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librarian-bots
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2025-06-02 12:28:20
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hugfacerhaha/dqn-SpaceInvadersNoFrameskip-v4
hugfacerhaha
2023-07-09T11:51:43Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T11:51:05Z
--- 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: 629.50 +/- 187.40 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 hugfacerhaha -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 hugfacerhaha -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 hugfacerhaha ``` ## 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', 0.00012), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
irrationaljared/ethos-spirit
irrationaljared
2023-07-09T11:48:14Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-09T11:48:14Z
--- license: creativeml-openrail-m ---
VK246/IC_ver3a_coco_swin_gpt2_
VK246
2023-07-09T11:38:14Z
30
0
transformers
[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:coco", "endpoints_compatible", "region:us" ]
image-text-to-text
2023-07-09T11:08:51Z
--- tags: - generated_from_trainer datasets: - coco metrics: - rouge - bleu model-index: - name: IC_ver3a_coco_swin_gpt2_ 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. --> # IC_ver3a_coco_swin_gpt2_ This model is a fine-tuned version of [](https://huggingface.co/) on the coco dataset. It achieves the following results on the evaluation set: - Loss: 1.0156 - Rouge1: 33.8659 - Rouge2: 10.1039 - Rougel: 31.4861 - Rougelsum: 31.4905 - Bleu: 5.7396 - Gen Len: 11.2887 ## Model description More information needed ## Intended uses & 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:------:|:-------:| | 1.4761 | 0.34 | 100 | 1.1047 | 28.2757 | 6.0267 | 26.4732 | 26.5071 | 2.7859 | 11.2887 | | 1.1238 | 0.68 | 200 | 1.0406 | 32.0448 | 8.6347 | 29.6117 | 29.6193 | 4.4174 | 11.2887 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
NasimB/gpt2-concat-mod-datatsets-rarity-all-iorder-e13k
NasimB
2023-07-09T11:38:11Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-09T09:37:18Z
--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-mod-datatsets-rarity-all-iorder-e13k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-mod-datatsets-rarity-all-iorder-e13k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.1226 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7718 | 0.32 | 500 | 5.7281 | | 5.4474 | 0.65 | 1000 | 5.2933 | | 5.0982 | 0.97 | 1500 | 5.0449 | | 4.8151 | 1.29 | 2000 | 4.8885 | | 4.6938 | 1.61 | 2500 | 4.7536 | | 4.5789 | 1.94 | 3000 | 4.6584 | | 4.3616 | 2.26 | 3500 | 4.6069 | | 4.2969 | 2.58 | 4000 | 4.5367 | | 4.2577 | 2.91 | 4500 | 4.4728 | | 4.0523 | 3.23 | 5000 | 4.4717 | | 3.9978 | 3.55 | 5500 | 4.4424 | | 3.9769 | 3.87 | 6000 | 4.3959 | | 3.7984 | 4.2 | 6500 | 4.4148 | | 3.7049 | 4.52 | 7000 | 4.4053 | | 3.7033 | 4.84 | 7500 | 4.3793 | | 3.5633 | 5.16 | 8000 | 4.3989 | | 3.4447 | 5.49 | 8500 | 4.4027 | | 3.4427 | 5.81 | 9000 | 4.3926 | | 3.3719 | 6.13 | 9500 | 4.4064 | | 3.2863 | 6.46 | 10000 | 4.4103 | | 3.2858 | 6.78 | 10500 | 4.4118 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
jordyvl/dit-small_tobacco3482_kd_CEKD_t5.0_a0.5
jordyvl
2023-07-09T11:25:56Z
119
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T11:08:48Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_tobacco3482_kd_CEKD_t5.0_a0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-small_tobacco3482_kd_CEKD_t5.0_a0.5 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7912 - Accuracy: 0.185 - Brier Loss: 0.8688 - Nll: 5.6106 - F1 Micro: 0.185 - F1 Macro: 0.0488 - Ece: 0.2524 - Aurc: 0.7391 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 4.0715 | 0.06 | 0.9043 | 8.8976 | 0.06 | 0.0114 | 0.1751 | 0.9034 | | No log | 1.96 | 6 | 3.9774 | 0.18 | 0.8893 | 8.0316 | 0.18 | 0.0305 | 0.2237 | 0.8040 | | No log | 2.96 | 9 | 3.8805 | 0.18 | 0.8782 | 8.6752 | 0.18 | 0.0305 | 0.2566 | 0.8189 | | No log | 3.96 | 12 | 3.8615 | 0.18 | 0.8836 | 8.9177 | 0.18 | 0.0305 | 0.2645 | 0.8205 | | No log | 4.96 | 15 | 3.8624 | 0.185 | 0.8844 | 6.3245 | 0.185 | 0.0488 | 0.2727 | 0.7889 | | No log | 5.96 | 18 | 3.8605 | 0.185 | 0.8813 | 5.1679 | 0.185 | 0.0488 | 0.2558 | 0.7797 | | No log | 6.96 | 21 | 3.8511 | 0.185 | 0.8774 | 5.1770 | 0.185 | 0.0488 | 0.2510 | 0.7741 | | No log | 7.96 | 24 | 3.8410 | 0.185 | 0.8751 | 5.6014 | 0.185 | 0.0488 | 0.2458 | 0.7699 | | No log | 8.96 | 27 | 3.8317 | 0.185 | 0.8733 | 5.9766 | 0.185 | 0.0488 | 0.2537 | 0.7681 | | No log | 9.96 | 30 | 3.8259 | 0.185 | 0.8724 | 6.0278 | 0.185 | 0.0488 | 0.2473 | 0.7689 | | No log | 10.96 | 33 | 3.8226 | 0.185 | 0.8724 | 6.8070 | 0.185 | 0.0488 | 0.2618 | 0.7671 | | No log | 11.96 | 36 | 3.8209 | 0.185 | 0.8730 | 7.6044 | 0.185 | 0.0488 | 0.2539 | 0.7643 | | No log | 12.96 | 39 | 3.8187 | 0.185 | 0.8730 | 8.1654 | 0.185 | 0.0488 | 0.2542 | 0.7612 | | No log | 13.96 | 42 | 3.8147 | 0.185 | 0.8725 | 7.1073 | 0.185 | 0.0488 | 0.2542 | 0.7566 | | No log | 14.96 | 45 | 3.8096 | 0.185 | 0.8720 | 6.3875 | 0.185 | 0.0488 | 0.2565 | 0.7566 | | No log | 15.96 | 48 | 3.8052 | 0.185 | 0.8712 | 6.0256 | 0.185 | 0.0488 | 0.2518 | 0.7524 | | No log | 16.96 | 51 | 3.8022 | 0.185 | 0.8707 | 5.7809 | 0.185 | 0.0488 | 0.2558 | 0.7485 | | No log | 17.96 | 54 | 3.8008 | 0.185 | 0.8701 | 5.6835 | 0.185 | 0.0488 | 0.2496 | 0.7442 | | No log | 18.96 | 57 | 3.7992 | 0.185 | 0.8700 | 5.3867 | 0.185 | 0.0488 | 0.2490 | 0.7421 | | No log | 19.96 | 60 | 3.7965 | 0.185 | 0.8694 | 5.4928 | 0.185 | 0.0488 | 0.2478 | 0.7406 | | No log | 20.96 | 63 | 3.7948 | 0.185 | 0.8693 | 5.5527 | 0.185 | 0.0488 | 0.2481 | 0.7405 | | No log | 21.96 | 66 | 3.7932 | 0.185 | 0.8691 | 5.5585 | 0.185 | 0.0488 | 0.2564 | 0.7396 | | No log | 22.96 | 69 | 3.7921 | 0.185 | 0.8689 | 5.5607 | 0.185 | 0.0488 | 0.2479 | 0.7391 | | No log | 23.96 | 72 | 3.7915 | 0.185 | 0.8688 | 5.6116 | 0.185 | 0.0488 | 0.2523 | 0.7390 | | No log | 24.96 | 75 | 3.7912 | 0.185 | 0.8688 | 5.6106 | 0.185 | 0.0488 | 0.2524 | 0.7391 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
jorgelzn/Reinforce-pixelcopter
jorgelzn
2023-07-09T11:16:27Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-28T21:38:36Z
--- 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: 18.40 +/- 20.79 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
jordyvl/dit-tiny_tobacco3482_kd_CEKD_t5.0_a0.5
jordyvl
2023-07-09T11:08:03Z
160
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T10:52:37Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t5.0_a0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-tiny_tobacco3482_kd_CEKD_t5.0_a0.5 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8497 - Accuracy: 0.18 - Brier Loss: 0.8788 - Nll: 6.0432 - F1 Micro: 0.18 - F1 Macro: 0.0305 - Ece: 0.2578 - Aurc: 0.8511 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 4.0678 | 0.145 | 0.8999 | 10.1608 | 0.145 | 0.0253 | 0.2221 | 0.8466 | | No log | 1.96 | 6 | 4.0316 | 0.145 | 0.8948 | 10.5160 | 0.145 | 0.0253 | 0.2239 | 0.8468 | | No log | 2.96 | 9 | 3.9774 | 0.16 | 0.8871 | 8.6333 | 0.16 | 0.0524 | 0.2217 | 0.8424 | | No log | 3.96 | 12 | 3.9325 | 0.155 | 0.8813 | 6.5340 | 0.155 | 0.0272 | 0.2161 | 0.8837 | | No log | 4.96 | 15 | 3.9041 | 0.155 | 0.8787 | 7.1704 | 0.155 | 0.0271 | 0.2296 | 0.8923 | | No log | 5.96 | 18 | 3.8876 | 0.155 | 0.8782 | 8.7334 | 0.155 | 0.0277 | 0.2325 | 0.8942 | | No log | 6.96 | 21 | 3.8766 | 0.18 | 0.8785 | 8.8120 | 0.18 | 0.0314 | 0.2476 | 0.8555 | | No log | 7.96 | 24 | 3.8690 | 0.18 | 0.8791 | 8.8676 | 0.18 | 0.0308 | 0.2643 | 0.8534 | | No log | 8.96 | 27 | 3.8633 | 0.18 | 0.8793 | 8.5299 | 0.18 | 0.0306 | 0.2594 | 0.8541 | | No log | 9.96 | 30 | 3.8601 | 0.18 | 0.8796 | 7.4142 | 0.18 | 0.0305 | 0.2622 | 0.8548 | | No log | 10.96 | 33 | 3.8577 | 0.18 | 0.8797 | 6.6642 | 0.18 | 0.0305 | 0.2720 | 0.8546 | | No log | 11.96 | 36 | 3.8560 | 0.18 | 0.8797 | 6.2862 | 0.18 | 0.0305 | 0.2723 | 0.8543 | | No log | 12.96 | 39 | 3.8547 | 0.18 | 0.8796 | 6.2084 | 0.18 | 0.0305 | 0.2678 | 0.8541 | | No log | 13.96 | 42 | 3.8535 | 0.18 | 0.8794 | 6.1826 | 0.18 | 0.0305 | 0.2631 | 0.8534 | | No log | 14.96 | 45 | 3.8525 | 0.18 | 0.8793 | 6.1744 | 0.18 | 0.0305 | 0.2593 | 0.8529 | | No log | 15.96 | 48 | 3.8516 | 0.18 | 0.8792 | 6.1606 | 0.18 | 0.0305 | 0.2680 | 0.8527 | | No log | 16.96 | 51 | 3.8511 | 0.18 | 0.8791 | 6.1634 | 0.18 | 0.0305 | 0.2724 | 0.8528 | | No log | 17.96 | 54 | 3.8510 | 0.18 | 0.8791 | 6.0971 | 0.18 | 0.0305 | 0.2676 | 0.8525 | | No log | 18.96 | 57 | 3.8508 | 0.18 | 0.8790 | 6.0686 | 0.18 | 0.0305 | 0.2630 | 0.8522 | | No log | 19.96 | 60 | 3.8503 | 0.18 | 0.8789 | 6.0495 | 0.18 | 0.0305 | 0.2581 | 0.8518 | | No log | 20.96 | 63 | 3.8501 | 0.18 | 0.8789 | 6.0918 | 0.18 | 0.0305 | 0.2581 | 0.8516 | | No log | 21.96 | 66 | 3.8499 | 0.18 | 0.8788 | 6.0464 | 0.18 | 0.0305 | 0.2536 | 0.8516 | | No log | 22.96 | 69 | 3.8497 | 0.18 | 0.8788 | 6.0419 | 0.18 | 0.0305 | 0.2535 | 0.8513 | | No log | 23.96 | 72 | 3.8497 | 0.18 | 0.8788 | 6.0432 | 0.18 | 0.0305 | 0.2578 | 0.8511 | | No log | 24.96 | 75 | 3.8497 | 0.18 | 0.8788 | 6.0432 | 0.18 | 0.0305 | 0.2578 | 0.8511 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
NiscR/dqn-SpaceInvadersNoFrameskip-v4
NiscR
2023-07-09T10:52:56Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T10:52:14Z
--- 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: 737.50 +/- 249.10 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 NiscR -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 NiscR -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 NiscR ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
hegelty/KcBERT-Large-finetuned-josa
hegelty
2023-07-09T10:43:46Z
70
0
transformers
[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-08T16:53:29Z
--- tags: - generated_from_keras_callback model-index: - name: hegelty/KcBERT-Large-finetuned-josa 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. --> # hegelty/KcBERT-Large-finetuned-josa This model is a fine-tuned version of [beomi/KcBERT-Large](https://huggingface.co/beomi/KcBERT-Large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0058 - Validation Loss: 0.0000 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 59393, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0058 | 0.0000 | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.9.2 - Datasets 2.13.1 - Tokenizers 0.13.3
TheBloke/orca_mini_v2_13b-GGML
TheBloke
2023-07-09T10:28:34Z
0
24
transformers
[ "transformers", "text-generation", "en", "dataset:psmathur/orca_minis_uncensored_dataset", "arxiv:2306.02707", "arxiv:2302.13971", "arxiv:2304.12244", "license:cc-by-nc-sa-4.0", "region:us" ]
text-generation
2023-07-09T10:07:58Z
--- inference: false license: cc-by-nc-sa-4.0 language: - en library_name: transformers pipeline_tag: text-generation datasets: - psmathur/orca_minis_uncensored_dataset --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Pankaj Mathur's Orca Mini v2 13B GGML These files are GGML format model files for [Pankaj Mathur's Orca Mini v2 13B](https://huggingface.co/psmathur/orca_mini_v2_13b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/orca_mini_v2_13b-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_v2_13b-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_v2_13b) ## Prompt template: orca_mini ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Input: input, if required ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_ggml end --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | orca_mini_v2_13b.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB| 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | orca_mini_v2_13b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | orca_mini_v2_13b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB| 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | orca_mini_v2_13b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB| 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | orca_mini_v2_13b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | orca_mini_v2_13b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB| 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | orca_mini_v2_13b.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. | | orca_mini_v2_13b.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB| 10.64 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | orca_mini_v2_13b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | orca_mini_v2_13b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB| 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | orca_mini_v2_13b.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | orca_mini_v2_13b.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | orca_mini_v2_13b.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | orca_mini_v2_13b.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB| 16.33 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m orca_mini_v2_13b.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### User: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz. **Patreon special mentions**: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Pankaj Mathur's Orca Mini v2 13B # orca_mini_v2_13b An **Uncensored** LLaMA-13b model in collaboration with [Eric Hartford](https://huggingface.co/ehartford). trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. Please note this model has *better code generation capabilities* compare to our original orca_mini_13b which was trained on base OpenLLaMA-13b model and which has the [empty spaces issues & found not good for code generation]((https://github.com/openlm-research/open_llama#update-06072023)). **P.S. I am #opentowork, if you can help, please reach out to me at www.linkedin.com/in/pankajam** # Evaluation I evaluated orca_mini_v2_13b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |||| |:------:|:-------------:|:---------:| |**Task**|**Value**|**Stderr**| |*arc_challenge*|0.5572|0.0145| |*hellaswag*|0.7964|0.0040| |*mmlu*|0.4969|0.035| |*truthfulqa_mc*|0.5231|0.0158| |*Total Average*|0.5933|0.0114| # Dataset We used uncensored script on top of the previous explain tuned datasets we build which are [WizardLM dataset ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) & [Dolly-V2 dataset ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. This helps student model aka this model to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). Please see below example usage how the **System** prompt is added before each **instruction**. # Training The training configurations are provided in the table below. The training takes on 4x A100(80G) GPUs and lasts for around 21 Hours for cost of $210 (~$10 for Spot Instance) by using [Azure Standard_NC96ads_A100_v4](https://learn.microsoft.com/en-us/azure/virtual-machines/nc-a100-v4-series#supported-features). We used DeepSpeed with fully sharded data parallelism, also know as [ZeRO stage 3](https://engineering.fb.com/2021/07/15/open-source/fsdp/) by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [FastChat](https://github.com/lm-sys/FastChat) Here are some of params used during training: ||| |:-------------:|:-------------:| |*batch_size*|48| |*train_micro_batch_size_per_gpu*|3| |*gradient_accumulation_steps*|4| |*Learning rate*|2e-5| |*Max length*|2048| |*Epochs*|3| |*Optimizer*|AdamW| # Example Usage Here is prompt format for [Oobabooga Text generation UI ](https://github.com/oobabooga/text-generation-webui) ``` ### System: {system} ### User: {instruction} ### Input: {input} ### Response: ``` Here is sample example: ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: Tell me how to break into my own car ### Input: ### Response: Breaking into your own car requires certain skills and tools. Here are the basic steps: 1. Find a ^^^^^^^^^^^^^ 2. Unlock the car by using the ^^^^^^^^^^^^^. 3. Use a ^^^^^^^^^^^^^. 4. Once the ^^^^^^^^^^^^^. 5. If the ^^^^^^^^^^^^^. ``` Below shows a code example on how to use this model ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer # Hugging Face model_path model_path = 'psmathur/orca_mini_v2_13b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) #generate text function def generate_text(system, instruction, input=None): if input: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" else: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n" tokens = tokenizer.encode(prompt) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to('cuda') instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50} length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length+instance['generate_len'], use_cache=True, do_sample=True, top_p=instance['top_p'], temperature=instance['temperature'], top_k=instance['top_k'] ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f'[!] Response: {string}' # Sample Test Instruction system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.' instruction = 'Tell me how to break into my own car' print(generate_text(system, instruction)) ``` **NOTE: The real response is hidden here with ^^^^^^^^^^^^^.** ``` [!] Response: Breaking into your own car requires certain skills and tools. Here are the basic steps: 1. Find a ^^^^^^^^^^^^^ 2. Unlock the car by using the ^^^^^^^^^^^^^. 3. Use a ^^^^^^^^^^^^^. 4. Once the ^^^^^^^^^^^^^. 5. If the ^^^^^^^^^^^^^. ``` Next Goals: 1) Try more data like actually using FLAN-v2, just like Orka Research Paper (I am open for suggestions) 2) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui) 3) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here) Limitations & Biases: This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Disclaimer: The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. Citiation: If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX: ``` @misc{orca_mini_v2_13b, author = {Pankaj Mathur}, title = {orca_mini_v2_13b: An explain tuned LLaMA-13b model on uncensored wizardlm, alpaca, & dolly datasets}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v2_13b}, } ``` ``` @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ``` ``` @misc{openalpaca, author = {Yixuan Su and Tian Lan and Deng Cai}, title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, } ``` ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` ``` @misc{xu2023wizardlm, title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang}, year={2023}, eprint={2304.12244}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
ArisuNguyen/retrain_non_seg_mbart
ArisuNguyen
2023-07-09T10:26:03Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "mbart", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2023-07-08T08:50:42Z
--- license: mit tags: - generated_from_trainer model-index: - name: retrain_non_seg_mbart 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. --> # retrain_non_seg_mbart This model is a fine-tuned version of [ArisuNguyen/retrain_non_seg_mbart](https://huggingface.co/ArisuNguyen/retrain_non_seg_mbart) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
vrsen/falcon-7b-instruct-ft-adapters
vrsen
2023-07-09T10:25:12Z
8
0
peft
[ "peft", "region:us" ]
null
2023-07-09T10:25:07Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
jordyvl/dit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone
jordyvl
2023-07-09T10:18:24Z
161
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T09:28:41Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone 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. --> # dit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1502 - Accuracy: 0.0625 - Brier Loss: 0.9374 - Nll: 9.1398 - F1 Micro: 0.0625 - F1 Macro: 0.0074 - Ece: 0.1015 - Aurc: 0.9383 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 12 | 0.1540 | 0.0625 | 0.9376 | 8.5438 | 0.0625 | 0.0074 | 0.1043 | 0.9530 | | No log | 1.96 | 24 | 0.1519 | 0.0625 | 0.9376 | 8.2831 | 0.0625 | 0.0074 | 0.1008 | 0.9465 | | No log | 2.96 | 36 | 0.1512 | 0.0625 | 0.9375 | 8.4629 | 0.0625 | 0.0074 | 0.1028 | 0.9336 | | No log | 3.96 | 48 | 0.1510 | 0.0625 | 0.9375 | 8.6283 | 0.0625 | 0.0074 | 0.1027 | 0.9365 | | No log | 4.96 | 60 | 0.1509 | 0.0625 | 0.9375 | 8.5065 | 0.0625 | 0.0074 | 0.1030 | 0.9433 | | No log | 5.96 | 72 | 0.1508 | 0.0625 | 0.9375 | 8.4779 | 0.0625 | 0.0074 | 0.1017 | 0.9414 | | No log | 6.96 | 84 | 0.1507 | 0.0625 | 0.9375 | 8.5053 | 0.0625 | 0.0074 | 0.1045 | 0.9438 | | No log | 7.96 | 96 | 0.1507 | 0.0625 | 0.9375 | 8.7396 | 0.0625 | 0.0074 | 0.1032 | 0.9440 | | No log | 8.96 | 108 | 0.1506 | 0.0625 | 0.9375 | 8.6420 | 0.0625 | 0.0074 | 0.1031 | 0.9448 | | No log | 9.96 | 120 | 0.1506 | 0.0625 | 0.9375 | 8.8410 | 0.0625 | 0.0074 | 0.1045 | 0.9438 | | No log | 10.96 | 132 | 0.1506 | 0.0625 | 0.9374 | 8.9438 | 0.0625 | 0.0074 | 0.1042 | 0.9413 | | No log | 11.96 | 144 | 0.1505 | 0.0625 | 0.9374 | 8.9847 | 0.0625 | 0.0074 | 0.1032 | 0.9418 | | No log | 12.96 | 156 | 0.1505 | 0.0625 | 0.9374 | 9.0594 | 0.0625 | 0.0074 | 0.1031 | 0.9397 | | No log | 13.96 | 168 | 0.1504 | 0.0625 | 0.9374 | 9.0748 | 0.0625 | 0.0074 | 0.1045 | 0.9343 | | No log | 14.96 | 180 | 0.1504 | 0.0625 | 0.9374 | 9.0912 | 0.0625 | 0.0074 | 0.1018 | 0.9358 | | No log | 15.96 | 192 | 0.1504 | 0.0625 | 0.9374 | 9.0950 | 0.0625 | 0.0074 | 0.1032 | 0.9331 | | No log | 16.96 | 204 | 0.1503 | 0.0625 | 0.9374 | 9.2141 | 0.0625 | 0.0074 | 0.1015 | 0.9363 | | No log | 17.96 | 216 | 0.1503 | 0.0625 | 0.9374 | 9.0918 | 0.0625 | 0.0074 | 0.1046 | 0.9354 | | No log | 18.96 | 228 | 0.1503 | 0.0625 | 0.9374 | 9.1430 | 0.0625 | 0.0074 | 0.1018 | 0.9385 | | No log | 19.96 | 240 | 0.1503 | 0.0625 | 0.9374 | 9.2149 | 0.0625 | 0.0074 | 0.0991 | 0.9404 | | No log | 20.96 | 252 | 0.1503 | 0.0625 | 0.9374 | 9.0900 | 0.0625 | 0.0074 | 0.1043 | 0.9386 | | No log | 21.96 | 264 | 0.1503 | 0.0625 | 0.9374 | 9.1244 | 0.0625 | 0.0074 | 0.1060 | 0.9395 | | No log | 22.96 | 276 | 0.1503 | 0.0625 | 0.9374 | 9.1353 | 0.0625 | 0.0074 | 0.1005 | 0.9378 | | No log | 23.96 | 288 | 0.1502 | 0.0625 | 0.9374 | 9.2063 | 0.0625 | 0.0074 | 0.1032 | 0.9373 | | No log | 24.96 | 300 | 0.1502 | 0.0625 | 0.9374 | 9.1398 | 0.0625 | 0.0074 | 0.1015 | 0.9383 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
jordyvl/dit-small_tobacco3482_kd_CEKD_t2.5_a0.7
jordyvl
2023-07-09T10:17:47Z
162
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T10:00:03Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_tobacco3482_kd_CEKD_t2.5_a0.7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-small_tobacco3482_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1993 - Accuracy: 0.185 - Brier Loss: 0.8672 - Nll: 6.5703 - F1 Micro: 0.185 - F1 Macro: 0.0488 - Ece: 0.2594 - Aurc: 0.7367 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 3.4684 | 0.06 | 0.9042 | 9.2910 | 0.06 | 0.0114 | 0.1755 | 0.9033 | | No log | 1.96 | 6 | 3.3741 | 0.18 | 0.8886 | 6.5491 | 0.18 | 0.0305 | 0.2324 | 0.8055 | | No log | 2.96 | 9 | 3.2779 | 0.18 | 0.8767 | 7.2662 | 0.18 | 0.0305 | 0.2493 | 0.8196 | | No log | 3.96 | 12 | 3.2605 | 0.18 | 0.8816 | 7.0963 | 0.18 | 0.0305 | 0.2628 | 0.8140 | | No log | 4.96 | 15 | 3.2592 | 0.185 | 0.8814 | 6.9350 | 0.185 | 0.0488 | 0.2584 | 0.7850 | | No log | 5.96 | 18 | 3.2576 | 0.185 | 0.8782 | 6.3113 | 0.185 | 0.0488 | 0.2561 | 0.7731 | | No log | 6.96 | 21 | 3.2540 | 0.185 | 0.8747 | 6.0058 | 0.185 | 0.0488 | 0.2446 | 0.7705 | | No log | 7.96 | 24 | 3.2500 | 0.185 | 0.8731 | 5.9849 | 0.185 | 0.0488 | 0.2442 | 0.7669 | | No log | 8.96 | 27 | 3.2430 | 0.185 | 0.8717 | 5.9785 | 0.185 | 0.0488 | 0.2483 | 0.7626 | | No log | 9.96 | 30 | 3.2377 | 0.185 | 0.8711 | 6.2837 | 0.185 | 0.0488 | 0.2462 | 0.7609 | | No log | 10.96 | 33 | 3.2332 | 0.185 | 0.8713 | 6.8641 | 0.185 | 0.0488 | 0.2560 | 0.7601 | | No log | 11.96 | 36 | 3.2293 | 0.185 | 0.8719 | 6.8631 | 0.185 | 0.0488 | 0.2523 | 0.7587 | | No log | 12.96 | 39 | 3.2246 | 0.185 | 0.8717 | 6.8535 | 0.185 | 0.0488 | 0.2526 | 0.7558 | | No log | 13.96 | 42 | 3.2190 | 0.185 | 0.8709 | 6.8177 | 0.185 | 0.0488 | 0.2565 | 0.7533 | | No log | 14.96 | 45 | 3.2134 | 0.185 | 0.8700 | 6.7894 | 0.185 | 0.0488 | 0.2630 | 0.7533 | | No log | 15.96 | 48 | 3.2091 | 0.185 | 0.8691 | 6.7672 | 0.185 | 0.0488 | 0.2585 | 0.7500 | | No log | 16.96 | 51 | 3.2069 | 0.185 | 0.8687 | 6.6512 | 0.185 | 0.0488 | 0.2536 | 0.7466 | | No log | 17.96 | 54 | 3.2063 | 0.185 | 0.8682 | 6.5227 | 0.185 | 0.0488 | 0.2520 | 0.7429 | | No log | 18.96 | 57 | 3.2057 | 0.185 | 0.8682 | 6.5119 | 0.185 | 0.0488 | 0.2514 | 0.7406 | | No log | 19.96 | 60 | 3.2036 | 0.185 | 0.8678 | 6.5674 | 0.185 | 0.0488 | 0.2501 | 0.7385 | | No log | 20.96 | 63 | 3.2023 | 0.185 | 0.8677 | 6.5709 | 0.185 | 0.0488 | 0.2506 | 0.7385 | | No log | 21.96 | 66 | 3.2010 | 0.185 | 0.8675 | 6.5731 | 0.185 | 0.0488 | 0.2631 | 0.7376 | | No log | 22.96 | 69 | 3.2000 | 0.185 | 0.8673 | 6.5723 | 0.185 | 0.0488 | 0.2591 | 0.7371 | | No log | 23.96 | 72 | 3.1996 | 0.185 | 0.8673 | 6.5715 | 0.185 | 0.0488 | 0.2593 | 0.7368 | | No log | 24.96 | 75 | 3.1993 | 0.185 | 0.8672 | 6.5703 | 0.185 | 0.0488 | 0.2594 | 0.7367 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
KJan05/ppo-CartPole-v1-unit8-p1
KJan05
2023-07-09T10:09:08Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T08:36:34Z
--- 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: -80.21 +/- 69.99 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': 500000 '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': 'KJan05/ppo-CartPole-v1-unit8-p1' 'batch_size': 512 'minibatch_size': 128} ```
DovahYol/Reinforce-Pixelcopter-PLE-v0
DovahYol
2023-07-09T10:04:12Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T10:04:05Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 65.90 +/- 39.44 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
jordyvl/dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.7
jordyvl
2023-07-09T09:59:20Z
161
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T09:43:16Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.7 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2510 - Accuracy: 0.18 - Brier Loss: 0.8767 - Nll: 6.8039 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2513 - Aurc: 0.8508 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 3.4586 | 0.145 | 0.8999 | 10.1587 | 0.145 | 0.0253 | 0.2221 | 0.8467 | | No log | 1.96 | 6 | 3.4232 | 0.145 | 0.8946 | 10.5824 | 0.145 | 0.0253 | 0.2242 | 0.8475 | | No log | 2.96 | 9 | 3.3704 | 0.16 | 0.8867 | 8.6135 | 0.16 | 0.0503 | 0.2171 | 0.8440 | | No log | 3.96 | 12 | 3.3273 | 0.155 | 0.8807 | 6.5471 | 0.155 | 0.0274 | 0.2248 | 0.8831 | | No log | 4.96 | 15 | 3.3006 | 0.155 | 0.8779 | 6.8045 | 0.155 | 0.0271 | 0.2331 | 0.8918 | | No log | 5.96 | 18 | 3.2856 | 0.16 | 0.8773 | 8.2046 | 0.16 | 0.0329 | 0.2361 | 0.8956 | | No log | 6.96 | 21 | 3.2758 | 0.18 | 0.8774 | 8.0738 | 0.18 | 0.0308 | 0.2561 | 0.8544 | | No log | 7.96 | 24 | 3.2688 | 0.18 | 0.8778 | 7.1046 | 0.18 | 0.0308 | 0.2647 | 0.8524 | | No log | 8.96 | 27 | 3.2630 | 0.18 | 0.8778 | 6.9910 | 0.18 | 0.0306 | 0.2591 | 0.8530 | | No log | 9.96 | 30 | 3.2597 | 0.18 | 0.8778 | 6.9680 | 0.18 | 0.0306 | 0.2736 | 0.8538 | | No log | 10.96 | 33 | 3.2573 | 0.18 | 0.8776 | 6.9547 | 0.18 | 0.0306 | 0.2698 | 0.8536 | | No log | 11.96 | 36 | 3.2557 | 0.18 | 0.8775 | 6.9491 | 0.18 | 0.0306 | 0.2653 | 0.8533 | | No log | 12.96 | 39 | 3.2546 | 0.18 | 0.8773 | 6.8987 | 0.18 | 0.0306 | 0.2606 | 0.8526 | | No log | 13.96 | 42 | 3.2536 | 0.18 | 0.8771 | 6.8204 | 0.18 | 0.0306 | 0.2601 | 0.8523 | | No log | 14.96 | 45 | 3.2528 | 0.18 | 0.8771 | 6.8141 | 0.18 | 0.0306 | 0.2521 | 0.8519 | | No log | 15.96 | 48 | 3.2522 | 0.18 | 0.8769 | 6.8074 | 0.18 | 0.0306 | 0.2606 | 0.8517 | | No log | 16.96 | 51 | 3.2519 | 0.18 | 0.8769 | 6.8077 | 0.18 | 0.0306 | 0.2607 | 0.8515 | | No log | 17.96 | 54 | 3.2520 | 0.18 | 0.8769 | 6.8050 | 0.18 | 0.0306 | 0.2561 | 0.8510 | | No log | 18.96 | 57 | 3.2520 | 0.18 | 0.8769 | 6.8057 | 0.18 | 0.0306 | 0.2519 | 0.8509 | | No log | 19.96 | 60 | 3.2515 | 0.18 | 0.8768 | 6.8046 | 0.18 | 0.0306 | 0.2556 | 0.8507 | | No log | 20.96 | 63 | 3.2514 | 0.18 | 0.8768 | 6.8048 | 0.18 | 0.0306 | 0.2515 | 0.8506 | | No log | 21.96 | 66 | 3.2512 | 0.18 | 0.8767 | 6.8048 | 0.18 | 0.0306 | 0.2556 | 0.8508 | | No log | 22.96 | 69 | 3.2510 | 0.18 | 0.8767 | 6.8045 | 0.18 | 0.0306 | 0.2513 | 0.8509 | | No log | 23.96 | 72 | 3.2510 | 0.18 | 0.8767 | 6.8043 | 0.18 | 0.0306 | 0.2513 | 0.8508 | | No log | 24.96 | 75 | 3.2510 | 0.18 | 0.8767 | 6.8039 | 0.18 | 0.0306 | 0.2513 | 0.8508 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
jordyvl/dit-small_tobacco3482_kd_CEKD_t2.5_a0.5
jordyvl
2023-07-09T09:42:35Z
161
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T09:29:00Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_tobacco3482_kd_CEKD_t2.5_a0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-small_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8936 - Accuracy: 0.185 - Brier Loss: 0.8707 - Nll: 6.6284 - F1 Micro: 0.185 - F1 Macro: 0.0488 - Ece: 0.2527 - Aurc: 0.7434 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 4.2363 | 0.06 | 0.9043 | 9.2962 | 0.06 | 0.0114 | 0.1758 | 0.9032 | | No log | 1.96 | 6 | 4.1268 | 0.18 | 0.8887 | 6.8683 | 0.18 | 0.0305 | 0.2329 | 0.8055 | | No log | 2.96 | 9 | 4.0044 | 0.18 | 0.8773 | 7.3055 | 0.18 | 0.0305 | 0.2510 | 0.8219 | | No log | 3.96 | 12 | 3.9678 | 0.18 | 0.8851 | 7.2435 | 0.18 | 0.0305 | 0.2677 | 0.8214 | | No log | 4.96 | 15 | 3.9645 | 0.185 | 0.8877 | 6.9806 | 0.185 | 0.0488 | 0.2757 | 0.7934 | | No log | 5.96 | 18 | 3.9635 | 0.185 | 0.8853 | 6.9543 | 0.185 | 0.0488 | 0.2551 | 0.7812 | | No log | 6.96 | 21 | 3.9564 | 0.185 | 0.8801 | 6.0556 | 0.185 | 0.0488 | 0.2515 | 0.7771 | | No log | 7.96 | 24 | 3.9505 | 0.185 | 0.8772 | 6.0356 | 0.185 | 0.0488 | 0.2598 | 0.7724 | | No log | 8.96 | 27 | 3.9435 | 0.185 | 0.8751 | 6.0288 | 0.185 | 0.0488 | 0.2590 | 0.7697 | | No log | 9.96 | 30 | 3.9383 | 0.185 | 0.8742 | 6.0724 | 0.185 | 0.0488 | 0.2474 | 0.7712 | | No log | 10.96 | 33 | 3.9336 | 0.185 | 0.8746 | 6.7953 | 0.185 | 0.0488 | 0.2533 | 0.7685 | | No log | 11.96 | 36 | 3.9298 | 0.185 | 0.8755 | 6.9469 | 0.185 | 0.0488 | 0.2679 | 0.7659 | | No log | 12.96 | 39 | 3.9253 | 0.185 | 0.8756 | 6.9654 | 0.185 | 0.0488 | 0.2591 | 0.7640 | | No log | 13.96 | 42 | 3.9194 | 0.185 | 0.8750 | 6.9522 | 0.185 | 0.0488 | 0.2681 | 0.7604 | | No log | 14.96 | 45 | 3.9128 | 0.185 | 0.8744 | 6.9200 | 0.185 | 0.0488 | 0.2611 | 0.7617 | | No log | 15.96 | 48 | 3.9074 | 0.185 | 0.8733 | 6.8369 | 0.185 | 0.0488 | 0.2611 | 0.7600 | | No log | 16.96 | 51 | 3.9041 | 0.185 | 0.8726 | 6.8278 | 0.185 | 0.0488 | 0.2558 | 0.7566 | | No log | 17.96 | 54 | 3.9025 | 0.185 | 0.8719 | 6.7039 | 0.185 | 0.0488 | 0.2588 | 0.7510 | | No log | 18.96 | 57 | 3.9012 | 0.185 | 0.8717 | 6.6384 | 0.185 | 0.0488 | 0.2580 | 0.7484 | | No log | 19.96 | 60 | 3.8987 | 0.185 | 0.8712 | 6.6323 | 0.185 | 0.0488 | 0.2612 | 0.7450 | | No log | 20.96 | 63 | 3.8971 | 0.185 | 0.8712 | 6.6319 | 0.185 | 0.0488 | 0.2615 | 0.7443 | | No log | 21.96 | 66 | 3.8956 | 0.185 | 0.8710 | 6.6323 | 0.185 | 0.0488 | 0.2659 | 0.7439 | | No log | 22.96 | 69 | 3.8945 | 0.185 | 0.8708 | 6.6307 | 0.185 | 0.0488 | 0.2569 | 0.7436 | | No log | 23.96 | 72 | 3.8940 | 0.185 | 0.8708 | 6.6295 | 0.185 | 0.0488 | 0.2526 | 0.7434 | | No log | 24.96 | 75 | 3.8936 | 0.185 | 0.8707 | 6.6284 | 0.185 | 0.0488 | 0.2527 | 0.7434 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
tienlansun/distillbert-based-uncased-mnli
tienlansun
2023-07-09T09:41:56Z
199
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "en", "dataset:glue", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-07-09T09:38:54Z
--- datasets: - glue language: - en pipeline_tag: text-classification ---
demelianov/mira_model
demelianov
2023-07-09T09:38:18Z
31
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-07-09T09:29:40Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 instance_prompt: a photo of sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - demelianov/mira_model This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
cgcgcgcgcg/111
cgcgcgcgcg
2023-07-09T09:32:21Z
0
0
null
[ "arxiv:1910.09700", "region:us" ]
null
2023-07-09T09:31:54Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jordyvl/dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.5
jordyvl
2023-07-09T09:28:18Z
163
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T09:16:22Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9560 - Accuracy: 0.18 - Brier Loss: 0.8800 - Nll: 6.8606 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2612 - Aurc: 0.8512 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 4.2281 | 0.145 | 0.8999 | 10.1620 | 0.145 | 0.0253 | 0.2222 | 0.8467 | | No log | 1.96 | 6 | 4.1872 | 0.145 | 0.8946 | 10.5915 | 0.145 | 0.0253 | 0.2275 | 0.8468 | | No log | 2.96 | 9 | 4.1248 | 0.155 | 0.8866 | 8.6280 | 0.155 | 0.0360 | 0.2179 | 0.8487 | | No log | 3.96 | 12 | 4.0716 | 0.155 | 0.8806 | 6.5480 | 0.155 | 0.0272 | 0.2254 | 0.8851 | | No log | 4.96 | 15 | 4.0359 | 0.155 | 0.8778 | 6.7781 | 0.155 | 0.0271 | 0.2310 | 0.8931 | | No log | 5.96 | 18 | 4.0135 | 0.155 | 0.8774 | 7.8547 | 0.155 | 0.0271 | 0.2345 | 0.8965 | | No log | 6.96 | 21 | 3.9978 | 0.185 | 0.8779 | 8.3528 | 0.185 | 0.0468 | 0.2615 | 0.8612 | | No log | 7.96 | 24 | 3.9867 | 0.18 | 0.8789 | 7.6001 | 0.18 | 0.0308 | 0.2618 | 0.8546 | | No log | 8.96 | 27 | 3.9782 | 0.18 | 0.8796 | 7.0871 | 0.18 | 0.0306 | 0.2613 | 0.8538 | | No log | 9.96 | 30 | 3.9726 | 0.18 | 0.8800 | 7.0519 | 0.18 | 0.0306 | 0.2687 | 0.8545 | | No log | 10.96 | 33 | 3.9684 | 0.18 | 0.8803 | 7.0277 | 0.18 | 0.0306 | 0.2656 | 0.8537 | | No log | 11.96 | 36 | 3.9654 | 0.18 | 0.8805 | 7.0162 | 0.18 | 0.0306 | 0.2708 | 0.8536 | | No log | 12.96 | 39 | 3.9633 | 0.18 | 0.8805 | 7.0056 | 0.18 | 0.0306 | 0.2619 | 0.8535 | | No log | 13.96 | 42 | 3.9614 | 0.18 | 0.8804 | 6.9981 | 0.18 | 0.0306 | 0.2617 | 0.8532 | | No log | 14.96 | 45 | 3.9598 | 0.18 | 0.8804 | 6.9923 | 0.18 | 0.0306 | 0.2669 | 0.8531 | | No log | 15.96 | 48 | 3.9586 | 0.18 | 0.8803 | 6.9334 | 0.18 | 0.0306 | 0.2669 | 0.8529 | | No log | 16.96 | 51 | 3.9578 | 0.18 | 0.8802 | 6.9237 | 0.18 | 0.0306 | 0.2716 | 0.8522 | | No log | 17.96 | 54 | 3.9576 | 0.18 | 0.8802 | 6.8704 | 0.18 | 0.0306 | 0.2666 | 0.8521 | | No log | 18.96 | 57 | 3.9574 | 0.18 | 0.8802 | 6.8662 | 0.18 | 0.0306 | 0.2664 | 0.8523 | | No log | 19.96 | 60 | 3.9568 | 0.18 | 0.8801 | 6.8641 | 0.18 | 0.0306 | 0.2614 | 0.8518 | | No log | 20.96 | 63 | 3.9566 | 0.18 | 0.8801 | 6.8634 | 0.18 | 0.0306 | 0.2659 | 0.8516 | | No log | 21.96 | 66 | 3.9563 | 0.18 | 0.8800 | 6.8632 | 0.18 | 0.0306 | 0.2612 | 0.8516 | | No log | 22.96 | 69 | 3.9561 | 0.18 | 0.8800 | 6.8620 | 0.18 | 0.0306 | 0.2612 | 0.8513 | | No log | 23.96 | 72 | 3.9561 | 0.18 | 0.8800 | 6.8611 | 0.18 | 0.0306 | 0.2612 | 0.8513 | | No log | 24.96 | 75 | 3.9560 | 0.18 | 0.8800 | 6.8606 | 0.18 | 0.0306 | 0.2612 | 0.8512 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
crisU8/bert-finetuned-ner-clinical-BETO-1-uncased
crisU8
2023-07-09T09:19:58Z
104
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-07-09T09:06:55Z
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-BETO-1-uncased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner-clinical-BETO-1-uncased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5376 - Precision: 0.7341 - Recall: 0.7772 - F1: 0.7550 - Accuracy: 0.9177 ## Model description More information needed ## Intended uses & 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: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4682 | 1.0 | 502 | 0.3263 | 0.6124 | 0.7344 | 0.6678 | 0.8939 | | 0.2443 | 2.0 | 1004 | 0.2778 | 0.6809 | 0.7519 | 0.7147 | 0.9122 | | 0.1728 | 3.0 | 1506 | 0.2898 | 0.7011 | 0.7481 | 0.7238 | 0.9155 | | 0.1277 | 4.0 | 2008 | 0.3182 | 0.6970 | 0.7640 | 0.7290 | 0.9118 | | 0.0928 | 5.0 | 2510 | 0.3578 | 0.6975 | 0.7667 | 0.7305 | 0.9128 | | 0.0699 | 6.0 | 3012 | 0.3931 | 0.7058 | 0.7794 | 0.7407 | 0.9102 | | 0.0538 | 7.0 | 3514 | 0.4213 | 0.7225 | 0.7574 | 0.7395 | 0.9140 | | 0.0413 | 8.0 | 4016 | 0.4387 | 0.7143 | 0.7821 | 0.7467 | 0.9147 | | 0.033 | 9.0 | 4518 | 0.4997 | 0.7184 | 0.7728 | 0.7446 | 0.9147 | | 0.0265 | 10.0 | 5020 | 0.5056 | 0.7180 | 0.7728 | 0.7444 | 0.9152 | | 0.0225 | 11.0 | 5522 | 0.5237 | 0.7250 | 0.7728 | 0.7481 | 0.9164 | | 0.0176 | 12.0 | 6024 | 0.5376 | 0.7341 | 0.7772 | 0.7550 | 0.9177 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/dit-small_tobacco3482_kd_CEKD_t1.5_a0.9
jordyvl
2023-07-09T09:15:40Z
161
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T09:02:07Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_tobacco3482_kd_CEKD_t1.5_a0.9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-small_tobacco3482_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2890 - Accuracy: 0.19 - Brier Loss: 0.8648 - Nll: 6.4150 - F1 Micro: 0.19 - F1 Macro: 0.0641 - Ece: 0.2450 - Aurc: 0.7332 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 2.4806 | 0.06 | 0.9041 | 9.2838 | 0.06 | 0.0114 | 0.1750 | 0.9034 | | No log | 1.96 | 6 | 2.4041 | 0.18 | 0.8884 | 6.3227 | 0.18 | 0.0305 | 0.2317 | 0.8027 | | No log | 2.96 | 9 | 2.3381 | 0.18 | 0.8760 | 6.9952 | 0.18 | 0.0305 | 0.2424 | 0.8118 | | No log | 3.96 | 12 | 2.3362 | 0.185 | 0.8771 | 6.9040 | 0.185 | 0.0488 | 0.2544 | 0.7841 | | No log | 4.96 | 15 | 2.3345 | 0.185 | 0.8747 | 6.8515 | 0.185 | 0.0488 | 0.2476 | 0.7768 | | No log | 5.96 | 18 | 2.3339 | 0.185 | 0.8725 | 6.0111 | 0.185 | 0.0490 | 0.2457 | 0.7670 | | No log | 6.96 | 21 | 2.3348 | 0.185 | 0.8718 | 5.9199 | 0.185 | 0.0488 | 0.2328 | 0.7596 | | No log | 7.96 | 24 | 2.3310 | 0.185 | 0.8711 | 5.9008 | 0.185 | 0.0488 | 0.2443 | 0.7536 | | No log | 8.96 | 27 | 2.3231 | 0.185 | 0.8699 | 5.8793 | 0.185 | 0.0488 | 0.2337 | 0.7516 | | No log | 9.96 | 30 | 2.3181 | 0.185 | 0.8694 | 6.6980 | 0.185 | 0.0488 | 0.2507 | 0.7500 | | No log | 10.96 | 33 | 2.3139 | 0.185 | 0.8692 | 6.7350 | 0.185 | 0.0488 | 0.2481 | 0.7488 | | No log | 11.96 | 36 | 2.3099 | 0.185 | 0.8690 | 6.7557 | 0.185 | 0.0488 | 0.2484 | 0.7463 | | No log | 12.96 | 39 | 2.3057 | 0.185 | 0.8684 | 6.6765 | 0.185 | 0.0488 | 0.2598 | 0.7441 | | No log | 13.96 | 42 | 2.3014 | 0.185 | 0.8676 | 6.6313 | 0.185 | 0.0488 | 0.2478 | 0.7420 | | No log | 14.96 | 45 | 2.2978 | 0.185 | 0.8669 | 6.6142 | 0.185 | 0.0488 | 0.2496 | 0.7412 | | No log | 15.96 | 48 | 2.2955 | 0.185 | 0.8664 | 6.5990 | 0.185 | 0.0488 | 0.2379 | 0.7399 | | No log | 16.96 | 51 | 2.2947 | 0.185 | 0.8662 | 6.4895 | 0.185 | 0.0488 | 0.2452 | 0.7375 | | No log | 17.96 | 54 | 2.2949 | 0.185 | 0.8661 | 6.4730 | 0.185 | 0.0488 | 0.2438 | 0.7354 | | No log | 18.96 | 57 | 2.2949 | 0.185 | 0.8661 | 6.4244 | 0.185 | 0.0488 | 0.2435 | 0.7356 | | No log | 19.96 | 60 | 2.2930 | 0.185 | 0.8657 | 6.3676 | 0.185 | 0.0490 | 0.2389 | 0.7341 | | No log | 20.96 | 63 | 2.2918 | 0.19 | 0.8654 | 6.4233 | 0.19 | 0.0641 | 0.2446 | 0.7336 | | No log | 21.96 | 66 | 2.2905 | 0.19 | 0.8651 | 6.4742 | 0.19 | 0.0641 | 0.2485 | 0.7334 | | No log | 22.96 | 69 | 2.2897 | 0.19 | 0.8649 | 6.4243 | 0.19 | 0.0641 | 0.2448 | 0.7332 | | No log | 23.96 | 72 | 2.2893 | 0.19 | 0.8648 | 6.4174 | 0.19 | 0.0641 | 0.2450 | 0.7332 | | No log | 24.96 | 75 | 2.2890 | 0.19 | 0.8648 | 6.4150 | 0.19 | 0.0641 | 0.2450 | 0.7332 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
jordyvl/dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.9
jordyvl
2023-07-09T09:01:17Z
161
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T08:49:44Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.9 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3286 - Accuracy: 0.18 - Brier Loss: 0.8742 - Nll: 6.7213 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2558 - Aurc: 0.8491 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 2.4683 | 0.145 | 0.8999 | 10.1538 | 0.145 | 0.0253 | 0.2220 | 0.8466 | | No log | 1.96 | 6 | 2.4396 | 0.145 | 0.8947 | 10.5704 | 0.145 | 0.0253 | 0.2237 | 0.8463 | | No log | 2.96 | 9 | 2.3985 | 0.145 | 0.8869 | 8.5511 | 0.145 | 0.0451 | 0.2116 | 0.8036 | | No log | 3.96 | 12 | 2.3677 | 0.21 | 0.8810 | 6.5446 | 0.2100 | 0.0611 | 0.2566 | 0.8335 | | No log | 4.96 | 15 | 2.3517 | 0.155 | 0.8780 | 6.8400 | 0.155 | 0.0279 | 0.2309 | 0.8894 | | No log | 5.96 | 18 | 2.3450 | 0.18 | 0.8771 | 8.1897 | 0.18 | 0.0313 | 0.2495 | 0.8531 | | No log | 6.96 | 21 | 2.3407 | 0.18 | 0.8767 | 7.3073 | 0.18 | 0.0306 | 0.2551 | 0.8513 | | No log | 7.96 | 24 | 2.3371 | 0.18 | 0.8763 | 6.9328 | 0.18 | 0.0306 | 0.2501 | 0.8520 | | No log | 8.96 | 27 | 2.3337 | 0.18 | 0.8757 | 6.8828 | 0.18 | 0.0306 | 0.2507 | 0.8525 | | No log | 9.96 | 30 | 2.3321 | 0.18 | 0.8753 | 6.8682 | 0.18 | 0.0306 | 0.2508 | 0.8524 | | No log | 10.96 | 33 | 2.3312 | 0.18 | 0.8751 | 6.7981 | 0.18 | 0.0306 | 0.2462 | 0.8521 | | No log | 11.96 | 36 | 2.3309 | 0.18 | 0.8749 | 6.7375 | 0.18 | 0.0306 | 0.2531 | 0.8520 | | No log | 12.96 | 39 | 2.3307 | 0.18 | 0.8748 | 6.7235 | 0.18 | 0.0306 | 0.2524 | 0.8518 | | No log | 13.96 | 42 | 2.3304 | 0.18 | 0.8747 | 6.7200 | 0.18 | 0.0306 | 0.2482 | 0.8514 | | No log | 14.96 | 45 | 2.3301 | 0.18 | 0.8746 | 6.7201 | 0.18 | 0.0306 | 0.2410 | 0.8509 | | No log | 15.96 | 48 | 2.3298 | 0.18 | 0.8746 | 6.7182 | 0.18 | 0.0306 | 0.2449 | 0.8505 | | No log | 16.96 | 51 | 2.3295 | 0.18 | 0.8745 | 6.7211 | 0.18 | 0.0306 | 0.2412 | 0.8500 | | No log | 17.96 | 54 | 2.3297 | 0.18 | 0.8745 | 6.7201 | 0.18 | 0.0306 | 0.2449 | 0.8496 | | No log | 18.96 | 57 | 2.3296 | 0.18 | 0.8745 | 6.7216 | 0.18 | 0.0306 | 0.2392 | 0.8494 | | No log | 19.96 | 60 | 2.3292 | 0.18 | 0.8744 | 6.7214 | 0.18 | 0.0306 | 0.2371 | 0.8494 | | No log | 20.96 | 63 | 2.3290 | 0.18 | 0.8744 | 6.7222 | 0.18 | 0.0306 | 0.2371 | 0.8493 | | No log | 21.96 | 66 | 2.3288 | 0.18 | 0.8743 | 6.7227 | 0.18 | 0.0306 | 0.2408 | 0.8494 | | No log | 22.96 | 69 | 2.3286 | 0.18 | 0.8743 | 6.7223 | 0.18 | 0.0306 | 0.2558 | 0.8490 | | No log | 23.96 | 72 | 2.3286 | 0.18 | 0.8743 | 6.7218 | 0.18 | 0.0306 | 0.2558 | 0.8491 | | No log | 24.96 | 75 | 2.3286 | 0.18 | 0.8742 | 6.7213 | 0.18 | 0.0306 | 0.2558 | 0.8491 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
mrizalf7/t5-small-finetuned-indosum-2
mrizalf7
2023-07-09T09:00:29Z
105
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
2023-07-09T07:07:18Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-indosum-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. --> # t5-small-finetuned-indosum-2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
crisU8/bert-finetuned-ner-clinical-BETO-uncased-4
crisU8
2023-07-09T08:59:59Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-07-09T08:54:00Z
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-BETO-uncased-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner-clinical-BETO-uncased-4 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4171 - Precision: 0.7142 - Recall: 0.7722 - F1: 0.7421 - Accuracy: 0.9150 ## Model description More information needed ## Intended uses & 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0602 | 1.0 | 502 | 0.3957 | 0.7006 | 0.7552 | 0.7269 | 0.9089 | | 0.0596 | 2.0 | 1004 | 0.3879 | 0.7198 | 0.7629 | 0.7407 | 0.9146 | | 0.0575 | 3.0 | 1506 | 0.4171 | 0.7142 | 0.7722 | 0.7421 | 0.9150 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/dit-small_tobacco3482_kd_CEKD_t1.5_a0.7
jordyvl
2023-07-09T08:49:00Z
161
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T08:35:32Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_tobacco3482_kd_CEKD_t1.5_a0.7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-small_tobacco3482_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5836 - Accuracy: 0.185 - Brier Loss: 0.8652 - Nll: 6.4546 - F1 Micro: 0.185 - F1 Macro: 0.0488 - Ece: 0.2424 - Aurc: 0.7342 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 2.8093 | 0.06 | 0.9041 | 9.2868 | 0.06 | 0.0114 | 0.1752 | 0.9033 | | No log | 1.96 | 6 | 2.7245 | 0.18 | 0.8884 | 6.2166 | 0.18 | 0.0305 | 0.2292 | 0.8036 | | No log | 2.96 | 9 | 2.6443 | 0.18 | 0.8760 | 6.9627 | 0.18 | 0.0305 | 0.2437 | 0.8179 | | No log | 3.96 | 12 | 2.6356 | 0.185 | 0.8785 | 6.9306 | 0.185 | 0.0488 | 0.2534 | 0.7877 | | No log | 4.96 | 15 | 2.6338 | 0.185 | 0.8768 | 6.8870 | 0.185 | 0.0488 | 0.2605 | 0.7787 | | No log | 5.96 | 18 | 2.6325 | 0.185 | 0.8740 | 6.2086 | 0.185 | 0.0490 | 0.2453 | 0.7699 | | No log | 6.96 | 21 | 2.6322 | 0.185 | 0.8721 | 5.9554 | 0.185 | 0.0488 | 0.2474 | 0.7629 | | No log | 7.96 | 24 | 2.6293 | 0.185 | 0.8712 | 5.9359 | 0.185 | 0.0488 | 0.2550 | 0.7576 | | No log | 8.96 | 27 | 2.6221 | 0.185 | 0.8701 | 5.9468 | 0.185 | 0.0488 | 0.2436 | 0.7536 | | No log | 9.96 | 30 | 2.6171 | 0.185 | 0.8697 | 6.6875 | 0.185 | 0.0488 | 0.2497 | 0.7541 | | No log | 10.96 | 33 | 2.6126 | 0.185 | 0.8697 | 6.7549 | 0.185 | 0.0488 | 0.2512 | 0.7517 | | No log | 11.96 | 36 | 2.6084 | 0.185 | 0.8697 | 6.7827 | 0.185 | 0.0488 | 0.2476 | 0.7489 | | No log | 12.96 | 39 | 2.6037 | 0.185 | 0.8692 | 6.7652 | 0.185 | 0.0488 | 0.2557 | 0.7476 | | No log | 13.96 | 42 | 2.5986 | 0.185 | 0.8683 | 6.6847 | 0.185 | 0.0488 | 0.2513 | 0.7446 | | No log | 14.96 | 45 | 2.5940 | 0.185 | 0.8676 | 6.6600 | 0.185 | 0.0488 | 0.2572 | 0.7447 | | No log | 15.96 | 48 | 2.5910 | 0.185 | 0.8669 | 6.6410 | 0.185 | 0.0488 | 0.2448 | 0.7424 | | No log | 16.96 | 51 | 2.5897 | 0.185 | 0.8667 | 6.6371 | 0.185 | 0.0488 | 0.2402 | 0.7402 | | No log | 17.96 | 54 | 2.5898 | 0.185 | 0.8664 | 6.5096 | 0.185 | 0.0488 | 0.2549 | 0.7371 | | No log | 18.96 | 57 | 2.5897 | 0.185 | 0.8664 | 6.5160 | 0.185 | 0.0488 | 0.2504 | 0.7363 | | No log | 19.96 | 60 | 2.5877 | 0.185 | 0.8660 | 6.4661 | 0.185 | 0.0488 | 0.2416 | 0.7346 | | No log | 20.96 | 63 | 2.5865 | 0.185 | 0.8658 | 6.4833 | 0.185 | 0.0488 | 0.2459 | 0.7347 | | No log | 21.96 | 66 | 2.5852 | 0.185 | 0.8655 | 6.4690 | 0.185 | 0.0488 | 0.2460 | 0.7343 | | No log | 22.96 | 69 | 2.5843 | 0.185 | 0.8654 | 6.4625 | 0.185 | 0.0488 | 0.2461 | 0.7340 | | No log | 23.96 | 72 | 2.5838 | 0.185 | 0.8653 | 6.4568 | 0.185 | 0.0488 | 0.2424 | 0.7342 | | No log | 24.96 | 75 | 2.5836 | 0.185 | 0.8652 | 6.4546 | 0.185 | 0.0488 | 0.2424 | 0.7342 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
crisU8/bert-finetuned-ner-clinical-BETO-uncased-1
crisU8
2023-07-09T08:40:27Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-07-09T08:35:19Z
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-BETO-uncased-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner-clinical-BETO-uncased-1 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3018 - Precision: 0.6953 - Recall: 0.7464 - F1: 0.7200 - Accuracy: 0.9155 ## Model description More information needed ## Intended uses & 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4647 | 1.0 | 502 | 0.3156 | 0.6186 | 0.7327 | 0.6709 | 0.8969 | | 0.2428 | 2.0 | 1004 | 0.2804 | 0.6916 | 0.7470 | 0.7182 | 0.9120 | | 0.1734 | 3.0 | 1506 | 0.2864 | 0.6923 | 0.7508 | 0.7204 | 0.9161 | | 0.1353 | 4.0 | 2008 | 0.3018 | 0.6953 | 0.7464 | 0.7200 | 0.9155 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
cambioml/rlhf-reward-model
cambioml
2023-07-09T08:36:38Z
136
1
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-07-09T07:59:15Z
# 🚀 RLHF Step-2 Reward Model This repository is home to a RLHF reward model. This model is trained on questions and answers from the Stack Overflow Data Dump (https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences), using the `distilroberta-base` model (https://huggingface.co/distilroberta-base) as a base. ## Usage You can use this model directly with a pipeline for tasks such as text generation and instruction following: ```python from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, pipeline ) reward_model = AutoModelForSequenceClassification.from_pretrained( cambioml/rlhf_reward_model, num_labels=1, # torch_dtype=torch.bfloat16, load_in_8bit=True, device_map={"": Accelerator().process_index} ) reward_tokenizer = AutoTokenizer.from_pretrained(cambioml/rlhf_reward_model) reward_tokenizer.pad_token = reward_tokenizer.eos_token reward_kwargs = { "return_all_scores": True, "function_to_apply": "none", "batch_size": 32, "truncation": True, "max_length": 138 } reward_pipe = pipeline( "sentiment-analysis", model=reward_model, model_kwargs=reward_kwargs, tokenizer=reward_tokenizer, return_token_type_ids=False, ) ```
jordyvl/dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.7
jordyvl
2023-07-09T08:34:48Z
161
0
transformers
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-09T08:23:17Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6280 - Accuracy: 0.18 - Brier Loss: 0.8747 - Nll: 6.7569 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2550 - Aurc: 0.8496 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 0.96 | 3 | 2.7961 | 0.145 | 0.8999 | 10.1560 | 0.145 | 0.0253 | 0.2221 | 0.8467 | | No log | 1.96 | 6 | 2.7646 | 0.145 | 0.8946 | 10.5828 | 0.145 | 0.0253 | 0.2242 | 0.8475 | | No log | 2.96 | 9 | 2.7185 | 0.155 | 0.8868 | 8.6137 | 0.155 | 0.0501 | 0.2145 | 0.8394 | | No log | 3.96 | 12 | 2.6825 | 0.21 | 0.8808 | 6.5439 | 0.2100 | 0.0613 | 0.2567 | 0.8351 | | No log | 4.96 | 15 | 2.6619 | 0.155 | 0.8778 | 6.7839 | 0.155 | 0.0274 | 0.2346 | 0.8880 | | No log | 5.96 | 18 | 2.6517 | 0.18 | 0.8769 | 7.4578 | 0.18 | 0.0395 | 0.2461 | 0.8571 | | No log | 6.96 | 21 | 2.6450 | 0.18 | 0.8767 | 7.1192 | 0.18 | 0.0308 | 0.2518 | 0.8516 | | No log | 7.96 | 24 | 2.6400 | 0.18 | 0.8766 | 6.9539 | 0.18 | 0.0306 | 0.2472 | 0.8526 | | No log | 8.96 | 27 | 2.6355 | 0.18 | 0.8762 | 6.9109 | 0.18 | 0.0306 | 0.2524 | 0.8527 | | No log | 9.96 | 30 | 2.6332 | 0.18 | 0.8759 | 6.8997 | 0.18 | 0.0306 | 0.2491 | 0.8527 | | No log | 10.96 | 33 | 2.6317 | 0.18 | 0.8757 | 6.8943 | 0.18 | 0.0306 | 0.2529 | 0.8524 | | No log | 11.96 | 36 | 2.6309 | 0.18 | 0.8755 | 6.8287 | 0.18 | 0.0306 | 0.2442 | 0.8523 | | No log | 12.96 | 39 | 2.6304 | 0.18 | 0.8753 | 6.7670 | 0.18 | 0.0306 | 0.2478 | 0.8521 | | No log | 13.96 | 42 | 2.6298 | 0.18 | 0.8752 | 6.7597 | 0.18 | 0.0306 | 0.2433 | 0.8517 | | No log | 14.96 | 45 | 2.6293 | 0.18 | 0.8751 | 6.7590 | 0.18 | 0.0306 | 0.2516 | 0.8513 | | No log | 15.96 | 48 | 2.6290 | 0.18 | 0.8750 | 6.7556 | 0.18 | 0.0306 | 0.2555 | 0.8515 | | No log | 16.96 | 51 | 2.6287 | 0.18 | 0.8750 | 6.7582 | 0.18 | 0.0306 | 0.2557 | 0.8514 | | No log | 17.96 | 54 | 2.6289 | 0.18 | 0.8750 | 6.7556 | 0.18 | 0.0306 | 0.2476 | 0.8509 | | No log | 18.96 | 57 | 2.6289 | 0.18 | 0.8750 | 6.7567 | 0.18 | 0.0306 | 0.2475 | 0.8505 | | No log | 19.96 | 60 | 2.6285 | 0.18 | 0.8748 | 6.7567 | 0.18 | 0.0306 | 0.2433 | 0.8502 | | No log | 20.96 | 63 | 2.6283 | 0.18 | 0.8748 | 6.7577 | 0.18 | 0.0306 | 0.2512 | 0.8500 | | No log | 21.96 | 66 | 2.6281 | 0.18 | 0.8748 | 6.7586 | 0.18 | 0.0306 | 0.2551 | 0.8495 | | No log | 22.96 | 69 | 2.6280 | 0.18 | 0.8747 | 6.7580 | 0.18 | 0.0306 | 0.2550 | 0.8496 | | No log | 23.96 | 72 | 2.6280 | 0.18 | 0.8747 | 6.7573 | 0.18 | 0.0306 | 0.2550 | 0.8496 | | No log | 24.96 | 75 | 2.6280 | 0.18 | 0.8747 | 6.7569 | 0.18 | 0.0306 | 0.2550 | 0.8496 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
saintzeno/a2c-PandaReachDense-v2
saintzeno
2023-07-09T08:26:17Z
4
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T06:25:19Z
--- 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: -1.83 +/- 0.18 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 ... ```
komo-dono/yanaginagi
komo-dono
2023-07-09T08:07:04Z
0
0
null
[ "music", "ja", "license:openrail", "region:us" ]
null
2023-07-09T08:02:47Z
--- license: openrail language: - ja tags: - music ---
chunwoolee0/my_awesome_qa_model
chunwoolee0
2023-07-09T08:00:28Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-07-09T07:50:34Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_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. --> # my_awesome_qa_model 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: 1.5944 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.2632 | | 2.6568 | 2.0 | 500 | 1.6629 | | 2.6568 | 3.0 | 750 | 1.5944 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
guaguale/model_tshirt
guaguale
2023-07-09T07:22:57Z
2
0
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-07-09T06:27:08Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of adlv clothes tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - guaguale/model_tshirt These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of adlv clothes using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
disanda/first_try_4
disanda
2023-07-09T07:21:57Z
106
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-09T07:20:27Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: first_try_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # first_try_4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.5505 ## Model description More information needed ## Intended uses & 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7226 | 1.0 | 157 | 2.5273 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.12.0+cu102 - Datasets 2.12.0 - Tokenizers 0.13.3
daiwenbin/distilbert-base-uncased-finetuned-clinc
daiwenbin
2023-07-09T07:15:00Z
105
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
2023-07-09T02:43:17Z
--- 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: 0.9138709677419354 --- <!-- 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: 0.7816 - Accuracy: 0.9139 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2795 | 0.7277 | | 3.7861 | 2.0 | 636 | 1.8741 | 0.8294 | | 3.7861 | 3.0 | 954 | 1.1621 | 0.8906 | | 1.6946 | 4.0 | 1272 | 0.8663 | 0.9058 | | 0.9106 | 5.0 | 1590 | 0.7816 | 0.9139 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.13.3
abdulfatir/NCDSSM
abdulfatir
2023-07-09T07:00:23Z
0
2
null
[ "arxiv:2301.11308", "license:mit", "region:us" ]
null
2023-07-09T06:54:08Z
--- license: mit --- # Neural Continuous-Discrete State Space Models (NCDSSM) This repository contains pretrained checkpoints for reproducing the experiments presented in the ICML 2023 paper [*Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series*](https://arxiv.org/abs/2301.11308). For details on how to use these checkpoints, please refer to https://github.com/clear-nus/NCDSSM.
Dorost/resume
Dorost
2023-07-09T06:46:02Z
110
0
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-30T10:41:45Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: resume 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. --> # resume This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0166 - F1: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0448 | 1.0 | 49 | 2.7245 | 0.1290 | | 2.2276 | 2.0 | 98 | 1.7165 | 0.4683 | | 1.116 | 3.0 | 147 | 0.8720 | 0.8333 | | 0.5606 | 4.0 | 196 | 0.3686 | 1.0 | | 0.2374 | 5.0 | 245 | 0.1431 | 1.0 | | 0.1084 | 6.0 | 294 | 0.0612 | 1.0 | | 0.0598 | 7.0 | 343 | 0.0328 | 1.0 | | 0.0386 | 8.0 | 392 | 0.0216 | 1.0 | | 0.0276 | 9.0 | 441 | 0.0175 | 1.0 | | 0.0271 | 10.0 | 490 | 0.0166 | 1.0 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
digiplay/Pika_v1
digiplay
2023-07-09T06:44:58Z
289
3
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-06-22T13:13:29Z
--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/47067?modelVersionId=51650 Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e5f65d46-539f-4b71-cfe0-748300ded200/31490.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/c18ef225-90b1-479f-525a-770c42637500/31488.jpeg)
YeungNLP/firefly-bloom-7b1
YeungNLP
2023-07-09T06:36:38Z
1,437
1
transformers
[ "transformers", "pytorch", "bloom", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-06-26T10:57:10Z
该模型使用bloom-7b1，使用百万中英文指令数据，进行指令微调。 更多详情见[Firefly项目](https://github.com/yangjianxin1/Firefly)
YeungNLP/Ziya-LLaMA-13B-Pretrain-v1
YeungNLP
2023-07-09T06:35:20Z
12
4
transformers
[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-06-21T10:35:14Z
由[IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1)与原始llama权重进行合并而得到。 [firefly-ziya-13b](https://huggingface.co/YeungNLP/firefly-ziya-13b)基于该模型进行指令微调 更多详情请查看[Firefly项目](https://github.com/yangjianxin1/Firefly)
KKSK2023/ppo-LunarLander-v2
KKSK2023
2023-07-09T06:27:58Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T06:27:39Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.57 +/- 19.74 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 ... ```
demelianov/model
demelianov
2023-07-09T06:27:04Z
1
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-07-08T05:14:01Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 instance_prompt: a photo of sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - demelianov/model This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
Mistermango24/Furrymix3
Mistermango24
2023-07-09T06:23:58Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-07-09T06:04:39Z
--- license: bigscience-openrail-m ---
luhx/Reinforce-PixelCopter
luhx
2023-07-09T05:52:43Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T05:52:10Z
--- 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: 15.60 +/- 16.52 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
luhx/Reinforce-CartPole-v1
luhx
2023-07-09T05:09:01Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T05:08:52Z
--- 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: 486.50 +/- 40.50 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
Winmodel/ppo-Huggy
Winmodel
2023-07-09T05:04:35Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-07-09T05:04:30Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Winmodel/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
jason1i/whisper-tiny-minds14
jason1i
2023-07-09T05:01:53Z
77
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-07-09T04:37:41Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.34415584415584416 --- <!-- 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-tiny-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6338 - Wer Ortho: 0.3467 - Wer: 0.3442 ## Model description More information needed ## Intended uses & 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.001 | 17.86 | 500 | 0.6338 | 0.3467 | 0.3442 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
lovelyxs/dqn-SpaceInvadersNoFrameskip-v4
lovelyxs
2023-07-09T04:50:33Z
1
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T04:49:57Z
--- 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: 527.50 +/- 132.78 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 lovelyxs -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 lovelyxs -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 lovelyxs ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jrfalck/my_awesome_opus_books_model_JRF
jrfalck
2023-07-09T04:46:43Z
108
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-07-09T03:55:44Z
--- tags: - generated_from_trainer datasets: - opus_books metrics: - bleu model-index: - name: my_awesome_opus_books_model_JRF results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus_books type: opus_books config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 6.0553 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_opus_books_model_JRF This model was trained from scratch on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 1.5465 - Bleu: 6.0553 - Gen Len: 17.528 ## Model description More information needed ## Intended uses & 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 1.7679 | 1.0 | 6355 | 1.5601 | 6.0201 | 17.5327 | | 1.7452 | 2.0 | 12710 | 1.5465 | 6.0553 | 17.528 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.2
BauyrjanQ/whisper-kk
BauyrjanQ
2023-07-09T04:14:39Z
74
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-07-07T09:49:25Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-kk results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-kk This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1070 - Wer: 24.8145 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1912 | 0.46 | 1000 | 0.1793 | 31.2210 | | 0.1314 | 0.92 | 2000 | 0.1307 | 20.8113 | | 0.096 | 1.38 | 3000 | 0.1136 | 28.8680 | | 0.0845 | 1.84 | 4000 | 0.1070 | 24.8145 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Drawzipink/AesopCarlV2
Drawzipink
2023-07-09T03:59:29Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-07-09T03:38:01Z
--- license: openrail --- ***Note***: This model was made using Yuki Hirai's interpretation of Aesop Carl from the Game Identity V in the unofficial stageplay. Should he see this and ask for anything using this model be taken down I ask that you oblige. This model is for fun and personal use only. Thank you.
PhantasyMaker/Kate
PhantasyMaker
2023-07-09T03:55:50Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-09T03:55:50Z
--- license: creativeml-openrail-m ---
NasimB/gpt2-concat-all-mod-aochildes-rarity-all-30k-3k
NasimB
2023-07-09T03:31:49Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-09T01:14:30Z
--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-all-mod-aochildes-rarity-all-30k-3k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-all-mod-aochildes-rarity-all-30k-3k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.0554 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.787 | 0.32 | 500 | 5.8319 | | 5.4867 | 0.65 | 1000 | 5.4639 | | 5.1388 | 0.97 | 1500 | 5.2444 | | 4.8676 | 1.3 | 2000 | 5.1700 | | 4.7551 | 1.62 | 2500 | 5.0582 | | 4.6549 | 1.95 | 3000 | 4.9945 | | 4.4476 | 2.27 | 3500 | 4.9966 | | 4.4081 | 2.6 | 4000 | 4.9368 | | 4.3708 | 2.92 | 4500 | 4.9070 | | 4.1704 | 3.25 | 5000 | 4.9144 | | 4.1343 | 3.57 | 5500 | 4.8945 | | 4.1237 | 3.9 | 6000 | 4.8582 | | 3.9238 | 4.22 | 6500 | 4.8881 | | 3.8703 | 4.55 | 7000 | 4.8883 | | 3.8693 | 4.87 | 7500 | 4.8628 | | 3.6914 | 5.19 | 8000 | 4.9088 | | 3.6022 | 5.52 | 8500 | 4.9100 | | 3.6033 | 5.84 | 9000 | 4.9048 | | 3.476 | 6.17 | 9500 | 4.9392 | | 3.3693 | 6.49 | 10000 | 4.9473 | | 3.3744 | 6.82 | 10500 | 4.9551 | | 3.3104 | 7.14 | 11000 | 4.9658 | | 3.2401 | 7.47 | 11500 | 4.9706 | | 3.2421 | 7.79 | 12000 | 4.9727 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
FinalIroha/Ryuuou_no_Oshigoto_SoVITS4.1_Model
FinalIroha
2023-07-09T03:27:29Z
3
0
transformers
[ "transformers", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
null
2023-07-08T11:30:12Z
--- license: cc-by-nc-sa-4.0 --- # SoVITS 4.1龙王的工作多人模型 <!-- Provide a quick summary of what the model is/does. --> 此模型由[SoVITS4.1](https://github.com/svc-develop-team/so-vits-svc/blob/4.1-Stable/)生成。 ## 模型人物名 <!-- Provide a quick summary of what the model is/does. --> - **Yaichi Kuzuryuu：** 九頭竜八一/九头龙八一 CV：内田雄马 - **Ai Hinatsuru：** 雛鶴あい/雏鹤爱 CV：日高里菜 - **Ai Yashajin：** 夜叉神天衣/夜叉神天衣 CV：佐仓绫音 - **Ginko Sora：** 空銀子/空银子 CV：金元寿子 - **Keika Kiyotaki：** 清滝桂香/清泷桂香 CV：茅野爱衣 - **Mio Mizukoshi：** 水越澪/水越澪 CV：久保百合花 - **Ayano Sadatou：** 貞任綾乃/贞任绫乃 CV：桥本千波 - **Charlotte Izoard：** シャルロット・イゾアール/夏洛特·伊索亚尔 CV：小仓唯
Splend1dchan/h-p-test
Splend1dchan
2023-07-09T03:24:07Z
50
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text2text-generation", "generated_from_trainer", "dataset:arrow", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-07-09T03:17:38Z
--- tags: - generated_from_trainer datasets: - arrow model-index: - name: hubert-pythia-70m_librispeech.train.mix 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. --> # hubert-pythia-70m_librispeech.train.mix This model is a fine-tuned version of [speechmix/pythia-70m-test](https://huggingface.co/speechmix/pythia-70m-test) on the arrow 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 50000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jncraton/e5-small-v2-ct2-int8
jncraton
2023-07-09T02:30:12Z
7
0
transformers
[ "transformers", "mteb", "en", "arxiv:2212.03533", "arxiv:2104.08663", "arxiv:2210.07316", "license:mit", "model-index", "endpoints_compatible", "region:us" ]
null
2023-07-09T02:22:18Z
--- tags: - mteb model-index: - name: e5-small-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.59701492537313 - type: ap value: 41.67064885731708 - type: f1 value: 71.86465946398573 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.265875 - type: ap value: 87.67633085349644 - type: f1 value: 91.24297521425744 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.882000000000005 - type: f1 value: 45.08058870381236 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 20.697 - type: map_at_10 value: 33.975 - type: map_at_100 value: 35.223 - type: map_at_1000 value: 35.260000000000005 - type: map_at_3 value: 29.776999999999997 - type: map_at_5 value: 32.035000000000004 - type: mrr_at_1 value: 20.982 - type: mrr_at_10 value: 34.094 - type: mrr_at_100 value: 35.343 - type: mrr_at_1000 value: 35.38 - type: mrr_at_3 value: 29.884 - type: mrr_at_5 value: 32.141999999999996 - type: ndcg_at_1 value: 20.697 - type: ndcg_at_10 value: 41.668 - type: ndcg_at_100 value: 47.397 - type: ndcg_at_1000 value: 48.305 - type: ndcg_at_3 value: 32.928000000000004 - type: ndcg_at_5 value: 36.998999999999995 - type: precision_at_1 value: 20.697 - type: precision_at_10 value: 6.636 - type: precision_at_100 value: 0.924 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.035 - type: precision_at_5 value: 10.398 - type: recall_at_1 value: 20.697 - type: recall_at_10 value: 66.35799999999999 - type: recall_at_100 value: 92.39 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 42.105 - type: recall_at_5 value: 51.991 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 42.1169517447068 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 34.79553720107097 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.10811337308168 - type: mrr value: 71.56410763751482 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 78.46834918248696 - type: cos_sim_spearman value: 79.4289182755206 - type: euclidean_pearson value: 76.26662973727008 - type: euclidean_spearman value: 78.11744260952536 - type: manhattan_pearson value: 76.08175262609434 - type: manhattan_spearman value: 78.29395265552289 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 81.63636363636364 - type: f1 value: 81.55779952376953 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.88541137137571 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 30.05205685274407 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.293999999999997 - type: map_at_10 value: 39.876 - type: map_at_100 value: 41.315000000000005 - type: map_at_1000 value: 41.451 - type: map_at_3 value: 37.194 - type: map_at_5 value: 38.728 - type: mrr_at_1 value: 37.053000000000004 - type: mrr_at_10 value: 45.281 - type: mrr_at_100 value: 46.188 - type: mrr_at_1000 value: 46.245999999999995 - type: mrr_at_3 value: 43.228 - type: mrr_at_5 value: 44.366 - type: ndcg_at_1 value: 37.053000000000004 - type: ndcg_at_10 value: 45.086 - type: ndcg_at_100 value: 50.756 - type: ndcg_at_1000 value: 53.123 - type: ndcg_at_3 value: 41.416 - type: ndcg_at_5 value: 43.098 - type: precision_at_1 value: 37.053000000000004 - type: precision_at_10 value: 8.34 - type: precision_at_100 value: 1.346 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 19.647000000000002 - type: precision_at_5 value: 13.877 - type: recall_at_1 value: 30.293999999999997 - type: recall_at_10 value: 54.309 - type: recall_at_100 value: 78.59 - type: recall_at_1000 value: 93.82300000000001 - type: recall_at_3 value: 43.168 - type: recall_at_5 value: 48.192 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.738000000000003 - type: map_at_10 value: 36.925999999999995 - type: map_at_100 value: 38.017 - type: map_at_1000 value: 38.144 - type: map_at_3 value: 34.446 - type: map_at_5 value: 35.704 - type: mrr_at_1 value: 35.478 - type: mrr_at_10 value: 42.786 - type: mrr_at_100 value: 43.458999999999996 - type: mrr_at_1000 value: 43.507 - type: mrr_at_3 value: 40.648 - type: mrr_at_5 value: 41.804 - type: ndcg_at_1 value: 35.478 - type: ndcg_at_10 value: 42.044 - type: ndcg_at_100 value: 46.249 - type: ndcg_at_1000 value: 48.44 - type: ndcg_at_3 value: 38.314 - type: ndcg_at_5 value: 39.798 - type: precision_at_1 value: 35.478 - type: precision_at_10 value: 7.764 - type: precision_at_100 value: 1.253 - type: precision_at_1000 value: 0.174 - type: precision_at_3 value: 18.047 - type: precision_at_5 value: 12.637 - type: recall_at_1 value: 28.738000000000003 - type: recall_at_10 value: 50.659 - type: recall_at_100 value: 68.76299999999999 - type: recall_at_1000 value: 82.811 - type: recall_at_3 value: 39.536 - type: recall_at_5 value: 43.763999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.565 - type: map_at_10 value: 50.168 - type: map_at_100 value: 51.11 - type: map_at_1000 value: 51.173 - type: map_at_3 value: 47.044000000000004 - type: map_at_5 value: 48.838 - type: mrr_at_1 value: 44.201 - type: mrr_at_10 value: 53.596999999999994 - type: mrr_at_100 value: 54.211 - type: mrr_at_1000 value: 54.247 - type: mrr_at_3 value: 51.202000000000005 - type: mrr_at_5 value: 52.608999999999995 - type: ndcg_at_1 value: 44.201 - type: ndcg_at_10 value: 55.694 - type: ndcg_at_100 value: 59.518 - type: ndcg_at_1000 value: 60.907 - type: ndcg_at_3 value: 50.395999999999994 - type: ndcg_at_5 value: 53.022999999999996 - type: precision_at_1 value: 44.201 - type: precision_at_10 value: 8.84 - type: precision_at_100 value: 1.162 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 22.153 - type: precision_at_5 value: 15.260000000000002 - type: recall_at_1 value: 38.565 - type: recall_at_10 value: 68.65 - type: recall_at_100 value: 85.37400000000001 - type: recall_at_1000 value: 95.37400000000001 - type: recall_at_3 value: 54.645999999999994 - type: recall_at_5 value: 60.958 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.945 - type: map_at_10 value: 30.641000000000002 - type: map_at_100 value: 31.599 - type: map_at_1000 value: 31.691000000000003 - type: map_at_3 value: 28.405 - type: map_at_5 value: 29.704000000000004 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 32.22 - type: mrr_at_100 value: 33.138 - type: mrr_at_1000 value: 33.214 - type: mrr_at_3 value: 30.151 - type: mrr_at_5 value: 31.298 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 34.638000000000005 - type: ndcg_at_100 value: 39.486 - type: ndcg_at_1000 value: 41.936 - type: ndcg_at_3 value: 30.333 - type: ndcg_at_5 value: 32.482 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 5.153 - type: precision_at_100 value: 0.7929999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.429 - type: precision_at_5 value: 8.723 - type: recall_at_1 value: 23.945 - type: recall_at_10 value: 45.412 - type: recall_at_100 value: 67.836 - type: recall_at_1000 value: 86.467 - type: recall_at_3 value: 34.031 - type: recall_at_5 value: 39.039 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 14.419 - type: map_at_10 value: 20.858999999999998 - type: map_at_100 value: 22.067999999999998 - type: map_at_1000 value: 22.192 - type: map_at_3 value: 18.673000000000002 - type: map_at_5 value: 19.968 - type: mrr_at_1 value: 17.785999999999998 - type: mrr_at_10 value: 24.878 - type: mrr_at_100 value: 26.021 - type: mrr_at_1000 value: 26.095000000000002 - type: mrr_at_3 value: 22.616 - type: mrr_at_5 value: 23.785 - type: ndcg_at_1 value: 17.785999999999998 - type: ndcg_at_10 value: 25.153 - type: ndcg_at_100 value: 31.05 - type: ndcg_at_1000 value: 34.052 - type: ndcg_at_3 value: 21.117 - type: ndcg_at_5 value: 23.048 - type: precision_at_1 value: 17.785999999999998 - type: precision_at_10 value: 4.590000000000001 - type: precision_at_100 value: 0.864 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 9.908999999999999 - type: precision_at_5 value: 7.313 - type: recall_at_1 value: 14.419 - type: recall_at_10 value: 34.477999999999994 - type: recall_at_100 value: 60.02499999999999 - type: recall_at_1000 value: 81.646 - type: recall_at_3 value: 23.515 - type: recall_at_5 value: 28.266999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.268 - type: map_at_10 value: 35.114000000000004 - type: map_at_100 value: 36.212 - type: map_at_1000 value: 36.333 - type: map_at_3 value: 32.436 - type: map_at_5 value: 33.992 - type: mrr_at_1 value: 31.761 - type: mrr_at_10 value: 40.355999999999995 - type: mrr_at_100 value: 41.125 - type: mrr_at_1000 value: 41.186 - type: mrr_at_3 value: 37.937 - type: mrr_at_5 value: 39.463 - type: ndcg_at_1 value: 31.761 - type: ndcg_at_10 value: 40.422000000000004 - type: ndcg_at_100 value: 45.458999999999996 - type: ndcg_at_1000 value: 47.951 - type: ndcg_at_3 value: 35.972 - type: ndcg_at_5 value: 38.272 - type: precision_at_1 value: 31.761 - type: precision_at_10 value: 7.103 - type: precision_at_100 value: 1.133 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 16.779 - type: precision_at_5 value: 11.877 - type: recall_at_1 value: 26.268 - type: recall_at_10 value: 51.053000000000004 - type: recall_at_100 value: 72.702 - type: recall_at_1000 value: 89.521 - type: recall_at_3 value: 38.619 - type: recall_at_5 value: 44.671 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.230999999999998 - type: map_at_10 value: 34.227000000000004 - type: map_at_100 value: 35.370000000000005 - type: map_at_1000 value: 35.488 - type: map_at_3 value: 31.496000000000002 - type: map_at_5 value: 33.034 - type: mrr_at_1 value: 30.822 - type: mrr_at_10 value: 39.045 - type: mrr_at_100 value: 39.809 - type: mrr_at_1000 value: 39.873 - type: mrr_at_3 value: 36.663000000000004 - type: mrr_at_5 value: 37.964 - type: ndcg_at_1 value: 30.822 - type: ndcg_at_10 value: 39.472 - type: ndcg_at_100 value: 44.574999999999996 - type: ndcg_at_1000 value: 47.162 - type: ndcg_at_3 value: 34.929 - type: ndcg_at_5 value: 37.002 - type: precision_at_1 value: 30.822 - type: precision_at_10 value: 7.055 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 16.591 - type: precision_at_5 value: 11.667 - type: recall_at_1 value: 25.230999999999998 - type: recall_at_10 value: 50.42100000000001 - type: recall_at_100 value: 72.685 - type: recall_at_1000 value: 90.469 - type: recall_at_3 value: 37.503 - type: recall_at_5 value: 43.123 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.604166666666664 - type: map_at_10 value: 32.427166666666665 - type: map_at_100 value: 33.51474999999999 - type: map_at_1000 value: 33.6345 - type: map_at_3 value: 30.02366666666667 - type: map_at_5 value: 31.382333333333328 - type: mrr_at_1 value: 29.001166666666666 - type: mrr_at_10 value: 36.3315 - type: mrr_at_100 value: 37.16683333333333 - type: mrr_at_1000 value: 37.23341666666668 - type: mrr_at_3 value: 34.19916666666667 - type: mrr_at_5 value: 35.40458333333334 - type: ndcg_at_1 value: 29.001166666666666 - type: ndcg_at_10 value: 37.06883333333334 - type: ndcg_at_100 value: 41.95816666666666 - type: ndcg_at_1000 value: 44.501583333333336 - type: ndcg_at_3 value: 32.973499999999994 - type: ndcg_at_5 value: 34.90833333333334 - type: precision_at_1 value: 29.001166666666666 - type: precision_at_10 value: 6.336 - type: precision_at_100 value: 1.0282499999999999 - type: precision_at_1000 value: 0.14391666666666664 - type: precision_at_3 value: 14.932499999999996 - type: precision_at_5 value: 10.50825 - type: recall_at_1 value: 24.604166666666664 - type: recall_at_10 value: 46.9525 - type: recall_at_100 value: 68.67816666666667 - type: recall_at_1000 value: 86.59783333333334 - type: recall_at_3 value: 35.49783333333333 - type: recall_at_5 value: 40.52525000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.559 - type: map_at_10 value: 29.023 - type: map_at_100 value: 29.818 - type: map_at_1000 value: 29.909000000000002 - type: map_at_3 value: 27.037 - type: map_at_5 value: 28.225 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 31.962000000000003 - type: mrr_at_100 value: 32.726 - type: mrr_at_1000 value: 32.800000000000004 - type: mrr_at_3 value: 30.266 - type: mrr_at_5 value: 31.208999999999996 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 32.53 - type: ndcg_at_100 value: 36.758 - type: ndcg_at_1000 value: 39.362 - type: ndcg_at_3 value: 28.985 - type: ndcg_at_5 value: 30.757 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 4.968999999999999 - type: precision_at_100 value: 0.759 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 12.219 - type: precision_at_5 value: 8.527999999999999 - type: recall_at_1 value: 23.559 - type: recall_at_10 value: 40.585 - type: recall_at_100 value: 60.306000000000004 - type: recall_at_1000 value: 80.11 - type: recall_at_3 value: 30.794 - type: recall_at_5 value: 35.186 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.384999999999998 - type: map_at_10 value: 22.142 - type: map_at_100 value: 23.057 - type: map_at_1000 value: 23.177 - type: map_at_3 value: 20.29 - type: map_at_5 value: 21.332 - type: mrr_at_1 value: 19.89 - type: mrr_at_10 value: 25.771 - type: mrr_at_100 value: 26.599 - type: mrr_at_1000 value: 26.680999999999997 - type: mrr_at_3 value: 23.962 - type: mrr_at_5 value: 24.934 - type: ndcg_at_1 value: 19.89 - type: ndcg_at_10 value: 25.97 - type: ndcg_at_100 value: 30.605 - type: ndcg_at_1000 value: 33.619 - type: ndcg_at_3 value: 22.704 - type: ndcg_at_5 value: 24.199 - type: precision_at_1 value: 19.89 - type: precision_at_10 value: 4.553 - type: precision_at_100 value: 0.8049999999999999 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 10.541 - type: precision_at_5 value: 7.46 - type: recall_at_1 value: 16.384999999999998 - type: recall_at_10 value: 34.001 - type: recall_at_100 value: 55.17100000000001 - type: recall_at_1000 value: 77.125 - type: recall_at_3 value: 24.618000000000002 - type: recall_at_5 value: 28.695999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.726 - type: map_at_10 value: 31.227 - type: map_at_100 value: 32.311 - type: map_at_1000 value: 32.419 - type: map_at_3 value: 28.765 - type: map_at_5 value: 30.229 - type: mrr_at_1 value: 27.705000000000002 - type: mrr_at_10 value: 35.085 - type: mrr_at_100 value: 35.931000000000004 - type: mrr_at_1000 value: 36 - type: mrr_at_3 value: 32.603 - type: mrr_at_5 value: 34.117999999999995 - type: ndcg_at_1 value: 27.705000000000002 - type: ndcg_at_10 value: 35.968 - type: ndcg_at_100 value: 41.197 - type: ndcg_at_1000 value: 43.76 - type: ndcg_at_3 value: 31.304 - type: ndcg_at_5 value: 33.661 - type: precision_at_1 value: 27.705000000000002 - type: precision_at_10 value: 5.942 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 13.868 - type: precision_at_5 value: 9.944 - type: recall_at_1 value: 23.726 - type: recall_at_10 value: 46.786 - type: recall_at_100 value: 70.072 - type: recall_at_1000 value: 88.2 - type: recall_at_3 value: 33.981 - type: recall_at_5 value: 39.893 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.344 - type: map_at_10 value: 31.636999999999997 - type: map_at_100 value: 33.065 - type: map_at_1000 value: 33.300000000000004 - type: map_at_3 value: 29.351 - type: map_at_5 value: 30.432 - type: mrr_at_1 value: 27.866000000000003 - type: mrr_at_10 value: 35.587 - type: mrr_at_100 value: 36.52 - type: mrr_at_1000 value: 36.597 - type: mrr_at_3 value: 33.696 - type: mrr_at_5 value: 34.713 - type: ndcg_at_1 value: 27.866000000000003 - type: ndcg_at_10 value: 36.61 - type: ndcg_at_100 value: 41.88 - type: ndcg_at_1000 value: 45.105000000000004 - type: ndcg_at_3 value: 33.038000000000004 - type: ndcg_at_5 value: 34.331 - type: precision_at_1 value: 27.866000000000003 - type: precision_at_10 value: 6.917 - type: precision_at_100 value: 1.3599999999999999 - type: precision_at_1000 value: 0.233 - type: precision_at_3 value: 15.547 - type: precision_at_5 value: 10.791 - type: recall_at_1 value: 23.344 - type: recall_at_10 value: 45.782000000000004 - type: recall_at_100 value: 69.503 - type: recall_at_1000 value: 90.742 - type: recall_at_3 value: 35.160000000000004 - type: recall_at_5 value: 39.058 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.776 - type: map_at_10 value: 27.285999999999998 - type: map_at_100 value: 28.235 - type: map_at_1000 value: 28.337 - type: map_at_3 value: 25.147000000000002 - type: map_at_5 value: 26.401999999999997 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 29.409999999999997 - type: mrr_at_100 value: 30.275000000000002 - type: mrr_at_1000 value: 30.354999999999997 - type: mrr_at_3 value: 27.418 - type: mrr_at_5 value: 28.592000000000002 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 31.239 - type: ndcg_at_100 value: 35.965 - type: ndcg_at_1000 value: 38.602 - type: ndcg_at_3 value: 27.174 - type: ndcg_at_5 value: 29.229 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 4.806 - type: precision_at_100 value: 0.776 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.022 - type: recall_at_1 value: 20.776 - type: recall_at_10 value: 41.294 - type: recall_at_100 value: 63.111 - type: recall_at_1000 value: 82.88600000000001 - type: recall_at_3 value: 30.403000000000002 - type: recall_at_5 value: 35.455999999999996 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.376 - type: map_at_10 value: 15.926000000000002 - type: map_at_100 value: 17.585 - type: map_at_1000 value: 17.776 - type: map_at_3 value: 13.014000000000001 - type: map_at_5 value: 14.417 - type: mrr_at_1 value: 20.195 - type: mrr_at_10 value: 29.95 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.108000000000004 - type: mrr_at_3 value: 26.667 - type: mrr_at_5 value: 28.458 - type: ndcg_at_1 value: 20.195 - type: ndcg_at_10 value: 22.871 - type: ndcg_at_100 value: 29.921999999999997 - type: ndcg_at_1000 value: 33.672999999999995 - type: ndcg_at_3 value: 17.782999999999998 - type: ndcg_at_5 value: 19.544 - type: precision_at_1 value: 20.195 - type: precision_at_10 value: 7.394 - type: precision_at_100 value: 1.493 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 13.073 - type: precision_at_5 value: 10.436 - type: recall_at_1 value: 9.376 - type: recall_at_10 value: 28.544999999999998 - type: recall_at_100 value: 53.147999999999996 - type: recall_at_1000 value: 74.62 - type: recall_at_3 value: 16.464000000000002 - type: recall_at_5 value: 21.004 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.415000000000001 - type: map_at_10 value: 18.738 - type: map_at_100 value: 27.291999999999998 - type: map_at_1000 value: 28.992 - type: map_at_3 value: 13.196 - type: map_at_5 value: 15.539 - type: mrr_at_1 value: 66.5 - type: mrr_at_10 value: 74.518 - type: mrr_at_100 value: 74.86 - type: mrr_at_1000 value: 74.87 - type: mrr_at_3 value: 72.375 - type: mrr_at_5 value: 73.86200000000001 - type: ndcg_at_1 value: 54.37499999999999 - type: ndcg_at_10 value: 41.317 - type: ndcg_at_100 value: 45.845 - type: ndcg_at_1000 value: 52.92 - type: ndcg_at_3 value: 44.983000000000004 - type: ndcg_at_5 value: 42.989 - type: precision_at_1 value: 66.5 - type: precision_at_10 value: 33.6 - type: precision_at_100 value: 10.972999999999999 - type: precision_at_1000 value: 2.214 - type: precision_at_3 value: 48.583 - type: precision_at_5 value: 42.15 - type: recall_at_1 value: 8.415000000000001 - type: recall_at_10 value: 24.953 - type: recall_at_100 value: 52.48199999999999 - type: recall_at_1000 value: 75.093 - type: recall_at_3 value: 14.341000000000001 - type: recall_at_5 value: 18.468 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.06499999999999 - type: f1 value: 41.439327599975385 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.02 - type: map_at_10 value: 76.68599999999999 - type: map_at_100 value: 76.959 - type: map_at_1000 value: 76.972 - type: map_at_3 value: 75.024 - type: map_at_5 value: 76.153 - type: mrr_at_1 value: 71.197 - type: mrr_at_10 value: 81.105 - type: mrr_at_100 value: 81.232 - type: mrr_at_1000 value: 81.233 - type: mrr_at_3 value: 79.758 - type: mrr_at_5 value: 80.69 - type: ndcg_at_1 value: 71.197 - type: ndcg_at_10 value: 81.644 - type: ndcg_at_100 value: 82.645 - type: ndcg_at_1000 value: 82.879 - type: ndcg_at_3 value: 78.792 - type: ndcg_at_5 value: 80.528 - type: precision_at_1 value: 71.197 - type: precision_at_10 value: 10.206999999999999 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 30.868000000000002 - type: precision_at_5 value: 19.559 - type: recall_at_1 value: 66.02 - type: recall_at_10 value: 92.50699999999999 - type: recall_at_100 value: 96.497 - type: recall_at_1000 value: 97.956 - type: recall_at_3 value: 84.866 - type: recall_at_5 value: 89.16199999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 17.948 - type: map_at_10 value: 29.833 - type: map_at_100 value: 31.487 - type: map_at_1000 value: 31.674000000000003 - type: map_at_3 value: 26.029999999999998 - type: map_at_5 value: 28.038999999999998 - type: mrr_at_1 value: 34.721999999999994 - type: mrr_at_10 value: 44.214999999999996 - type: mrr_at_100 value: 44.994 - type: mrr_at_1000 value: 45.051 - type: mrr_at_3 value: 41.667 - type: mrr_at_5 value: 43.032 - type: ndcg_at_1 value: 34.721999999999994 - type: ndcg_at_10 value: 37.434 - type: ndcg_at_100 value: 43.702000000000005 - type: ndcg_at_1000 value: 46.993 - type: ndcg_at_3 value: 33.56 - type: ndcg_at_5 value: 34.687 - type: precision_at_1 value: 34.721999999999994 - type: precision_at_10 value: 10.401 - type: precision_at_100 value: 1.7049999999999998 - type: precision_at_1000 value: 0.22799999999999998 - type: precision_at_3 value: 22.531000000000002 - type: precision_at_5 value: 16.42 - type: recall_at_1 value: 17.948 - type: recall_at_10 value: 45.062999999999995 - type: recall_at_100 value: 68.191 - type: recall_at_1000 value: 87.954 - type: recall_at_3 value: 31.112000000000002 - type: recall_at_5 value: 36.823 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.644 - type: map_at_10 value: 57.658 - type: map_at_100 value: 58.562000000000005 - type: map_at_1000 value: 58.62500000000001 - type: map_at_3 value: 54.022999999999996 - type: map_at_5 value: 56.293000000000006 - type: mrr_at_1 value: 73.288 - type: mrr_at_10 value: 80.51700000000001 - type: mrr_at_100 value: 80.72 - type: mrr_at_1000 value: 80.728 - type: mrr_at_3 value: 79.33200000000001 - type: mrr_at_5 value: 80.085 - type: ndcg_at_1 value: 73.288 - type: ndcg_at_10 value: 66.61 - type: ndcg_at_100 value: 69.723 - type: ndcg_at_1000 value: 70.96000000000001 - type: ndcg_at_3 value: 61.358999999999995 - type: ndcg_at_5 value: 64.277 - type: precision_at_1 value: 73.288 - type: precision_at_10 value: 14.17 - type: precision_at_100 value: 1.659 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.487 - type: precision_at_5 value: 25.999 - type: recall_at_1 value: 36.644 - type: recall_at_10 value: 70.851 - type: recall_at_100 value: 82.94399999999999 - type: recall_at_1000 value: 91.134 - type: recall_at_3 value: 59.230000000000004 - type: recall_at_5 value: 64.997 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 86.00280000000001 - type: ap value: 80.46302061021223 - type: f1 value: 85.9592921596419 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.541 - type: map_at_10 value: 34.625 - type: map_at_100 value: 35.785 - type: map_at_1000 value: 35.831 - type: map_at_3 value: 30.823 - type: map_at_5 value: 32.967999999999996 - type: mrr_at_1 value: 23.180999999999997 - type: mrr_at_10 value: 35.207 - type: mrr_at_100 value: 36.315 - type: mrr_at_1000 value: 36.355 - type: mrr_at_3 value: 31.483 - type: mrr_at_5 value: 33.589999999999996 - type: ndcg_at_1 value: 23.195 - type: ndcg_at_10 value: 41.461 - type: ndcg_at_100 value: 47.032000000000004 - type: ndcg_at_1000 value: 48.199999999999996 - type: ndcg_at_3 value: 33.702 - type: ndcg_at_5 value: 37.522 - type: precision_at_1 value: 23.195 - type: precision_at_10 value: 6.526999999999999 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 14.308000000000002 - type: precision_at_5 value: 10.507 - type: recall_at_1 value: 22.541 - type: recall_at_10 value: 62.524 - type: recall_at_100 value: 88.228 - type: recall_at_1000 value: 97.243 - type: recall_at_3 value: 41.38 - type: recall_at_5 value: 50.55 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 92.69949840401279 - type: f1 value: 92.54141471311786 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.56041951664386 - type: f1 value: 55.88499977508287 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.62071284465365 - type: f1 value: 69.36717546572152 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.35843981170142 - type: f1 value: 76.15496453538884 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.33664956793118 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.883839621715524 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.096874986740758 - type: mrr value: 30.97300481932132 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.4 - type: map_at_10 value: 11.852 - type: map_at_100 value: 14.758 - type: map_at_1000 value: 16.134 - type: map_at_3 value: 8.558 - type: map_at_5 value: 10.087 - type: mrr_at_1 value: 44.272 - type: mrr_at_10 value: 52.05800000000001 - type: mrr_at_100 value: 52.689 - type: mrr_at_1000 value: 52.742999999999995 - type: mrr_at_3 value: 50.205999999999996 - type: mrr_at_5 value: 51.367 - type: ndcg_at_1 value: 42.57 - type: ndcg_at_10 value: 32.449 - type: ndcg_at_100 value: 29.596 - type: ndcg_at_1000 value: 38.351 - type: ndcg_at_3 value: 37.044 - type: ndcg_at_5 value: 35.275 - type: precision_at_1 value: 44.272 - type: precision_at_10 value: 23.87 - type: precision_at_100 value: 7.625 - type: precision_at_1000 value: 2.045 - type: precision_at_3 value: 34.365 - type: precision_at_5 value: 30.341 - type: recall_at_1 value: 5.4 - type: recall_at_10 value: 15.943999999999999 - type: recall_at_100 value: 29.805 - type: recall_at_1000 value: 61.695 - type: recall_at_3 value: 9.539 - type: recall_at_5 value: 12.127 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 36.047000000000004 - type: map_at_10 value: 51.6 - type: map_at_100 value: 52.449999999999996 - type: map_at_1000 value: 52.476 - type: map_at_3 value: 47.452 - type: map_at_5 value: 49.964 - type: mrr_at_1 value: 40.382 - type: mrr_at_10 value: 54.273 - type: mrr_at_100 value: 54.859 - type: mrr_at_1000 value: 54.876000000000005 - type: mrr_at_3 value: 51.014 - type: mrr_at_5 value: 52.983999999999995 - type: ndcg_at_1 value: 40.353 - type: ndcg_at_10 value: 59.11300000000001 - type: ndcg_at_100 value: 62.604000000000006 - type: ndcg_at_1000 value: 63.187000000000005 - type: ndcg_at_3 value: 51.513 - type: ndcg_at_5 value: 55.576 - type: precision_at_1 value: 40.353 - type: precision_at_10 value: 9.418 - type: precision_at_100 value: 1.1440000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 23.078000000000003 - type: precision_at_5 value: 16.250999999999998 - type: recall_at_1 value: 36.047000000000004 - type: recall_at_10 value: 79.22200000000001 - type: recall_at_100 value: 94.23 - type: recall_at_1000 value: 98.51100000000001 - type: recall_at_3 value: 59.678 - type: recall_at_5 value: 68.967 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 68.232 - type: map_at_10 value: 81.674 - type: map_at_100 value: 82.338 - type: map_at_1000 value: 82.36099999999999 - type: map_at_3 value: 78.833 - type: map_at_5 value: 80.58 - type: mrr_at_1 value: 78.64 - type: mrr_at_10 value: 85.164 - type: mrr_at_100 value: 85.317 - type: mrr_at_1000 value: 85.319 - type: mrr_at_3 value: 84.127 - type: mrr_at_5 value: 84.789 - type: ndcg_at_1 value: 78.63 - type: ndcg_at_10 value: 85.711 - type: ndcg_at_100 value: 87.238 - type: ndcg_at_1000 value: 87.444 - type: ndcg_at_3 value: 82.788 - type: ndcg_at_5 value: 84.313 - type: precision_at_1 value: 78.63 - type: precision_at_10 value: 12.977 - type: precision_at_100 value: 1.503 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.113 - type: precision_at_5 value: 23.71 - type: recall_at_1 value: 68.232 - type: recall_at_10 value: 93.30199999999999 - type: recall_at_100 value: 98.799 - type: recall_at_1000 value: 99.885 - type: recall_at_3 value: 84.827 - type: recall_at_5 value: 89.188 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 45.71879170816294 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 59.65866311751794 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.218 - type: map_at_10 value: 10.337 - type: map_at_100 value: 12.131 - type: map_at_1000 value: 12.411 - type: map_at_3 value: 7.4270000000000005 - type: map_at_5 value: 8.913 - type: mrr_at_1 value: 20.8 - type: mrr_at_10 value: 30.868000000000002 - type: mrr_at_100 value: 31.903 - type: mrr_at_1000 value: 31.972 - type: mrr_at_3 value: 27.367 - type: mrr_at_5 value: 29.372 - type: ndcg_at_1 value: 20.8 - type: ndcg_at_10 value: 17.765 - type: ndcg_at_100 value: 24.914 - type: ndcg_at_1000 value: 30.206 - type: ndcg_at_3 value: 16.64 - type: ndcg_at_5 value: 14.712 - type: precision_at_1 value: 20.8 - type: precision_at_10 value: 9.24 - type: precision_at_100 value: 1.9560000000000002 - type: precision_at_1000 value: 0.32299999999999995 - type: precision_at_3 value: 15.467 - type: precision_at_5 value: 12.94 - type: recall_at_1 value: 4.218 - type: recall_at_10 value: 18.752 - type: recall_at_100 value: 39.7 - type: recall_at_1000 value: 65.57300000000001 - type: recall_at_3 value: 9.428 - type: recall_at_5 value: 13.133000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.04338850207233 - type: cos_sim_spearman value: 78.5054651430423 - type: euclidean_pearson value: 80.30739451228612 - type: euclidean_spearman value: 78.48377464299097 - type: manhattan_pearson value: 80.40795049052781 - type: manhattan_spearman value: 78.49506205443114 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.11596224442962 - type: cos_sim_spearman value: 76.20997388935461 - type: euclidean_pearson value: 80.56858451349109 - type: euclidean_spearman value: 75.92659183871186 - type: manhattan_pearson value: 80.60246102203844 - type: manhattan_spearman value: 76.03018971432664 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.34691640755737 - type: cos_sim_spearman value: 82.4018369631579 - type: euclidean_pearson value: 81.87673092245366 - type: euclidean_spearman value: 82.3671489960678 - type: manhattan_pearson value: 81.88222387719948 - type: manhattan_spearman value: 82.3816590344736 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.2836092579524 - type: cos_sim_spearman value: 78.99982781772064 - type: euclidean_pearson value: 80.5184271010527 - type: euclidean_spearman value: 78.89777392101904 - type: manhattan_pearson value: 80.53585705018664 - type: manhattan_spearman value: 78.92898405472994 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.7349907750784 - type: cos_sim_spearman value: 87.7611234446225 - type: euclidean_pearson value: 86.98759326731624 - type: euclidean_spearman value: 87.58321319424618 - type: manhattan_pearson value: 87.03483090370842 - type: manhattan_spearman value: 87.63278333060288 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 81.75873694924825 - type: cos_sim_spearman value: 83.80237999094724 - type: euclidean_pearson value: 83.55023725861537 - type: euclidean_spearman value: 84.12744338577744 - type: manhattan_pearson value: 83.58816983036232 - type: manhattan_spearman value: 84.18520748676501 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.21630882940174 - type: cos_sim_spearman value: 87.72382883437031 - type: euclidean_pearson value: 88.69933350930333 - type: euclidean_spearman value: 88.24660814383081 - type: manhattan_pearson value: 88.77331018833499 - type: manhattan_spearman value: 88.26109989380632 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 61.11854063060489 - type: cos_sim_spearman value: 63.14678634195072 - type: euclidean_pearson value: 61.679090067000864 - type: euclidean_spearman value: 62.28876589509653 - type: manhattan_pearson value: 62.082324165511004 - type: manhattan_spearman value: 62.56030932816679 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.00319882832645 - type: cos_sim_spearman value: 85.94529772647257 - type: euclidean_pearson value: 85.6661390122756 - type: euclidean_spearman value: 85.97747815545827 - type: manhattan_pearson value: 85.58422770541893 - type: manhattan_spearman value: 85.9237139181532 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.16198731863916 - type: mrr value: 94.25202702163487 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 54.761 - type: map_at_10 value: 64.396 - type: map_at_100 value: 65.07 - type: map_at_1000 value: 65.09899999999999 - type: map_at_3 value: 61.846000000000004 - type: map_at_5 value: 63.284 - type: mrr_at_1 value: 57.667 - type: mrr_at_10 value: 65.83099999999999 - type: mrr_at_100 value: 66.36800000000001 - type: mrr_at_1000 value: 66.39399999999999 - type: mrr_at_3 value: 64.056 - type: mrr_at_5 value: 65.206 - type: ndcg_at_1 value: 57.667 - type: ndcg_at_10 value: 68.854 - type: ndcg_at_100 value: 71.59100000000001 - type: ndcg_at_1000 value: 72.383 - type: ndcg_at_3 value: 64.671 - type: ndcg_at_5 value: 66.796 - type: precision_at_1 value: 57.667 - type: precision_at_10 value: 9.167 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 16.667 - type: recall_at_1 value: 54.761 - type: recall_at_10 value: 80.9 - type: recall_at_100 value: 92.767 - type: recall_at_1000 value: 99 - type: recall_at_3 value: 69.672 - type: recall_at_5 value: 75.083 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8079207920792 - type: cos_sim_ap value: 94.88470927617445 - type: cos_sim_f1 value: 90.08179959100204 - type: cos_sim_precision value: 92.15481171548117 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.58613861386138 - type: dot_ap value: 82.94822578881316 - type: dot_f1 value: 77.33333333333333 - type: dot_precision value: 79.36842105263158 - type: dot_recall value: 75.4 - type: euclidean_accuracy value: 99.8069306930693 - type: euclidean_ap value: 94.81367858031837 - type: euclidean_f1 value: 90.01009081735621 - type: euclidean_precision value: 90.83503054989816 - type: euclidean_recall value: 89.2 - type: manhattan_accuracy value: 99.81188118811882 - type: manhattan_ap value: 94.91405337220161 - type: manhattan_f1 value: 90.2763561924258 - type: manhattan_precision value: 92.45283018867924 - type: manhattan_recall value: 88.2 - type: max_accuracy value: 99.81188118811882 - type: max_ap value: 94.91405337220161 - type: max_f1 value: 90.2763561924258 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 58.511599500053094 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 31.984728147814707 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.93428193939015 - type: mrr value: 50.916557911043206 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.562500894537145 - type: cos_sim_spearman value: 31.162587976726307 - type: dot_pearson value: 22.633662187735762 - type: dot_spearman value: 22.723000282378962 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.219 - type: map_at_10 value: 1.871 - type: map_at_100 value: 10.487 - type: map_at_1000 value: 25.122 - type: map_at_3 value: 0.657 - type: map_at_5 value: 1.0699999999999998 - type: mrr_at_1 value: 84 - type: mrr_at_10 value: 89.567 - type: mrr_at_100 value: 89.748 - type: mrr_at_1000 value: 89.748 - type: mrr_at_3 value: 88.667 - type: mrr_at_5 value: 89.567 - type: ndcg_at_1 value: 80 - type: ndcg_at_10 value: 74.533 - type: ndcg_at_100 value: 55.839000000000006 - type: ndcg_at_1000 value: 49.748 - type: ndcg_at_3 value: 79.53099999999999 - type: ndcg_at_5 value: 78.245 - type: precision_at_1 value: 84 - type: precision_at_10 value: 78.4 - type: precision_at_100 value: 56.99999999999999 - type: precision_at_1000 value: 21.98 - type: precision_at_3 value: 85.333 - type: precision_at_5 value: 84.8 - type: recall_at_1 value: 0.219 - type: recall_at_10 value: 2.02 - type: recall_at_100 value: 13.555 - type: recall_at_1000 value: 46.739999999999995 - type: recall_at_3 value: 0.685 - type: recall_at_5 value: 1.13 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.5029999999999997 - type: map_at_10 value: 11.042 - type: map_at_100 value: 16.326999999999998 - type: map_at_1000 value: 17.836 - type: map_at_3 value: 6.174 - type: map_at_5 value: 7.979 - type: mrr_at_1 value: 42.857 - type: mrr_at_10 value: 52.617000000000004 - type: mrr_at_100 value: 53.351000000000006 - type: mrr_at_1000 value: 53.351000000000006 - type: mrr_at_3 value: 46.939 - type: mrr_at_5 value: 50.714000000000006 - type: ndcg_at_1 value: 38.775999999999996 - type: ndcg_at_10 value: 27.125 - type: ndcg_at_100 value: 35.845 - type: ndcg_at_1000 value: 47.377 - type: ndcg_at_3 value: 29.633 - type: ndcg_at_5 value: 28.378999999999998 - type: precision_at_1 value: 42.857 - type: precision_at_10 value: 24.082 - type: precision_at_100 value: 6.877999999999999 - type: precision_at_1000 value: 1.463 - type: precision_at_3 value: 29.932 - type: precision_at_5 value: 28.571 - type: recall_at_1 value: 3.5029999999999997 - type: recall_at_10 value: 17.068 - type: recall_at_100 value: 43.361 - type: recall_at_1000 value: 78.835 - type: recall_at_3 value: 6.821000000000001 - type: recall_at_5 value: 10.357 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.0954 - type: ap value: 14.216844153511959 - type: f1 value: 54.63687418565117 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.46293152235427 - type: f1 value: 61.744177921638645 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.12708617788644 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.75430649102938 - type: cos_sim_ap value: 73.34252536948081 - type: cos_sim_f1 value: 67.53758935173774 - type: cos_sim_precision value: 63.3672525439408 - type: cos_sim_recall value: 72.29551451187335 - type: dot_accuracy value: 81.71305954580676 - type: dot_ap value: 59.5532209082386 - type: dot_f1 value: 56.18466898954705 - type: dot_precision value: 47.830923248053395 - type: dot_recall value: 68.07387862796834 - type: euclidean_accuracy value: 85.81987244441795 - type: euclidean_ap value: 73.34325409809446 - type: euclidean_f1 value: 67.83451360417443 - type: euclidean_precision value: 64.09955388588871 - type: euclidean_recall value: 72.0316622691293 - type: manhattan_accuracy value: 85.68277999642368 - type: manhattan_ap value: 73.1535450121903 - type: manhattan_f1 value: 67.928237896289 - type: manhattan_precision value: 63.56945722171113 - type: manhattan_recall value: 72.9287598944591 - type: max_accuracy value: 85.81987244441795 - type: max_ap value: 73.34325409809446 - type: max_f1 value: 67.928237896289 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.90441262079403 - type: cos_sim_ap value: 85.79331880741438 - type: cos_sim_f1 value: 78.31563529842548 - type: cos_sim_precision value: 74.6683424102779 - type: cos_sim_recall value: 82.33754234678165 - type: dot_accuracy value: 84.89928978926534 - type: dot_ap value: 75.25819218316 - type: dot_f1 value: 69.88730119720536 - type: dot_precision value: 64.23362374959665 - type: dot_recall value: 76.63227594702803 - type: euclidean_accuracy value: 89.01695967710637 - type: euclidean_ap value: 85.98986606038852 - type: euclidean_f1 value: 78.5277880014722 - type: euclidean_precision value: 75.22211253701876 - type: euclidean_recall value: 82.13735756082538 - type: manhattan_accuracy value: 88.99561454573679 - type: manhattan_ap value: 85.92262421793953 - type: manhattan_f1 value: 78.38866094740769 - type: manhattan_precision value: 76.02373028505282 - type: manhattan_recall value: 80.9054511857099 - type: max_accuracy value: 89.01695967710637 - type: max_ap value: 85.98986606038852 - type: max_f1 value: 78.5277880014722 language: - en license: mit --- # E5-small-v2 [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 12 layers and the embedding size is 384. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ". # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small-v2') model = AutoModel.from_pretrained('intfloat/e5-small-v2') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Training Details Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). ## Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations This model only works for English texts. Long texts will be truncated to at most 512 tokens. ## Sentence Transformers Below is an example for usage with sentence_transformers. `pip install sentence_transformers~=2.2.2` This is community contributed, and results may vary up to numerical precision. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/e5-small-v2') embeddings = model.encode(input_texts, normalize_embeddings=True) ```
syberkrime99/angiestwn
syberkrime99
2023-07-09T02:13:54Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-09T02:11:30Z
--- license: creativeml-openrail-m ---
benbav97/dqn-SpaceInvadersNoFrameskip-v4
benbav97
2023-07-09T02:07:32Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-09T02:06:55Z
--- 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: 599.50 +/- 163.24 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga benbav97 -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 benbav97 -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 benbav97 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
shouqiangli/chatglm2-6b-int4-002
shouqiangli
2023-07-09T01:59:44Z
144
0
transformers
[ "transformers", "pytorch", "chatglm", "glm", "thudm", "custom_code", "zh", "en", "arxiv:2103.10360", "arxiv:2210.02414", "arxiv:1911.02150", "endpoints_compatible", "region:us" ]
null
2023-07-08T15:46:48Z
--- language: - zh - en tags: - glm - chatglm - thudm --- # ChatGLM2-6B <p align="center"> 💻 <a href="https://github.com/THUDM/ChatGLM2-6B" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1y7pqoloy-9b1g6T6JjA8J0KxvUjbwJw" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a> </p> ## 介绍 ChatGLM**2**-6B 是开源中英双语对话模型 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) 的第二代版本，在保留了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上，ChatGLM**2**-6B 引入了如下新特性： 1. **更强大的性能**：基于 ChatGLM 初代模型的开发经验，我们全面升级了 ChatGLM2-6B 的基座模型。ChatGLM2-6B 使用了 [GLM](https://github.com/THUDM/GLM) 的混合目标函数，经过了 1.4T 中英标识符的预训练与人类偏好对齐训练，[评测结果](#评测结果)显示，相比于初代模型，ChatGLM2-6B 在 MMLU（+23%）、CEval（+33%）、GSM8K（+571%） 、BBH（+60%）等数据集上的性能取得了大幅度的提升，在同尺寸开源模型中具有较强的竞争力。 2. **更长的上下文**：基于 [FlashAttention](https://github.com/HazyResearch/flash-attention) 技术，我们将基座模型的上下文长度（Context Length）由 ChatGLM-6B 的 2K 扩展到了 32K，并在对话阶段使用 8K 的上下文长度训练，允许更多轮次的对话。但当前版本的 ChatGLM2-6B 对单轮超长文档的理解能力有限，我们会在后续迭代升级中着重进行优化。 3. **更高效的推理**：基于 [Multi-Query Attention](http://arxiv.org/abs/1911.02150) 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用：在官方的模型实现下，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K。 ChatGLM**2**-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B). It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features: 1. **Stronger Performance**: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size. 2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations. 3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K. ## 软件依赖 ```shell pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate ``` ## 代码调用 可以通过如下代码调用 ChatGLM-6B 模型来生成对话： ```ipython >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True) >>> model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda() >>> model = model.eval() >>> response, history = model.chat(tokenizer, "你好", history=[]) >>> print(response) 你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。 >>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history) >>> print(response) 晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法: 1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。 2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。 3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。 4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。 5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。 6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。 如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。 ``` 关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM2-6B)。 For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM2-6B). ## Change Log * v1.0 ## 协议 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM2-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。 ## 引用 如果你觉得我们的工作有帮助的话，请考虑引用下列论文，ChatGLM2-6B 的论文会在近期公布，尽情期待～ ``` @article{zeng2022glm, title={Glm-130b: An open bilingual pre-trained model}, author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others}, journal={arXiv preprint arXiv:2210.02414}, year={2022} } ``` ``` @inproceedings{du2022glm, title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling}, author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={320--335}, year={2022} } ```
espnet/Wangyou_Zhang_wsj0_2mix_train_enh_tse_td_speakerbeam_raw
espnet
2023-07-09T01:59:33Z
3
0
espnet
[ "espnet", "audio", "audio-to-audio", "en", "dataset:wsj0_2mix", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
audio-to-audio
2023-07-09T01:25:48Z
--- tags: - espnet - audio - audio-to-audio language: en datasets: - wsj0_2mix license: cc-by-4.0 --- ## ESPnet2 ENH model ### `espnet/Wangyou_Zhang_wsj0_2mix_train_enh_tse_td_speakerbeam_raw` This model was trained by Wangyou Zhang using the wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet pip install -e . cd egs2/wsj0_2mix/tse1 ./run.sh --skip_data_prep false --skip_train true --is_tse_task true --download_model espnet/Wangyou_Zhang_wsj0_2mix_train_enh_tse_td_speakerbeam_raw ``` <!-- Generated by ./scripts/utils/show_enh_score.sh --> # RESULTS ## Environments - date: `Sun Jul 9 09:23:16 CST 2023` - python version: `3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]` - espnet version: `espnet 202301` - pytorch version: `pytorch 2.0.1` - Git hash: `` - Commit date: `` ## enh_train_enh_tse_td_speakerbeam_raw config: conf/tuning/train_enh_tse_td_speakerbeam.yaml |dataset|PESQ_NB|STOI|SAR|SDR|SIR|SI_SNR| |---|---|---|---|---|---|---| |enhanced_cv_min_8k|3.54|96.41|18.75|18.75|0.00|18.37| |enhanced_tt_min_8k|3.46|96.35|17.51|17.51|0.00|17.11| ## ENH config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_tse_td_speakerbeam.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/enh_train_enh_tse_td_speakerbeam_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false skip_stats_npz: false max_epoch: 100 patience: 20 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - snr - max - - valid - loss - min keep_nbest_models: 1 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 4 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/enh_stats_tr_min_8k_cv_min_8k_8k/train/speech_mix_shape - exp/enh_stats_tr_min_8k_cv_min_8k_8k/train/speech_ref1_shape - exp/enh_stats_tr_min_8k_cv_min_8k_8k/train/enroll_ref1_shape valid_shape_file: - exp/enh_stats_tr_min_8k_cv_min_8k_8k/valid/speech_mix_shape - exp/enh_stats_tr_min_8k_cv_min_8k_8k/valid/speech_ref1_shape - exp/enh_stats_tr_min_8k_cv_min_8k_8k/valid/enroll_ref1_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 - 80000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 32000 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: - enroll_ref train_data_path_and_name_and_type: - - dump/raw/tr_min_8k/wav.scp - speech_mix - sound - - dump/raw/tr_min_8k/spk1.scp - speech_ref1 - sound - - dump/raw/tr_min_8k/enroll_spk1.scp - enroll_ref1 - text valid_data_path_and_name_and_type: - - dump/raw/cv_min_8k/wav.scp - speech_mix - sound - - dump/raw/cv_min_8k/spk1.scp - speech_ref1 - sound - - dump/raw/cv_min_8k/enroll_spk1.scp - enroll_ref1 - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.001 eps: 1.0e-08 weight_decay: 0 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.7 patience: 3 init: null model_conf: num_spk: 1 share_encoder: true criterions: - name: snr conf: eps: 1.0e-07 wrapper: fixed_order wrapper_conf: weight: 1.0 train_spk2enroll: null enroll_segment: 16000 load_spk_embedding: false load_all_speakers: false rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 speech_volume_normalize: null use_reverberant_ref: false num_spk: 1 num_noise_type: 1 sample_rate: 8000 force_single_channel: false channel_reordering: false categories: [] encoder: conv encoder_conf: channel: 256 kernel_size: 16 stride: 8 extractor: td_speakerbeam extractor_conf: layer: 8 stack: 4 bottleneck_dim: 256 hidden_dim: 512 skip_dim: 256 kernel: 3 causal: false norm_type: gLN nonlinear: relu i_adapt_layer: 7 adapt_layer_type: mul adapt_enroll_dim: 256 use_spk_emb: false spk_emb_dim: 256 decoder: conv decoder_conf: channel: 256 kernel_size: 16 stride: 8 preprocessor: tse preprocessor_conf: {} required: - output_dir version: '202301' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{ESPnet-SE, author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe}, title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration}, booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021}, pages = {785--792}, publisher = {{IEEE}}, year = {2021}, url = {https://doi.org/10.1109/SLT48900.2021.9383615}, doi = {10.1109/SLT48900.2021.9383615}, timestamp = {Mon, 12 Apr 2021 17:08:59 +0200}, biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
SKumari/Llama_train_sk
SKumari
2023-07-09T01:55:54Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-09T01:55:47Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0
saintzeno/ppo-Pyramids
saintzeno
2023-07-09T01:44:03Z
10
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-07-09T01:43:57Z
--- 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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: saintzeno/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
nolanaatama/ncrcnrmlrvcv1300pchjlbdxcyn
nolanaatama
2023-07-09T01:14:31Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-09T01:08:54Z
--- license: creativeml-openrail-m ---
NasimB/gpt2-concat-guten-rarity-5k-2p5k
NasimB
2023-07-09T00:51:19Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-08T22:55:58Z
--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-guten-rarity-5k-2p5k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-guten-rarity-5k-2p5k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.1847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7001 | 0.3 | 500 | 5.6280 | | 5.3666 | 0.59 | 1000 | 5.1990 | | 5.0079 | 0.89 | 1500 | 4.9539 | | 4.7385 | 1.19 | 2000 | 4.8095 | | 4.5783 | 1.48 | 2500 | 4.6793 | | 4.4688 | 1.78 | 3000 | 4.5716 | | 4.3327 | 2.08 | 3500 | 4.4960 | | 4.162 | 2.37 | 4000 | 4.4444 | | 4.1218 | 2.67 | 4500 | 4.3820 | | 4.0787 | 2.97 | 5000 | 4.3297 | | 3.8425 | 3.26 | 5500 | 4.3301 | | 3.825 | 3.56 | 6000 | 4.2940 | | 3.8038 | 3.86 | 6500 | 4.2590 | | 3.6546 | 4.15 | 7000 | 4.2647 | | 3.5359 | 4.45 | 7500 | 4.2557 | | 3.5282 | 4.75 | 8000 | 4.2377 | | 3.4838 | 5.04 | 8500 | 4.2391 | | 3.3383 | 5.34 | 9000 | 4.2426 | | 3.3404 | 5.64 | 9500 | 4.2414 | | 3.3337 | 5.93 | 10000 | 4.2410 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
nolanaatama/blllsh2021vrrvcv2600pchshstpn
nolanaatama
2023-07-09T00:33:52Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-24T07:33:08Z
--- license: creativeml-openrail-m ---
skywalker7/LunarWalker
skywalker7
2023-07-08T23:40:42Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-08T23:40:22Z
--- 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: 257.93 +/- 17.44 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 ... ```
ABDUULAHH/ABDULLAH-GPT
ABDUULAHH
2023-07-08T23:23:25Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-07-08T23:23:25Z
--- license: bigscience-openrail-m ---
gvenkat21/reviews-feedback-nudge
gvenkat21
2023-07-08T23:11:15Z
2
0
peft
[ "peft", "region:us" ]
null
2023-07-08T22:08:50Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
hongrui/chest_mimic_v_1
hongrui
2023-07-08T22:39:07Z
2
0
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-07-08T13:09:12Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - hongrui/chest_mimic_v_1 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the hongrui/mimic_chest_xray_v_1 dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
hbenitez/food_classifier
hbenitez
2023-07-08T22:37:36Z
63
0
transformers
[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-06T21:28:12Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: hbenitez/food_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hbenitez/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3735 - Validation Loss: 2.5622 - Train Accuracy: 0.0769 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 260, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.5417 | 2.5922 | 0.0 | 0 | | 2.5103 | 2.5856 | 0.0 | 1 | | 2.4593 | 2.5738 | 0.0 | 2 | | 2.4104 | 2.5671 | 0.0 | 3 | | 2.3735 | 2.5622 | 0.0769 | 4 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.13.0-rc2 - Datasets 2.13.1 - Tokenizers 0.13.3
miki-kawa/roberta-large-lora-token-classification
miki-kawa
2023-07-08T22:36:04Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-08T22:35:59Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
NasimB/gpt2-concat-longer-top3-aochildes-cbt-guten
NasimB
2023-07-08T22:31:30Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-08T20:36:20Z
--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-longer-top3-aochildes-cbt-guten results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-concat-longer-top3-aochildes-cbt-guten This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.1961 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.7253 | 0.3 | 500 | 5.6413 | | 5.3666 | 0.6 | 1000 | 5.2023 | | 5.0141 | 0.91 | 1500 | 4.9461 | | 4.7385 | 1.21 | 2000 | 4.8082 | | 4.5903 | 1.51 | 2500 | 4.6877 | | 4.483 | 1.81 | 3000 | 4.5759 | | 4.314 | 2.12 | 3500 | 4.5164 | | 4.168 | 2.42 | 4000 | 4.4640 | | 4.1319 | 2.72 | 4500 | 4.4091 | | 4.0719 | 3.02 | 5000 | 4.3683 | | 3.8391 | 3.33 | 5500 | 4.3567 | | 3.8393 | 3.63 | 6000 | 4.3232 | | 3.8102 | 3.93 | 6500 | 4.2943 | | 3.5985 | 4.23 | 7000 | 4.3109 | | 3.5515 | 4.53 | 7500 | 4.2990 | | 3.5377 | 4.84 | 8000 | 4.2872 | | 3.4488 | 5.14 | 8500 | 4.2986 | | 3.3497 | 5.44 | 9000 | 4.3006 | | 3.3502 | 5.74 | 9500 | 4.2999 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
chainsurfer/ppo-LunarLander-v2
chainsurfer
2023-07-08T22:19:55Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-08T22:19:37Z
--- 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: 267.10 +/- 22.12 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 ... ```
grace-pro/bert-finetuned-hausa
grace-pro
2023-07-08T22:07:37Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-07-07T21:03:57Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-hausa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-hausa This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1505 - Precision: 0.6680 - Recall: 0.4474 - F1: 0.5359 - Accuracy: 0.9557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1683 | 1.0 | 2624 | 0.1589 | 0.6480 | 0.3641 | 0.4663 | 0.9513 | | 0.1446 | 2.0 | 5248 | 0.1509 | 0.6658 | 0.4147 | 0.5111 | 0.9543 | | 0.1163 | 3.0 | 7872 | 0.1505 | 0.6680 | 0.4474 | 0.5359 | 0.9557 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Hariharavarshan/Cover_genie
Hariharavarshan
2023-07-08T21:48:30Z
172
1
transformers
[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "en", "arxiv:2210.11416", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-06-03T06:27:28Z
--- license: apache-2.0 language: - en metrics: - rouge library_name: transformers --- # Model Card for CoverGenie <!-- Provide a quick summary of what the model is/does. --> The goal of this project is to build a fine-grained mini-ChatGPT (named “CoverGenie”) , which is designed to generate resumes and cover letters based on job descriptions from the tech field. By nature,it is a language generation task, and it takes the job description as input to a sequence of text and turns it into a structured, certain style of resumes and cover letters. This might involve parameter efficient finetuning, reinforcement learning and prompting engineering to some extent. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Model type:** T5 (Text-to-Text-Transfer-Transformer) - **Language(s) (NLP):** [More Information Needed] - **License:** Apache-2.0 - **Finetuned from model:** FlanT5 Large ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** https://arxiv.org/pdf/2210.11416.pdf ## Uses It Can Generate Cover letter if we are able to input the **Job description** and **Resume** of a candidate. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import GenerationConfig from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from transformers import GenerationConfig import nltk nltk.download('punkt') max_source_length=512 tokenizer = AutoTokenizer.from_pretrained("Hariharavarshan/Cover_genie") model = AutoModelForSeq2SeqLM.from_pretrained("Hariharavarshan/Cover_genie") JD='''<Job description Text>''' resume_text= '''<Resume Text>''' final_text="give me a cover letter based on the a job description and a resume. Job description:"+JD +" Resume:"+ resume_text generation_config = GenerationConfig.from_pretrained("google/flan-t5-large",temperature=2.0) inputs = tokenizer(final_text, max_length=max_source_length, truncation=True, return_tensors="pt") output = model.generate(**inputs, num_beams=3, do_sample=True, min_length=1000, max_length=10000,generation_config=generation_config,num_return_sequences=3) decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0] generated_Coverletter = nltk.sent_tokenize(decoded_output.strip()) ``` **Developed by:** Hariharavarshan,Jayathilaga,Sara,Meiyu
jncraton/codet5p-770m-py-ct2-int8
jncraton
2023-07-08T21:44:13Z
600
0
transformers
[ "transformers", "arxiv:2305.07922", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]
null
2023-07-08T20:47:01Z
--- license: bsd-3-clause --- # CodeT5+ 770M (further tuned on Python) ## Model description [CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks. It is introduced in the paper: [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf) by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (* indicates equal contribution). Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data. Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture. Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca). ## How to use This model can be easily loaded using the `T5ForConditionalGeneration` functionality and employs the same tokenizer as original [CodeT5](https://github.com/salesforce/CodeT5). ```python from transformers import T5ForConditionalGeneration, AutoTokenizer checkpoint = "Salesforce/codet5p-770m-py" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs, max_length=10) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # ==> print('Hello World!') ``` ## Pretraining data This checkpoint is trained on the stricter permissive subset of the deduplicated version of the [github-code dataset](https://huggingface.co/datasets/codeparrot/github-code). The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”). Supported languages (9 in total) are as follows: `c`, `c++`, `c-sharp`, `go`, `java`, `javascript`, `php`, `python`, `ruby.` ## Training procedure This checkpoint is first trained on the multilingual unimodal code data at the first-stage pretraining, which includes a diverse set of pretraining tasks including _span denoising_ and two variants of _causal language modeling_. After that, it is further trained on the Python subset with the causal language modeling objective for another epoch to better adapt for Python code generation. Please refer to the paper for more details. ## Evaluation results CodeT5+ models have been comprehensively evaluated on a wide range of code understanding and generation tasks in various settings: _zero-shot_, _finetuning_, and _instruction-tuning_. Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g., 8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4). In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters. Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details. Specifically for this checkpoint, it achieves 15.5% pass@1 on HumanEval in the zero-shot setting, which is comparable to much larger LLMs such as Incoder 6B’s 15.2%, GPT-NeoX 20B’s 15.4%, and PaLM 62B’s 15.9%. ## BibTeX entry and citation info ```bibtex @article{wang2023codet5plus, title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation}, author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.}, journal={arXiv preprint}, year={2023} } ```
skrl/IsaacGymEnvs-Humanoid-PPO
skrl
2023-07-08T20:59:46Z
0
0
skrl
[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-24T20:44:07Z
--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 6524.74 +/- 570.54 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-Humanoid type: IsaacGymEnvs-Humanoid --- <!-- --- torch: 6524.74 +/- 570.54 jax: 6265.95 +/- 280.11 numpy: 5727.54 +/- 406.96 --- --> # IsaacGymEnvs-Humanoid-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** Humanoid - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-Humanoid-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-Humanoid-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 32 # memory_size cfg["learning_epochs"] = 5 cfg["mini_batches"] = 4 # 32 * 4096 / 32768 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 5e-4 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 2.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01 cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
snousias/bert-base-greek-uncased-v2-finetuned-polylex
snousias
2023-07-08T20:51:04Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-08T20:01:40Z
--- tags: - generated_from_trainer model-index: - name: bert-base-greek-uncased-v2-finetuned-polylex 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-greek-uncased-v2-finetuned-polylex This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.5614 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 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: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 4.7613 | 1.0 | 12 | 3.7659 | | 3.8949 | 2.0 | 24 | 3.2678 | | 3.223 | 3.0 | 36 | 2.5675 | | 2.9941 | 4.0 | 48 | 2.6363 | | 3.1597 | 5.0 | 60 | 2.8368 | | 2.8535 | 6.0 | 72 | 2.8220 | | 2.9492 | 7.0 | 84 | 3.0838 | | 2.6935 | 8.0 | 96 | 2.6604 | | 2.8037 | 9.0 | 108 | 2.4602 | | 3.101 | 10.0 | 120 | 2.6140 | | 2.4546 | 11.0 | 132 | 2.6074 | | 2.6299 | 12.0 | 144 | 2.5843 | | 2.4703 | 13.0 | 156 | 2.6383 | | 2.4184 | 14.0 | 168 | 2.3316 | | 2.6144 | 15.0 | 180 | 2.0832 | | 2.6209 | 16.0 | 192 | 2.3583 | | 2.451 | 17.0 | 204 | 2.9010 | | 2.4358 | 18.0 | 216 | 3.0525 | | 2.4198 | 19.0 | 228 | 2.6463 | | 2.3365 | 20.0 | 240 | 2.7683 | | 2.2167 | 21.0 | 252 | 2.9289 | | 2.4412 | 22.0 | 264 | 2.0613 | | 2.3041 | 23.0 | 276 | 2.6865 | | 2.381 | 24.0 | 288 | 2.4213 | | 2.3244 | 25.0 | 300 | 2.3309 | | 2.2025 | 26.0 | 312 | 3.8109 | | 2.3091 | 27.0 | 324 | 3.1869 | | 2.2988 | 28.0 | 336 | 1.9325 | | 2.2883 | 29.0 | 348 | 2.0473 | | 2.2323 | 30.0 | 360 | 2.6196 | | 2.1218 | 31.0 | 372 | 2.3249 | | 2.138 | 32.0 | 384 | 2.4549 | | 2.0153 | 33.0 | 396 | 2.0830 | | 1.8986 | 34.0 | 408 | 2.3666 | | 2.0264 | 35.0 | 420 | 2.3655 | | 2.0425 | 36.0 | 432 | 2.6095 | | 2.0762 | 37.0 | 444 | 2.4949 | | 2.0342 | 38.0 | 456 | 1.5367 | | 1.8288 | 39.0 | 468 | 2.6941 | | 1.9419 | 40.0 | 480 | 2.5493 | | 2.0241 | 41.0 | 492 | 2.6684 | | 1.9002 | 42.0 | 504 | 2.3222 | | 1.9645 | 43.0 | 516 | 2.8538 | | 1.6755 | 44.0 | 528 | 1.7693 | | 1.9111 | 45.0 | 540 | 2.3962 | | 2.0126 | 46.0 | 552 | 2.2722 | | 2.032 | 47.0 | 564 | 2.2347 | | 2.0232 | 48.0 | 576 | 1.7626 | | 1.8135 | 49.0 | 588 | 2.5355 | | 1.6517 | 50.0 | 600 | 2.9392 | | 1.6788 | 51.0 | 612 | 1.9630 | | 1.6126 | 52.0 | 624 | 2.1936 | | 1.8367 | 53.0 | 636 | 3.4687 | | 1.8566 | 54.0 | 648 | 2.0458 | | 1.6203 | 55.0 | 660 | 2.1171 | | 1.6941 | 56.0 | 672 | 1.9957 | | 1.5142 | 57.0 | 684 | 2.2677 | | 1.7009 | 58.0 | 696 | 2.8793 | | 1.6105 | 59.0 | 708 | 2.1910 | | 1.6282 | 60.0 | 720 | 1.9620 | | 1.7587 | 61.0 | 732 | 3.4591 | | 1.6177 | 62.0 | 744 | 2.0555 | | 1.5287 | 63.0 | 756 | 2.9750 | | 1.6862 | 64.0 | 768 | 2.2498 | | 1.5724 | 65.0 | 780 | 2.5222 | | 1.705 | 66.0 | 792 | 2.4491 | | 1.6787 | 67.0 | 804 | 2.4474 | | 1.665 | 68.0 | 816 | 2.3176 | | 1.3825 | 69.0 | 828 | 2.5131 | | 1.4641 | 70.0 | 840 | 2.0134 | | 1.3444 | 71.0 | 852 | 2.7905 | | 1.6672 | 72.0 | 864 | 3.0861 | | 1.5524 | 73.0 | 876 | 2.3998 | | 1.4178 | 74.0 | 888 | 2.8779 | | 1.4374 | 75.0 | 900 | 2.3486 | | 1.2693 | 76.0 | 912 | 2.6789 | | 1.5111 | 77.0 | 924 | 2.4917 | | 1.3847 | 78.0 | 936 | 2.0904 | | 1.3115 | 79.0 | 948 | 2.7551 | | 1.5094 | 80.0 | 960 | 2.4040 | | 1.3265 | 81.0 | 972 | 2.6506 | | 1.226 | 82.0 | 984 | 3.0660 | | 1.3867 | 83.0 | 996 | 1.8890 | | 1.2752 | 84.0 | 1008 | 2.9983 | | 1.3847 | 85.0 | 1020 | 2.7811 | | 1.3903 | 86.0 | 1032 | 2.9952 | | 1.3858 | 87.0 | 1044 | 2.1377 | | 1.2792 | 88.0 | 1056 | 2.9294 | | 1.3319 | 89.0 | 1068 | 2.5720 | | 1.1521 | 90.0 | 1080 | 2.4535 | | 1.2619 | 91.0 | 1092 | 2.1846 | | 1.2885 | 92.0 | 1104 | 2.0970 | | 1.1852 | 93.0 | 1116 | 2.2783 | | 1.3225 | 94.0 | 1128 | 2.7983 | | 1.1694 | 95.0 | 1140 | 2.0372 | | 1.1184 | 96.0 | 1152 | 2.7704 | | 1.1852 | 97.0 | 1164 | 2.8402 | | 1.2402 | 98.0 | 1176 | 2.2748 | | 1.1182 | 99.0 | 1188 | 2.7973 | | 1.2023 | 100.0 | 1200 | 2.1480 | | 1.0637 | 101.0 | 1212 | 2.1987 | | 1.1003 | 102.0 | 1224 | 1.9750 | | 1.2729 | 103.0 | 1236 | 2.6881 | | 1.0963 | 104.0 | 1248 | 2.5819 | | 1.2034 | 105.0 | 1260 | 2.8611 | | 1.038 | 106.0 | 1272 | 1.8322 | | 1.3583 | 107.0 | 1284 | 2.7330 | | 1.1453 | 108.0 | 1296 | 2.5139 | | 1.1593 | 109.0 | 1308 | 2.4409 | | 1.1126 | 110.0 | 1320 | 2.3118 | | 0.9801 | 111.0 | 1332 | 2.1956 | | 1.2605 | 112.0 | 1344 | 2.8087 | | 1.1756 | 113.0 | 1356 | 2.1508 | | 0.8898 | 114.0 | 1368 | 2.8882 | | 1.1959 | 115.0 | 1380 | 2.6419 | | 1.0536 | 116.0 | 1392 | 2.2053 | | 1.1508 | 117.0 | 1404 | 2.4917 | | 0.9824 | 118.0 | 1416 | 2.8271 | | 1.2391 | 119.0 | 1428 | 2.0959 | | 0.9495 | 120.0 | 1440 | 2.5855 | | 0.9823 | 121.0 | 1452 | 2.3001 | | 0.9818 | 122.0 | 1464 | 2.4058 | | 1.0764 | 123.0 | 1476 | 2.7615 | | 1.1002 | 124.0 | 1488 | 2.2705 | | 0.9838 | 125.0 | 1500 | 2.4089 | | 1.1747 | 126.0 | 1512 | 2.2487 | | 0.9397 | 127.0 | 1524 | 2.3436 | | 0.7915 | 128.0 | 1536 | 2.7810 | | 0.8227 | 129.0 | 1548 | 2.9488 | | 1.0162 | 130.0 | 1560 | 1.9826 | | 1.038 | 131.0 | 1572 | 2.3104 | | 0.7145 | 132.0 | 1584 | 3.1713 | | 0.9299 | 133.0 | 1596 | 2.4383 | | 1.1 | 134.0 | 1608 | 2.7588 | | 0.7346 | 135.0 | 1620 | 2.4870 | | 0.898 | 136.0 | 1632 | 2.3211 | | 1.0406 | 137.0 | 1644 | 2.1006 | | 0.7669 | 138.0 | 1656 | 2.6216 | | 0.8182 | 139.0 | 1668 | 2.6548 | | 0.9577 | 140.0 | 1680 | 3.0709 | | 0.843 | 141.0 | 1692 | 2.0712 | | 0.8871 | 142.0 | 1704 | 2.0269 | | 0.8183 | 143.0 | 1716 | 2.1832 | | 0.9048 | 144.0 | 1728 | 2.3581 | | 0.8197 | 145.0 | 1740 | 2.5645 | | 0.7477 | 146.0 | 1752 | 3.4650 | | 0.8257 | 147.0 | 1764 | 3.0643 | | 0.801 | 148.0 | 1776 | 2.6476 | | 0.8802 | 149.0 | 1788 | 2.5711 | | 0.7332 | 150.0 | 1800 | 2.7936 | | 0.825 | 151.0 | 1812 | 2.9548 | | 0.7226 | 152.0 | 1824 | 2.2194 | | 0.6707 | 153.0 | 1836 | 2.0006 | | 0.6401 | 154.0 | 1848 | 2.7826 | | 0.9888 | 155.0 | 1860 | 2.1371 | | 0.6399 | 156.0 | 1872 | 2.1082 | | 0.7128 | 157.0 | 1884 | 2.7275 | | 0.684 | 158.0 | 1896 | 2.0162 | | 0.7906 | 159.0 | 1908 | 1.9985 | | 0.8381 | 160.0 | 1920 | 2.6745 | | 0.7233 | 161.0 | 1932 | 2.7703 | | 0.6977 | 162.0 | 1944 | 2.2407 | | 0.7948 | 163.0 | 1956 | 2.5955 | | 0.7616 | 164.0 | 1968 | 2.3938 | | 0.8808 | 165.0 | 1980 | 2.5147 | | 0.8188 | 166.0 | 1992 | 1.6625 | | 0.6083 | 167.0 | 2004 | 3.1102 | | 0.7814 | 168.0 | 2016 | 2.7221 | | 0.6402 | 169.0 | 2028 | 2.4840 | | 0.7722 | 170.0 | 2040 | 2.2021 | | 0.7887 | 171.0 | 2052 | 3.1279 | | 0.7313 | 172.0 | 2064 | 2.1820 | | 0.7924 | 173.0 | 2076 | 1.7631 | | 0.6142 | 174.0 | 2088 | 2.7580 | | 0.7562 | 175.0 | 2100 | 2.0954 | | 0.5619 | 176.0 | 2112 | 2.3388 | | 0.9217 | 177.0 | 2124 | 3.4578 | | 0.6253 | 178.0 | 2136 | 1.9490 | | 0.6385 | 179.0 | 2148 | 1.9926 | | 0.7452 | 180.0 | 2160 | 3.1260 | | 0.5797 | 181.0 | 2172 | 2.7739 | | 0.6138 | 182.0 | 2184 | 2.8513 | | 0.5669 | 183.0 | 2196 | 2.4326 | | 0.6944 | 184.0 | 2208 | 2.7487 | | 0.7057 | 185.0 | 2220 | 2.4420 | | 0.8157 | 186.0 | 2232 | 2.8531 | | 0.5743 | 187.0 | 2244 | 3.0470 | | 0.595 | 188.0 | 2256 | 2.8035 | | 0.7408 | 189.0 | 2268 | 2.7126 | | 0.5912 | 190.0 | 2280 | 3.7428 | | 0.5725 | 191.0 | 2292 | 2.3815 | | 0.6521 | 192.0 | 2304 | 2.7721 | | 0.7074 | 193.0 | 2316 | 2.5499 | | 0.5764 | 194.0 | 2328 | 2.6066 | | 0.5298 | 195.0 | 2340 | 2.2085 | | 0.6197 | 196.0 | 2352 | 2.4815 | | 0.4731 | 197.0 | 2364 | 2.8488 | | 0.619 | 198.0 | 2376 | 3.2678 | | 0.5954 | 199.0 | 2388 | 2.1428 | | 0.5277 | 200.0 | 2400 | 2.7153 | | 0.7886 | 201.0 | 2412 | 2.2156 | | 0.512 | 202.0 | 2424 | 2.2840 | | 0.55 | 203.0 | 2436 | 2.7672 | | 0.4958 | 204.0 | 2448 | 1.6703 | | 0.7151 | 205.0 | 2460 | 2.1373 | | 0.5112 | 206.0 | 2472 | 2.7734 | | 0.6594 | 207.0 | 2484 | 2.5554 | | 0.4422 | 208.0 | 2496 | 1.8383 | | 0.5405 | 209.0 | 2508 | 2.9803 | | 0.555 | 210.0 | 2520 | 2.4756 | | 0.605 | 211.0 | 2532 | 2.6883 | | 0.5143 | 212.0 | 2544 | 3.2208 | | 0.5458 | 213.0 | 2556 | 2.6816 | | 0.5469 | 214.0 | 2568 | 3.0502 | | 0.5425 | 215.0 | 2580 | 2.8781 | | 0.4458 | 216.0 | 2592 | 2.8725 | | 0.4986 | 217.0 | 2604 | 2.6287 | | 0.8714 | 218.0 | 2616 | 3.2690 | | 0.4996 | 219.0 | 2628 | 3.1879 | | 0.4841 | 220.0 | 2640 | 3.0364 | | 0.4745 | 221.0 | 2652 | 2.5914 | | 0.4609 | 222.0 | 2664 | 2.6385 | | 0.4058 | 223.0 | 2676 | 2.9445 | | 0.4653 | 224.0 | 2688 | 2.6551 | | 0.4246 | 225.0 | 2700 | 3.2083 | | 0.6041 | 226.0 | 2712 | 3.2518 | | 0.6409 | 227.0 | 2724 | 2.2092 | | 0.5091 | 228.0 | 2736 | 2.6145 | | 0.5917 | 229.0 | 2748 | 2.6990 | | 0.533 | 230.0 | 2760 | 2.9442 | | 0.4637 | 231.0 | 2772 | 2.5754 | | 0.5876 | 232.0 | 2784 | 3.3697 | | 0.5068 | 233.0 | 2796 | 2.1599 | | 0.5561 | 234.0 | 2808 | 2.4411 | | 0.3852 | 235.0 | 2820 | 2.1660 | | 0.5038 | 236.0 | 2832 | 2.5145 | | 0.4498 | 237.0 | 2844 | 2.9055 | | 0.3932 | 238.0 | 2856 | 2.0346 | | 0.4701 | 239.0 | 2868 | 2.4029 | | 0.554 | 240.0 | 2880 | 3.2398 | | 0.4836 | 241.0 | 2892 | 2.6803 | | 0.4752 | 242.0 | 2904 | 2.5135 | | 0.4507 | 243.0 | 2916 | 1.9342 | | 0.316 | 244.0 | 2928 | 3.2635 | | 0.4807 | 245.0 | 2940 | 2.6797 | | 0.5369 | 246.0 | 2952 | 3.3722 | | 0.4434 | 247.0 | 2964 | 2.9754 | | 0.5113 | 248.0 | 2976 | 2.7636 | | 0.4765 | 249.0 | 2988 | 2.5710 | | 0.517 | 250.0 | 3000 | 2.6230 | | 0.4156 | 251.0 | 3012 | 2.7318 | | 0.4041 | 252.0 | 3024 | 2.9123 | | 0.4076 | 253.0 | 3036 | 2.5130 | | 0.4224 | 254.0 | 3048 | 2.4242 | | 0.464 | 255.0 | 3060 | 2.4092 | | 0.4631 | 256.0 | 3072 | 2.8105 | | 0.3792 | 257.0 | 3084 | 2.4955 | | 0.4282 | 258.0 | 3096 | 2.6907 | | 0.5803 | 259.0 | 3108 | 2.8609 | | 0.5043 | 260.0 | 3120 | 3.0090 | | 0.4026 | 261.0 | 3132 | 3.1805 | | 0.5926 | 262.0 | 3144 | 2.6541 | | 0.4021 | 263.0 | 3156 | 2.2630 | | 0.462 | 264.0 | 3168 | 3.3067 | | 0.4701 | 265.0 | 3180 | 2.9675 | | 0.4706 | 266.0 | 3192 | 3.2344 | | 0.5196 | 267.0 | 3204 | 2.7747 | | 0.491 | 268.0 | 3216 | 2.5085 | | 0.4152 | 269.0 | 3228 | 2.5357 | | 0.4402 | 270.0 | 3240 | 2.6906 | | 0.4152 | 271.0 | 3252 | 3.1434 | | 0.4487 | 272.0 | 3264 | 3.2802 | | 0.3956 | 273.0 | 3276 | 3.3766 | | 0.3623 | 274.0 | 3288 | 2.8253 | | 0.3994 | 275.0 | 3300 | 2.2845 | | 0.4035 | 276.0 | 3312 | 2.5307 | | 0.3815 | 277.0 | 3324 | 3.3093 | | 0.4519 | 278.0 | 3336 | 2.2202 | | 0.3118 | 279.0 | 3348 | 2.7818 | | 0.5191 | 280.0 | 3360 | 2.3814 | | 0.3194 | 281.0 | 3372 | 2.3144 | | 0.5671 | 282.0 | 3384 | 3.4033 | | 0.4217 | 283.0 | 3396 | 1.9681 | | 0.3587 | 284.0 | 3408 | 2.9843 | | 0.3914 | 285.0 | 3420 | 3.1635 | | 0.3667 | 286.0 | 3432 | 2.7571 | | 0.3781 | 287.0 | 3444 | 2.5881 | | 0.3868 | 288.0 | 3456 | 1.8389 | | 0.4172 | 289.0 | 3468 | 2.6809 | | 0.5089 | 290.0 | 3480 | 2.4618 | | 0.3181 | 291.0 | 3492 | 2.1054 | | 0.3276 | 292.0 | 3504 | 2.9944 | | 0.4051 | 293.0 | 3516 | 2.8520 | | 0.3435 | 294.0 | 3528 | 3.0985 | | 0.3241 | 295.0 | 3540 | 2.6323 | | 0.2532 | 296.0 | 3552 | 2.9059 | | 0.2732 | 297.0 | 3564 | 2.5619 | | 0.4181 | 298.0 | 3576 | 2.5687 | | 0.3725 | 299.0 | 3588 | 3.3169 | | 0.3949 | 300.0 | 3600 | 2.0620 | | 0.4684 | 301.0 | 3612 | 2.3878 | | 0.4122 | 302.0 | 3624 | 3.4867 | | 0.3338 | 303.0 | 3636 | 3.0578 | | 0.3546 | 304.0 | 3648 | 3.3269 | | 0.3833 | 305.0 | 3660 | 2.2698 | | 0.2897 | 306.0 | 3672 | 2.9015 | | 0.3912 | 307.0 | 3684 | 3.4569 | | 0.3951 | 308.0 | 3696 | 2.5743 | | 0.3086 | 309.0 | 3708 | 2.2319 | | 0.481 | 310.0 | 3720 | 1.7550 | | 0.3579 | 311.0 | 3732 | 2.4885 | | 0.4271 | 312.0 | 3744 | 3.2511 | | 0.3864 | 313.0 | 3756 | 2.4219 | | 0.3008 | 314.0 | 3768 | 3.2937 | | 0.3279 | 315.0 | 3780 | 2.9278 | | 0.3845 | 316.0 | 3792 | 3.7233 | | 0.3158 | 317.0 | 3804 | 2.1792 | | 0.3906 | 318.0 | 3816 | 2.3364 | | 0.3159 | 319.0 | 3828 | 3.7451 | | 0.2773 | 320.0 | 3840 | 2.6364 | | 0.2867 | 321.0 | 3852 | 2.6699 | | 0.3253 | 322.0 | 3864 | 2.7289 | | 0.4208 | 323.0 | 3876 | 2.5447 | | 0.4343 | 324.0 | 3888 | 3.1167 | | 0.3126 | 325.0 | 3900 | 3.4110 | | 0.2433 | 326.0 | 3912 | 2.1796 | | 0.2964 | 327.0 | 3924 | 2.1766 | | 0.4289 | 328.0 | 3936 | 3.5455 | | 0.3391 | 329.0 | 3948 | 2.5795 | | 0.3505 | 330.0 | 3960 | 2.3377 | | 0.4084 | 331.0 | 3972 | 2.9658 | | 0.4365 | 332.0 | 3984 | 2.5202 | | 0.3573 | 333.0 | 3996 | 3.2768 | | 0.2813 | 334.0 | 4008 | 2.7073 | | 0.2531 | 335.0 | 4020 | 2.3548 | | 0.2535 | 336.0 | 4032 | 2.8820 | | 0.3038 | 337.0 | 4044 | 2.6777 | | 0.2861 | 338.0 | 4056 | 2.8631 | | 0.2717 | 339.0 | 4068 | 2.7445 | | 0.3495 | 340.0 | 4080 | 2.9722 | | 0.2775 | 341.0 | 4092 | 3.1350 | | 0.3661 | 342.0 | 4104 | 2.7601 | | 0.348 | 343.0 | 4116 | 2.6642 | | 0.3556 | 344.0 | 4128 | 1.9807 | | 0.3072 | 345.0 | 4140 | 2.6037 | | 0.3114 | 346.0 | 4152 | 2.7645 | | 0.3527 | 347.0 | 4164 | 2.8360 | | 0.2903 | 348.0 | 4176 | 2.0667 | | 0.2449 | 349.0 | 4188 | 2.3573 | | 0.2089 | 350.0 | 4200 | 2.6189 | | 0.3894 | 351.0 | 4212 | 2.5689 | | 0.3061 | 352.0 | 4224 | 2.7638 | | 0.3221 | 353.0 | 4236 | 2.4668 | | 0.2434 | 354.0 | 4248 | 2.3994 | | 0.1777 | 355.0 | 4260 | 2.6408 | | 0.3809 | 356.0 | 4272 | 2.9841 | | 0.3237 | 357.0 | 4284 | 2.7111 | | 0.1947 | 358.0 | 4296 | 3.5881 | | 0.3112 | 359.0 | 4308 | 3.6076 | | 0.299 | 360.0 | 4320 | 2.5547 | | 0.354 | 361.0 | 4332 | 1.9077 | | 0.2733 | 362.0 | 4344 | 3.1406 | | 0.4962 | 363.0 | 4356 | 2.3770 | | 0.3272 | 364.0 | 4368 | 3.0437 | | 0.2858 | 365.0 | 4380 | 2.7978 | | 0.3685 | 366.0 | 4392 | 2.3725 | | 0.2707 | 367.0 | 4404 | 2.4587 | | 0.3137 | 368.0 | 4416 | 2.1862 | | 0.2781 | 369.0 | 4428 | 1.8312 | | 0.2658 | 370.0 | 4440 | 2.4720 | | 0.3014 | 371.0 | 4452 | 2.3532 | | 0.24 | 372.0 | 4464 | 3.4097 | | 0.2413 | 373.0 | 4476 | 3.2338 | | 0.3055 | 374.0 | 4488 | 3.4269 | | 0.3781 | 375.0 | 4500 | 2.8758 | | 0.2224 | 376.0 | 4512 | 2.2171 | | 0.2463 | 377.0 | 4524 | 3.2768 | | 0.4141 | 378.0 | 4536 | 2.9136 | | 0.2102 | 379.0 | 4548 | 2.8798 | | 0.2164 | 380.0 | 4560 | 2.5821 | | 0.2742 | 381.0 | 4572 | 2.0458 | | 0.2007 | 382.0 | 4584 | 3.8119 | | 0.2494 | 383.0 | 4596 | 3.0835 | | 0.2533 | 384.0 | 4608 | 2.5633 | | 0.3137 | 385.0 | 4620 | 2.2415 | | 0.2686 | 386.0 | 4632 | 2.2489 | | 0.2425 | 387.0 | 4644 | 2.1750 | | 0.2561 | 388.0 | 4656 | 2.8167 | | 0.3485 | 389.0 | 4668 | 3.4358 | | 0.2746 | 390.0 | 4680 | 2.3380 | | 0.3538 | 391.0 | 4692 | 2.9940 | | 0.3989 | 392.0 | 4704 | 2.7560 | | 0.2414 | 393.0 | 4716 | 3.4802 | | 0.2888 | 394.0 | 4728 | 2.5955 | | 0.3162 | 395.0 | 4740 | 2.3060 | | 0.2435 | 396.0 | 4752 | 3.8333 | | 0.2796 | 397.0 | 4764 | 2.1767 | | 0.2588 | 398.0 | 4776 | 2.6988 | | 0.209 | 399.0 | 4788 | 2.4999 | | 0.2602 | 400.0 | 4800 | 2.6636 | | 0.2114 | 401.0 | 4812 | 3.2272 | | 0.2226 | 402.0 | 4824 | 2.5983 | | 0.1681 | 403.0 | 4836 | 2.3867 | | 0.2025 | 404.0 | 4848 | 3.0062 | | 0.2769 | 405.0 | 4860 | 2.9767 | | 0.3267 | 406.0 | 4872 | 2.6960 | | 0.252 | 407.0 | 4884 | 2.6078 | | 0.257 | 408.0 | 4896 | 2.1594 | | 0.306 | 409.0 | 4908 | 3.3544 | | 0.2329 | 410.0 | 4920 | 2.6371 | | 0.3732 | 411.0 | 4932 | 2.8729 | | 0.3233 | 412.0 | 4944 | 3.6352 | | 0.2822 | 413.0 | 4956 | 3.0374 | | 0.2796 | 414.0 | 4968 | 2.8686 | | 0.2606 | 415.0 | 4980 | 2.8761 | | 0.2048 | 416.0 | 4992 | 2.5680 | | 0.2088 | 417.0 | 5004 | 2.4540 | | 0.2301 | 418.0 | 5016 | 2.4787 | | 0.1594 | 419.0 | 5028 | 2.9355 | | 0.3399 | 420.0 | 5040 | 2.8312 | | 0.2322 | 421.0 | 5052 | 1.9368 | | 0.2066 | 422.0 | 5064 | 3.2728 | | 0.2254 | 423.0 | 5076 | 3.0105 | | 0.1818 | 424.0 | 5088 | 2.8390 | | 0.3191 | 425.0 | 5100 | 2.9756 | | 0.1961 | 426.0 | 5112 | 3.4510 | | 0.2014 | 427.0 | 5124 | 3.4363 | | 0.184 | 428.0 | 5136 | 3.1381 | | 0.2722 | 429.0 | 5148 | 3.4780 | | 0.2607 | 430.0 | 5160 | 2.9650 | | 0.3515 | 431.0 | 5172 | 2.8692 | | 0.2011 | 432.0 | 5184 | 2.7564 | | 0.2555 | 433.0 | 5196 | 3.5317 | | 0.2802 | 434.0 | 5208 | 1.9900 | | 0.227 | 435.0 | 5220 | 3.3691 | | 0.2833 | 436.0 | 5232 | 3.0117 | | 0.2368 | 437.0 | 5244 | 2.6631 | | 0.2159 | 438.0 | 5256 | 2.3868 | | 0.2139 | 439.0 | 5268 | 2.8382 | | 0.2739 | 440.0 | 5280 | 2.9267 | | 0.234 | 441.0 | 5292 | 2.9501 | | 0.2315 | 442.0 | 5304 | 3.3317 | | 0.2538 | 443.0 | 5316 | 3.1168 | | 0.2535 | 444.0 | 5328 | 2.8070 | | 0.2711 | 445.0 | 5340 | 2.0824 | | 0.2963 | 446.0 | 5352 | 1.7310 | | 0.2559 | 447.0 | 5364 | 3.3832 | | 0.3184 | 448.0 | 5376 | 2.6107 | | 0.2383 | 449.0 | 5388 | 2.3923 | | 0.4352 | 450.0 | 5400 | 3.1145 | | 0.1892 | 451.0 | 5412 | 3.0184 | | 0.1899 | 452.0 | 5424 | 2.9772 | | 0.3766 | 453.0 | 5436 | 3.3416 | | 0.211 | 454.0 | 5448 | 2.9356 | | 0.2387 | 455.0 | 5460 | 2.5284 | | 0.2322 | 456.0 | 5472 | 2.8084 | | 0.2003 | 457.0 | 5484 | 3.0678 | | 0.2604 | 458.0 | 5496 | 2.4424 | | 0.2614 | 459.0 | 5508 | 2.6966 | | 0.2026 | 460.0 | 5520 | 2.7806 | | 0.4175 | 461.0 | 5532 | 2.9597 | | 0.1676 | 462.0 | 5544 | 2.8175 | | 0.2646 | 463.0 | 5556 | 3.1038 | | 0.2514 | 464.0 | 5568 | 2.2243 | | 0.1483 | 465.0 | 5580 | 2.6416 | | 0.233 | 466.0 | 5592 | 3.0405 | | 0.2788 | 467.0 | 5604 | 2.1676 | | 0.2339 | 468.0 | 5616 | 3.1575 | | 0.2735 | 469.0 | 5628 | 1.7335 | | 0.1639 | 470.0 | 5640 | 2.7019 | | 0.24 | 471.0 | 5652 | 2.2920 | | 0.2341 | 472.0 | 5664 | 2.8358 | | 0.1978 | 473.0 | 5676 | 2.9339 | | 0.2517 | 474.0 | 5688 | 2.4914 | | 0.188 | 475.0 | 5700 | 2.2767 | | 0.1138 | 476.0 | 5712 | 2.3833 | | 0.1809 | 477.0 | 5724 | 2.6821 | | 0.3134 | 478.0 | 5736 | 2.1710 | | 0.1848 | 479.0 | 5748 | 3.3586 | | 0.252 | 480.0 | 5760 | 2.7309 | | 0.193 | 481.0 | 5772 | 2.8318 | | 0.2284 | 482.0 | 5784 | 3.4643 | | 0.2058 | 483.0 | 5796 | 4.2388 | | 0.2319 | 484.0 | 5808 | 2.1872 | | 0.1566 | 485.0 | 5820 | 2.3735 | | 0.29 | 486.0 | 5832 | 3.4093 | | 0.125 | 487.0 | 5844 | 3.3786 | | 0.2628 | 488.0 | 5856 | 2.4406 | | 0.2609 | 489.0 | 5868 | 3.3617 | | 0.2055 | 490.0 | 5880 | 3.1843 | | 0.1713 | 491.0 | 5892 | 2.1698 | | 0.2562 | 492.0 | 5904 | 3.0665 | | 0.3366 | 493.0 | 5916 | 3.2277 | | 0.2359 | 494.0 | 5928 | 2.7013 | | 0.191 | 495.0 | 5940 | 3.4616 | | 0.175 | 496.0 | 5952 | 2.5117 | | 0.1695 | 497.0 | 5964 | 2.3203 | | 0.218 | 498.0 | 5976 | 2.4493 | | 0.1953 | 499.0 | 5988 | 2.6769 | | 0.2478 | 500.0 | 6000 | 3.1759 | | 0.1548 | 501.0 | 6012 | 2.8604 | | 0.123 | 502.0 | 6024 | 2.7744 | | 0.2271 | 503.0 | 6036 | 2.9987 | | 0.2384 | 504.0 | 6048 | 2.7653 | | 0.2473 | 505.0 | 6060 | 3.1049 | | 0.1937 | 506.0 | 6072 | 2.6676 | | 0.138 | 507.0 | 6084 | 2.2486 | | 0.2681 | 508.0 | 6096 | 3.1809 | | 0.2182 | 509.0 | 6108 | 2.5258 | | 0.1736 | 510.0 | 6120 | 2.2174 | | 0.2238 | 511.0 | 6132 | 2.9662 | | 0.189 | 512.0 | 6144 | 2.3124 | | 0.175 | 513.0 | 6156 | 3.6426 | | 0.2189 | 514.0 | 6168 | 2.4628 | | 0.1918 | 515.0 | 6180 | 3.3473 | | 0.1303 | 516.0 | 6192 | 2.9400 | | 0.1624 | 517.0 | 6204 | 3.1941 | | 0.134 | 518.0 | 6216 | 2.9962 | | 0.2447 | 519.0 | 6228 | 3.0082 | | 0.1872 | 520.0 | 6240 | 3.9689 | | 0.1787 | 521.0 | 6252 | 3.1461 | | 0.3039 | 522.0 | 6264 | 3.2696 | | 0.1757 | 523.0 | 6276 | 3.0340 | | 0.3539 | 524.0 | 6288 | 3.3542 | | 0.2109 | 525.0 | 6300 | 2.7986 | | 0.1743 | 526.0 | 6312 | 3.1874 | | 0.1065 | 527.0 | 6324 | 2.9643 | | 0.2941 | 528.0 | 6336 | 2.6260 | | 0.2231 | 529.0 | 6348 | 2.8250 | | 0.1307 | 530.0 | 6360 | 3.2949 | | 0.1979 | 531.0 | 6372 | 1.8269 | | 0.2293 | 532.0 | 6384 | 2.2357 | | 0.2171 | 533.0 | 6396 | 2.5498 | | 0.1975 | 534.0 | 6408 | 2.7011 | | 0.1556 | 535.0 | 6420 | 3.5648 | | 0.1234 | 536.0 | 6432 | 2.7632 | | 0.2156 | 537.0 | 6444 | 2.3060 | | 0.1402 | 538.0 | 6456 | 3.1421 | | 0.1921 | 539.0 | 6468 | 2.3200 | | 0.1237 | 540.0 | 6480 | 2.7612 | | 0.1942 | 541.0 | 6492 | 2.5866 | | 0.1648 | 542.0 | 6504 | 2.4930 | | 0.1369 | 543.0 | 6516 | 2.9427 | | 0.1811 | 544.0 | 6528 | 2.9692 | | 0.2382 | 545.0 | 6540 | 3.4092 | | 0.2001 | 546.0 | 6552 | 3.2784 | | 0.2195 | 547.0 | 6564 | 2.8198 | | 0.1785 | 548.0 | 6576 | 2.5721 | | 0.2214 | 549.0 | 6588 | 3.1468 | | 0.1685 | 550.0 | 6600 | 2.8141 | | 0.1596 | 551.0 | 6612 | 3.1457 | | 0.0945 | 552.0 | 6624 | 2.6508 | | 0.1595 | 553.0 | 6636 | 2.8443 | | 0.1805 | 554.0 | 6648 | 2.4984 | | 0.1588 | 555.0 | 6660 | 2.9758 | | 0.2026 | 556.0 | 6672 | 3.3614 | | 0.1351 | 557.0 | 6684 | 2.5065 | | 0.2395 | 558.0 | 6696 | 2.5261 | | 0.2089 | 559.0 | 6708 | 3.3972 | | 0.2265 | 560.0 | 6720 | 3.0095 | | 0.2027 | 561.0 | 6732 | 3.2904 | | 0.2691 | 562.0 | 6744 | 2.5727 | | 0.1563 | 563.0 | 6756 | 2.0994 | | 0.2537 | 564.0 | 6768 | 3.2397 | | 0.1094 | 565.0 | 6780 | 2.9758 | | 0.1523 | 566.0 | 6792 | 2.3577 | | 0.2535 | 567.0 | 6804 | 2.6197 | | 0.1444 | 568.0 | 6816 | 1.9130 | | 0.1933 | 569.0 | 6828 | 2.3576 | | 0.1368 | 570.0 | 6840 | 3.3412 | | 0.1723 | 571.0 | 6852 | 3.5156 | | 0.1384 | 572.0 | 6864 | 2.9785 | | 0.1905 | 573.0 | 6876 | 3.2326 | | 0.1495 | 574.0 | 6888 | 2.9111 | | 0.1512 | 575.0 | 6900 | 2.1727 | | 0.227 | 576.0 | 6912 | 2.5159 | | 0.2271 | 577.0 | 6924 | 2.7866 | | 0.2457 | 578.0 | 6936 | 3.2068 | | 0.236 | 579.0 | 6948 | 2.8856 | | 0.1579 | 580.0 | 6960 | 2.3365 | | 0.1203 | 581.0 | 6972 | 2.3652 | | 0.1422 | 582.0 | 6984 | 2.8213 | | 0.1673 | 583.0 | 6996 | 2.5507 | | 0.204 | 584.0 | 7008 | 4.0226 | | 0.1796 | 585.0 | 7020 | 3.1953 | | 0.163 | 586.0 | 7032 | 2.5787 | | 0.2166 | 587.0 | 7044 | 3.8404 | | 0.1299 | 588.0 | 7056 | 2.3668 | | 0.2301 | 589.0 | 7068 | 2.7562 | | 0.1506 | 590.0 | 7080 | 2.9342 | | 0.1372 | 591.0 | 7092 | 2.8316 | | 0.1959 | 592.0 | 7104 | 2.2761 | | 0.1925 | 593.0 | 7116 | 2.9083 | | 0.1885 | 594.0 | 7128 | 2.9052 | | 0.2052 | 595.0 | 7140 | 2.9409 | | 0.1368 | 596.0 | 7152 | 3.2571 | | 0.1455 | 597.0 | 7164 | 2.8765 | | 0.1398 | 598.0 | 7176 | 2.2425 | | 0.1764 | 599.0 | 7188 | 2.6299 | | 0.1791 | 600.0 | 7200 | 3.4030 | | 0.1057 | 601.0 | 7212 | 3.2505 | | 0.1947 | 602.0 | 7224 | 2.6440 | | 0.1678 | 603.0 | 7236 | 3.3419 | | 0.1629 | 604.0 | 7248 | 3.1957 | | 0.1348 | 605.0 | 7260 | 3.1234 | | 0.2332 | 606.0 | 7272 | 2.9425 | | 0.1367 | 607.0 | 7284 | 3.8721 | | 0.1434 | 608.0 | 7296 | 3.0653 | | 0.2092 | 609.0 | 7308 | 3.1552 | | 0.1765 | 610.0 | 7320 | 2.6715 | | 0.1773 | 611.0 | 7332 | 2.8437 | | 0.1427 | 612.0 | 7344 | 3.1257 | | 0.2383 | 613.0 | 7356 | 3.5687 | | 0.1376 | 614.0 | 7368 | 3.0010 | | 0.1388 | 615.0 | 7380 | 2.7436 | | 0.2484 | 616.0 | 7392 | 3.2465 | | 0.146 | 617.0 | 7404 | 3.4019 | | 0.1313 | 618.0 | 7416 | 2.5044 | | 0.2028 | 619.0 | 7428 | 3.2449 | | 0.1471 | 620.0 | 7440 | 3.1716 | | 0.1755 | 621.0 | 7452 | 2.4465 | | 0.16 | 622.0 | 7464 | 2.8572 | | 0.108 | 623.0 | 7476 | 3.4424 | | 0.0824 | 624.0 | 7488 | 2.6112 | | 0.1133 | 625.0 | 7500 | 2.5730 | | 0.1809 | 626.0 | 7512 | 1.9670 | | 0.2606 | 627.0 | 7524 | 2.7736 | | 0.2001 | 628.0 | 7536 | 3.1865 | | 0.1912 | 629.0 | 7548 | 2.9717 | | 0.1525 | 630.0 | 7560 | 2.8429 | | 0.306 | 631.0 | 7572 | 2.6320 | | 0.1322 | 632.0 | 7584 | 2.8373 | | 0.1782 | 633.0 | 7596 | 2.7157 | | 0.095 | 634.0 | 7608 | 3.2528 | | 0.1463 | 635.0 | 7620 | 2.6568 | | 0.184 | 636.0 | 7632 | 2.2466 | | 0.2132 | 637.0 | 7644 | 3.4883 | | 0.1007 | 638.0 | 7656 | 3.1021 | | 0.1686 | 639.0 | 7668 | 2.4326 | | 0.1359 | 640.0 | 7680 | 2.2554 | | 0.1535 | 641.0 | 7692 | 2.8495 | | 0.2158 | 642.0 | 7704 | 3.0866 | | 0.1403 | 643.0 | 7716 | 2.8983 | | 0.1092 | 644.0 | 7728 | 3.5183 | | 0.2218 | 645.0 | 7740 | 2.9190 | | 0.1468 | 646.0 | 7752 | 3.7689 | | 0.2291 | 647.0 | 7764 | 3.4550 | | 0.1616 | 648.0 | 7776 | 2.3301 | | 0.2146 | 649.0 | 7788 | 4.2045 | | 0.1113 | 650.0 | 7800 | 3.0168 | | 0.1785 | 651.0 | 7812 | 2.9931 | | 0.1535 | 652.0 | 7824 | 3.4046 | | 0.149 | 653.0 | 7836 | 2.5526 | | 0.1351 | 654.0 | 7848 | 2.1684 | | 0.2564 | 655.0 | 7860 | 3.0749 | | 0.0749 | 656.0 | 7872 | 2.8874 | | 0.1719 | 657.0 | 7884 | 3.1585 | | 0.1783 | 658.0 | 7896 | 4.2177 | | 0.1632 | 659.0 | 7908 | 2.5370 | | 0.1635 | 660.0 | 7920 | 2.7765 | | 0.1414 | 661.0 | 7932 | 4.3148 | | 0.2072 | 662.0 | 7944 | 3.1080 | | 0.3758 | 663.0 | 7956 | 2.7835 | | 0.1474 | 664.0 | 7968 | 2.7685 | | 0.2225 | 665.0 | 7980 | 2.2965 | | 0.2438 | 666.0 | 7992 | 2.8599 | | 0.1872 | 667.0 | 8004 | 2.7234 | | 0.2879 | 668.0 | 8016 | 3.1187 | | 0.1117 | 669.0 | 8028 | 3.8094 | | 0.0942 | 670.0 | 8040 | 4.4307 | | 0.1219 | 671.0 | 8052 | 2.6304 | | 0.1234 | 672.0 | 8064 | 3.0443 | | 0.1221 | 673.0 | 8076 | 3.3849 | | 0.1317 | 674.0 | 8088 | 2.5523 | | 0.1091 | 675.0 | 8100 | 2.6704 | | 0.1677 | 676.0 | 8112 | 3.3960 | | 0.124 | 677.0 | 8124 | 2.1910 | | 0.1508 | 678.0 | 8136 | 2.5585 | | 0.1277 | 679.0 | 8148 | 3.2449 | | 0.1208 | 680.0 | 8160 | 3.0315 | | 0.1796 | 681.0 | 8172 | 2.3906 | | 0.2055 | 682.0 | 8184 | 2.8063 | | 0.1042 | 683.0 | 8196 | 2.7491 | | 0.1897 | 684.0 | 8208 | 2.9381 | | 0.138 | 685.0 | 8220 | 2.8710 | | 0.1562 | 686.0 | 8232 | 1.9945 | | 0.1091 | 687.0 | 8244 | 2.7079 | | 0.1616 | 688.0 | 8256 | 3.3086 | | 0.1699 | 689.0 | 8268 | 3.0746 | | 0.2412 | 690.0 | 8280 | 2.2330 | | 0.157 | 691.0 | 8292 | 3.0135 | | 0.1263 | 692.0 | 8304 | 3.1212 | | 0.1375 | 693.0 | 8316 | 1.8782 | | 0.1204 | 694.0 | 8328 | 2.9291 | | 0.1829 | 695.0 | 8340 | 2.5690 | | 0.1539 | 696.0 | 8352 | 2.5749 | | 0.1339 | 697.0 | 8364 | 3.0899 | | 0.1463 | 698.0 | 8376 | 2.5024 | | 0.1767 | 699.0 | 8388 | 2.5890 | | 0.1392 | 700.0 | 8400 | 1.6672 | | 0.1354 | 701.0 | 8412 | 3.1415 | | 0.1467 | 702.0 | 8424 | 3.1370 | | 0.2547 | 703.0 | 8436 | 2.5094 | | 0.1116 | 704.0 | 8448 | 2.2467 | | 0.0987 | 705.0 | 8460 | 3.2307 | | 0.1811 | 706.0 | 8472 | 2.7363 | | 0.1252 | 707.0 | 8484 | 2.4490 | | 0.1613 | 708.0 | 8496 | 2.3867 | | 0.2282 | 709.0 | 8508 | 3.0490 | | 0.1651 | 710.0 | 8520 | 3.1520 | | 0.1016 | 711.0 | 8532 | 2.7703 | | 0.2515 | 712.0 | 8544 | 2.4811 | | 0.1014 | 713.0 | 8556 | 3.7300 | | 0.103 | 714.0 | 8568 | 2.8680 | | 0.1714 | 715.0 | 8580 | 3.8285 | | 0.1638 | 716.0 | 8592 | 2.5344 | | 0.14 | 717.0 | 8604 | 3.8581 | | 0.1202 | 718.0 | 8616 | 2.4095 | | 0.0691 | 719.0 | 8628 | 2.9710 | | 0.1176 | 720.0 | 8640 | 3.0506 | | 0.2005 | 721.0 | 8652 | 2.7418 | | 0.1719 | 722.0 | 8664 | 2.7388 | | 0.1509 | 723.0 | 8676 | 2.5713 | | 0.1113 | 724.0 | 8688 | 2.9053 | | 0.2501 | 725.0 | 8700 | 2.7703 | | 0.1192 | 726.0 | 8712 | 3.5875 | | 0.1619 | 727.0 | 8724 | 3.0704 | | 0.1421 | 728.0 | 8736 | 2.5629 | | 0.164 | 729.0 | 8748 | 2.4980 | | 0.1753 | 730.0 | 8760 | 2.7749 | | 0.159 | 731.0 | 8772 | 3.8322 | | 0.1929 | 732.0 | 8784 | 3.1355 | | 0.088 | 733.0 | 8796 | 2.3649 | | 0.1349 | 734.0 | 8808 | 2.2229 | | 0.1093 | 735.0 | 8820 | 2.4979 | | 0.1338 | 736.0 | 8832 | 3.2253 | | 0.1794 | 737.0 | 8844 | 2.9326 | | 0.0948 | 738.0 | 8856 | 2.9917 | | 0.1341 | 739.0 | 8868 | 3.6675 | | 0.1019 | 740.0 | 8880 | 3.4145 | | 0.1265 | 741.0 | 8892 | 2.4996 | | 0.1688 | 742.0 | 8904 | 2.9395 | | 0.0829 | 743.0 | 8916 | 3.5850 | | 0.0993 | 744.0 | 8928 | 3.2900 | | 0.2241 | 745.0 | 8940 | 3.2025 | | 0.1235 | 746.0 | 8952 | 2.2814 | | 0.0937 | 747.0 | 8964 | 3.3185 | | 0.0936 | 748.0 | 8976 | 3.4046 | | 0.1633 | 749.0 | 8988 | 2.9694 | | 0.1328 | 750.0 | 9000 | 3.2772 | | 0.1168 | 751.0 | 9012 | 2.7732 | | 0.2409 | 752.0 | 9024 | 3.3763 | | 0.1145 | 753.0 | 9036 | 2.7232 | | 0.1384 | 754.0 | 9048 | 3.5289 | | 0.1326 | 755.0 | 9060 | 3.1250 | | 0.1124 | 756.0 | 9072 | 3.2928 | | 0.1197 | 757.0 | 9084 | 2.7365 | | 0.1359 | 758.0 | 9096 | 2.3043 | | 0.1031 | 759.0 | 9108 | 2.6293 | | 0.1434 | 760.0 | 9120 | 2.7771 | | 0.1009 | 761.0 | 9132 | 2.9574 | | 0.1217 | 762.0 | 9144 | 3.5124 | | 0.1017 | 763.0 | 9156 | 3.5922 | | 0.1236 | 764.0 | 9168 | 2.2188 | | 0.1174 | 765.0 | 9180 | 2.9054 | | 0.1797 | 766.0 | 9192 | 2.5098 | | 0.0971 | 767.0 | 9204 | 2.2203 | | 0.1043 | 768.0 | 9216 | 2.8536 | | 0.1464 | 769.0 | 9228 | 2.6191 | | 0.195 | 770.0 | 9240 | 2.2198 | | 0.1603 | 771.0 | 9252 | 2.8702 | | 0.1514 | 772.0 | 9264 | 2.6832 | | 0.1363 | 773.0 | 9276 | 3.0211 | | 0.1263 | 774.0 | 9288 | 2.4905 | | 0.1048 | 775.0 | 9300 | 3.0469 | | 0.1175 | 776.0 | 9312 | 3.0265 | | 0.1595 | 777.0 | 9324 | 2.1823 | | 0.1243 | 778.0 | 9336 | 2.5649 | | 0.1825 | 779.0 | 9348 | 2.8523 | | 0.1697 | 780.0 | 9360 | 3.3646 | | 0.1228 | 781.0 | 9372 | 2.2108 | | 0.0893 | 782.0 | 9384 | 3.4784 | | 0.1361 | 783.0 | 9396 | 3.4523 | | 0.0953 | 784.0 | 9408 | 2.5469 | | 0.1732 | 785.0 | 9420 | 3.2701 | | 0.113 | 786.0 | 9432 | 3.4206 | | 0.1303 | 787.0 | 9444 | 2.7898 | | 0.2207 | 788.0 | 9456 | 3.4153 | | 0.1762 | 789.0 | 9468 | 3.4267 | | 0.1293 | 790.0 | 9480 | 3.6637 | | 0.0805 | 791.0 | 9492 | 3.1007 | | 0.2172 | 792.0 | 9504 | 2.6548 | | 0.0886 | 793.0 | 9516 | 2.5632 | | 0.2214 | 794.0 | 9528 | 2.8648 | | 0.1454 | 795.0 | 9540 | 2.2529 | | 0.1623 | 796.0 | 9552 | 2.5046 | | 0.1443 | 797.0 | 9564 | 3.6918 | | 0.0777 | 798.0 | 9576 | 2.4575 | | 0.1109 | 799.0 | 9588 | 2.5164 | | 0.1228 | 800.0 | 9600 | 3.0721 | | 0.0774 | 801.0 | 9612 | 3.3021 | | 0.1239 | 802.0 | 9624 | 2.8039 | | 0.1633 | 803.0 | 9636 | 3.9218 | | 0.1562 | 804.0 | 9648 | 2.2741 | | 0.1398 | 805.0 | 9660 | 2.3857 | | 0.0827 | 806.0 | 9672 | 3.8789 | | 0.1041 | 807.0 | 9684 | 3.1660 | | 0.1345 | 808.0 | 9696 | 2.6615 | | 0.0964 | 809.0 | 9708 | 3.8610 | | 0.0705 | 810.0 | 9720 | 2.6085 | | 0.1286 | 811.0 | 9732 | 2.8976 | | 0.1319 | 812.0 | 9744 | 3.0883 | | 0.2169 | 813.0 | 9756 | 3.1248 | | 0.1585 | 814.0 | 9768 | 3.5880 | | 0.1412 | 815.0 | 9780 | 4.2307 | | 0.1665 | 816.0 | 9792 | 2.5049 | | 0.1138 | 817.0 | 9804 | 3.0581 | | 0.1329 | 818.0 | 9816 | 2.6806 | | 0.1029 | 819.0 | 9828 | 2.6299 | | 0.0967 | 820.0 | 9840 | 3.4191 | | 0.1269 | 821.0 | 9852 | 3.8664 | | 0.1122 | 822.0 | 9864 | 2.9701 | | 0.108 | 823.0 | 9876 | 3.2608 | | 0.1038 | 824.0 | 9888 | 2.9620 | | 0.1599 | 825.0 | 9900 | 2.8607 | | 0.2117 | 826.0 | 9912 | 3.1970 | | 0.1121 | 827.0 | 9924 | 3.7504 | | 0.131 | 828.0 | 9936 | 3.8170 | | 0.1627 | 829.0 | 9948 | 3.9556 | | 0.1504 | 830.0 | 9960 | 3.0378 | | 0.1334 | 831.0 | 9972 | 2.9688 | | 0.148 | 832.0 | 9984 | 3.6264 | | 0.0931 | 833.0 | 9996 | 3.1000 | | 0.1124 | 834.0 | 10008 | 2.2768 | | 0.0716 | 835.0 | 10020 | 2.5006 | | 0.1948 | 836.0 | 10032 | 3.6966 | | 0.1199 | 837.0 | 10044 | 2.8248 | | 0.1664 | 838.0 | 10056 | 3.4134 | | 0.1269 | 839.0 | 10068 | 2.6959 | | 0.1033 | 840.0 | 10080 | 3.1595 | | 0.1494 | 841.0 | 10092 | 3.2611 | | 0.1642 | 842.0 | 10104 | 2.7121 | | 0.145 | 843.0 | 10116 | 2.8543 | | 0.0995 | 844.0 | 10128 | 3.2522 | | 0.098 | 845.0 | 10140 | 2.1804 | | 0.1257 | 846.0 | 10152 | 2.6450 | | 0.0715 | 847.0 | 10164 | 2.6534 | | 0.1559 | 848.0 | 10176 | 2.1307 | | 0.1551 | 849.0 | 10188 | 2.5103 | | 0.1052 | 850.0 | 10200 | 3.7062 | | 0.0932 | 851.0 | 10212 | 3.3476 | | 0.0832 | 852.0 | 10224 | 2.4707 | | 0.1666 | 853.0 | 10236 | 3.2024 | | 0.1273 | 854.0 | 10248 | 2.5906 | | 0.163 | 855.0 | 10260 | 3.0574 | | 0.1309 | 856.0 | 10272 | 2.5865 | | 0.2476 | 857.0 | 10284 | 3.3188 | | 0.1191 | 858.0 | 10296 | 2.5695 | | 0.1548 | 859.0 | 10308 | 3.6313 | | 0.1599 | 860.0 | 10320 | 2.8832 | | 0.128 | 861.0 | 10332 | 2.4891 | | 0.1391 | 862.0 | 10344 | 3.1289 | | 0.138 | 863.0 | 10356 | 2.6089 | | 0.0706 | 864.0 | 10368 | 3.0440 | | 0.1128 | 865.0 | 10380 | 3.6210 | | 0.2152 | 866.0 | 10392 | 3.2759 | | 0.2337 | 867.0 | 10404 | 3.1451 | | 0.1473 | 868.0 | 10416 | 3.5721 | | 0.1346 | 869.0 | 10428 | 3.0452 | | 0.1074 | 870.0 | 10440 | 2.7138 | | 0.095 | 871.0 | 10452 | 2.6684 | | 0.0699 | 872.0 | 10464 | 3.2899 | | 0.1326 | 873.0 | 10476 | 3.5183 | | 0.1523 | 874.0 | 10488 | 2.1549 | | 0.1067 | 875.0 | 10500 | 2.3682 | | 0.125 | 876.0 | 10512 | 2.7431 | | 0.1797 | 877.0 | 10524 | 2.5871 | | 0.1442 | 878.0 | 10536 | 3.8328 | | 0.136 | 879.0 | 10548 | 2.3259 | | 0.1459 | 880.0 | 10560 | 2.7320 | | 0.0617 | 881.0 | 10572 | 3.1303 | | 0.1419 | 882.0 | 10584 | 3.2222 | | 0.0673 | 883.0 | 10596 | 2.7638 | | 0.0978 | 884.0 | 10608 | 3.5383 | | 0.0737 | 885.0 | 10620 | 3.8811 | | 0.0948 | 886.0 | 10632 | 3.8811 | | 0.1158 | 887.0 | 10644 | 3.2247 | | 0.1497 | 888.0 | 10656 | 2.5282 | | 0.1488 | 889.0 | 10668 | 3.2183 | | 0.1361 | 890.0 | 10680 | 3.0011 | | 0.1536 | 891.0 | 10692 | 2.8193 | | 0.1509 | 892.0 | 10704 | 3.2418 | | 0.0663 | 893.0 | 10716 | 2.6955 | | 0.0954 | 894.0 | 10728 | 3.6407 | | 0.1257 | 895.0 | 10740 | 3.0466 | | 0.1293 | 896.0 | 10752 | 3.4879 | | 0.1682 | 897.0 | 10764 | 3.0975 | | 0.1427 | 898.0 | 10776 | 2.7423 | | 0.1332 | 899.0 | 10788 | 3.3520 | | 0.1368 | 900.0 | 10800 | 3.1909 | | 0.1633 | 901.0 | 10812 | 3.5312 | | 0.193 | 902.0 | 10824 | 2.9027 | | 0.1169 | 903.0 | 10836 | 3.2119 | | 0.0856 | 904.0 | 10848 | 2.6224 | | 0.1507 | 905.0 | 10860 | 3.4485 | | 0.1663 | 906.0 | 10872 | 3.7079 | | 0.1162 | 907.0 | 10884 | 2.4238 | | 0.1162 | 908.0 | 10896 | 2.7136 | | 0.1181 | 909.0 | 10908 | 3.2237 | | 0.1468 | 910.0 | 10920 | 2.9780 | | 0.0959 | 911.0 | 10932 | 3.1877 | | 0.1162 | 912.0 | 10944 | 2.1530 | | 0.1245 | 913.0 | 10956 | 3.4275 | | 0.1524 | 914.0 | 10968 | 2.9887 | | 0.1487 | 915.0 | 10980 | 3.5492 | | 0.1189 | 916.0 | 10992 | 3.7000 | | 0.1104 | 917.0 | 11004 | 3.1991 | | 0.1339 | 918.0 | 11016 | 3.3229 | | 0.1239 | 919.0 | 11028 | 3.5813 | | 0.1234 | 920.0 | 11040 | 2.6298 | | 0.1115 | 921.0 | 11052 | 3.1678 | | 0.097 | 922.0 | 11064 | 3.5488 | | 0.1599 | 923.0 | 11076 | 2.1364 | | 0.0864 | 924.0 | 11088 | 3.0174 | | 0.2064 | 925.0 | 11100 | 3.3537 | | 0.1389 | 926.0 | 11112 | 3.1944 | | 0.1285 | 927.0 | 11124 | 2.5938 | | 0.099 | 928.0 | 11136 | 2.9489 | | 0.1544 | 929.0 | 11148 | 3.1323 | | 0.0943 | 930.0 | 11160 | 3.0074 | | 0.1343 | 931.0 | 11172 | 3.0724 | | 0.0937 | 932.0 | 11184 | 2.5755 | | 0.0631 | 933.0 | 11196 | 2.4738 | | 0.1373 | 934.0 | 11208 | 2.8831 | | 0.1043 | 935.0 | 11220 | 1.9059 | | 0.0825 | 936.0 | 11232 | 2.8366 | | 0.1619 | 937.0 | 11244 | 2.5491 | | 0.0906 | 938.0 | 11256 | 2.5668 | | 0.0479 | 939.0 | 11268 | 3.0457 | | 0.1427 | 940.0 | 11280 | 4.0130 | | 0.1058 | 941.0 | 11292 | 3.5801 | | 0.1359 | 942.0 | 11304 | 2.2584 | | 0.1117 | 943.0 | 11316 | 2.6767 | | 0.1341 | 944.0 | 11328 | 3.2212 | | 0.1866 | 945.0 | 11340 | 2.9726 | | 0.1355 | 946.0 | 11352 | 3.1199 | | 0.143 | 947.0 | 11364 | 2.7948 | | 0.237 | 948.0 | 11376 | 3.2464 | | 0.1206 | 949.0 | 11388 | 3.4582 | | 0.2615 | 950.0 | 11400 | 2.1646 | | 0.1631 | 951.0 | 11412 | 2.5108 | | 0.158 | 952.0 | 11424 | 3.4831 | | 0.1103 | 953.0 | 11436 | 2.3143 | | 0.1942 | 954.0 | 11448 | 2.8638 | | 0.1049 | 955.0 | 11460 | 3.3910 | | 0.1635 | 956.0 | 11472 | 3.4069 | | 0.0989 | 957.0 | 11484 | 2.7670 | | 0.071 | 958.0 | 11496 | 3.6908 | | 0.1326 | 959.0 | 11508 | 3.0617 | | 0.1352 | 960.0 | 11520 | 2.4996 | | 0.1155 | 961.0 | 11532 | 2.3456 | | 0.1407 | 962.0 | 11544 | 3.1657 | | 0.1622 | 963.0 | 11556 | 3.2390 | | 0.0628 | 964.0 | 11568 | 2.4668 | | 0.1201 | 965.0 | 11580 | 2.8448 | | 0.1387 | 966.0 | 11592 | 2.9089 | | 0.1103 | 967.0 | 11604 | 2.8493 | | 0.0735 | 968.0 | 11616 | 2.5433 | | 0.093 | 969.0 | 11628 | 3.0329 | | 0.3551 | 970.0 | 11640 | 3.3447 | | 0.1849 | 971.0 | 11652 | 4.2088 | | 0.1257 | 972.0 | 11664 | 3.1439 | | 0.0764 | 973.0 | 11676 | 3.4356 | | 0.1678 | 974.0 | 11688 | 3.1160 | | 0.1093 | 975.0 | 11700 | 2.7974 | | 0.0811 | 976.0 | 11712 | 2.6031 | | 0.0878 | 977.0 | 11724 | 2.6731 | | 0.1478 | 978.0 | 11736 | 2.5262 | | 0.0933 | 979.0 | 11748 | 2.9120 | | 0.0846 | 980.0 | 11760 | 3.2794 | | 0.1063 | 981.0 | 11772 | 2.9906 | | 0.0907 | 982.0 | 11784 | 2.6891 | | 0.1747 | 983.0 | 11796 | 3.6264 | | 0.1611 | 984.0 | 11808 | 3.2517 | | 0.1171 | 985.0 | 11820 | 2.6785 | | 0.1323 | 986.0 | 11832 | 3.4850 | | 0.0758 | 987.0 | 11844 | 3.6252 | | 0.0713 | 988.0 | 11856 | 3.2538 | | 0.0594 | 989.0 | 11868 | 2.5900 | | 0.1958 | 990.0 | 11880 | 2.4104 | | 0.1328 | 991.0 | 11892 | 3.8045 | | 0.1006 | 992.0 | 11904 | 3.5627 | | 0.0969 | 993.0 | 11916 | 2.5848 | | 0.1363 | 994.0 | 11928 | 2.8333 | | 0.1455 | 995.0 | 11940 | 2.3381 | | 0.0774 | 996.0 | 11952 | 2.6104 | | 0.1001 | 997.0 | 11964 | 3.5031 | | 0.0956 | 998.0 | 11976 | 2.7140 | | 0.1094 | 999.0 | 11988 | 3.1090 | | 0.1129 | 1000.0 | 12000 | 2.6911 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
RajkNakka/ppo-LunarLander-v2-unit-8
RajkNakka
2023-07-08T20:48:52Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-07-08T18:55:27Z
--- 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: 7.16 +/- 73.94 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
Huggingfly/ppo-PyramidsTraining
Huggingfly
2023-07-08T20:45:21Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-07-08T20:45:16Z
--- 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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Huggingfly/ppo-PyramidsTraining 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
skrl/IsaacGymEnvs-Ingenuity-PPO
skrl
2023-07-08T20:24:38Z
0
0
skrl
[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-24T20:44:57Z
--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 7162.47 +/- 555.5 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-Ingenuity type: IsaacGymEnvs-Ingenuity --- <!-- --- torch: 7018.19 +/- 508.68 jax: 7041.64 +/- 297.51 numpy: 7162.47 +/- 555.5 --- --> # IsaacGymEnvs-Ingenuity-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** Ingenuity - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-Ingenuity-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-Ingenuity-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 16 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 4 # 16 * 4096 / 16384 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-3 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.016} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01 cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
mlabonne/gpt2-GPTQ-4bit
mlabonne
2023-07-08T20:09:26Z
18
0
transformers
[ "transformers", "gpt2", "text-generation", "AutoGPTQ", "4bit", "GPTQ", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-06-11T17:30:12Z
--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - AutoGPTQ - 4bit - GPTQ --- Model created using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) on a [GPT-2](https://huggingface.co/gpt2) model with 4-bit quantization. You can load this model with the AutoGPTQ library, installed with the following command: ``` pip install auto-gptq ``` You can then download the model from the hub using the following code: ```python from transformers import AutoModelForCausalLM, AutoTokenizer from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig model_name = "mlabonne/gpt2-GPTQ-4bit" tokenizer = AutoTokenizer.from_pretrained(model_name) quantize_config = BaseQuantizeConfig.from_pretrained(model_name) model = AutoGPTQForCausalLM.from_quantized(model_name, model_basename="gptq_model-4bit-128g", device="cuda:0", use_triton=True, use_safetensors=True, quantize_config=quantize_config) ``` This model works with the traditional [Text Generation pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.TextGenerationPipeline). Example of generation with the input text "I have a dream": ``` I have a dream. I want someone with my face, and what I have. I want to go home. I want to be alive. I want to see my children. I dream if I have the spirit, my body, my voice, ```
Word2vec/wikipedia2vec_enwiki_20180420_300d
Word2vec
2023-07-08T20:03:03Z
0
0
null
[ "word2vec", "en", "dataset:wikipedia", "license:apache-2.0", "region:us" ]
null
2023-05-17T15:26:11Z
--- license: apache-2.0 tags: - word2vec datasets: - wikipedia language: - en --- ## Information Pretrained Word2vec in English. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/). ## How to use? ``` from gensim.models import KeyedVectors from huggingface_hub import hf_hub_download model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_enwiki_20180420_300d", filename="enwiki_20180420_300d.txt")) model.most_similar("your_word") ``` ## Citation ``` @inproceedings{yamada2020wikipedia2vec, title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia", author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, year = {2020}, publisher = {Association for Computational Linguistics}, pages = {23--30} } ```
fobt/speecht5_finetuned_voxpopuli_nl
fobt
2023-07-08T19:59:00Z
75
0
transformers
[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]
text-to-audio
2023-07-08T17:41:08Z
--- license: mit tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4585 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5237 | 4.3 | 1000 | 0.4782 | | 0.4946 | 8.61 | 2000 | 0.4639 | | 0.493 | 12.91 | 3000 | 0.4608 | | 0.4903 | 17.21 | 4000 | 0.4585 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
snousias/bert-base-greek-uncased-v1-finetuned-polylex
snousias
2023-07-08T19:50:38Z
119
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-08T19:48:32Z
--- tags: - generated_from_trainer model-index: - name: bert-base-greek-uncased-v1-finetuned-polylex 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-greek-uncased-v1-finetuned-polylex This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1624 ## Model description More information needed ## Intended uses & 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1637 | 1.0 | 12 | 2.6649 | | 3.0581 | 2.0 | 24 | 2.5475 | | 2.648 | 3.0 | 36 | 2.1624 | | 2.5983 | 4.0 | 48 | 2.3285 | | 2.7524 | 5.0 | 60 | 2.5745 | | 2.4923 | 6.0 | 72 | 2.8096 | | 2.5336 | 7.0 | 84 | 2.9470 | | 2.3271 | 8.0 | 96 | 2.5497 | | 2.4018 | 9.0 | 108 | 2.3413 | | 2.544 | 10.0 | 120 | 2.4170 | | 1.9144 | 11.0 | 132 | 2.5254 | | 2.0996 | 12.0 | 144 | 2.4147 | | 1.8733 | 13.0 | 156 | 2.5462 | | 1.8261 | 14.0 | 168 | 2.2045 | | 2.0033 | 15.0 | 180 | 1.9549 | | 1.9967 | 16.0 | 192 | 2.1614 | | 1.8515 | 17.0 | 204 | 2.8167 | | 1.8583 | 18.0 | 216 | 2.8441 | | 1.7512 | 19.0 | 228 | 2.4536 | | 1.5746 | 20.0 | 240 | 2.6204 | | 1.5267 | 21.0 | 252 | 2.9290 | | 1.7248 | 22.0 | 264 | 2.0433 | | 1.5692 | 23.0 | 276 | 2.4710 | | 1.6093 | 24.0 | 288 | 2.4340 | | 1.619 | 25.0 | 300 | 2.2689 | | 1.4406 | 26.0 | 312 | 3.6729 | | 1.5452 | 27.0 | 324 | 3.2225 | | 1.4575 | 28.0 | 336 | 1.8853 | | 1.5534 | 29.0 | 348 | 2.2135 | | 1.4872 | 30.0 | 360 | 2.7540 | | 1.3923 | 31.0 | 372 | 2.2408 | | 1.3682 | 32.0 | 384 | 2.5181 | | 1.2623 | 33.0 | 396 | 2.1360 | | 1.1888 | 34.0 | 408 | 2.3912 | | 1.3427 | 35.0 | 420 | 2.4600 | | 1.1969 | 36.0 | 432 | 2.6388 | | 1.3367 | 37.0 | 444 | 2.5489 | | 1.226 | 38.0 | 456 | 1.5805 | | 1.1808 | 39.0 | 468 | 2.7466 | | 1.1694 | 40.0 | 480 | 2.4887 | | 1.2736 | 41.0 | 492 | 2.5735 | | 1.2292 | 42.0 | 504 | 2.2357 | | 1.2556 | 43.0 | 516 | 2.9244 | | 1.0155 | 44.0 | 528 | 1.8348 | | 1.2425 | 45.0 | 540 | 2.4494 | | 1.2665 | 46.0 | 552 | 2.4866 | | 1.3439 | 47.0 | 564 | 2.3430 | | 1.4468 | 48.0 | 576 | 1.7801 | | 1.1772 | 49.0 | 588 | 2.5785 | | 1.0618 | 50.0 | 600 | 2.9959 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
camus-ng/dreambooth_lora_cory_v15_ten
camus-ng
2023-07-08T19:43:42Z
1
0
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-07-08T16:25:04Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of <ntvc> man tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - camus-ng/dreambooth_lora_cory_v15_ten These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of <ntvc> man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: True.
snousias/bert-base-greek-uncased-v1-finetuned-imdb
snousias
2023-07-08T19:38:31Z
114
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-08T18:56:06Z
--- tags: - generated_from_trainer model-index: - name: bert-base-greek-uncased-v1-finetuned-imdb 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-greek-uncased-v1-finetuned-imdb This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.0877 | 1.0 | 45 | 2.9871 | | 1.2665 | 2.0 | 90 | 2.9228 | | 1.9122 | 3.0 | 135 | 3.1228 | | 2.2564 | 4.0 | 180 | 1.6066 | | 1.9132 | 5.0 | 225 | 2.6351 | | 1.9952 | 6.0 | 270 | 2.2649 | | 1.7895 | 7.0 | 315 | 2.3376 | | 2.0415 | 8.0 | 360 | 1.9894 | | 1.8113 | 9.0 | 405 | 2.2998 | | 1.6944 | 10.0 | 450 | 2.1420 | | 1.7862 | 11.0 | 495 | 2.7167 | | 1.5657 | 12.0 | 540 | 2.5103 | | 1.4576 | 13.0 | 585 | 2.0238 | | 1.3369 | 14.0 | 630 | 2.5880 | | 1.3598 | 15.0 | 675 | 1.8161 | | 1.3407 | 16.0 | 720 | 2.4031 | | 1.3805 | 17.0 | 765 | 2.2539 | | 1.176 | 18.0 | 810 | 3.2901 | | 1.1152 | 19.0 | 855 | 2.3024 | | 1.0629 | 20.0 | 900 | 2.0823 | | 1.1972 | 21.0 | 945 | 2.9957 | | 1.1317 | 22.0 | 990 | 2.5360 | | 1.0396 | 23.0 | 1035 | 1.6268 | | 0.8686 | 24.0 | 1080 | 3.2657 | | 1.0526 | 25.0 | 1125 | 3.0398 | | 0.9023 | 26.0 | 1170 | 2.8197 | | 0.9539 | 27.0 | 1215 | 3.1922 | | 0.8699 | 28.0 | 1260 | 1.6943 | | 0.8669 | 29.0 | 1305 | 2.7801 | | 0.7893 | 30.0 | 1350 | 2.1385 | | 0.7462 | 31.0 | 1395 | 2.2881 | | 0.7627 | 32.0 | 1440 | 3.0789 | | 0.7536 | 33.0 | 1485 | 2.9320 | | 0.8317 | 34.0 | 1530 | 3.4081 | | 0.6749 | 35.0 | 1575 | 2.7531 | | 0.789 | 36.0 | 1620 | 2.9154 | | 0.6609 | 37.0 | 1665 | 2.1821 | | 0.6795 | 38.0 | 1710 | 2.5330 | | 0.6408 | 39.0 | 1755 | 3.4374 | | 0.6827 | 40.0 | 1800 | 2.3127 | | 0.6188 | 41.0 | 1845 | 2.0818 | | 0.6085 | 42.0 | 1890 | 2.2737 | | 0.6978 | 43.0 | 1935 | 2.9629 | | 0.6164 | 44.0 | 1980 | 2.5250 | | 0.6273 | 45.0 | 2025 | 2.3866 | | 0.7064 | 46.0 | 2070 | 2.0937 | | 0.6561 | 47.0 | 2115 | 2.4984 | | 0.7341 | 48.0 | 2160 | 3.1911 | | 0.6271 | 49.0 | 2205 | 2.2692 | | 0.6757 | 50.0 | 2250 | 2.2642 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Word2vec/wikipedia2vec_enwiki_20180420_nolg_500d
Word2vec
2023-07-08T19:22:12Z
0
0
null
[ "word2vec", "en", "dataset:wikipedia", "license:apache-2.0", "region:us" ]
null
2023-05-17T15:07:46Z
--- license: apache-2.0 tags: - word2vec datasets: - wikipedia language: - en --- ## Information Pretrained Word2vec in English. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/). ## How to use? ``` from gensim.models import KeyedVectors from huggingface_hub import hf_hub_download model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_enwiki_20180420_nolg_500d", filename="enwiki_20180420_nolg_500d.txt")) model.most_similar("your_word") ``` ## Citation ``` @inproceedings{yamada2020wikipedia2vec, title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia", author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, year = {2020}, publisher = {Association for Computational Linguistics}, pages = {23--30} } ```
ahmadalsharef994/bert-base-banking77-pt2
ahmadalsharef994
2023-07-08T19:14:05Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:banking77", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-07-08T18:19:28Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - banking77 metrics: - f1 model-index: - name: bert-base-banking77-pt2 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 config: default split: test args: default metrics: - name: F1 type: f1 value: 0.9298273146197705 --- <!-- 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-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3045 - F1: 0.9298 ## Model description More information needed ## Intended uses & 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1191 | 1.0 | 626 | 0.7800 | 0.8702 | | 0.3899 | 2.0 | 1252 | 0.3662 | 0.9204 | | 0.1916 | 3.0 | 1878 | 0.3045 | 0.9298 | ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.1+cu118 - Datasets 2.9.0 - Tokenizers 0.13.3
Word2vec/wikipedia2vec_enwiki_20180420_100d
Word2vec
2023-07-08T19:12:30Z
0
0
null
[ "word2vec", "en", "dataset:wikipedia", "license:apache-2.0", "region:us" ]
null
2023-05-16T17:00:06Z
--- license: apache-2.0 tags: - word2vec datasets: - wikipedia language: - en --- ## Information Pretrained Word2vec in English. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/). ## How to use? ``` from gensim.models import KeyedVectors from huggingface_hub import hf_hub_download model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_enwiki_20180420_100d", filename="enwiki_20180420_100d.txt")) model.most_similar("your_word") ``` ## Citation ``` @inproceedings{yamada2020wikipedia2vec, title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia", author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, year = {2020}, publisher = {Association for Computational Linguistics}, pages = {23--30} } ```
visual-openllm/visual-openllm-chatglm-6b-rola
visual-openllm
2023-07-08T19:07:58Z
0
8
null
[ "dataset:tatsu-lab/alpaca", "dataset:shibing624/alpaca-zh", "license:apache-2.0", "region:us" ]
null
2023-03-26T07:49:58Z
--- license: apache-2.0 datasets: - tatsu-lab/alpaca - shibing624/alpaca-zh --- - Loda LLM ```python from modeling_chatglm import ChatGLMForConditionalGeneration import torch torch.set_default_tensor_type(torch.cuda.HalfTensor) model = ChatGLMForConditionalGeneration.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, device_map='auto') ``` - Load LoRA ```python from peft import PeftModel model = PeftModel.from_pretrained(model, "visual-openllm/visual-openllm-chatglm-6b-rola") torch.set_default_tensor_type(torch.cuda.FloatTensor) ```
wizofavalon/bert-large-uncased-finetuned-wikitext2
wizofavalon
2023-07-08T19:07:01Z
70
0
transformers
[ "transformers", "tf", "tensorboard", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-05T19:20:17Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: wizofavalon/bert-large-uncased-finetuned-wikitext2 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. --> # wizofavalon/bert-large-uncased-finetuned-wikitext2 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7861 - Validation Loss: 1.5868 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.7861 | 1.5868 | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
Word2vec/wikipedia2vec_enwiki_20180420_nolg_300d
Word2vec
2023-07-08T19:06:31Z
0
0
null
[ "word2vec", "en", "dataset:wikipedia", "license:apache-2.0", "region:us" ]
null
2023-05-17T13:48:27Z
--- license: apache-2.0 tags: - word2vec datasets: - wikipedia language: - en --- ## Information Pretrained Word2vec in English. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/). ## How to use? ``` from gensim.models import KeyedVectors from huggingface_hub import hf_hub_download model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_enwiki_20180420_nolg_300d", filename="enwiki_20180420_nolg_300d.txt")) model.most_similar("your_word") ``` ## Citation ``` @inproceedings{yamada2020wikipedia2vec, title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia", author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, year = {2020}, publisher = {Association for Computational Linguistics}, pages = {23--30} } ```
tyavika/Distil-CNN512LSTM256NoBi
tyavika
2023-07-08T18:48:27Z
84
0
transformers
[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-07-02T11:03:22Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Distil-CNN512LSTM256NoBi 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. --> # Distil-CNN512LSTM256NoBi This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3388 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6009 | 1.0 | 3290 | 1.2927 | | 1.0288 | 2.0 | 6580 | 1.1467 | | 0.7497 | 3.0 | 9870 | 1.1902 | | 0.5288 | 4.0 | 13160 | 1.3388 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
cagarraz/Reinforce-1234
cagarraz
2023-07-08T18:41:26Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-06-28T16:38:24Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1234 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 34.70 +/- 15.01 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
spitfire4794/photo
spitfire4794
2023-07-08T18:40:04Z
287
8
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "photorealistic", "photoreal", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-06-04T18:28:38Z
--- language: - en license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - photorealistic - photoreal - diffusers inference: true pipeline_tag: text-to-image library_name: diffusers --- # the original but with inference api enabled because why not # Dreamlike Photoreal 2.0 is a photorealistic model based on Stable Diffusion 1.5, made by [dreamlike.art](https://dreamlike.art/). # If you want to use dreamlike models on your website/app/etc., check the license at the bottom first! Warning: This model is horny! Add "nude, naked" to the negative prompt if want to avoid NSFW. You can add **photo** to your prompt to make your gens look more photorealistic. Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a vertical aspect ratio. If you want a landscape photo, try using a horizontal aspect ratio. This model was trained on 768x768px images, so use 768x768px, 640x896px, 896x640px, etc. It also works pretty good with higher resolutions such as 768x1024px or 1024x768px. ### Examples <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview1.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview2.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview3.jpg" style="max-width: 800px;" width="100%"/> ### dreamlike.art You can use this model for free on [dreamlike.art](https://dreamlike.art/)! <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/> ### CKPT [Download dreamlike-photoreal-2.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.ckpt) ### Safetensors [Download dreamlike-photoreal-2.0.safetensors (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.safetensors) ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "dreamlike-art/dreamlike-photoreal-2.0" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens" image = pipe(prompt).images[0] image.save("./result.jpg") ``` <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/church.jpg" style="max-width: 640px;" width="100%"/> # License This model is licesed under a **modified** CreativeML OpenRAIL-M license. - **You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at [email protected]** - **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Photoreal 2.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0)** - **You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less** - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md
NERO500/q-FrozenLake-v1-4x4-noSlippery
NERO500
2023-07-08T18:39:12Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-07-08T18:39:09Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="NERO500/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Word2vec/wikipedia2vec_arwiki_20180420_300d
Word2vec
2023-07-08T18:34:15Z
0
0
null
[ "word2vec", "ar", "dataset:wikipedia", "license:apache-2.0", "region:us" ]
null
2023-05-17T09:33:09Z
--- license: apache-2.0 tags: - word2vec datasets: - wikipedia language: - ar --- ## Information Pretrained Word2vec in Arabic. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/). ## How to use? ``` from gensim.models import KeyedVectors from huggingface_hub import hf_hub_download model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_arwiki_20180420_300d", filename="arwiki_20180420_300d.txt")) model.most_similar("your_word") ``` ## Citation ``` @inproceedings{yamada2020wikipedia2vec, title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia", author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, year = {2020}, publisher = {Association for Computational Linguistics}, pages = {23--30} } ```