Datasets:
librarian-bots
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model_cards_with_metadata

Dataset card Data Studio Files
xet
Community
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2020-02-15 11:33:14
2025-09-13 00:37:47
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alea31415/yumechi-akiba-maid-sensou
alea31415
2022-12-12T00:14:41Z
0
3
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2022-12-08T21:55:34Z
--- license: creativeml-openrail-m --- Prompt with **"yumechi"** It is also possible to get **"nagomi"** though it is not working very well **Training details:** - Trained with [TheLastBen's fast-DreamBooth notebook](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) - data set: 76 concept images + around the same number of custmized reg images - learning rate 1.5e-6 or 2e-6 for 5000 steps - text encoder rate 15% **Problems:** It turns out this model has a bunch of problems and is quite hard to use. 1. On one hand, it feels like the model is undertrained as it often fails to capture the key characteristics of the characters, and in particular often has problem with hairstyles. 2. On the other hand, it feels like the model is overtrained as we have little flexibility on the style and the background. It tends to throw just red or pink single color background. 3. Overall, when we try to alter the background or other things, the character gets altered as well. This is clearly due to the bias of the dataset. A more diverse dataset will be needed to improve the model. **Example generations:** ![1966680319-yumechi](https://huggingface.co/alea31415/yumechi-akiba-maid-sensou/resolve/main/1966680319-yumechi.png) ![4238311060-3350512545-yumechi](https://huggingface.co/alea31415/yumechi-akiba-maid-sensou/resolve/main/4238311060-3350512545-yumechi.png) ![4238311061-4154525082-yumechi](https://huggingface.co/alea31415/yumechi-akiba-maid-sensou/resolve/main/4238311061-4154525082-yumechi.png) ![4238311069-2598248771-(yumechi)](https://huggingface.co/alea31415/yumechi-akiba-maid-sensou/resolve/main/4238311069-2598248771-(yumechi).png) ![4238311072-1587927987-(nagomi)](https://huggingface.co/alea31415/yumechi-akiba-maid-sensou/resolve/main/4238311072-1587927987-(nagomi).png) ![4238311074-934538101-(nagomi)](https://huggingface.co/alea31415/yumechi-akiba-maid-sensou/resolve/main/4238311074-934538101-(nagomi).png)
jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-8_sixties-2_s103
jonatasgrosman
2022-12-11T23:34:23Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T23:33:50Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_xls-r_age_teens-8_sixties-2_s103 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
ConstantineK/huh
ConstantineK
2022-12-11T23:29:55Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T00:23:29Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: x86 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 293.85 +/- 16.47 name: mean_reward verified: false --- # **x86** Agent playing **LunarLander-v2** This is a trained model of a **x86** 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 ... ```
ecemisildar/ppo-LunarLander-v2
ecemisildar
2022-12-11T23:27:06Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T23:26:34Z
--- 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: 252.82 +/- 25.62 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 ... ```
Permanganat/ppo-LunarLander-v2
Permanganat
2022-12-11T23:27:01Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T23:26:32Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.50 +/- 19.61 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 ... ```
jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s717
jonatasgrosman
2022-12-11T23:21:16Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T23:20:08Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s717 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
Pech82/ppo-LunarLander-v2
Pech82
2022-12-11T23:00:48Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T23:00:22Z
--- 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: 256.41 +/- 26.76 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 ... ```
jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s246
jonatasgrosman
2022-12-11T22:55:05Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T22:54:22Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s246 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-10_sixties-0_s807
jonatasgrosman
2022-12-11T22:50:52Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T22:50:38Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_xls-r_age_teens-10_sixties-0_s807 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-10_sixties-0_s364
jonatasgrosman
2022-12-11T22:43:39Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T22:43:22Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_xls-r_age_teens-10_sixties-0_s364 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-0_sixties-10_s847
jonatasgrosman
2022-12-11T22:40:17Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T22:40:00Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_xls-r_age_teens-0_sixties-10_s847 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
amitkayal/whisper-small-hi
amitkayal
2022-12-11T22:30:57Z
6
0
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-07T10:53:02Z
--- language: - hi license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-small-hi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: hi metrics: - name: Wer type: wer value: 21.749971340135275 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5191 - Wer: 21.7500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0055 | 7.02 | 1000 | 0.4214 | 22.0939 | | 0.0003 | 14.03 | 2000 | 0.4978 | 21.7070 | | 0.0002 | 22.0 | 3000 | 0.5191 | 21.7500 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.10.0 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
bjarlestam/ppo-LunarLander-v2
bjarlestam
2022-12-11T22:22:46Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T22:22:25Z
--- 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: 261.61 +/- 38.77 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 ... ```
alx-ai/noggles-v21-6400-best
alx-ai
2022-12-11T22:16:31Z
2
1
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2022-12-11T22:02:07Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### noggles_v21_6400_BEST Dreambooth model trained by alxdfy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/alxdfy/noggles-v21-6400-best/resolve/main/sample_images/vitalik.png)
sanchit-gandhi/whisper-small-tt-1k-steps
sanchit-gandhi
2022-12-11T22:15:42Z
7
1
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "tt", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:09:36Z
--- language: - tt license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Tatar results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 tt type: mozilla-foundation/common_voice_11_0 config: tt split: test args: tt metrics: - name: Wer type: wer value: 33.104473386183464 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Tatar This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 tt dataset. It achieves the following results on the evaluation set: - Loss: 0.4091 - Wer: 33.1045 ## Model description More information needed ## 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0212 | 5.04 | 1000 | 0.4091 | 33.1045 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221210+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
Arch4ngel/ppo-LunarLander-v3
Arch4ngel
2022-12-11T22:02:17Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T20:38:34Z
--- 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: 275.85 +/- 20.59 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 ... ```
alea31415/ryza-atelier-ryza
alea31415
2022-12-11T21:52:16Z
0
2
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2022-10-02T15:20:59Z
--- license: creativeml-openrail-m --- Prompt with **"rizaDB anime girl"** **Training details (as far as I remember):** - Trained with [JoePenna Dreambooth repository](https://github.com/JoePenna/Dreambooth-Stable-Diffusion) - data set: 54 concept images + a number of custom reg images - default learning rate for 4500 steps **Example generations:** ![00037-2764761054-rizaDB](https://huggingface.co/alea31415/ryza-atelier-ryza/resolve/main/00037-2764761054-rizaDB.png) ![00061-3019598551-rizaDB](https://huggingface.co/alea31415/ryza-atelier-ryza/resolve/main/00061-3019598551-rizaDB.png) ![00226-1649286225-HQ](https://huggingface.co/alea31415/ryza-atelier-ryza/resolve/main/00226-1649286225-HQ.png) ![00231-1843099238-HQ](https://huggingface.co/alea31415/ryza-atelier-ryza/resolve/main/00231-1843099238-HQ.png) ![00244-3595448462-HQ](https://huggingface.co/alea31415/ryza-atelier-ryza/resolve/main/00244-3595448462-HQ.png)
RamonAnkersmit/ppo-Huggy
RamonAnkersmit
2022-12-11T21:38:12Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-11T21:25:31Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: RamonAnkersmit/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Jairnetojp/ppo-Huggy
Jairnetojp
2022-12-11T21:32:26Z
6
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-11T21:32:20Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: Jairnetojp/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
redevaaa/fin5
redevaaa
2022-12-11T21:27:59Z
15
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:fin", "license:cc-by-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-11T21:11:49Z
--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - fin metrics: - precision - recall - f1 - accuracy model-index: - name: fin5 results: - task: name: Token Classification type: token-classification dataset: name: fin type: fin config: default split: train args: default metrics: - name: Precision type: precision value: 0.9243027888446215 - name: Recall type: recall value: 0.9243027888446215 - name: F1 type: f1 value: 0.9243027888446215 - name: Accuracy type: accuracy value: 0.9908666100254885 --- <!-- 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. --> # fin5 This model is a fine-tuned version of [nlpaueb/sec-bert-shape](https://huggingface.co/nlpaueb/sec-bert-shape) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0752 - Precision: 0.9243 - Recall: 0.9243 - F1: 0.9243 - Accuracy: 0.9909 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 129 | 0.0825 | 0.8327 | 0.8924 | 0.8615 | 0.9811 | | No log | 2.0 | 258 | 0.0633 | 0.8593 | 0.9243 | 0.8906 | 0.9866 | | No log | 3.0 | 387 | 0.0586 | 0.9038 | 0.9363 | 0.9198 | 0.9894 | | 0.0547 | 4.0 | 516 | 0.0607 | 0.9357 | 0.9283 | 0.932 | 0.9911 | | 0.0547 | 5.0 | 645 | 0.0656 | 0.9216 | 0.9363 | 0.9289 | 0.9904 | | 0.0547 | 6.0 | 774 | 0.0692 | 0.9249 | 0.9323 | 0.9286 | 0.9909 | | 0.0547 | 7.0 | 903 | 0.0716 | 0.9246 | 0.9283 | 0.9264 | 0.9904 | | 0.0019 | 8.0 | 1032 | 0.0742 | 0.9213 | 0.9323 | 0.9267 | 0.9909 | | 0.0019 | 9.0 | 1161 | 0.0748 | 0.9246 | 0.9283 | 0.9264 | 0.9909 | | 0.0019 | 10.0 | 1290 | 0.0752 | 0.9243 | 0.9243 | 0.9243 | 0.9909 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s945
jonatasgrosman
2022-12-11T20:52:38Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:52:27Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s945 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s338
jonatasgrosman
2022-12-11T20:49:57Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:49:46Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s338 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
redevaaa/fin4
redevaaa
2022-12-11T20:48:07Z
13
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:fin", "license:cc-by-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-11T20:32:23Z
--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - fin metrics: - precision - recall - f1 - accuracy model-index: - name: fin4 results: - task: name: Token Classification type: token-classification dataset: name: fin type: fin config: default split: train args: default metrics: - name: Precision type: precision value: 0.9209486166007905 - name: Recall type: recall value: 0.9282868525896414 - name: F1 type: f1 value: 0.9246031746031745 - name: Accuracy type: accuracy value: 0.9913080347678609 --- <!-- 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. --> # fin4 This model is a fine-tuned version of [nlpaueb/sec-bert-num](https://huggingface.co/nlpaueb/sec-bert-num) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0549 - Precision: 0.9209 - Recall: 0.9283 - F1: 0.9246 - Accuracy: 0.9913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 129 | 0.1041 | 0.8242 | 0.8406 | 0.8323 | 0.9788 | | No log | 2.0 | 258 | 0.0511 | 0.9173 | 0.9283 | 0.9228 | 0.9902 | | No log | 3.0 | 387 | 0.0430 | 0.9102 | 0.9283 | 0.9191 | 0.9907 | | 0.0598 | 4.0 | 516 | 0.0501 | 0.9368 | 0.9442 | 0.9405 | 0.9922 | | 0.0598 | 5.0 | 645 | 0.0436 | 0.9325 | 0.9363 | 0.9344 | 0.9924 | | 0.0598 | 6.0 | 774 | 0.0489 | 0.9433 | 0.9283 | 0.9357 | 0.9917 | | 0.0598 | 7.0 | 903 | 0.0499 | 0.932 | 0.9283 | 0.9301 | 0.9919 | | 0.0028 | 8.0 | 1032 | 0.0537 | 0.9209 | 0.9283 | 0.9246 | 0.9913 | | 0.0028 | 9.0 | 1161 | 0.0540 | 0.9170 | 0.9243 | 0.9206 | 0.9911 | | 0.0028 | 10.0 | 1290 | 0.0549 | 0.9209 | 0.9283 | 0.9246 | 0.9913 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
alexisamachine/lunarLanderBaseline3
alexisamachine
2022-12-11T20:31:28Z
2
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T20:29:35Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.86 +/- 18.90 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 ... ```
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-10_sixties-0_s460
jonatasgrosman
2022-12-11T20:30:32Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:30:20Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-10_sixties-0_s460 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-10_sixties-0_s380
jonatasgrosman
2022-12-11T20:27:38Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:27:27Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-10_sixties-0_s380 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-0_sixties-10_s288
jonatasgrosman
2022-12-11T20:21:25Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:21:14Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-0_sixties-10_s288 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-0_sixties-10_s113
jonatasgrosman
2022-12-11T20:17:34Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:17:23Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-0_sixties-10_s113 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-5_sixties-5_s938
jonatasgrosman
2022-12-11T20:14:03Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:13:52Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-5_sixties-5_s938 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
sanchit-gandhi/whisper-small-de-1k-steps
sanchit-gandhi
2022-12-11T20:09:28Z
12
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "de", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T16:43:34Z
--- language: - de license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small German results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 de type: mozilla-foundation/common_voice_11_0 config: de split: test args: de metrics: - name: Wer type: wer value: 12.519375143207581 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small German This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 de dataset. It achieves the following results on the evaluation set: - Loss: 0.2490 - Wer: 12.5194 ## Model description More information needed ## 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2189 | 1.0 | 1000 | 0.2490 | 12.5194 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221210+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-5_sixties-5_s200
jonatasgrosman
2022-12-11T20:08:22Z
5
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T20:08:11Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_xls-r_age_teens-5_sixties-5_s200 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
rbat/ppo-LunarLander
rbat
2022-12-11T20:03:52Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T20:03:32Z
--- 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: -568.60 +/- 154.25 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Moussmous/ppo-Huggy
Moussmous
2022-12-11T20:00:35Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-11T20:00:29Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: Moussmous/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
claterza/ppo-Huggy-v1
claterza
2022-12-11T19:48:19Z
3
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-11T19:48:11Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: claterza/ppo-Huggy-v1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Wtesqie/lunarFirst
Wtesqie
2022-12-11T19:35:47Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T19:19:34Z
--- 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: -159.71 +/- 33.02 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 ... ```
jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s607
jonatasgrosman
2022-12-11T19:21:49Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T19:21:30Z
--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s607 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s42
jonatasgrosman
2022-12-11T19:18:56Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T19:18:45Z
--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s42 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s132
jonatasgrosman
2022-12-11T19:16:28Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T19:16:17Z
--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_fr_vp-100k_age_teens-8_sixties-2_s132 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
alea31415/hutari-bocchi-the-rock
alea31415
2022-12-11T19:13:57Z
0
5
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2022-12-11T19:03:09Z
--- license: creativeml-openrail-m --- Prompt with **"hutari"** **Training details:** - Trained with [TheLastBen's fast-DreamBooth notebook](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) - data set: around 20 concept images + around 50 custmized reg images, the concept images are then duplicated to balance the two - learning rate 2e-6 for 5000 steps - text encoder rate 15% **Example generations:** ![3712879356-hutari](https://huggingface.co/alea31415/hutari-bocchi-the-rock/resolve/main/3712879356-hutari.png) ![170473768-beautiful](https://huggingface.co/alea31415/hutari-bocchi-the-rock/resolve/main/170473768-beautiful.png) ![4077722094-hutari](https://huggingface.co/alea31415/hutari-bocchi-the-rock/resolve/main/4077722094-hutari.png) ![925299796-beautiful](https://huggingface.co/alea31415/hutari-bocchi-the-rock/resolve/main/925299796-beautiful.png) ![3820948984-beautiful](https://huggingface.co/alea31415/hutari-bocchi-the-rock/resolve/main/3820948984-beautiful.png)
jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-2_sixties-8_s869
jonatasgrosman
2022-12-11T19:13:37Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T19:13:26Z
--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_fr_vp-100k_age_teens-2_sixties-8_s869 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
hdty/davlan-multilingual
hdty
2022-12-11T19:11:11Z
13
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-11T15:58:51Z
--- tags: - generated_from_trainer model-index: - name: davlan-multilingual 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. --> # davlan-multilingual This model is a fine-tuned version of [Davlan/bert-base-multilingual-cased-ner-hrl](https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0990 - Overall Precision: 0.7095 - Overall Recall: 0.7332 - Overall F1: 0.7211 - Overall Accuracy: 0.9716 - Humanprod F1: 0.2593 - Loc F1: 0.7493 - Org F1: 0.5574 - Per F1: 0.7592 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Humanprod F1 | Loc F1 | Org F1 | Per F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:------:|:------:|:------:| | 0.2962 | 1.0 | 613 | 0.1025 | 0.6794 | 0.7090 | 0.6939 | 0.9681 | 0.0 | 0.7315 | 0.5026 | 0.7296 | | 0.0854 | 2.0 | 1226 | 0.0990 | 0.7095 | 0.7332 | 0.7211 | 0.9716 | 0.2593 | 0.7493 | 0.5574 | 0.7592 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.7.1+cpu - Datasets 2.7.1 - Tokenizers 0.13.2
jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-2_sixties-8_s724
jonatasgrosman
2022-12-11T19:11:06Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T19:10:55Z
--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_fr_vp-100k_age_teens-2_sixties-8_s724 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-2_sixties-8_s420
jonatasgrosman
2022-12-11T19:08:21Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T19:08:09Z
--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_fr_vp-100k_age_teens-2_sixties-8_s420 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
thennal/whisper-small-ml-imasc
thennal
2022-12-11T19:04:01Z
12
3
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ml", "dataset:thennal/imasc", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-06T17:24:29Z
--- language: - ml license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - thennal/imasc metrics: - wer model-index: - name: Whisper Small Ml - IMaSC results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: ICFOSS Malayalam Speech Corpus type: thennal/imasc config: ml split: test args: ml metrics: - name: Wer type: wer value: 10.945945945945947 --- # Whisper Small Ml - IMaSC This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [ICFOSS Malayalam Speech Corpus](https://huggingface.co/datasets/thennal/IMaSC) dataset. It achieves the following results on the evaluation set: - Loss: 0.0244 - Wer: 10.9459 - Cer: 1.7890 ## Model description More information needed ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
jonatasgrosman/exp_w2v2r_fr_vp-100k_age_teens-10_sixties-0_s451
jonatasgrosman
2022-12-11T19:00:28Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T19:00:17Z
--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_fr_vp-100k_age_teens-10_sixties-0_s451 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
alea31415/nanachi-made-in-abyss
alea31415
2022-12-11T18:52:08Z
0
5
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2022-10-02T15:32:59Z
--- license: creativeml-openrail-m --- Prompt with **"nanachiDB cute furry girl"** **Training details (as far as I remember):** - Trained with [JoePenna Dreambooth repository](https://github.com/JoePenna/Dreambooth-Stable-Diffusion) - data set: 42 concept images + a number of custom reg images - default learning rate for 5000 steps - trained on top of yiffy-e15? **Example generations:** ![00025-2249204502-wallpaper](https://huggingface.co/alea31415/nanachi-made-in-abyss/resolve/main/00025-2249204502-wallpaper.png) ![00029-3984737103-wallpaper](https://huggingface.co/alea31415/nanachi-made-in-abyss/resolve/main/00029-3984737103-wallpaper.png) ![00088-4000752502-nanachiDB](https://huggingface.co/alea31415/nanachi-made-in-abyss/resolve/main/00088-4000752502-nanachiDB.png) ![00090-2600473557-nanachiDB](https://huggingface.co/alea31415/nanachi-made-in-abyss/resolve/main/00090-2600473557-nanachiDB.png) ![00074-476540115-nanachiDB](https://huggingface.co/alea31415/nanachi-made-in-abyss/resolve/main/00074-476540115-nanachiDB.png) ![00008-3331942115-HQ](https://huggingface.co/alea31415/nanachi-made-in-abyss/resolve/main/00008-3331942115-HQ.png)
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-8_sixties-2_s284
jonatasgrosman
2022-12-11T18:38:08Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T18:37:58Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-8_sixties-2_s284 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-2_sixties-8_s869
jonatasgrosman
2022-12-11T18:32:58Z
5
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T18:32:47Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-2_sixties-8_s869 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
amitkayal/whisper-tiny-hi
amitkayal
2022-12-11T18:31:44Z
30
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-05T14:00:20Z
--- language: - hi license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-tiny-hi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: hi metrics: - name: Wer type: wer value: 43.88685085406397 --- <!-- 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-hi This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7990 - Wer: 43.8869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1747 | 7.02 | 1000 | 0.5674 | 41.6800 | | 0.0466 | 14.03 | 2000 | 0.7042 | 43.7378 | | 0.0174 | 22.0 | 3000 | 0.7990 | 43.8869 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.10.0 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-2_sixties-8_s481
jonatasgrosman
2022-12-11T18:29:47Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T18:29:37Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-2_sixties-8_s481 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
SamoaJon/ppo-LunarLander-v2-TEST
SamoaJon
2022-12-11T18:27:15Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T18:26:48Z
--- 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: 272.44 +/- 17.07 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 ... ```
Conflictx/AnimeScreencap
Conflictx
2022-12-11T18:18:49Z
0
91
null
[ "text-to-image", "v2.0", "Embedding", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2022-12-06T18:45:55Z
--- license: creativeml-openrail-m tags: - text-to-image - v2.0 - Embedding --- Textual Inversion Embedding by ConflictX For SD 2.x trained on 768x768 images from anime sources. Install by downloading the step embedding, and put it in the \embeddings folder A beautiful artstyle, this one focused on warm environments, with a focus on movie stylized anime. This one has a bit more difficulty to get faces right, but it is possible. Use keyword: AnimeScreenCap Images: ![01243-759871936-a beautiful pond, volumetrics, photorealistic, from a movie, (AnimeScreenCap_1.0), analog, very grainy, film still,.png](https://s3.amazonaws.com/moonup/production/uploads/1670352727158-6303c53d7373aacccd859bbd.png) ![01244-999652193-a beautiful japanese castle, volumetrics, photorealistic, from a movie, (AnimeScreenCap_1.0), analog, very grainy, film still,.png](https://s3.amazonaws.com/moonup/production/uploads/1670352732990-6303c53d7373aacccd859bbd.png) ![01245-4037853542-a beautiful japanese boat on a river, volumetrics, photorealistic, from a movie, (AnimeScreenCap_1.0), analog, very grainy, film.png](https://s3.amazonaws.com/moonup/production/uploads/1670352835385-6303c53d7373aacccd859bbd.png) ![01247-2689419970-warm cup of coffee on a table, volumetrics, photorealistic, from a movie, (AnimeScreenCap_1.0), analog, very grainy, film still,.png](https://s3.amazonaws.com/moonup/production/uploads/1670352930862-6303c53d7373aacccd859bbd.png) ![01185-2676837077-steampunk tokyo, a ninja wearing a ninja suit roaming the streets at night, , (AnimeScreenCap_1.25) , steampunk, volumetrics, ph.png](https://s3.amazonaws.com/moonup/production/uploads/1670353171805-6303c53d7373aacccd859bbd.png) Mixes with my other embeddings: Vikingpunk: ![01199-2670180534-closeup of a viking cyberpunk sword, (AnimeScreenCap_1.25) , steampunk, volumetrics, photorealistic, tiny magical lights, from a.png](https://s3.amazonaws.com/moonup/production/uploads/1670352979388-6303c53d7373aacccd859bbd.png) Chempunk: ![01206-1025396846-a toxic lab with green mist hanging in it, (AnimeScreenCap_1.2) , glowing vials, volumetrics, photorealistic, tiny toxic green l.png](https://s3.amazonaws.com/moonup/production/uploads/1670353010183-6303c53d7373aacccd859bbd.png) Kipaki: ![01192-1803232961-closeup of an egyptian temple, (AnimeScreenCap_1.25) , steampunk, volumetrics, photorealistic, tiny magical lights, from a movie.png](https://s3.amazonaws.com/moonup/production/uploads/1670353032099-6303c53d7373aacccd859bbd.png) Candypunk: ![01268-1980997927-the face of a beautiful anime japanese woman wearing a sailor moon dress, volumetrics, photorealistic, from a movie, (AnimeScree.png](https://s3.amazonaws.com/moonup/production/uploads/1670355406032-6303c53d7373aacccd859bbd.png)
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-10_sixties-0_s613
jonatasgrosman
2022-12-11T18:18:48Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T18:18:33Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-10_sixties-0_s613 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-10_sixties-0_s261
jonatasgrosman
2022-12-11T18:13:38Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T18:13:26Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-10_sixties-0_s261 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-0_sixties-10_s464
jonatasgrosman
2022-12-11T18:08:11Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T18:08:00Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-0_sixties-10_s464 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
Hossein-Bodaghi/CA_Market
Hossein-Bodaghi
2022-12-11T18:05:14Z
0
0
null
[ "license:cc-by-nc-sa-4.0", "region:us" ]
null
2022-12-11T18:05:12Z
--- license: cc-by-nc-sa-4.0 ---
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-5_sixties-5_s625
jonatasgrosman
2022-12-11T18:02:54Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T18:02:37Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-5_sixties-5_s625 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
Glen/ppo-LunarLander-v2
Glen
2022-12-11T17:58:19Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T17:57:57Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.00 +/- 20.12 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 ... ```
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-5_sixties-5_s12
jonatasgrosman
2022-12-11T17:57:35Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:57:24Z
--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - es datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_es_vp-100k_age_teens-5_sixties-5_s12 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
Kwaku/gpt2-finetuned-banking77
Kwaku
2022-12-11T17:54:30Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "eng", "dataset:banking77", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-07-20T01:06:19Z
--- language: eng datasets: - banking77 --- # GPT2 Fine-Tuned Banking 77 This is a fine-tuned version of the GPT2 model. It's best suited for text-generation. ## Model Description Kwaku/gpt2-finetuned-banking77 was fine tuned on the [banking77](https://huggingface.co/datasets/banking77) dataset, which is "composed of online banking queries annotated with their corresponding intents." ## Intended Uses and Limitations Given the magnitude of the [Microsoft DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large) model, the author resorted to fine-tuning the gpt2 model for the creation of a chatbot. The intent was for the chatbot to emulate a banking customer agent, hence the use of the banking77 dataset. However, when the fine-tuned model was deployed in the chatbot, the results were undesirable. Its responses were inappropriate and unnecessarily long. The last word of its response is repeated numerously, a major glitch in it. The model performs better in text-generation but is prone to generating banking-related text because of the corpus it was trained on. ### How to use You can use this model directly with a pipeline for text generation: ```python >>>from transformers import pipeline >>> model_name = "Kwaku/gpt2-finetuned-banking77" >>> generator = pipeline("text-generation", model=model_name) >>> result = generator("My money is", max_length=15, num_return_sequences=2) >>> print(result) [{'generated_text': 'My money is stuck in ATM pending. Please cancel this transaction and refund it'}, {'generated_text': 'My money is missing. How do I get a second card, and how'}] ``` ### Limitations and bias For users who want a diverse text-generator, this model's tendency to generate mostly bank-related text will be a drawback. It also inherits [the biases of its parent model, the GPT2](https://huggingface.co/gpt2#limitations-and-bias).
jonatasgrosman/exp_w2v2r_en_vp-100k_age_teens-8_sixties-2_s741
jonatasgrosman
2022-12-11T17:54:29Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:54:19Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_vp-100k_age_teens-8_sixties-2_s741 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_en_vp-100k_age_teens-8_sixties-2_s71
jonatasgrosman
2022-12-11T17:50:49Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:50:38Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_vp-100k_age_teens-8_sixties-2_s71 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_en_vp-100k_age_teens-2_sixties-8_s769
jonatasgrosman
2022-12-11T17:44:28Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:44:15Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_vp-100k_age_teens-2_sixties-8_s769 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_en_vp-100k_age_teens-0_sixties-10_s539
jonatasgrosman
2022-12-11T17:24:55Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:24:43Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_vp-100k_age_teens-0_sixties-10_s539 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
RajMoodley/ppo-Huggy
RajMoodley
2022-12-11T17:21:18Z
3
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-11T17:21:12Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: RajMoodley/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
jonatasgrosman/exp_w2v2r_en_vp-100k_age_teens-5_sixties-5_s649
jonatasgrosman
2022-12-11T17:17:12Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:17:01Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_vp-100k_age_teens-5_sixties-5_s649 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_en_vp-100k_age_teens-5_sixties-5_s197
jonatasgrosman
2022-12-11T17:14:23Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:14:12Z
--- language: - en license: apache-2.0 tags: - automatic-speech-recognition - en datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_en_vp-100k_age_teens-5_sixties-5_s197 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-8_sixties-2_s786
jonatasgrosman
2022-12-11T17:09:21Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:09:09Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-8_sixties-2_s786 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-8_sixties-2_s538
jonatasgrosman
2022-12-11T17:06:16Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:06:05Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-8_sixties-2_s538 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
kjul/ppo-Huggy
kjul
2022-12-11T17:06:03Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-11T17:05:57Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: kjul/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Kwaku/social_media_sa
Kwaku
2022-12-11T17:05:23Z
4
1
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "eng", "dataset:banking77", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-11-21T23:20:43Z
--- language: eng datasets: - banking77 --- # Social Media Sentiment Analysis Model This is a fine-tuned version of the Distilbert model. It's best suited for sentiment-analysis. ## Model Description Social Media Sentiment Analysis Model was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle." ## Intended Uses and Limitations This model is meant for sentiment-analysis. Because it was trained on a corpus of tweets, it is familiar with social media jargons. ### How to use You can use this model directly with a pipeline for text generation: ```python >>>from transformers import pipeline >>> model_name = "Kwaku/social_media_sa" >>> generator = pipeline("sentiment-analysis", model=model_name) >>> result = generator("I like this model") >>> print(result) Generated output: [{'label': 'positive', 'score': 0.9494990110397339}] ``` ### Limitations and bias This model inherits the bias of its parent, [Distilbert](https://huggingface.co/models?other=distilbert). Besides that, it was trained on only 1000 randomly selected sequences, and thus does not achieve a high probability rate. It does fairly well nonetheless.
Kwaku/social_media_sa_finetuned_1
Kwaku
2022-12-11T17:04:42Z
10
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "eng", "dataset:banking77", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-11-22T00:06:16Z
--- language: eng datasets: - banking77 --- # Social Media Sentiment Analysis Model (Finetuned) This is a fine-tuned version of the [Social Media Sentiment Analysis Model](https://huggingface.co/Kwaku/social_media_sa) which is a finetuned version of [Distilbert](https://huggingface.co/models?other=distilbert). It's best suited for sentiment-analysis. ## Model Description Social Media Sentiment Analysis Model was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle." ## Intended Uses and Limitations This model is meant for sentiment-analysis. Because it was trained on a corpus of tweets, it is familiar with social media jargons. ### How to use You can use this model directly with a pipeline for text generation: ```python >>>from transformers import pipeline >>> model_name = "Kwaku/social_media_sa_finetuned_1" >>> generator = pipeline("sentiment-analysis", model=model_name) >>> result = generator("I like this model") >>> print(result) Generated output: [{'label': 'positive', 'score': 0.9494990110397339}] ``` ### Limitations and bias This model inherits the bias of its parent, [Distilbert](https://huggingface.co/models?other=distilbert). Besides that, it was trained on only 1000 randomly selected sequences, and thus does not achieve a high probability rate. It does fairly well nonetheless.
PakanunNoa/ppo-Huggy
PakanunNoa
2022-12-11T17:02:56Z
3
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-11T17:02:48Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: PakanunNoa/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s510
jonatasgrosman
2022-12-11T17:00:35Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T17:00:23Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s510 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
nemanjar/ppo-LunarLander-v2
nemanjar
2022-12-11T16:57:50Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-07T20:28:25Z
--- 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: 287.80 +/- 16.52 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-10_sixties-0_s693
jonatasgrosman
2022-12-11T16:53:19Z
4
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T16:53:08Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-10_sixties-0_s693 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-10_sixties-0_s362
jonatasgrosman
2022-12-11T16:50:03Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T16:49:52Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-10_sixties-0_s362 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-0_sixties-10_s50
jonatasgrosman
2022-12-11T16:41:44Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T16:41:31Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-0_sixties-10_s50 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
sanchit-gandhi/whisper-small-cy-1k-steps
sanchit-gandhi
2022-12-11T16:39:15Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "cy", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T14:43:38Z
--- language: - cy license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Welsh results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 cy type: mozilla-foundation/common_voice_11_0 config: cy split: test args: cy metrics: - name: Wer type: wer value: 29.784318398474742 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Welsh This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 cy dataset. It achieves the following results on the evaluation set: - Loss: 0.4594 - Wer: 29.7843 ## Model description More information needed ## 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1288 | 3.02 | 1000 | 0.4594 | 29.7843 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221210+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-0_sixties-10_s278
jonatasgrosman
2022-12-11T16:36:11Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T16:35:52Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-0_sixties-10_s278 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-5_sixties-5_s852
jonatasgrosman
2022-12-11T16:29:07Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "de", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T16:28:56Z
--- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2r_de_vp-100k_age_teens-5_sixties-5_s852 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
EffyLi/bert-base-uncased-finetuned-ner-finetuned-ner
EffyLi
2022-12-11T16:18:36Z
12
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-11T16:17:20Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 model-index: - name: bert-base-uncased-finetuned-ner-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-ner-finetuned-ner This model is a fine-tuned version of [EffyLi/bert-base-uncased-finetuned-ner](https://huggingface.co/EffyLi/bert-base-uncased-finetuned-ner) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0 - Datasets 2.7.1 - Tokenizers 0.11.0
kjul/ppo-LunarLander-v2
kjul
2022-12-11T16:00:18Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T15:52:39Z
--- 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: -166.90 +/- 40.93 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 ... ```
teddy322/wav2vec2-large-xls-r-300m-kor-lr-5e-4
teddy322
2022-12-11T15:48:33Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:zeroth_korean_asr", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-07T16:02:52Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - zeroth_korean_asr model-index: - name: wav2vec2-large-xls-r-300m-kor-lr-5e-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. --> # wav2vec2-large-xls-r-300m-kor-lr-5e-4 This model is a fine-tuned version of [teddy322/wav2vec2-large-xls-r-300m-kor-lr-5e-4](https://huggingface.co/teddy322/wav2vec2-large-xls-r-300m-kor-lr-5e-4) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6605 - eval_wer: 0.4005 - eval_runtime: 150.1937 - eval_samples_per_second: 3.043 - eval_steps_per_second: 0.386 - epoch: 7.87 - step: 2800 ## Model description More information needed ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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 - num_epochs: 20 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
parinzee/whisper-base-th-newmm
parinzee
2022-12-11T15:46:59Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "th", "dataset:mozilla-foundation/common_voice_11_0", "dataset:google/fleurs", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-10T09:28:58Z
--- language: - th license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 - google/fleurs model-index: - name: Whisper Base Thai Newmm Tokenized - Parinthapat Pengpun 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 Base Thai Newmm Tokenized - Parinthapat Pengpun This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 and the FLEURS datasets. It achieves the following results on the evaluation set: - eval_loss: 0.5888 - eval_wer: 67.3381 - eval_cer: 32.4281 - eval_runtime: 6393.9778 - eval_samples_per_second: 1.709 - eval_steps_per_second: 0.214 - epoch: 1.0 - step: 2000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 7500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
khaled5321/PPO-LunarLander-v2
khaled5321
2022-12-11T15:25:36Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-10T19:54:12Z
--- 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: 296.58 +/- 16.76 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 ... ```
AI-MeisterBin/ko-sentence-bert-MeisterBin
AI-MeisterBin
2022-12-11T14:52:37Z
4
0
transformers
[ "transformers", "pytorch", "tf", "roberta", "feature-extraction", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2022-12-11T10:19:49Z
심리상담 챗봇 메아리를 만들기 위한 버트 모델입니다. 챗봇 https://ai-meisterbin-project-chatbot-main-chatbot-qj3hxl.streamlit.app/ 깃허브 https://github.com/AI-MeisterBin/project_chatbot
sanchit-gandhi/whisper-small-kab-1k-steps
sanchit-gandhi
2022-12-11T14:43:28Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ka", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T11:22:40Z
--- language: - ka license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Georgian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 kab type: mozilla-foundation/common_voice_11_0 config: kab split: test args: kab metrics: - name: Wer type: wer value: 53.84203447245193 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Georgian This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 kab dataset. It achieves the following results on the evaluation set: - Loss: 0.6125 - Wer: 53.8420 ## Model description More information needed ## 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5555 | 1.06 | 1000 | 0.6125 | 53.8420 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221210+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
Gweizheng/ppo-LunarLander-v2
Gweizheng
2022-12-11T14:33:29Z
5
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T14:33:05Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.72 +/- 22.34 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 ... ```
anuragshas/whisper-large-v2-hi-v2
anuragshas
2022-12-11T14:33:10Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T06:34:14Z
--- language: - hi license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large-v2 Hindi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 hi type: mozilla-foundation/common_voice_11_0 config: hi split: test args: hi metrics: - name: Wer type: wer value: 12.457650398315174 --- <!-- 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 Large-v2 Hindi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 hi dataset. It achieves the following results on the evaluation set: - Loss: 0.1870 - Wer: 12.4577 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 300 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2097 | 0.37 | 100 | 0.2616 | 17.6701 | | 0.1578 | 0.73 | 200 | 0.2108 | 14.0990 | | 0.0806 | 1.1 | 300 | 0.1870 | 12.4577 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
polejowska/vit-base-patch16-224-in21k-eurosat
polejowska
2022-12-11T14:30:36Z
36
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-12-11T11:58:34Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9801587301587301 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-eurosat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3394 - Accuracy: 0.9802 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7076 | 0.98 | 33 | 0.6119 | 0.9696 | | 0.4469 | 1.98 | 66 | 0.4190 | 0.9788 | | 0.3497 | 2.98 | 99 | 0.3555 | 0.9788 | | 0.3048 | 3.98 | 132 | 0.3394 | 0.9802 | | 0.2983 | 4.98 | 165 | 0.3394 | 0.9802 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
lewtun/ddpm-celebahq-finetuned-butterflies-2epochs
lewtun
2022-12-11T14:02:43Z
1
0
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2022-12-11T14:01:18Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('lewtun/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
mrgreat1110/bert-finetuned-ner
mrgreat1110
2022-12-11T13:41:30Z
13
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-11T13:20:48Z
--- license: mit tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.934260639178672 - name: Recall type: recall value: 0.9495119488387749 - name: F1 type: f1 value: 0.9418245555462816 - name: Accuracy type: accuracy value: 0.9860923058809677 --- <!-- 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 This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0883 - Precision: 0.9343 - Recall: 0.9495 - F1: 0.9418 - Accuracy: 0.9861 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.02 | 1.0 | 1756 | 0.0944 | 0.9189 | 0.9381 | 0.9284 | 0.9833 | | 0.011 | 2.0 | 3512 | 0.0809 | 0.9358 | 0.9514 | 0.9435 | 0.9862 | | 0.0032 | 3.0 | 5268 | 0.0883 | 0.9343 | 0.9495 | 0.9418 | 0.9861 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
paulkm/autotrain-lottery_v2-2420075390
paulkm
2022-12-11T13:32:25Z
4
0
transformers
[ "transformers", "pytorch", "autotrain", "text-classification", "zh", "dataset:paulkm/autotrain-data-lottery_v2", "co2_eq_emissions", "endpoints_compatible", "region:us" ]
text-classification
2022-12-11T13:30:55Z
--- tags: - autotrain - text-classification language: - zh widget: - text: "I love AutoTrain 🤗" datasets: - paulkm/autotrain-data-lottery_v2 co2_eq_emissions: emissions: 0.013953144730323944 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2420075390 - CO2 Emissions (in grams): 0.0140 ## Validation Metrics - Loss: 0.117 - Accuracy: 0.966 - Precision: 0.965 - Recall: 0.960 - AUC: 0.990 - F1: 0.962 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/paulkm/autotrain-lottery_v2-2420075390 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("paulkm/autotrain-lottery_v2-2420075390", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("paulkm/autotrain-lottery_v2-2420075390", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
mokryak/ppo-LunarLander-v2
mokryak
2022-12-11T12:41:01Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T10:25:08Z
--- 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: 292.82 +/- 17.26 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 ... ```
sanchit-gandhi/whisper-small-sl-1k-steps
sanchit-gandhi
2022-12-11T11:22:31Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "sl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-11T10:15:40Z
--- language: - sl license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Slovenian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 sl type: mozilla-foundation/common_voice_11_0 config: sl split: test args: sl metrics: - name: Wer type: wer value: 26.588921282798832 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Slovenian This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 sl dataset. It achieves the following results on the evaluation set: - Loss: 0.4625 - Wer: 26.5889 ## Model description More information needed ## 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0027 | 13.01 | 1000 | 0.4625 | 26.5889 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221210+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
jesusfbes/ppo-LunarLander-v2
jesusfbes
2022-12-11T10:27:20Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-11T10:26:51Z
--- 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: 247.93 +/- 32.87 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 ... ```
bnriiitb/whisper-small-te
bnriiitb
2022-12-11T09:11:11Z
13
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "te", "dataset:Chai_Bisket_Stories_16-08-2021_14-17", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-11-21T19:28:59Z
--- language: - te license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - Chai_Bisket_Stories_16-08-2021_14-17 metrics: - wer model-index: - name: Whisper Small Telugu - Naga Budigam results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Chai_Bisket_Stories_16-08-2021_14-17 type: Chai_Bisket_Stories_16-08-2021_14-17 config: None split: None args: 'config: te, split: test' metrics: - name: Wer type: wer value: 77.48711850971065 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Telugu - Naga Budigam This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chai_Bisket_Stories_16-08-2021_14-17 dataset. It achieves the following results on the evaluation set: - Loss: 0.7063 - Wer: 77.4871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2933 | 2.62 | 500 | 0.3849 | 86.6429 | | 0.0692 | 5.24 | 1000 | 0.3943 | 82.7190 | | 0.0251 | 7.85 | 1500 | 0.4720 | 82.4415 | | 0.0098 | 10.47 | 2000 | 0.5359 | 81.6092 | | 0.0061 | 13.09 | 2500 | 0.5868 | 75.9413 | | 0.0025 | 15.71 | 3000 | 0.6235 | 76.6944 | | 0.0009 | 18.32 | 3500 | 0.6634 | 78.3987 | | 0.0005 | 20.94 | 4000 | 0.6776 | 77.1700 | | 0.0002 | 23.56 | 4500 | 0.6995 | 78.2798 | | 0.0001 | 26.18 | 5000 | 0.7063 | 77.4871 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
CarpetCleaningofFriscoTX/CarpetCleaningofFriscoTX
CarpetCleaningofFriscoTX
2022-12-11T08:54:26Z
0
0
null
[ "license:other", "region:us" ]
null
2022-12-11T08:53:52Z
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CarpetCleaningLewisvilleTX/UpholsteryCleaningLewisvilleTX
CarpetCleaningLewisvilleTX
2022-12-11T08:51:06Z
0
0
null
[ "license:other", "region:us" ]
null
2022-12-11T08:50:33Z
--- license: other --- Upholstery Cleaning Lewisville TX https://carpetcleaninglewisville.com/upholstery-cleaning.html 972-338-5376 We have all been in situations where someone accidentally spills wine on your couch during a family get-together or where children misbehave and throw food all over your furniture.You can't go back to these times.However, Carpet Cleaning Lewisville, TX can get rid of all of these unsightly stains and give your upholstery a new look and scent.