7.6078 \| \| 0.0003 \| 7.06 \| 9000 \| 0.2244 \| 7.6262 \| \| 0.0002 \| 8.04 \| 10000 \| 0.2253 \| 7.5624 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.1.dev0 - Tokenizers 0.13.2
Carlosrelao/Reinforce-CartPole1	Carlosrelao	2023-04-26T14:53:34Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T14:53:22Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 459.40 +/- 121.80 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
murlina/ppo-LunarLander-v2	murlina	2023-04-26T14:45:14Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-25T17:12:35Z	--- 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: 243.30 +/- 32.31 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
gRaphael/ppo-LunarLander-v2	gRaphael	2023-04-26T14:36:49Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T14:27:38Z	--- 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: 264.49 +/- 14.91 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 ... ```
MiniMinMax/Reinforce-Pixelcopter	MiniMinMax	2023-04-26T14:32:03Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T14:31:59Z	--- 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: 42.40 +/- 24.45 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
doarisono/Doar	doarisono	2023-04-26T14:28:16Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T14:13:06Z	--- license: creativeml-openrail-m ---
Ashfaq60/Ashfaq	Ashfaq60	2023-04-26T14:27:27Z	0	0	null	[ "region:us" ]	null	2023-04-26T13:07:28Z	--- license: artistic-2.0 ---hi
rawmt/finetuning-sentiment-model-3000-samples	rawmt	2023-04-26T14:24:05Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-26T14:10:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples 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: - Loss: 0.3114 - Accuracy: 0.8733 - F1: 0.8742 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3
uisikdag/42000news_turkish_bert_uncased_finetune	uisikdag	2023-04-26T14:21:30Z	185	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-14T14:37:45Z	--- license: mit tags: - generated_from_trainer model-index: - name: umit_42000news_bertuncased 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. --> # umit_42000news_bertuncased This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 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: 3 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Sinsinnati/hf_workshop_extra	Sinsinnati	2023-04-26T14:18:43Z	76	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "text-classification", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-04-26T14:08:49Z	--- pipeline_tag: text-classification widget: - text: He loves learning new things. - text: I go to university every day. ---
BlueAvenir/proseiben_events_activities_announcements	BlueAvenir	2023-04-26T14:17:55Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-04-26T14:17:33Z	--- 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 653 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 653, "warmup_steps": 66, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->
ndhieunguyen/Reinforce-PixelCopter	ndhieunguyen	2023-04-26T14:15:26Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-04-24T01:44:42Z	--- 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: 46.20 +/- 34.66 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
Dewa/ppo-Lunar_rl-v5	Dewa	2023-04-26T14:01:40Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T14:00:55Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -114.16 +/- 28.04 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 100000 'learning_rate': 0.004 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.92 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Dewa/ppo-Lunar_rl-v5' 'batch_size': 512 'minibatch_size': 128} ```
Dewa/dqn-SpaceInvadersNoFrameskip-v4-version-6	Dewa	2023-04-26T13:39:48Z	3	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T12:50:28Z	--- 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: 274.50 +/- 31.50 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 Dewa -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 Dewa -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 Dewa ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
PaulineSanchez/autotrain-translation_food_english_to_french-52830124391	PaulineSanchez	2023-04-26T13:36:23Z	223	2	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "autotrain", "translation", "en", "fr", "dataset:PaulineSanchez/autotrain-data-translation_food_english_to_french", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-04-26T13:14:44Z	--- tags: - autotrain - translation language: - en - fr datasets: - PaulineSanchez/autotrain-data-translation_food_english_to_french co2_eq_emissions: emissions: 8.23780867881086 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 52830124391 - CO2 Emissions (in grams): 8.2378 ## Validation Metrics - Loss: 0.539 - SacreBLEU: 61.476 - Gen len: 12.913
Sergendel/Reinforce-PixelCopter_v2	Sergendel	2023-04-26T13:35:08Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T13:35:05Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter_v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 30.60 +/- 30.90 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
Yonadav/summarization_t5base_en_to_kjven	Yonadav	2023-04-26T13:32:17Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-26T07:40:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: summarization_t5base_en_to_kjven 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. --> # summarization_t5base_en_to_kjven This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8324 - Bleu: 21.2143 - Gen Len: 18.1685 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 1.0735 \| 1.0 \| 2860 \| 0.9479 \| 21.3913 \| 18.1219 \| \| 0.9776 \| 2.0 \| 5720 \| 0.8750 \| 22.1711 \| 18.1307 \| \| 0.918 \| 3.0 \| 8580 \| 0.8317 \| 22.6915 \| 18.1381 \| \| 0.8741 \| 4.0 \| 11440 \| 0.8039 \| 23.0856 \| 18.1468 \| \| 0.8489 \| 5.0 \| 14300 \| 0.7841 \| 23.3573 \| 18.1455 \| \| 0.8169 \| 6.0 \| 17160 \| 0.7664 \| 23.5073 \| 18.1493 \| \| 0.7965 \| 7.0 \| 20020 \| 0.7532 \| 23.6919 \| 18.1495 \| \| 0.78 \| 8.0 \| 22880 \| 0.7411 \| 23.8445 \| 18.1461 \| \| 0.7568 \| 9.0 \| 25740 \| 0.7338 \| 23.86 \| 18.155 \| \| 0.7496 \| 10.0 \| 28600 \| 0.7228 \| 23.953 \| 18.1511 \| \| 0.7411 \| 11.0 \| 31460 \| 0.7175 \| 24.0327 \| 18.1511 \| \| 0.8376 \| 12.0 \| 34320 \| 0.8114 \| 23.311 \| 18.1319 \| \| 1.1918 \| 13.0 \| 37180 \| 0.9686 \| 21.5339 \| 18.1185 \| \| 1.0929 \| 14.0 \| 40040 \| 0.8978 \| 21.561 \| 18.1455 \| \| 1.0373 \| 15.0 \| 42900 \| 0.8617 \| 21.4942 \| 18.1542 \| \| 1.0165 \| 16.0 \| 45760 \| 0.8432 \| 21.3962 \| 18.1595 \| \| 0.9973 \| 17.0 \| 48620 \| 0.8340 \| 21.2558 \| 18.166 \| \| 0.9889 \| 18.0 \| 51480 \| 0.8326 \| 21.2238 \| 18.1687 \| \| 0.9909 \| 19.0 \| 54340 \| 0.8325 \| 21.2216 \| 18.1688 \| \| 0.9942 \| 20.0 \| 57200 \| 0.8324 \| 21.2143 \| 18.1685 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
josu/gpt-neo-1.3B-instruction	josu	2023-04-26T13:27:25Z	19	1	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-04-14T20:56:15Z	--- language: - pt widget: - text: Explique o que é inteligência artificial. - text: Explique o que é processamento de linguagem natural. --- ``` python from transformers import GenerationConfig from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("josu/gpt-neo-1.3B-instruction") tokenizer = AutoTokenizer.from_pretrained("josu/gpt-neo-1.3B-instruction") def generate_prompt(instruction, input=None): if input: return f"""Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido. ### Instrução: {instruction} ### Entrada: {input} ### Resposta:""" else: return f"""Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido. ### Instrução: {instruction} ### Resposta:""" generation_config = GenerationConfig( temperature=0.2, top_p=0.75, num_beams=4, ) def evaluate(instruction, input=None): prompt = generate_prompt(instruction, input) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=256 ) content = [] for s in generation_output.sequences: output = tokenizer.decode(s) content.append(output.split("### Resposta:")[1].strip()) return content ```
UsuallyPoncho/ppo-Huggy	UsuallyPoncho	2023-04-26T13:22:22Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-04-26T13:22:16Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://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: Find your model_id: UsuallyPoncho/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
XdSlams/fjhqgwkjwehhrfgir28	XdSlams	2023-04-26T13:06:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T07:09:24Z	--- license: creativeml-openrail-m ---
LarryAIDraw/jessicaGranblue_v10	LarryAIDraw	2023-04-26T13:06:13Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T12:59:23Z	--- license: creativeml-openrail-m --- https://civitai.com/models/50740/jessica-or-granblue-fantasy
LarryAIDraw/eremiteScorching_v10	LarryAIDraw	2023-04-26T13:05:56Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T12:59:04Z	--- license: creativeml-openrail-m --- https://civitai.com/models/50612/eremite-scorching-loremaster-genshin-impact
LarryAIDraw/towerOfFantasyFiona_v10	LarryAIDraw	2023-04-26T13:05:10Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T12:57:55Z	--- license: creativeml-openrail-m --- https://civitai.com/models/50216/tower-of-fantasy-fiona
LarryAIDraw/yamashiroAzurLane_v10	LarryAIDraw	2023-04-26T13:04:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T12:57:08Z	--- license: creativeml-openrail-m --- https://civitai.com/models/50909/yamashiroazur-lane
LarryAIDraw/sanjounoHaruhimeDunmachi_v10	LarryAIDraw	2023-04-26T13:04:13Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T12:56:21Z	--- license: creativeml-openrail-m --- https://civitai.com/models/51211/sanjouno-haruhime-dunmachi
LarryAIDraw/7thMarchHonkaiStar_v10	LarryAIDraw	2023-04-26T13:04:01Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T12:55:59Z	--- license: creativeml-openrail-m --- https://civitai.com/models/50469/7th-march-honkai-star-rail
alibidaran/Symptom2disease	alibidaran	2023-04-26T12:51:01Z	121	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-26T11:19:00Z	--- license: apache-2.0 pipeline_tag: text-classification ---
aravind-selvam/donut_finetuned_chart	aravind-selvam	2023-04-26T12:49:39Z	53	2	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-04-17T12:33:36Z	--- license: mit tags: - generated_from_trainer model-index: - name: donut_finetuned_chart 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. --> # donut_finetuned_chart This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an chart images dataset. It achieves the following results on the evaluation set: - Loss: 0.4957 - Cer: 0.2318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.4943 \| 1.0 \| 166 \| 0.6634 \| 0.2341 \| \| 0.475 \| 2.0 \| 333 \| 0.5370 \| 0.2320 \| \| 0.3009 \| 3.0 \| 500 \| 0.5051 \| 0.2318 \| \| 0.2611 \| 3.98 \| 664 \| 0.4957 \| 0.2318 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
DTorregrosa/sd-class-butterflies-64	DTorregrosa	2023-04-26T12:49:08Z	36	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-04-26T12:48:19Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('DTorregrosa/sd-class-butterflies-64') image = pipeline().images[0] image ```
Oleksandr2003/QA_model	Oleksandr2003	2023-04-26T12:48:53Z	29	0	transformers	[ "transformers", "pytorch", "tf", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-01-29T15:51:42Z	--- license: mit tags: - generated_from_trainer model-index: - name: QA_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # QA_model This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4719 \| 1.0 \| 620 \| 1.3108 \| \| 1.4047 \| 2.0 \| 1240 \| 1.1630 \| \| 1.1245 \| 3.0 \| 1860 \| 1.1429 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Dewa/dqn-SpaceInvadersNoFrameskip-v4-version-5	Dewa	2023-04-26T12:43:46Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T12:43:11Z	--- 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 Dewa -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 Dewa -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 Dewa ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Ubenwa/sb-ecapa-vggsound	Ubenwa	2023-04-26T12:37:18Z	7	1	speechbrain	[ "speechbrain", "embeddings", "Sound", "pytorch", "ECAPA-TDNN", "TDNN", "audio-classification", "en", "dataset:VGGSound", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]	audio-classification	2023-01-06T01:25:21Z	--- language: "en" thumbnail: tags: - speechbrain - embeddings - Sound - pytorch - ECAPA-TDNN - TDNN - audio-classification license: "apache-2.0" datasets: - VGGSound metrics: - Accuracy --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # Sound Recognition with ECAPA embeddings on VGGSound This repository provides all the necessary tools to perform sound recognition with SpeechBrain using a model pretrained on VGGSound. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance on the test set is: \| Release \| Error Rate (%) \|:-------------:\|:--------------:\| \| 28-02-23 \| 42.8 \| #### Referencing ECAPA ```@inproceedings{DBLP:conf/interspeech/DesplanquesTD20, author = {Brecht Desplanques and Jenthe Thienpondt and Kris Demuynck}, editor = {Helen Meng and Bo Xu and Thomas Fang Zheng}, title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation in {TDNN} Based Speaker Verification}, booktitle = {Interspeech 2020}, pages = {3830--3834}, publisher = {{ISCA}}, year = {2020}, } ``` #### Referencing VGGSound ```@inproceedings{chen2020vggsound, title={Vggsound: A large-scale audio-visual dataset}, author={Chen, Honglie and Xie, Weidi and Vedaldi, Andrea and Zisserman, Andrew}, booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={721--725}, year={2020}, organization={IEEE} } ``` # Citing SpeechBrain Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
jorgefedzhedz/distilbert-base-uncased-finetuned-cola	jorgefedzhedz	2023-04-26T12:33:20Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-26T12:09:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.541934635424655 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8224 - Matthews Correlation: 0.5419 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5231 \| 1.0 \| 535 \| 0.5305 \| 0.4003 \| \| 0.348 \| 2.0 \| 1070 \| 0.5013 \| 0.4885 \| \| 0.2353 \| 3.0 \| 1605 \| 0.5578 \| 0.5299 \| \| 0.1846 \| 4.0 \| 2140 \| 0.7711 \| 0.5176 \| \| 0.1363 \| 5.0 \| 2675 \| 0.8224 \| 0.5419 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
metrosir/sd	metrosir	2023-04-26T12:32:27Z	49	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	2023-04-25T13:01:04Z	# Chill Watcher consider deploy on: - huggingface inference point - replicate api - lightning.ai # platform comparison > all support autoscaling \|platform\|prediction speed\|charges\|deploy handiness\| \|-\|-\|-\|-\| \|huggingface\|fast:20s\|high:$0.6/hr (without autoscaling)\|easy:git push\| \|replicate\|fast if used frequently: 30s, slow if needs initialization: 5min\|low: $0.02 per generation\|difficult: build image and upload\| \|lightning.ai\|fast with app running: 20s, slow if idle: XXs\|low: free $30 per month, $0.18 per init, $0.02 per run\|easy: one command\| # platform deploy options ## huggingface > [docs](https://huggingface.co/docs/inference-endpoints/guides/custom_handler) - requirements: use pip packages in `requirements.txt` - `init()` and `predict()` function: use `handler.py`, implement the `EndpointHandler` class - more: modify `handler.py` for requests and inference and explore more highly-customized features - deploy: git (lfs) push to huggingface repository(the whole directory including models and weights, etc.), and use inference endpoints to deploy. Click and deploy automaticly, very simple. - call api: use the url provide by inference endpoints after endpoint is ready(build, initialize and in a "running" state), make a post request to the url using request schema definied in the `handler.py` ## replicate > [docs](https://replicate.com/docs/guides/push-a-model) - requirements: specify all requirements(pip packages, system packages, python version, cuda, etc.) in `cog.yaml` - `init()` and `predict()` function: use `predict.py`, implement the `Predictor` class - more: modify `predict.py` - deploy: 1. get a linux GPU machine with 60GB disk space; 2. install [cog](https://replicate.com/docs/guides/push-a-model) and [docker](https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository) 3. `git pull` the current repository from huggingface, including large model files 4. after `predict.py` and `cog.yaml` is correctly coded, run `cog login`, `cog push`, then cog will build a docker image locally and push the image to replicate. As the image could take 30GB or so disk space, it would cost a lot network bandwidth. - call api: if everything runs successfully and the docker image is pushed to replicate, you will see a web-ui and an API example directly in your replicate repository ## lightning.ai > docs: [code](https://lightning.ai/docs/app/stable/levels/basic/real_lightning_component_implementations.html), [deploy](https://lightning.ai/docs/app/stable/workflows/run_app_on_cloud/) - requirements: - pip packages are listed in `requirements.txt`, note that some requirements are different from those in huggingface, and you need to modify some lines in `requirements.txt` according to the comment in the `requirements.txt` - other pip packages, system packages and some big model weight files download commands, can be listed using a custom build config. Checkout `class CustomBuildConfig(BuildConfig)` in `app.py`. In a custom build config you can use many linux commands such as `wget` and `sudo apt-get update`. The custom build config will be executed on the `__init__()` of the `PythonServer` class - `init()` and `predict()` function: use `app.py`, implement the `PythonServer` class. Note: - some packages haven't been installed when the file is called(these packages may be installed when `__init__()` is called), so some import code should be in the function, not at the top of the file, or you may get import errors. - you can't save your own value to `PythonServer.self` unless it's predifined in the variables, so don't assign any self-defined variables to `self` - if you use the custom build config, you should implement `PythonServer`'s `__init()__` yourself, so don't forget to use the correct function signature - more: ... - deploy: - `pip install lightning` - prepare the directory on your local computer(no need to have a GPU) - list big files in the `.lightningignore` file to avoid big file upload and save deploy time cost - run `lightning run app app.py --cloud` in the local terminal, and it will upload the files in the directory to lightning cloud, and start deploying on the cloud - check error logs on the web-ui, use `all logs` - call api: only if the app starts successfully, you can see a valid url in the `settings` page of the web-ui. Open that url, and you can see the api ### some stackoverflow: install docker: - https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository install git-lfs: - https://github.com/git-lfs/git-lfs/blob/main/INSTALLING.md linux: ``` curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh \| sudo bash sudo apt-get install git-lfs ``` --- license: apache-2.0 ---
Dewa/pixelcopter_rl-v4	Dewa	2023-04-26T12:31:11Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T12:31:06Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter_rl-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 22.20 +/- 15.35 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
mtc/mbart-newsum	mtc	2023-04-26T12:29:27Z	106	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-29T13:47:33Z	# grizzled-interest-2023-03-29 This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the mtc/newsum2021 dataset. It achieves the following results on the test set: - Loss: 3.5178 - Rouge1: 31.4512 - Rouge2: 11.0965 - Rougel: 21.5021 - Rougelsum: 28.634 - Gen Len: 75.755 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 4 - 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: polynomial - lr_scheduler_warmup_steps: 500 - training_steps: 8000 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 3.6815 \| 0.89 \| 500 \| 3.5617 \| 29.5414 \| 10.5201 \| 20.056 \| 27.2581 \| 86.07 \| \| 3.4132 \| 1.79 \| 1000 \| 3.4133 \| 29.6393 \| 9.9545 \| 19.6903 \| 27.0861 \| 96.545 \| \| 3.198 \| 2.68 \| 1500 \| 3.3693 \| 29.8614 \| 10.4517 \| 20.1728 \| 27.3879 \| 94.31 \| \| 3.0292 \| 3.58 \| 2000 \| 3.3370 \| 30.6444 \| 11.5935 \| 21.1955 \| 28.2699 \| 87.355 \| \| 2.901 \| 4.47 \| 2500 \| 3.3440 \| 30.7453 \| 11.111 \| 21.2076 \| 28.269 \| 88.365 \| \| 2.7832 \| 5.37 \| 3000 \| 3.3758 \| 30.4995 \| 10.9025 \| 20.6601 \| 28.0575 \| 104.655 \| \| 2.6965 \| 6.26 \| 3500 \| 3.3793 \| 31.2287 \| 11.5544 \| 21.1909 \| 28.738 \| 88.47 \| \| 2.6475 \| 7.16 \| 4000 \| 3.4083 \| 32.0341 \| 11.9417 \| 22.2785 \| 29.2495 \| 84.095 \| \| 2.6196 \| 8.05 \| 4500 \| 3.4007 \| 30.8963 \| 11.3811 \| 21.3146 \| 28.3222 \| 90.875 \| \| 2.5574 \| 8.94 \| 5000 \| 3.4104 \| 32.3867 \| 12.0469 \| 21.9831 \| 29.5205 \| 87.46 \| \| 2.4977 \| 9.84 \| 5500 \| 3.4340 \| 32.5857 \| 12.5072 \| 22.6288 \| 30.1168 \| 79.87 \| \| 2.4362 \| 10.73 \| 6000 \| 3.4626 \| 31.9121 \| 11.8577 \| 22.3647 \| 29.3822 \| 85.17 \| \| 2.3977 \| 11.63 \| 6500 \| 3.4737 \| 32.0202 \| 12.0413 \| 22.5237 \| 29.5166 \| 77.905 \| \| 2.369 \| 12.52 \| 7000 \| 3.4890 \| 31.2516 \| 11.3416 \| 21.5711 \| 28.5465 \| 85.605 \| \| 2.3446 \| 13.42 \| 7500 \| 3.4949 \| 32.1277 \| 11.6876 \| 22.0244 \| 29.2239 \| 83.895 \| \| 2.3295 \| 14.31 \| 8000 \| 3.4976 \| 31.8729 \| 11.629 \| 21.9629 \| 28.9948 \| 84.47 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.0.dev20230220+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
vega6000/distilgpt2-finetuned-medical	vega6000	2023-04-26T12:22:34Z	188	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-04-26T09:26:10Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-medical results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-medical This model is a fine-tuned version of [vega6000/distilgpt2-finetuned-medical](https://huggingface.co/vega6000/distilgpt2-finetuned-medical) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 15 \| 2.0817 \| \| No log \| 2.0 \| 30 \| 1.9431 \| \| No log \| 3.0 \| 45 \| 1.8487 \| \| No log \| 4.0 \| 60 \| 1.7761 \| \| No log \| 5.0 \| 75 \| 1.7253 \| \| No log \| 6.0 \| 90 \| 1.6875 \| \| No log \| 7.0 \| 105 \| 1.6574 \| \| No log \| 8.0 \| 120 \| 1.6385 \| \| No log \| 9.0 \| 135 \| 1.6288 \| \| No log \| 10.0 \| 150 \| 1.6248 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
Zexois36/tokyolagi	Zexois36	2023-04-26T12:13:52Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T12:11:45Z	--- license: creativeml-openrail-m ---
DTorregrosa/sd-class-butterflies-32	DTorregrosa	2023-04-26T12:11:54Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-04-26T12:11:41Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('DTorregrosa/sd-class-butterflies-32') image = pipeline().images[0] image ```
pszemraj/pegasus-large-book-summary	pszemraj	2023-04-26T12:01:32Z	119	1	transformers	[ "transformers", "pytorch", "safetensors", "pegasus", "text2text-generation", "summarization", "en", "dataset:kmfoda/booksum", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- language: - en tags: - summarization - pegasus license: apache-2.0 datasets: - kmfoda/booksum metrics: - rouge widget: - text: "large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock." example_title: "earthquakes" - text: " A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a 'toolbox' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5)." example_title: "scientific paper" - text: " the big variety of data coming from diverse sources is one of the key properties of the big data phenomenon. It is, therefore, beneficial to understand how data is generated in various environments and scenarios, before looking at what should be done with this data and how to design the best possible architecture to accomplish this The evolution of IT architectures, described in Chapter 2, means that the data is no longer processed by a few big monolith systems, but rather by a group of services In parallel to the processing layer, the underlying data storage has also changed and became more distributed This, in turn, required a significant paradigm shift as the traditional approach to transactions (ACID) could no longer be supported. On top of this, cloud computing is becoming a major approach with the benefits of reducing costs and providing on-demand scalability but at the same time introducing concerns about privacy, data ownership, etc In the meantime the Internet continues its exponential growth: Every day both structured and unstructured data is published and available for processing: To achieve competitive advantage companies have to relate their corporate resources to external services, e.g. financial markets, weather forecasts, social media, etc While several of the sites provide some sort of API to access the data in a more orderly fashion; countless sources require advanced web mining and Natural Language Processing (NLP) processing techniques: Advances in science push researchers to construct new instruments for observing the universe O conducting experiments to understand even better the laws of physics and other domains. Every year humans have at their disposal new telescopes, space probes, particle accelerators, etc These instruments generate huge streams of data, which need to be stored and analyzed. The constant drive for efficiency in the industry motivates the introduction of new automation techniques and process optimization: This could not be done without analyzing the precise data that describe these processes. As more and more human tasks are automated, machines provide rich data sets, which can be analyzed in real-time to drive efficiency to new levels. Finally, it is now evident that the growth of the Internet of Things is becoming a major source of data. More and more of the devices are equipped with significant computational power and can generate a continuous data stream from their sensors. In the subsequent sections of this chapter, we will look at the domains described above to see what they generate in terms of data sets. We will compare the volumes but will also look at what is characteristic and important from their respective points of view. 3.1 The Internet is undoubtedly the largest database ever created by humans. While several well described; cleaned, and structured data sets have been made available through this medium, most of the resources are of an ambiguous, unstructured, incomplete or even erroneous nature. Still, several examples in the areas such as opinion mining, social media analysis, e-governance, etc, clearly show the potential lying in these resources. Those who can successfully mine and interpret the Internet data can gain unique insight and competitive advantage in their business An important area of data analytics on the edge of corporate IT and the Internet is Web Analytics." example_title: "data science textbook" inference: parameters: max_length: 64 no_repeat_ngram_size: 2 encoder_no_repeat_ngram_size: 3 repetition_penalty: 2.4 length_penalty: 0.5 num_beams: 4 early_stopping: True --- # checkpoints This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset. ## Model description More information needed ## Intended uses & limitations - standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first. - this was only trained on the dataset for an epoch but still provides reasonable results. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.10.3
kahkasha/distilbert-base-uncased-finetuned-squad	kahkasha	2023-04-26T12:01:28Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-04-25T11:55:56Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.219 \| 1.0 \| 5533 \| 1.1625 \| \| 0.9573 \| 2.0 \| 11066 \| 1.1382 \| \| 0.755 \| 3.0 \| 16599 \| 1.1594 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
iamjoy/ppo-Huggy-01	iamjoy	2023-04-26T11:58:32Z	16	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-04-26T11:58:25Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://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: Find your model_id: iamjoy/ppo-Huggy-01 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
seanghay/mt5-small-km-phoneme-reverse	seanghay	2023-04-26T11:41:52Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-26T10:45:20Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-km-phoneme-reverse 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-km-phoneme-reverse This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2416 - Rouge1: 30.9064 - Rouge2: 15.5474 - Rougel: 30.6746 - Rougelsum: 30.691 - Gen Len: 4.8282 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.6872 \| 1.0 \| 2515 \| 1.3202 \| 28.8555 \| 13.7602 \| 28.6841 \| 28.7043 \| 4.6996 \| \| 1.5052 \| 2.0 \| 5030 \| 1.2561 \| 30.5921 \| 15.3773 \| 30.3685 \| 30.3818 \| 4.8390 \| \| 1.5144 \| 3.0 \| 7545 \| 1.2416 \| 30.9064 \| 15.5474 \| 30.6746 \| 30.691 \| 4.8282 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
Stern5497/sBert-swiss-legal-base	Stern5497	2023-04-26T11:41:09Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-04-26T09:24:47Z	--- 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 14247 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 5000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1424, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
Dqcky/Gabagtha	Dqcky	2023-04-26T11:40:48Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-26T11:39:04Z	--- license: creativeml-openrail-m ---
jcrOrganisation/ppo-pyramids	jcrOrganisation	2023-04-26T11:40:20Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-04-26T11:40:14Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: jcrOrganisation/ppo-pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
NiamaLynn/sd-class-butterflies-32	NiamaLynn	2023-04-26T11:24:50Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-04-26T11:24:35Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('NiamaLynn/sd-class-butterflies-32') image = pipeline().images[0] image ```
pythonist/bert-base-cased-healthdemomodel	pythonist	2023-04-26T11:23:33Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-04-26T11:21:41Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-healthdemomodel 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-cased-healthdemomodel This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.5819 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 6.1760 \| \| No log \| 2.0 \| 2 \| 6.1161 \| \| No log \| 3.0 \| 3 \| 6.0619 \| \| No log \| 4.0 \| 4 \| 6.0120 \| \| No log \| 5.0 \| 5 \| 5.9641 \| \| No log \| 6.0 \| 6 \| 5.9177 \| \| No log \| 7.0 \| 7 \| 5.8738 \| \| No log \| 8.0 \| 8 \| 5.8334 \| \| No log \| 9.0 \| 9 \| 5.7938 \| \| No log \| 10.0 \| 10 \| 5.7589 \| \| No log \| 11.0 \| 11 \| 5.7289 \| \| No log \| 12.0 \| 12 \| 5.7019 \| \| No log \| 13.0 \| 13 \| 5.6746 \| \| No log \| 14.0 \| 14 \| 5.6499 \| \| No log \| 15.0 \| 15 \| 5.6293 \| \| No log \| 16.0 \| 16 \| 5.6122 \| \| No log \| 17.0 \| 17 \| 5.5995 \| \| No log \| 18.0 \| 18 \| 5.5905 \| \| No log \| 19.0 \| 19 \| 5.5848 \| \| No log \| 20.0 \| 20 \| 5.5819 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
worsty/dqn-SpaceInvadersNoFrameskip-v4-test6	worsty	2023-04-26T11:21:32Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T11:14:42Z	--- 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: 582.00 +/- 170.93 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 worsty -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 worsty -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 worsty ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

carolinetfls/plant-seedlings-model-beit-free-0-6

carolinetfls

2023-04-26T23:00:49Z

19

0

transformers

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

image-classification

2023-04-26T20:11:12Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: plant-seedlings-model-beit-free-0-6 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: 0.7475442043222004 ---  # plant-seedlings-model-beit-free-0-6 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7557 - Accuracy: 0.7475 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.4892 | 0.2 | 100 | 2.4909 | 0.0751 | | 2.4906 | 0.39 | 200 | 2.4886 | 0.0756 | | 2.3925 | 0.59 | 300 | 2.3344 | 0.1537 | | 2.31 | 0.79 | 400 | 2.3306 | 0.1464 | | 2.2355 | 0.98 | 500 | 2.2335 | 0.1778 | | 2.2642 | 1.18 | 600 | 2.1889 | 0.1807 | | 2.0806 | 1.38 | 700 | 2.3229 | 0.1680 | | 2.1013 | 1.57 | 800 | 2.1519 | 0.2004 | | 2.0094 | 1.77 | 900 | 2.0611 | 0.2146 | | 2.0387 | 1.96 | 1000 | 2.0413 | 0.2210 | | 2.0032 | 2.16 | 1100 | 1.9758 | 0.2618 | | 1.986 | 2.36 | 1200 | 1.9238 | 0.2638 | | 2.0885 | 2.55 | 1300 | 1.8944 | 0.2942 | | 1.8808 | 2.75 | 1400 | 1.9330 | 0.2868 | | 1.915 | 2.95 | 1500 | 1.8919 | 0.2814 | | 1.958 | 3.14 | 1600 | 1.8762 | 0.3114 | | 1.9001 | 3.34 | 1700 | 1.8389 | 0.3232 | | 1.8572 | 3.54 | 1800 | 1.7978 | 0.3487 | | 1.9969 | 3.73 | 1900 | 1.9371 | 0.3089 | | 1.9186 | 3.93 | 2000 | 1.8055 | 0.3502 | | 1.7591 | 4.13 | 2100 | 1.7695 | 0.3428 | | 1.8368 | 4.32 | 2200 | 1.7498 | 0.3502 | | 1.9842 | 4.52 | 2300 | 1.8049 | 0.3193 | | 1.7606 | 4.72 | 2400 | 1.6730 | 0.3954 | | 1.7787 | 4.91 | 2500 | 1.7104 | 0.3777 | | 1.6377 | 5.11 | 2600 | 1.6647 | 0.3870 | | 1.8834 | 5.3 | 2700 | 1.6325 | 0.3973 | | 1.6149 | 5.5 | 2800 | 1.6722 | 0.3787 | | 1.7038 | 5.7 | 2900 | 1.6425 | 0.3973 | | 1.682 | 5.89 | 3000 | 1.5927 | 0.4180 | | 1.6326 | 6.09 | 3100 | 1.4982 | 0.4622 | | 1.5687 | 6.29 | 3200 | 1.4440 | 0.4774 | | 1.3637 | 6.48 | 3300 | 1.4477 | 0.4877 | | 1.4079 | 6.68 | 3400 | 1.3827 | 0.5020 | | 1.3721 | 6.88 | 3500 | 1.4069 | 0.5010 | | 1.5675 | 7.07 | 3600 | 1.3595 | 0.5083 | | 1.5725 | 7.27 | 3700 | 1.3790 | 0.4956 | | 1.4522 | 7.47 | 3800 | 1.3116 | 0.5378 | | 1.4692 | 7.66 | 3900 | 1.3729 | 0.4980 | | 1.5073 | 7.86 | 4000 | 1.3799 | 0.5216 | | 1.2529 | 8.06 | 4100 | 1.2706 | 0.5486 | | 1.3727 | 8.25 | 4200 | 1.2519 | 0.5535 | | 1.2451 | 8.45 | 4300 | 1.2595 | 0.5648 | | 1.339 | 8.64 | 4400 | 1.3614 | 0.5172 | | 1.2858 | 8.84 | 4500 | 1.3028 | 0.5393 | | 1.1039 | 9.04 | 4600 | 1.2309 | 0.5771 | | 1.0351 | 9.23 | 4700 | 1.2678 | 0.5609 | | 1.1125 | 9.43 | 4800 | 1.2786 | 0.5624 | | 1.1667 | 9.63 | 4900 | 1.2131 | 0.5840 | | 1.1386 | 9.82 | 5000 | 1.1359 | 0.6154 | | 1.1888 | 10.02 | 5100 | 1.1309 | 0.6041 | | 1.1777 | 10.22 | 5200 | 1.1288 | 0.6287 | | 1.3693 | 10.41 | 5300 | 1.3827 | 0.5182 | | 1.1016 | 10.61 | 5400 | 1.2255 | 0.5594 | | 1.1527 | 10.81 | 5500 | 1.0772 | 0.6434 | | 1.1039 | 11.0 | 5600 | 1.1032 | 0.6100 | | 1.2502 | 11.2 | 5700 | 1.1230 | 0.6169 | | 1.0818 | 11.39 | 5800 | 1.0750 | 0.6302 | | 1.0872 | 11.59 | 5900 | 1.0397 | 0.6331 | | 1.0425 | 11.79 | 6000 | 1.0231 | 0.6483 | | 1.0791 | 11.98 | 6100 | 1.0250 | 0.6636 | | 0.9736 | 12.18 | 6200 | 1.0879 | 0.6267 | | 0.9788 | 12.38 | 6300 | 1.1334 | 0.5968 | | 0.8982 | 12.57 | 6400 | 0.9934 | 0.6528 | | 1.077 | 12.77 | 6500 | 0.9698 | 0.6812 | | 1.0347 | 12.97 | 6600 | 1.0265 | 0.6513 | | 0.9159 | 13.16 | 6700 | 0.9442 | 0.6788 | | 1.1187 | 13.36 | 6800 | 0.9738 | 0.6685 | | 0.9624 | 13.56 | 6900 | 1.0008 | 0.6699 | | 0.922 | 13.75 | 7000 | 0.9502 | 0.6906 | | 0.9317 | 13.95 | 7100 | 0.9687 | 0.6758 | | 0.9979 | 14.15 | 7200 | 0.9869 | 0.6768 | | 0.8362 | 14.34 | 7300 | 0.9220 | 0.6994 | | 0.8449 | 14.54 | 7400 | 0.9181 | 0.6861 | | 0.9678 | 14.73 | 7500 | 0.9789 | 0.6729 | | 0.9119 | 14.93 | 7600 | 0.8879 | 0.7009 | | 0.9517 | 15.13 | 7700 | 0.8816 | 0.6994 | | 0.9688 | 15.32 | 7800 | 0.8803 | 0.7117 | | 0.8625 | 15.52 | 7900 | 0.8782 | 0.7038 | | 0.9121 | 15.72 | 8000 | 0.8225 | 0.7191 | | 0.9035 | 15.91 | 8100 | 0.8649 | 0.7087 | | 0.8762 | 16.11 | 8200 | 0.8427 | 0.7102 | | 0.7708 | 16.31 | 8300 | 0.8685 | 0.7117 | | 0.8893 | 16.5 | 8400 | 0.8178 | 0.7264 | | 0.9584 | 16.7 | 8500 | 0.8709 | 0.7092 | | 0.757 | 16.9 | 8600 | 0.8244 | 0.7254 | | 0.8184 | 17.09 | 8700 | 0.8128 | 0.7240 | | 0.8858 | 17.29 | 8800 | 0.8360 | 0.7156 | | 0.7116 | 17.49 | 8900 | 0.7952 | 0.7279 | | 0.9579 | 17.68 | 9000 | 0.8263 | 0.7274 | | 0.7037 | 17.88 | 9100 | 0.7884 | 0.7348 | | 1.0359 | 18.07 | 9200 | 0.8118 | 0.7402 | | 1.067 | 18.27 | 9300 | 0.8203 | 0.7186 | | 0.8503 | 18.47 | 9400 | 0.7918 | 0.7362 | | 0.8552 | 18.66 | 9500 | 0.7972 | 0.7382 | | 0.7498 | 18.86 | 9600 | 0.8038 | 0.7343 | | 0.8542 | 19.06 | 9700 | 0.7799 | 0.7333 | | 0.9539 | 19.25 | 9800 | 0.7795 | 0.7333 | | 0.7369 | 19.45 | 9900 | 0.8103 | 0.7269 | | 0.6637 | 19.65 | 10000 | 0.7597 | 0.7441 | | 0.6712 | 19.84 | 10100 | 0.7557 | 0.7475 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Sergendel/a2c-AntBulletEnv-v0

Sergendel

2023-04-26T22:48:01Z

0

stable-baselines3

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

reinforcement-learning

2023-04-26T18:09:29Z

--- 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: 1226.61 +/- 481.06 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 ... ```

kerdel/GenerAd-AI

kerdel

2023-04-26T22:23:59Z

36

0

adapter-transformers

[ "adapter-transformers", "text-generation", "dataset:kerdel/generative_ai_sample", "license:bigscience-openrail-m", "region:us" ]

text-generation

2023-04-26T22:18:12Z

--- license: bigscience-openrail-m datasets: - kerdel/generative_ai_sample library_name: adapter-transformers pipeline_tag: text-generation ---

myklicious/Reinforce-CartPole-V1

myklicious

2023-04-26T21:49:52Z

0

null

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

reinforcement-learning

2023-04-26T21:49:40Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-V1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

FacehugmanIII/4x_foolhardy_Remacri

FacehugmanIII

2023-04-26T21:49:37Z

0

90

null

[ "art", "license:unknown", "region:us" ]

null

2023-04-26T21:39:16Z

--- license: unknown tags: - art --- Using the Remacri upscaler in Automatic1111: Get the '4x_foolhardy_Remacri.pth' file linked in this post Copy it to: \stable-diffusion-webui\models\ESRGAN Restart WebUI 4x_foolhardy_Remacri is now available in the Extras tab and for the SD Upscale script I didn't create this upscaler, I simply downloaded it from a random link on reddit and uploaded here as I couldn't find it anywhere else.

Dsfajardob/rl_course_vizdoom_health_gathering_supreme

Dsfajardob

2023-04-26T21:45:35Z

0

sample-factory

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

reinforcement-learning

2023-04-26T20:57:34Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.04 +/- 5.36 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Dsfajardob/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

jkorstad/a2c-PandaReachDense-v2

jkorstad

2023-04-26T21:18:32Z

0

stable-baselines3

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

reinforcement-learning

2023-04-26T05:03:19Z

--- 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: -2.03 +/- 1.11 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 ... ```

jmartin233/bloom-1b7-lora-reading-comprehension

jmartin233

2023-04-26T20:50:10Z

0

null

[ "text-generation", "en", "dataset:jmartin233/reading_comprehension_exercise_dataset_v2", "license:bigscience-openrail-m", "region:us" ]

text-generation

2023-04-26T19:25:42Z

--- license: bigscience-openrail-m datasets: - jmartin233/reading_comprehension_exercise_dataset_v2 language: - en pipeline_tag: text-generation --- # Model Card for Model ID The model generates short reading comprehension exercises for English teachers to use. ## Model Details ### Model Description  Finetuned using this process: https://github.com/FourthBrain/Building-Generative-AI-Apps/blob/main/GenerAd-AI/notebooks/%F0%9F%92%AE%20GenerAd%20AI%F0%9F%92%AE%20Fine%20tuning%20BLOOM.ipynb ## Uses English teachers can use the model to generate short texts that use specified types of grammar, and are written a specified level (beginner, intermediate or advanced.)

amitrajitbh1/distilroberta-base-finetuned-teen-2

amitrajitbh1

2023-04-26T20:49:20Z

125

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-04-26T20:14:09Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-teen-2 results: [] ---  # distilroberta-base-finetuned-teen-2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0436 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5736 | 1.0 | 157 | 3.3554 | | 3.1559 | 2.0 | 314 | 3.1532 | | 3.0252 | 3.0 | 471 | 3.0850 | | 2.858 | 4.0 | 628 | 2.9401 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

JuanDa14Sa/Taxi-v3-QLearn

JuanDa14Sa

2023-04-26T20:42:28Z

0

null

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

reinforcement-learning

2023-04-26T20:30:22Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-QLearn 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="JuanDa14Sa/Taxi-v3-QLearn", 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"]) ```

akadhim-ai/sd_aiconos-model-v1-2_400

akadhim-ai

2023-04-26T20:37:30Z

31

0

diffusers

[ "diffusers", "tensorboard", "art", "text-to-image", "en", "dataset:Ali-fb/ios_icons_2", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-04-26T14:02:51Z

--- license: openrail datasets: - Ali-fb/ios_icons_2 language: - en metrics: - accuracy library_name: diffusers pipeline_tag: text-to-image tags: - art ---

Pedrampedram/MarketMail-AI

Pedrampedram

2023-04-26T20:27:00Z

0

null

[ "marketing", "question-answering", "en", "dataset:Pedrampedram/MarketMail-AI-Dataset", "license:openrail", "region:us" ]

question-answering

2023-04-26T20:00:26Z

--- license: openrail datasets: - Pedrampedram/MarketMail-AI-Dataset language: - en pipeline_tag: question-answering tags: - marketing ---

khadija267/distilbert-base-uncased-distilled-clinc

khadija267

2023-04-26T20:25:28Z

110

0

transformers

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

text-classification

2023-04-26T00:36:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.947741935483871 ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2830 - Accuracy: 0.9477 ## Model description More information needed ## Intended uses & 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.8723 | 1.0 | 318 | 2.8941 | 0.7461 | | 2.2155 | 2.0 | 636 | 1.4516 | 0.8613 | | 1.0985 | 3.0 | 954 | 0.7466 | 0.9152 | | 0.5635 | 4.0 | 1272 | 0.4707 | 0.9358 | | 0.3294 | 5.0 | 1590 | 0.3628 | 0.9429 | | 0.221 | 6.0 | 1908 | 0.3173 | 0.9439 | | 0.1671 | 7.0 | 2226 | 0.2968 | 0.9477 | | 0.14 | 8.0 | 2544 | 0.2876 | 0.9484 | | 0.1263 | 9.0 | 2862 | 0.2838 | 0.9471 | | 0.1189 | 10.0 | 3180 | 0.2830 | 0.9477 | ### Framework versions - Transformers 4.11.3 - Pytorch 2.0.0+cu118 - Datasets 1.16.1 - Tokenizers 0.10.3

Dsfajardob/ppo-LunarLander-v2

Dsfajardob

2023-04-26T20:19:44Z

1

0

stable-baselines3

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

reinforcement-learning

2023-03-28T20:15:40Z

--- 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: 269.59 +/- 20.52 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 ... ```

minoosh/ast-finetuned-audioset-10-10-0.4593-finetuned-ie

minoosh

2023-04-26T20:09:47Z

7

0

transformers

[ "transformers", "pytorch", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]

audio-classification

2023-04-26T09:55:48Z

--- license: bsd-3-clause tags: - generated_from_trainer model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-ie results: [] ---  # ast-finetuned-audioset-10-10-0.4593-finetuned-ie This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 3.0698 - eval_accuracy: 0.6076 - eval_runtime: 163.7462 - eval_samples_per_second: 7.579 - eval_steps_per_second: 0.953 - epoch: 18.08 - step: 1844 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Framework versions - Transformers 4.27.4 - Pytorch 1.13.0 - Datasets 2.11.0 - Tokenizers 0.13.2

khadija267/distilbert-base-uncased-finetuned-clinc

khadija267

2023-04-26T20:02:43Z

106

0

transformers

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

text-classification

2023-04-25T23:46:09Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9161290322580645 ---  # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7754 - Accuracy: 0.9161 ## Model description More information needed ## Intended uses & 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2893 | 1.0 | 318 | 3.2831 | 0.7397 | | 2.6289 | 2.0 | 636 | 1.8731 | 0.8345 | | 1.5481 | 3.0 | 954 | 1.1580 | 0.89 | | 1.0137 | 4.0 | 1272 | 0.8584 | 0.9077 | | 0.7969 | 5.0 | 1590 | 0.7754 | 0.9161 | ### Framework versions - Transformers 4.11.3 - Pytorch 2.0.0+cu118 - Datasets 1.16.1 - Tokenizers 0.10.3

yerx/videomae-base-finetuned-basketball-subset-20epochs

yerx

2023-04-26T20:01:14Z

63

0

transformers

[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-04-26T18:56:04Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-basketball-subset-20epochs results: [] ---  # videomae-base-finetuned-basketball-subset-20epochs This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8785 - Accuracy: 0.1972 ## Model description More information needed ## Intended uses & 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: 1 - eval_batch_size: 1 - 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: 4060 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2525 | 0.05 | 200 | 0.7720 | 0.52 | | 0.8649 | 1.05 | 400 | 0.7721 | 0.48 | | 1.0703 | 2.05 | 600 | 1.3605 | 0.52 | | 0.606 | 3.05 | 800 | 1.0668 | 0.6 | | 2.0221 | 4.05 | 1000 | 1.1741 | 0.56 | | 1.2916 | 5.05 | 1200 | 1.4747 | 0.52 | | 1.4861 | 6.05 | 1400 | 1.1454 | 0.6 | | 1.3012 | 7.05 | 1600 | 1.6105 | 0.56 | | 1.3327 | 8.05 | 1800 | 1.2343 | 0.52 | | 2.077 | 9.05 | 2000 | 1.3243 | 0.6 | | 1.2349 | 10.05 | 2200 | 1.2044 | 0.6 | | 1.005 | 11.05 | 2400 | 1.6417 | 0.52 | | 1.1622 | 12.05 | 2600 | 1.3058 | 0.56 | | 0.8031 | 13.05 | 2800 | 0.6776 | 0.48 | | 0.8588 | 14.05 | 3000 | 1.1644 | 0.64 | | 0.8451 | 15.05 | 3200 | 0.8491 | 0.64 | | 1.1336 | 16.05 | 3400 | 1.0237 | 0.6 | | 1.5719 | 17.05 | 3600 | 1.0391 | 0.64 | | 0.4892 | 18.05 | 3800 | 0.9995 | 0.64 | | 1.2092 | 19.05 | 4000 | 0.9802 | 0.56 | | 0.9784 | 20.01 | 4060 | 0.9771 | 0.56 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Dsfajardob/ppo-LunarLander-v2-U8

Dsfajardob

2023-04-26T19:56:33Z

0

null

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

reinforcement-learning

2023-04-26T19:56:25Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -218.11 +/- 142.77 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Dsfajardob/ppo-LunarLander-v2-U8' 'batch_size': 512 'minibatch_size': 128} ```

Anyayolp/t5-end2end-questions-generation

Anyayolp

2023-04-26T19:56:27Z

161

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_modified_for_t5_qg", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-04-26T14:25:47Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_modified_for_t5_qg model-index: - name: t5-end2end-questions-generation results: [] ---  # t5-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. It achieves the following results on the evaluation set: - Loss: 1.5674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5884 | 0.34 | 100 | 1.9159 | | 1.9705 | 0.68 | 200 | 1.7310 | | 1.8439 | 1.02 | 300 | 1.6672 | | 1.7426 | 1.35 | 400 | 1.6382 | | 1.7147 | 1.69 | 500 | 1.6199 | | 1.6908 | 2.03 | 600 | 1.6053 | | 1.6315 | 2.37 | 700 | 1.5967 | | 1.627 | 2.71 | 800 | 1.5939 | | 1.6122 | 3.05 | 900 | 1.5877 | | 1.5706 | 3.39 | 1000 | 1.5861 | | 1.5708 | 3.73 | 1100 | 1.5742 | | 1.5534 | 4.06 | 1200 | 1.5798 | | 1.5351 | 4.4 | 1300 | 1.5738 | | 1.5226 | 4.74 | 1400 | 1.5757 | | 1.5187 | 5.08 | 1500 | 1.5727 | | 1.4963 | 5.42 | 1600 | 1.5710 | | 1.4841 | 5.76 | 1700 | 1.5668 | | 1.5025 | 6.1 | 1800 | 1.5688 | | 1.4778 | 6.44 | 1900 | 1.5717 | | 1.4769 | 6.77 | 2000 | 1.5674 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy

mrm8488

2023-04-26T19:55:30Z

651

6

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "medical", "colon", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-02T23:29:05Z

--- tags: - image-classification - pytorch - medical - colon metrics: - accuracy: 0.93 --- # Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification ## Demo ### Drag the following images to the widget to test the model - ![](https://i.imgur.com/2ykziCJ_d.webp?maxwidth=224&fidelity=grand) - ![](https://i.imgur.com/NfWPHkj_d.webp?maxwidth=224&fidelity=grand) - ![](https://i.imgur.com/C3RexVQ_d.webp?maxwidth=224&fidelity=grand) - ![](https://i.imgur.com/qcCYpN9_d.webp?maxwidth=224&fidelity=grand) ## Training You can find the code [here](https://github.com/qanastek/HugsVision/blob/main/recipes/kvasir_v2/binary_classification/Kvasir_v2_Image_Classifier.ipynb) ## Metrics ``` precision recall f1-score support dyed-lifted-polyps 0.95 0.93 0.94 60 dyed-resection-margins 0.97 0.95 0.96 64 esophagitis 0.93 0.79 0.85 67 normal-cecum 1.00 0.98 0.99 54 normal-pylorus 0.95 1.00 0.97 57 normal-z-line 0.82 0.93 0.87 67 polyps 0.92 0.92 0.92 52 ulcerative-colitis 0.93 0.95 0.94 59 accuracy 0.93 480 macro avg 0.93 0.93 0.93 480 weighted avg 0.93 0.93 0.93 480 ``` ## How to use ```py from transformers import ViTFeatureExtractor, ViTForImageClassification from hugsvision.inference.VisionClassifierInference import VisionClassifierInference path = "mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy" classifier = VisionClassifierInference( feature_extractor = ViTFeatureExtractor.from_pretrained(path), model = ViTForImageClassification.from_pretrained(path), ) img = "Your image path" label = classifier.predict(img_path=img) print("Predicted class:", label) ``` > Disclaimer: This model was trained for research only > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with &hearts; in Spain

gmongaras/gpt-anime-sub-1.3B

gmongaras

2023-04-26T19:53:42Z

142

5

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neo", "text-generation", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-02-01T05:26:48Z

--- license: openrail --- This model is fintuned using the following model: https://huggingface.co/EleutherAI/gpt-neo-1.3B The data was scraped from: https://www.kitsunekko.net/dirlist.php?dir=subtitles%2F To load, use: model = pipeline('text-generation',model="gmongaras/gpt-anime-sub-1.3B", tokenizer="EleutherAI/gpt-neo-1.3B")

Callidior/bert2bert-base-arxiv-titlegen

Callidior

2023-04-26T19:42:59Z

163

13

transformers

[ "transformers", "pytorch", "safetensors", "encoder-decoder", "text2text-generation", "summarization", "en", "dataset:arxiv_dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-03-02T23:29:04Z

--- language: - en tags: - summarization license: apache-2.0 datasets: - arxiv_dataset metrics: - rouge widget: - text: "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data." --- # Paper Title Generator Generates titles for computer science papers given an abstract. The model is a BERT2BERT Encoder-Decoder using the official `bert-base-uncased` checkpoint as initialization for the encoder and decoder. It was fine-tuned on 318,500 computer science papers posted on arXiv.org between 2007 and 2022 and achieved a 26.3% Rouge2 F1-Score on held-out validation data. **Live Demo:** [https://paper-titles.ey.r.appspot.com/](https://paper-titles.ey.r.appspot.com/)

PanEa/dolly-v2-gptj-enhanced-auto-gptq

PanEa

2023-04-26T19:16:11Z

6

1

transformers

[ "transformers", "gptj", "text-generation", "en", "dataset:vicgalle/alpaca-gpt4", "dataset:databricks/databricks-dolly-15k", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-04-26T08:08:50Z

--- license: afl-3.0 datasets: - vicgalle/alpaca-gpt4 - databricks/databricks-dolly-15k language: - en metrics: - perplexity pipeline_tag: text-generation ---

JgnMama/Erniee

JgnMama

2023-04-26T19:15:08Z

0

null

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

null

2023-04-26T19:12:31Z

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

Pennyyyyy/t5-end2end-questions-generation

Pennyyyyy

2023-04-26T19:10:40Z

162

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_modified_for_t5_qg", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-04-26T13:24:34Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_modified_for_t5_qg model-index: - name: t5-end2end-questions-generation results: [] ---  # t5-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. It achieves the following results on the evaluation set: - Loss: 1.5674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5884 | 0.34 | 100 | 1.9159 | | 1.9705 | 0.68 | 200 | 1.7310 | | 1.8439 | 1.02 | 300 | 1.6672 | | 1.7426 | 1.35 | 400 | 1.6382 | | 1.7147 | 1.69 | 500 | 1.6199 | | 1.6908 | 2.03 | 600 | 1.6053 | | 1.6315 | 2.37 | 700 | 1.5967 | | 1.627 | 2.71 | 800 | 1.5939 | | 1.6122 | 3.05 | 900 | 1.5877 | | 1.5706 | 3.39 | 1000 | 1.5861 | | 1.5708 | 3.73 | 1100 | 1.5742 | | 1.5534 | 4.06 | 1200 | 1.5798 | | 1.5351 | 4.4 | 1300 | 1.5738 | | 1.5226 | 4.74 | 1400 | 1.5757 | | 1.5187 | 5.08 | 1500 | 1.5727 | | 1.4963 | 5.42 | 1600 | 1.5710 | | 1.4841 | 5.76 | 1700 | 1.5668 | | 1.5025 | 6.1 | 1800 | 1.5688 | | 1.4778 | 6.44 | 1900 | 1.5717 | | 1.4769 | 6.77 | 2000 | 1.5674 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Sergendel/a2c-PandaReachDense-v2

Sergendel

2023-04-26T19:08:07Z

0

stable-baselines3

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

reinforcement-learning

2023-04-26T19:05:30Z

--- 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: -1.47 +/- 0.30 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 ... ```

anilkumar2444/a2c-AntBulletEnv-v0

anilkumar2444

2023-04-26T19:06:19Z

0

stable-baselines3

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

reinforcement-learning

2023-04-26T19:05:13Z

--- 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: 838.75 +/- 152.64 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 ... ```

sdesai/narrativa-finetuned-wmt22-en-pt-br-brwac

sdesai

2023-04-26T18:58:42Z

159

0

transformers

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

text2text-generation

2023-04-26T18:43:19Z

--- tags: - generated_from_trainer model-index: - name: narrativa-finetuned-wmt22-en-pt-br-brwac results: [] ---  # narrativa-finetuned-wmt22-en-pt-br-brwac This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-en-pt-translation](https://huggingface.co/Narrativa/mbart-large-50-finetuned-opus-en-pt-translation) on an unknown dataset. Also added brwac. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results - epoch = 3.0 - eval_bleu = 63.9813 - eval_gen_len = 12.3215 - eval_loss = 0.4894 - eval_runtime = 0:00:20.01 - eval_samples = 190 - eval_samples_per_second = 9.492 - eval_steps_per_second = 2.398 ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu116 - Datasets 2.4.0 - Tokenizers 0.12.1

ericrincon/Qtable_taxi

ericrincon

2023-04-26T18:52:52Z

0

null

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

reinforcement-learning

2023-04-26T18:52:50Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Qtable_taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.72 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="ericrincon/Qtable_taxi", 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"]) ```

ericrincon/Frozen_Lake_4x4

ericrincon

2023-04-26T18:50:48Z

0

null

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

reinforcement-learning

2023-04-26T18:50:46Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: Frozen_Lake_4x4 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="ericrincon/Frozen_Lake_4x4", 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"]) ```

email81227/poca-SoccerTwos

email81227

2023-04-26T18:38:17Z

0

ml-agents

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

reinforcement-learning

2023-04-26T18:07:55Z

--- 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: email81227/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

LecJackS/distilbert-base-uncased-finetuned-emotion

LecJackS

2023-04-26T18:36:36Z

106

0

transformers

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

text-classification

2023-04-26T18:23:29Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: split metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9244610483889744 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2193 - Accuracy: 0.9245 - F1: 0.9245 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8598 | 1.0 | 250 | 0.3274 | 0.9005 | 0.8966 | | 0.2584 | 2.0 | 500 | 0.2193 | 0.9245 | 0.9245 | ### Framework versions - Transformers 4.13.0 - Pytorch 2.0.0+cu118 - Datasets 2.8.0 - Tokenizers 0.10.3

Asif782/lora

Asif782

2023-04-26T18:36:19Z

0

null

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

null

2023-04-26T18:11:22Z

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

KigenCHESS/eng-sw_translation

KigenCHESS

2023-04-26T18:33:41Z

61

0

transformers

[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-04-26T18:30:31Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: KigenCHESS/eng-sw_translation results: [] ---  # KigenCHESS/eng-sw_translation This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-sw](https://huggingface.co/Helsinki-NLP/opus-mt-en-sw) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5372 - Validation Loss: 0.6656 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 424, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.9069 | 0.7022 | 0 | | 0.5372 | 0.6656 | 1 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.2

Raiden-1001/poca-SoccerTwos

Raiden-1001

2023-04-26T18:27:48Z

89

0

ml-agents

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

reinforcement-learning

2023-03-23T05:59:23Z

--- 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: Raiden-1001/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

dkerja/songhyekyo

dkerja

2023-04-26T18:14:39Z

0

null

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

null

2023-04-26T18:13:08Z

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

Pranjalya/a2c-AntBulletEnv-v0

Pranjalya

2023-04-26T18:09:15Z

0

stable-baselines3

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

reinforcement-learning

2023-04-26T18:08:06Z

--- 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: 1829.43 +/- 423.83 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 ... ```

freya48/nekoda

freya48

2023-04-26T18:08:00Z

0

null

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

null

2023-04-26T18:03:06Z

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

kaisar-barlybay-sse/qard-bert-base-multilingual-uncased_6

kaisar-barlybay-sse

2023-04-26T17:52:59Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

multiple-choice

2023-04-26T14:51:34Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: qard-bert-base-multilingual-uncased_6 results: [] ---  # qard-bert-base-multilingual-uncased_6 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3863 - Accuracy: 0.3074 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.399 | 1.0 | 501 | 1.3862 | 0.3373 | | 1.3921 | 2.0 | 1002 | 1.3862 | 0.3613 | | 1.3905 | 3.0 | 1503 | 1.3863 | 0.3273 | | 1.3903 | 4.0 | 2004 | 1.3863 | 0.2455 | | 1.3904 | 5.0 | 2505 | 1.3863 | 0.2834 | | 1.3898 | 6.0 | 3006 | 1.3863 | 0.3074 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

kaisar-barlybay-sse/qard-distilbert-base-multilingual-cased_6

kaisar-barlybay-sse

2023-04-26T17:33:35Z

101

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

multiple-choice

2023-04-26T14:19:40Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: qard-distilbert-base-multilingual-cased_6 results: [] ---  # qard-distilbert-base-multilingual-cased_6 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3254 - Accuracy: 0.4331 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3738 | 1.0 | 501 | 1.3361 | 0.4152 | | 1.2866 | 2.0 | 1002 | 1.3259 | 0.4212 | | 1.0942 | 3.0 | 1503 | 1.3760 | 0.4391 | | 0.8393 | 4.0 | 2004 | 1.6132 | 0.4291 | | 0.6062 | 5.0 | 2505 | 1.8334 | 0.4391 | | 0.4319 | 6.0 | 3006 | 2.3254 | 0.4331 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

YchKhan/EloQuence

YchKhan

2023-04-26T17:30:58Z

0

null

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

null

2023-04-26T17:22:36Z

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

shahukareem/bulhaa-cat

shahukareem

2023-04-26T17:10:07Z

31

0

diffusers

[ "diffusers", "pytorch", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth-hackathon", "animal", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-01-19T15:15:29Z

--- license: creativeml-openrail-m tags: - pytorch - diffusers - stable-diffusion - text-to-image - diffusion-models-class - dreambooth-hackathon - animal widget: - text: A cute and adorable photo of a cat --- # DreamBooth model for the naseemee concept trained by shahukareem on the shahukareem/cat dataset. This is a Stable Diffusion model fine-tuned on the bulhaa concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of bulhaa cat** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is a Stable Diffusion model fine-tuned on `cat` images for the animal theme. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('shahukareem/bulhaa-cat') image = pipeline().images[0] image ```

ericrincon/LunarLander-v2

ericrincon

2023-04-26T16:51:34Z

0

stable-baselines3

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

reinforcement-learning

2023-04-26T16:44:07Z

--- 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: 265.64 +/- 23.25 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 ... ```

sophiebottani/distilbert_squad_newsqa

sophiebottani

2023-04-26T16:22:54Z

11

0

transformers

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

question-answering

2023-04-26T07:12:32Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert_squad_newsqa results: [] datasets: - newsqa ---  # distilbert_squad_newsqa This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the NewsQA dataset. It achieves the following results on the evaluation set: - Loss: 1.6247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7181 | 1.0 | 6730 | 1.6477 | | 1.4932 | 2.0 | 13460 | 1.6274 | | 1.4426 | 3.0 | 20190 | 1.6247 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1+cu102 - Datasets 2.9.0 - Tokenizers 0.13.2

kaisar-barlybay-sse/qard-bert-base-multilingual-cased_6

kaisar-barlybay-sse

2023-04-26T15:57:26Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

multiple-choice

2023-04-26T11:43:17Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: qard-bert-base-multilingual-cased_6 results: [] ---  # qard-bert-base-multilingual-cased_6 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3526 - Accuracy: 0.3453 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3904 | 1.0 | 501 | 1.3831 | 0.3473 | | 1.3997 | 2.0 | 1002 | 1.3863 | 0.3234 | | 1.3937 | 3.0 | 1503 | 1.3865 | 0.2395 | | 1.3912 | 4.0 | 2004 | 1.3862 | 0.3253 | | 1.3919 | 5.0 | 2505 | 1.3861 | 0.3633 | | 1.3713 | 6.0 | 3006 | 1.3526 | 0.3453 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Sergendel/ppo-SnowballTarget

Sergendel

2023-04-26T15:33:35Z

8

0

ml-agents

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

reinforcement-learning

2023-04-26T15:33:30Z

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

bobLi/autotrain-burp-52899124622

bobLi

2023-04-26T15:30:07Z

105

0

transformers

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

text-classification

2023-04-26T15:28:52Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - bobLi/autotrain-data-burp co2_eq_emissions: emissions: 0.004479786338858913 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 52899124622 - CO2 Emissions (in grams): 0.0045 ## Validation Metrics - Loss: 0.000 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000 ## Usage You can use cURL to access this model: ``` $ 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/bobLi/autotrain-burp-52899124622 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("bobLi/autotrain-burp-52899124622", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("bobLi/autotrain-burp-52899124622", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

GANYANG/gpt-2

GANYANG

2023-04-26T15:24:20Z

0

null

[ "pytorch", "tensorboard", "generated_from_trainer", "license:mit", "region:us" ]

null

2023-04-12T03:39:20Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt-2 results: [] ---  # gpt-2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.837 | 0.51 | 200 | 1.7257 | | 1.7602 | 1.03 | 400 | 1.6777 | | 1.7341 | 1.54 | 600 | 1.6604 | ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.0 - Datasets 2.10.1 - Tokenizers 0.13.3

Supparesk/t5-end2end-questions-generation

Supparesk

2023-04-26T15:09:36Z

163

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_modified_for_t5_qg", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-04-24T17:56:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_modified_for_t5_qg model-index: - name: t5-end2end-questions-generation results: [] ---  # t5-end2end-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset. It achieves the following results on the evaluation set: - Loss: 1.5791 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.5888 | 0.34 | 100 | 1.9194 | | 1.9722 | 0.68 | 200 | 1.7316 | | 1.8479 | 1.02 | 300 | 1.6689 | | 1.7478 | 1.35 | 400 | 1.6409 | | 1.7204 | 1.69 | 500 | 1.6268 | | 1.6986 | 2.03 | 600 | 1.6105 | | 1.6437 | 2.37 | 700 | 1.6007 | | 1.639 | 2.71 | 800 | 1.5952 | | 1.6261 | 3.05 | 900 | 1.5909 | | 1.5915 | 3.39 | 1000 | 1.5861 | | 1.5917 | 3.73 | 1100 | 1.5829 | | 1.5772 | 4.06 | 1200 | 1.5788 | | 1.5697 | 4.4 | 1300 | 1.5800 | | 1.557 | 4.74 | 1400 | 1.5791 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

gaussalgo/MiniLM-L6-v2-Canard-Fullwiki

gaussalgo

2023-04-26T15:03:24Z

3

0

sentence-transformers

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

sentence-similarity

2023-04-26T15:02:58Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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) ``` ## 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 1988 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors

ckallur/ppo-LunarLander-v2

ckallur

2023-04-26T14:59:53Z

4

0

stable-baselines3

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

reinforcement-learning

2023-04-26T14:59:23Z

--- 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: 258.44 +/- 19.98 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 ... ```

lorenzoncina/whisper-medium-ru

lorenzoncina

2023-04-26T14:59:33Z

34

7

transformers

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

automatic-speech-recognition

2023-04-07T15:58:48Z

--- language: - ru license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Medium Russian results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 ru type: mozilla-foundation/common_voice_11_0 config: ru split: test args: ru metrics: - type: wer value: 7.562437929892964 name: Wer - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: ru_ru split: test metrics: - type: wer value: 10.92 name: WER ---  # Whisper Medium Russian This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 ru dataset. It achieves the following results on the evaluation set: - Loss: 0.2253 - Wer: 7.5624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1578 | 0.1 | 1000 | 0.1662 | 8.8290 | | 0.045 | 1.08 | 2000 | 0.1748 | 8.9148 | | 0.0176 | 2.06 | 3000 | 0.1889 | 8.7848 | | 0.0104 | 3.04 | 4000 | 0.1922 | 8.4354 | | 0.0051 | 4.02 | 5000 | 0.2034 | 8.1865 | | 0.0047 | 4.12 | 6000 | 0.2012 | 8.0455 | | 0.0018 | 5.1 | 7000 | 0.2117 | 7.6237 | | 0.0004 | 6.08 | 8000 | 0.2177 | 7.6078 | | 0.0003 | 7.06 | 9000 | 0.2244 | 7.6262 | | 0.0002 | 8.04 | 10000 | 0.2253 | 7.5624 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.1.dev0 - Tokenizers 0.13.2

Carlosrelao/Reinforce-CartPole1

Carlosrelao

2023-04-26T14:53:34Z

0

null

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

reinforcement-learning

2023-04-26T14:53:22Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 459.40 +/- 121.80 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

murlina/ppo-LunarLander-v2

murlina

2023-04-26T14:45:14Z

0

stable-baselines3

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

reinforcement-learning

2023-04-25T17:12:35Z

--- 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: 243.30 +/- 32.31 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

gRaphael/ppo-LunarLander-v2

gRaphael

2023-04-26T14:36:49Z

4

0

stable-baselines3

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

reinforcement-learning

2023-04-26T14:27:38Z

--- 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: 264.49 +/- 14.91 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 ... ```

MiniMinMax/Reinforce-Pixelcopter

MiniMinMax

2023-04-26T14:32:03Z

0

null

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

reinforcement-learning

2023-04-26T14:31:59Z

--- 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: 42.40 +/- 24.45 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

doarisono/Doar

doarisono

2023-04-26T14:28:16Z

0

null

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

null

2023-04-26T14:13:06Z

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

Ashfaq60/Ashfaq

Ashfaq60

2023-04-26T14:27:27Z

0

null

[ "region:us" ]

null

2023-04-26T13:07:28Z

--- license: artistic-2.0 ---hi

rawmt/finetuning-sentiment-model-3000-samples

rawmt

2023-04-26T14:24:05Z

105

0

transformers

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

text-classification

2023-04-26T14:10:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples 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: - Loss: 0.3114 - Accuracy: 0.8733 - F1: 0.8742 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3

uisikdag/42000news_turkish_bert_uncased_finetune

uisikdag

2023-04-26T14:21:30Z

185

0

transformers

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

text-classification

2023-03-14T14:37:45Z

--- license: mit tags: - generated_from_trainer model-index: - name: umit_42000news_bertuncased results: [] ---  # umit_42000news_bertuncased This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 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: 3 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Sinsinnati/hf_workshop_extra

Sinsinnati

2023-04-26T14:18:43Z

76

0

transformers

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

text-classification

2023-04-26T14:08:49Z

--- pipeline_tag: text-classification widget: - text: He loves learning new things. - text: I go to university every day. ---

BlueAvenir/proseiben_events_activities_announcements

BlueAvenir

2023-04-26T14:17:55Z

2

0

sentence-transformers

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

sentence-similarity

2023-04-26T14:17:33Z

--- 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 653 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 653, "warmup_steps": 66, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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

ndhieunguyen/Reinforce-PixelCopter

ndhieunguyen

2023-04-26T14:15:26Z

0

null

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

reinforcement-learning

2023-04-24T01:44:42Z

--- 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: 46.20 +/- 34.66 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

Dewa/ppo-Lunar_rl-v5

Dewa

2023-04-26T14:01:40Z

0

null

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

reinforcement-learning

2023-04-26T14:00:55Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -114.16 +/- 28.04 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 100000 'learning_rate': 0.004 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.92 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Dewa/ppo-Lunar_rl-v5' 'batch_size': 512 'minibatch_size': 128} ```

Dewa/dqn-SpaceInvadersNoFrameskip-v4-version-6

Dewa

2023-04-26T13:39:48Z

3

0

stable-baselines3

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

reinforcement-learning

2023-04-26T12:50:28Z

--- 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: 274.50 +/- 31.50 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 Dewa -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 Dewa -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 Dewa ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

PaulineSanchez/autotrain-translation_food_english_to_french-52830124391

PaulineSanchez

2023-04-26T13:36:23Z

223

2

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "autotrain", "translation", "en", "fr", "dataset:PaulineSanchez/autotrain-data-translation_food_english_to_french", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-04-26T13:14:44Z

--- tags: - autotrain - translation language: - en - fr datasets: - PaulineSanchez/autotrain-data-translation_food_english_to_french co2_eq_emissions: emissions: 8.23780867881086 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 52830124391 - CO2 Emissions (in grams): 8.2378 ## Validation Metrics - Loss: 0.539 - SacreBLEU: 61.476 - Gen len: 12.913

Sergendel/Reinforce-PixelCopter_v2

Sergendel

2023-04-26T13:35:08Z

0

null

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

reinforcement-learning

2023-04-26T13:35:05Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter_v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 30.60 +/- 30.90 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

Yonadav/summarization_t5base_en_to_kjven

Yonadav

2023-04-26T13:32:17Z

7

0

transformers

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

text2text-generation

2023-04-26T07:40:28Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: summarization_t5base_en_to_kjven results: [] ---  # summarization_t5base_en_to_kjven This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8324 - Bleu: 21.2143 - Gen Len: 18.1685 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.0735 | 1.0 | 2860 | 0.9479 | 21.3913 | 18.1219 | | 0.9776 | 2.0 | 5720 | 0.8750 | 22.1711 | 18.1307 | | 0.918 | 3.0 | 8580 | 0.8317 | 22.6915 | 18.1381 | | 0.8741 | 4.0 | 11440 | 0.8039 | 23.0856 | 18.1468 | | 0.8489 | 5.0 | 14300 | 0.7841 | 23.3573 | 18.1455 | | 0.8169 | 6.0 | 17160 | 0.7664 | 23.5073 | 18.1493 | | 0.7965 | 7.0 | 20020 | 0.7532 | 23.6919 | 18.1495 | | 0.78 | 8.0 | 22880 | 0.7411 | 23.8445 | 18.1461 | | 0.7568 | 9.0 | 25740 | 0.7338 | 23.86 | 18.155 | | 0.7496 | 10.0 | 28600 | 0.7228 | 23.953 | 18.1511 | | 0.7411 | 11.0 | 31460 | 0.7175 | 24.0327 | 18.1511 | | 0.8376 | 12.0 | 34320 | 0.8114 | 23.311 | 18.1319 | | 1.1918 | 13.0 | 37180 | 0.9686 | 21.5339 | 18.1185 | | 1.0929 | 14.0 | 40040 | 0.8978 | 21.561 | 18.1455 | | 1.0373 | 15.0 | 42900 | 0.8617 | 21.4942 | 18.1542 | | 1.0165 | 16.0 | 45760 | 0.8432 | 21.3962 | 18.1595 | | 0.9973 | 17.0 | 48620 | 0.8340 | 21.2558 | 18.166 | | 0.9889 | 18.0 | 51480 | 0.8326 | 21.2238 | 18.1687 | | 0.9909 | 19.0 | 54340 | 0.8325 | 21.2216 | 18.1688 | | 0.9942 | 20.0 | 57200 | 0.8324 | 21.2143 | 18.1685 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

josu/gpt-neo-1.3B-instruction

josu

2023-04-26T13:27:25Z

19

1

transformers

[ "transformers", "pytorch", "gpt_neo", "text-generation", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-04-14T20:56:15Z

--- language: - pt widget: - text: Explique o que é inteligência artificial. - text: Explique o que é processamento de linguagem natural. --- ``` python from transformers import GenerationConfig from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("josu/gpt-neo-1.3B-instruction") tokenizer = AutoTokenizer.from_pretrained("josu/gpt-neo-1.3B-instruction") def generate_prompt(instruction, input=None): if input: return f"""Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido. ### Instrução: {instruction} ### Entrada: {input} ### Resposta:""" else: return f"""Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido. ### Instrução: {instruction} ### Resposta:""" generation_config = GenerationConfig( temperature=0.2, top_p=0.75, num_beams=4, ) def evaluate(instruction, input=None): prompt = generate_prompt(instruction, input) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=256 ) content = [] for s in generation_output.sequences: output = tokenizer.decode(s) content.append(output.split("### Resposta:")[1].strip()) return content ```

UsuallyPoncho/ppo-Huggy

UsuallyPoncho

2023-04-26T13:22:22Z

0

ml-agents

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

reinforcement-learning

2023-04-26T13:22:16Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://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: Find your model_id: UsuallyPoncho/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

XdSlams/fjhqgwkjwehhrfgir28

XdSlams

2023-04-26T13:06:23Z

0

null

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

null

2023-04-26T07:09:24Z

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

LarryAIDraw/jessicaGranblue_v10

LarryAIDraw

2023-04-26T13:06:13Z

0

null

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

null

2023-04-26T12:59:23Z

--- license: creativeml-openrail-m --- https://civitai.com/models/50740/jessica-or-granblue-fantasy

LarryAIDraw/eremiteScorching_v10

LarryAIDraw

2023-04-26T13:05:56Z

0

null

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

null

2023-04-26T12:59:04Z

--- license: creativeml-openrail-m --- https://civitai.com/models/50612/eremite-scorching-loremaster-genshin-impact

LarryAIDraw/towerOfFantasyFiona_v10

LarryAIDraw

2023-04-26T13:05:10Z

0

null

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

null

2023-04-26T12:57:55Z

--- license: creativeml-openrail-m --- https://civitai.com/models/50216/tower-of-fantasy-fiona

LarryAIDraw/yamashiroAzurLane_v10

LarryAIDraw

2023-04-26T13:04:41Z

0

null

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

null

2023-04-26T12:57:08Z

--- license: creativeml-openrail-m --- https://civitai.com/models/50909/yamashiroazur-lane

LarryAIDraw/sanjounoHaruhimeDunmachi_v10

LarryAIDraw

2023-04-26T13:04:13Z

0

null

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

null

2023-04-26T12:56:21Z

--- license: creativeml-openrail-m --- https://civitai.com/models/51211/sanjouno-haruhime-dunmachi

LarryAIDraw/7thMarchHonkaiStar_v10

LarryAIDraw

2023-04-26T13:04:01Z

0

null

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

null

2023-04-26T12:55:59Z

--- license: creativeml-openrail-m --- https://civitai.com/models/50469/7th-march-honkai-star-rail

alibidaran/Symptom2disease

alibidaran

2023-04-26T12:51:01Z

121

0

transformers

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

text-classification

2023-04-26T11:19:00Z

--- license: apache-2.0 pipeline_tag: text-classification ---

aravind-selvam/donut_finetuned_chart

aravind-selvam

2023-04-26T12:49:39Z

53

2

transformers

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

image-text-to-text

2023-04-17T12:33:36Z

--- license: mit tags: - generated_from_trainer model-index: - name: donut_finetuned_chart results: [] ---  # donut_finetuned_chart This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an chart images dataset. It achieves the following results on the evaluation set: - Loss: 0.4957 - Cer: 0.2318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4943 | 1.0 | 166 | 0.6634 | 0.2341 | | 0.475 | 2.0 | 333 | 0.5370 | 0.2320 | | 0.3009 | 3.0 | 500 | 0.5051 | 0.2318 | | 0.2611 | 3.98 | 664 | 0.4957 | 0.2318 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

DTorregrosa/sd-class-butterflies-64

DTorregrosa

2023-04-26T12:49:08Z

36

0

diffusers

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

unconditional-image-generation

2023-04-26T12:48:19Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('DTorregrosa/sd-class-butterflies-64') image = pipeline().images[0] image ```

Oleksandr2003/QA_model

Oleksandr2003

2023-04-26T12:48:53Z

29

0

transformers

[ "transformers", "pytorch", "tf", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-01-29T15:51:42Z

--- license: mit tags: - generated_from_trainer model-index: - name: QA_model results: [] ---  # QA_model This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4719 | 1.0 | 620 | 1.3108 | | 1.4047 | 2.0 | 1240 | 1.1630 | | 1.1245 | 3.0 | 1860 | 1.1429 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Dewa/dqn-SpaceInvadersNoFrameskip-v4-version-5

Dewa

2023-04-26T12:43:46Z

0

stable-baselines3

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

reinforcement-learning

2023-04-26T12:43:11Z

--- 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 Dewa -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 Dewa -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 Dewa ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

Ubenwa/sb-ecapa-vggsound

Ubenwa

2023-04-26T12:37:18Z

7

1

speechbrain

[ "speechbrain", "embeddings", "Sound", "pytorch", "ECAPA-TDNN", "TDNN", "audio-classification", "en", "dataset:VGGSound", "arxiv:2106.04624", "license:apache-2.0", "region:us" ]

audio-classification

2023-01-06T01:25:21Z

--- language: "en" thumbnail: tags: - speechbrain - embeddings - Sound - pytorch - ECAPA-TDNN - TDNN - audio-classification license: "apache-2.0" datasets: - VGGSound metrics: - Accuracy --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> # Sound Recognition with ECAPA embeddings on VGGSound This repository provides all the necessary tools to perform sound recognition with SpeechBrain using a model pretrained on VGGSound. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance on the test set is: | Release | Error Rate (%) |:-------------:|:--------------:| | 28-02-23 | 42.8 | #### Referencing ECAPA ```@inproceedings{DBLP:conf/interspeech/DesplanquesTD20, author = {Brecht Desplanques and Jenthe Thienpondt and Kris Demuynck}, editor = {Helen Meng and Bo Xu and Thomas Fang Zheng}, title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation in {TDNN} Based Speaker Verification}, booktitle = {Interspeech 2020}, pages = {3830--3834}, publisher = {{ISCA}}, year = {2020}, } ``` #### Referencing VGGSound ```@inproceedings{chen2020vggsound, title={Vggsound: A large-scale audio-visual dataset}, author={Chen, Honglie and Xie, Weidi and Vedaldi, Andrea and Zisserman, Andrew}, booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={721--725}, year={2020}, organization={IEEE} } ``` # **Citing SpeechBrain** Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```

jorgefedzhedz/distilbert-base-uncased-finetuned-cola

jorgefedzhedz

2023-04-26T12:33:20Z

105

0

transformers

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

text-classification

2023-04-26T12:09:33Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.541934635424655 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8224 - Matthews Correlation: 0.5419 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5231 | 1.0 | 535 | 0.5305 | 0.4003 | | 0.348 | 2.0 | 1070 | 0.5013 | 0.4885 | | 0.2353 | 3.0 | 1605 | 0.5578 | 0.5299 | | 0.1846 | 4.0 | 2140 | 0.7711 | 0.5176 | | 0.1363 | 5.0 | 2675 | 0.8224 | 0.5419 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

metrosir/sd

metrosir

2023-04-26T12:32:27Z

49

0

transformers

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

null

2023-04-25T13:01:04Z

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

Dewa/pixelcopter_rl-v4

Dewa

2023-04-26T12:31:11Z

0

null

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

reinforcement-learning

2023-04-26T12:31:06Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter_rl-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 22.20 +/- 15.35 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

mtc/mbart-newsum

mtc

2023-04-26T12:29:27Z

106

0

transformers

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

text2text-generation

2023-03-29T13:47:33Z

# grizzled-interest-2023-03-29 This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the mtc/newsum2021 dataset. It achieves the following results on the test set: - Loss: 3.5178 - Rouge1: 31.4512 - Rouge2: 11.0965 - Rougel: 21.5021 - Rougelsum: 28.634 - Gen Len: 75.755 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 4 - 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: polynomial - lr_scheduler_warmup_steps: 500 - training_steps: 8000 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.6815 | 0.89 | 500 | 3.5617 | 29.5414 | 10.5201 | 20.056 | 27.2581 | 86.07 | | 3.4132 | 1.79 | 1000 | 3.4133 | 29.6393 | 9.9545 | 19.6903 | 27.0861 | 96.545 | | 3.198 | 2.68 | 1500 | 3.3693 | 29.8614 | 10.4517 | 20.1728 | 27.3879 | 94.31 | | 3.0292 | 3.58 | 2000 | 3.3370 | 30.6444 | 11.5935 | 21.1955 | 28.2699 | 87.355 | | 2.901 | 4.47 | 2500 | 3.3440 | 30.7453 | 11.111 | 21.2076 | 28.269 | 88.365 | | 2.7832 | 5.37 | 3000 | 3.3758 | 30.4995 | 10.9025 | 20.6601 | 28.0575 | 104.655 | | 2.6965 | 6.26 | 3500 | 3.3793 | 31.2287 | 11.5544 | 21.1909 | 28.738 | 88.47 | | 2.6475 | 7.16 | 4000 | 3.4083 | 32.0341 | 11.9417 | 22.2785 | 29.2495 | 84.095 | | 2.6196 | 8.05 | 4500 | 3.4007 | 30.8963 | 11.3811 | 21.3146 | 28.3222 | 90.875 | | 2.5574 | 8.94 | 5000 | 3.4104 | 32.3867 | 12.0469 | 21.9831 | 29.5205 | 87.46 | | 2.4977 | 9.84 | 5500 | 3.4340 | 32.5857 | 12.5072 | 22.6288 | 30.1168 | 79.87 | | 2.4362 | 10.73 | 6000 | 3.4626 | 31.9121 | 11.8577 | 22.3647 | 29.3822 | 85.17 | | 2.3977 | 11.63 | 6500 | 3.4737 | 32.0202 | 12.0413 | 22.5237 | 29.5166 | 77.905 | | 2.369 | 12.52 | 7000 | 3.4890 | 31.2516 | 11.3416 | 21.5711 | 28.5465 | 85.605 | | 2.3446 | 13.42 | 7500 | 3.4949 | 32.1277 | 11.6876 | 22.0244 | 29.2239 | 83.895 | | 2.3295 | 14.31 | 8000 | 3.4976 | 31.8729 | 11.629 | 21.9629 | 28.9948 | 84.47 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.0.dev20230220+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

vega6000/distilgpt2-finetuned-medical

vega6000

2023-04-26T12:22:34Z

188

0

transformers

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

text-generation

2023-04-26T09:26:10Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-medical results: [] ---  # distilgpt2-finetuned-medical This model is a fine-tuned version of [vega6000/distilgpt2-finetuned-medical](https://huggingface.co/vega6000/distilgpt2-finetuned-medical) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 15 | 2.0817 | | No log | 2.0 | 30 | 1.9431 | | No log | 3.0 | 45 | 1.8487 | | No log | 4.0 | 60 | 1.7761 | | No log | 5.0 | 75 | 1.7253 | | No log | 6.0 | 90 | 1.6875 | | No log | 7.0 | 105 | 1.6574 | | No log | 8.0 | 120 | 1.6385 | | No log | 9.0 | 135 | 1.6288 | | No log | 10.0 | 150 | 1.6248 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

Zexois36/tokyolagi

Zexois36

2023-04-26T12:13:52Z

0

null

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

null

2023-04-26T12:11:45Z

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

DTorregrosa/sd-class-butterflies-32

DTorregrosa

2023-04-26T12:11:54Z

32

0

diffusers

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

unconditional-image-generation

2023-04-26T12:11:41Z

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

pszemraj/pegasus-large-book-summary

pszemraj

2023-04-26T12:01:32Z

119

1

transformers

[ "transformers", "pytorch", "safetensors", "pegasus", "text2text-generation", "summarization", "en", "dataset:kmfoda/booksum", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-03-02T23:29:05Z

--- language: - en tags: - summarization - pegasus license: apache-2.0 datasets: - kmfoda/booksum metrics: - rouge widget: - text: "large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock." example_title: "earthquakes" - text: " A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a 'toolbox' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5)." example_title: "scientific paper" - text: " the big variety of data coming from diverse sources is one of the key properties of the big data phenomenon. It is, therefore, beneficial to understand how data is generated in various environments and scenarios, before looking at what should be done with this data and how to design the best possible architecture to accomplish this The evolution of IT architectures, described in Chapter 2, means that the data is no longer processed by a few big monolith systems, but rather by a group of services In parallel to the processing layer, the underlying data storage has also changed and became more distributed This, in turn, required a significant paradigm shift as the traditional approach to transactions (ACID) could no longer be supported. On top of this, cloud computing is becoming a major approach with the benefits of reducing costs and providing on-demand scalability but at the same time introducing concerns about privacy, data ownership, etc In the meantime the Internet continues its exponential growth: Every day both structured and unstructured data is published and available for processing: To achieve competitive advantage companies have to relate their corporate resources to external services, e.g. financial markets, weather forecasts, social media, etc While several of the sites provide some sort of API to access the data in a more orderly fashion; countless sources require advanced web mining and Natural Language Processing (NLP) processing techniques: Advances in science push researchers to construct new instruments for observing the universe O conducting experiments to understand even better the laws of physics and other domains. Every year humans have at their disposal new telescopes, space probes, particle accelerators, etc These instruments generate huge streams of data, which need to be stored and analyzed. The constant drive for efficiency in the industry motivates the introduction of new automation techniques and process optimization: This could not be done without analyzing the precise data that describe these processes. As more and more human tasks are automated, machines provide rich data sets, which can be analyzed in real-time to drive efficiency to new levels. Finally, it is now evident that the growth of the Internet of Things is becoming a major source of data. More and more of the devices are equipped with significant computational power and can generate a continuous data stream from their sensors. In the subsequent sections of this chapter, we will look at the domains described above to see what they generate in terms of data sets. We will compare the volumes but will also look at what is characteristic and important from their respective points of view. 3.1 The Internet is undoubtedly the largest database ever created by humans. While several well described; cleaned, and structured data sets have been made available through this medium, most of the resources are of an ambiguous, unstructured, incomplete or even erroneous nature. Still, several examples in the areas such as opinion mining, social media analysis, e-governance, etc, clearly show the potential lying in these resources. Those who can successfully mine and interpret the Internet data can gain unique insight and competitive advantage in their business An important area of data analytics on the edge of corporate IT and the Internet is Web Analytics." example_title: "data science textbook" inference: parameters: max_length: 64 no_repeat_ngram_size: 2 encoder_no_repeat_ngram_size: 3 repetition_penalty: 2.4 length_penalty: 0.5 num_beams: 4 early_stopping: True --- # checkpoints This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset. ## Model description More information needed ## Intended uses & limitations - standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first. - this was only trained on the dataset for an epoch but still provides reasonable results. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.10.3

kahkasha/distilbert-base-uncased-finetuned-squad

kahkasha

2023-04-26T12:01:28Z

4

0

transformers

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

question-answering

2023-04-25T11:55:56Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.219 | 1.0 | 5533 | 1.1625 | | 0.9573 | 2.0 | 11066 | 1.1382 | | 0.755 | 3.0 | 16599 | 1.1594 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

iamjoy/ppo-Huggy-01

iamjoy

2023-04-26T11:58:32Z

16

0

ml-agents

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

reinforcement-learning

2023-04-26T11:58:25Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://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: Find your model_id: iamjoy/ppo-Huggy-01 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

seanghay/mt5-small-km-phoneme-reverse

seanghay

2023-04-26T11:41:52Z

106

0

transformers

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

text2text-generation

2023-04-26T10:45:20Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-km-phoneme-reverse results: [] ---  # mt5-small-km-phoneme-reverse This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2416 - Rouge1: 30.9064 - Rouge2: 15.5474 - Rougel: 30.6746 - Rougelsum: 30.691 - Gen Len: 4.8282 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6872 | 1.0 | 2515 | 1.3202 | 28.8555 | 13.7602 | 28.6841 | 28.7043 | 4.6996 | | 1.5052 | 2.0 | 5030 | 1.2561 | 30.5921 | 15.3773 | 30.3685 | 30.3818 | 4.8390 | | 1.5144 | 3.0 | 7545 | 1.2416 | 30.9064 | 15.5474 | 30.6746 | 30.691 | 4.8282 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3

Stern5497/sBert-swiss-legal-base

Stern5497

2023-04-26T11:41:09Z

3

0

sentence-transformers

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

sentence-similarity

2023-04-26T09:24:47Z

--- 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 14247 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 5000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1424, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (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

Dqcky/Gabagtha

Dqcky

2023-04-26T11:40:48Z

0

null

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

null

2023-04-26T11:39:04Z

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

jcrOrganisation/ppo-pyramids

jcrOrganisation

2023-04-26T11:40:20Z

7

0

ml-agents

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

reinforcement-learning

2023-04-26T11:40:14Z

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

NiamaLynn/sd-class-butterflies-32

NiamaLynn

2023-04-26T11:24:50Z

37

0

diffusers

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

unconditional-image-generation

2023-04-26T11:24:35Z

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

pythonist/bert-base-cased-healthdemomodel

pythonist

2023-04-26T11:23:33Z

103

0

transformers

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

question-answering

2023-04-26T11:21:41Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-healthdemomodel results: [] ---  # bert-base-cased-healthdemomodel This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.5819 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 6.1760 | | No log | 2.0 | 2 | 6.1161 | | No log | 3.0 | 3 | 6.0619 | | No log | 4.0 | 4 | 6.0120 | | No log | 5.0 | 5 | 5.9641 | | No log | 6.0 | 6 | 5.9177 | | No log | 7.0 | 7 | 5.8738 | | No log | 8.0 | 8 | 5.8334 | | No log | 9.0 | 9 | 5.7938 | | No log | 10.0 | 10 | 5.7589 | | No log | 11.0 | 11 | 5.7289 | | No log | 12.0 | 12 | 5.7019 | | No log | 13.0 | 13 | 5.6746 | | No log | 14.0 | 14 | 5.6499 | | No log | 15.0 | 15 | 5.6293 | | No log | 16.0 | 16 | 5.6122 | | No log | 17.0 | 17 | 5.5995 | | No log | 18.0 | 18 | 5.5905 | | No log | 19.0 | 19 | 5.5848 | | No log | 20.0 | 20 | 5.5819 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

worsty/dqn-SpaceInvadersNoFrameskip-v4-test6

worsty

2023-04-26T11:21:32Z

4

0

stable-baselines3

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

reinforcement-learning

2023-04-26T11:14:42Z

--- 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: 582.00 +/- 170.93 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 worsty -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 worsty -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 worsty ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```