davanstrien/model_cards_with_readmes_with_duplicates

jackshoemaker/bert-finetuned-squad

jackshoemaker

bert

16

12

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

954

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

gatardochi/ppo-SnowballTarget

gatardochi

null

20

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

857

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

smilingface88/xlm-roberta-base-finetuned-panx-de-fr

smilingface88

xlm-roberta

10

2

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,321

# xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1629 - F1: 0.8584 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2904 | 1.0 | 715 | 0.1823 | 0.8286 | | 0.1446 | 2.0 | 1430 | 0.1626 | 0.8488 | | 0.0941 | 3.0 | 2145 | 0.1629 | 0.8584 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

marcowong02/bert-finetuned-squad

marcowong02

bert

10

9

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

954

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

BridgeTower/bridgetower-large-itm-mlm-itc

BridgeTower

null

7

124

transformers

0

null

true

false

mit

['en']

['conceptual_captions', 'conceptual_12m', 'sbu_captions', 'visual_genome', 'mscoco_captions']

null

0

['bridgetower', 'gaudi']

false

true

5,176

# BridgeTower large-itm-mlm-itc model The BridgeTower model was proposed in "BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning" by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The model was pretrained on English language using masked language modeling (MLM) and image text matching (ITM)objectives. It was introduced in [this paper](https://arxiv.org/pdf/2206.08657.pdf) and first released in [this repository](https://github.com/microsoft/BridgeTower). BridgeTower got accepted to [AAAI'23](https://aaai.org/Conferences/AAAI-23/). ## Model description The abstract from the paper is the following: Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder. Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BridgeTower, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the cross-modal encoder. This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BridgeTower achieves state-of-the-art performance on various downstream vision-language tasks. In particular, on the VQAv2 test-std set, BridgeTower achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs. Notably, when further scaling the model, BridgeTower achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets. ## Intended uses & limitations ### How to use Here is how to use this model to perform image and text matching: ```python from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval import requests from PIL import Image url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"] processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi") model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi") # forward pass scores = dict() for text in texts: # prepare inputs encoding = processor(image, text, return_tensors="pt") outputs = model(**encoding) scores[text] = outputs.logits[0,1].item() ``` Here is how to use this model to perform masked language modeling: ```python from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000360943.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") text = "a <mask> looking out of the window" processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi") model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-gaudi") # prepare inputs encoding = processor(image, text, return_tensors="pt") # forward pass outputs = model(**encoding) results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist()) print(results) #.a cat looking out of the window. ``` ## Training data The BridgeTower model was pretrained on four public image-caption datasets: - [Conceptual Captions (CC3M)](https://ai.google.com/research/ConceptualCaptions/) - [Conceptual 12M (CC12M)](https://github.com/google-research-datasets/conceptual-12m) - [SBU Captions](https://www.cs.rice.edu/~vo9/sbucaptions/) - [MSCOCO Captions](https://arxiv.org/pdf/1504.00325.pdf) - [Visual Genome](https://visualgenome.org/) The total number of unique images in the combined data is around 14M. ## Training procedure ### Pretraining The model was pre-trained for 10 epochs on an Intel AI supercomputing cluster using 512 Gaudis and 128 Xeons with a batch size of 2048. The optimizer used was AdamW with a learning rate of 1e-7. No data augmentation was used except for center-crop. The image resolution in pre-training is set to 294 x 294. ## Evaluation results Please refer to [Table 5](https://arxiv.org/pdf/2206.08657.pdf) for BridgeTower's performance on Image Retrieval and other downstream tasks. ### BibTeX entry and citation info ```bibtex @article{xu2022bridge, title={BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning}, author={Xu, Xiao and Wu, Chenfei and Rosenman, Shachar and Lal, Vasudev and Che, Wanxiang and Duan, Nan}, journal={arXiv preprint arXiv:2206.08657}, year={2022} } ```

DunnBC22/vit-base-patch16-224-in21k-weather-images-classification

DunnBC22

vit

15

0

transformers

1

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

2,308

# vit-base-patch16-224-in21k-weather-images-classification 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: 0.2255 - Accuracy: 0.9340 - Weighted f1: 0.9341 - Micro f1: 0.9340 - Macro f1: 0.9372 - Weighted recall: 0.9340 - Micro recall: 0.9340 - Macro recall: 0.9354 - Weighted precision: 0.9347 - Micro precision: 0.9340 - Macro precision: 0.9398 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| | 2.4333 | 1.0 | 337 | 0.3374 | 0.9036 | 0.9028 | 0.9036 | 0.9080 | 0.9036 | 0.9036 | 0.9002 | 0.9088 | 0.9036 | 0.9234 | | 0.4422 | 2.0 | 674 | 0.2504 | 0.9228 | 0.9226 | 0.9228 | 0.9285 | 0.9228 | 0.9228 | 0.9273 | 0.9248 | 0.9228 | 0.9318 | | 0.1051 | 3.0 | 1011 | 0.2255 | 0.9340 | 0.9341 | 0.9340 | 0.9372 | 0.9340 | 0.9340 | 0.9354 | 0.9347 | 0.9340 | 0.9398 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.12.1

gatardochi/ppo-Pyramids

gatardochi

null

16

2

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

833

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

amlannayak/finetuning-sentiment-model-3000-samples

amlannayak

distilbert

10

2

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,049

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3251 - Accuracy: 0.8767 - F1: 0.8787 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

lancechen/ppo-LunarLander-v2

lancechen

null

12

1

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

juye/_output

juye

null

38

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

356

# LoRA DreamBooth - juye/_output These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks woman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup

laion

null

12

1

open_clip

0

zero-shot-image-classification

false

mit

null

0

['zero-shot-image-classification', 'clip']

false

true

12,492

# Model card for CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) # Model Details ## Model Description A series of CLIP [ConvNeXt-Large](https://arxiv.org/abs/2201.03545) (w/ extra text depth, vision MLP head) models trained on the LAION-2B (english) subset of [LAION-5B](https://arxiv.org/abs/2210.08402) using [OpenCLIP](https://github.com/mlfoundations/open_clip). The models utilize: * the [timm](https://github.com/rwightman/pytorch-image-models) ConvNeXt-Large model (`convnext_large`) as the image tower * a MLP (`fc - gelu - drop - fc`) head in vision tower instead of the single projection of other CLIP models * a text tower with same width but 4 layers more depth than ViT-L / RN50x16 models (depth 16, embed dim 768). This 320x320 resolution model is a soup (weight average) of 3 fine-tunes of [CLIP-convnext_large_d.laion2B-s26B-b102K-augreg](https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg) at a higher resolution. It is an average of 3 fine-tunes from the final checkpoint of the original 256x256 training run w/ an additional ~2-3B samples for each fine-tune and a lower learning rate. Each fine-tune was a different learning rate (1e-4, 6e-5, 5e-5), and diff # of samples (3.2B, 2B, 2.5B). At 320x320, the ConvNext-Large-D is significantly more efficient than the L/14 model at 336x336 that OpenAI fine-tuned. L/14-336 model is 2.5x more GMAC, 2.8x more activations, and 1.22x more parameters. | Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) | | ----- | ------- | ---------- | ------------ | --------- | | [convnext_large_d.laion2b_s26b_b102k-augreg](https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg) | LAION-2B | 256x256 | RRC (0.33, 1.0), RE (0.35), SD (0.1), D(0.1) | 75.9 | | [convnext_large_d_320.laion2b_s29b_b131k-ft](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.6 | | [convnext_large_d_320.laion2b_s29b_b131k-ft-soup](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.9 | RRC = Random Resize Crop (crop pcts), RE = Random Erasing (prob), SD = Stochastic Depth (prob) -- image tower only, D = Dropout (prob) -- image tower head only LAION-A = LAION Aesthetic, an ~900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering. Model training done by Ross Wightman on the [stability.ai](https://stability.ai/) cluster. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with one of (see table in intro): * LAION-2B - A 2 billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). * LAION-Aesthetic - A 900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure All 320x320 model fine-tunes were trained with a global batch size of 131072 for 10-16 checkpoint intervals of 203.7M samples for a total of ~2-3B samples seen over fine-tune. For 320x320 models, a slurm script w/ srun below was used on 64 8-GPU (A100 40GB) nodes (Stability). ``` /opt/slurm/sbin/srun --cpu_bind=v --accel-bind=gn python -m training.main \ --save-frequency 1 \ --name "convnext_large_320" \ --pretrained ""/runs/convnext_large_256/epoch_128.pt" \ --resume 'latest' \ --train-data="pipe:aws s3 cp s3://mybucket/path/{laion{00000..xxxxx}.tar -" \ --train-num-samples 203666042 \ --dataset-type webdataset \ --precision amp_bfloat16 \ --beta2 0.98 \ --warmup 2000 \ --batch-size=256 \ --epochs=12 \ --dataset-resampled \ --aug-cfg use_timm=True scale='(0.5, 1.0)' re_prob=0.4 \ --clip-grad-norm 5.0 \ --lr 5e-5 \ --workers=6 \ --model "convnext_large_d_320" \ --seed 0 \ --ddp-static-graph \ --local-loss \ --gather-with-grad \ --grad-checkpointing ``` # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. ## Results The models achieve between 75.9 and 76.9 top-1 zero-shot accuracy on ImageNet-1k. Zero-shot curve of origina from-scratch 256x256 training: ![](convnext_large_zero_shot.png) An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) for compute used to train this model. # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenCLIP software ```bibtex @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` ``` @InProceedings{pmlr-v162-wortsman22a, title = {Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time}, author = {Wortsman, Mitchell and Ilharco, Gabriel and Gadre, Samir Ya and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Morcos, Ari S and Namkoong, Hongseok and Farhadi, Ali and Carmon, Yair and Kornblith, Simon and Schmidt, Ludwig}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {23965--23998}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf}, url = {https://proceedings.mlr.press/v162/wortsman22a.html} } ``` OpenAI CLIP paper ```bibtex @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` ```bibtex @Article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ```

Patrickrpds/mriverdb

Patrickrpds

null

19

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

421

### mriverdb Dreambooth model trained by Patrickrpds with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft

laion

null

10

1

open_clip

0

zero-shot-image-classification

false

mit

null

0

['zero-shot-image-classification', 'clip']

false

true

11,371

# Model card for CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) # Model Details ## Model Description A series of CLIP [ConvNeXt-Large](https://arxiv.org/abs/2201.03545) (w/ extra text depth, vision MLP head) models trained on the LAION-2B (english) subset of [LAION-5B](https://arxiv.org/abs/2210.08402) using [OpenCLIP](https://github.com/mlfoundations/open_clip). The models utilize: * the [timm](https://github.com/rwightman/pytorch-image-models) ConvNeXt-Large model (`convnext_large`) as the image tower * a MLP (`fc - gelu - drop - fc`) head in vision tower instead of the single projection of other CLIP models * a text tower with same width but 4 layers more depth than ViT-L / RN50x16 models (depth 16, embed dim 768). This 320x320 resolution model is a fine-tune of [CLIP-convnext_large_d.laion2B-s26B-b102K-augreg](https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg) at a higher resolution. It was fine-tune from the final checkpoint of the original 256x256 training run w/ an additional ~2.5B samples and a lower learning rate. At 320x320, the ConvNext-Large-D is significantly more efficient than the L/14 model at 336x336 that OpenAI fine-tuned. L/14-336 model is 2.5x more GMAC, 2.8x more activations, and 1.22x more parameters. | Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) | | ----- | ------- | ---------- | ------------ | --------- | | [convnext_large_d.laion2b_s26b_b102k-augreg](https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg) | LAION-2B | 256x256 | RRC (0.33, 1.0), RE (0.35), SD (0.1), D(0.1) | 75.9 | | [convnext_large_d_320.laion2b_s29b_b131k-ft](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.6 | | [convnext_large_d_320.laion2b_s29b_b131k-ft-soup](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.9 | RRC = Random Resize Crop (crop pcts), RE = Random Erasing (prob), SD = Stochastic Depth (prob) -- image tower only, D = Dropout (prob) -- image tower head only LAION-A = LAION Aesthetic, an ~900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering. Model training done by Ross Wightman on the [stability.ai](https://stability.ai/) cluster. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with one of (see table in intro): * LAION-2B - A 2 billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). * LAION-Aesthetic - A 900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure All 320x320 model fine-tunes were trained with a global batch size of 131072 for 10-16 checkpoint intervals of 203.7M samples for a total of ~2-3B samples seen over fine-tune. For 320x320 models, a slurm script w/ srun below was used on 64 8-GPU (A100 40GB) nodes (Stability). ``` /opt/slurm/sbin/srun --cpu_bind=v --accel-bind=gn python -m training.main \ --save-frequency 1 \ --name "convnext_large_320" \ --pretrained ""/runs/convnext_large_256/epoch_128.pt" \ --resume 'latest' \ --train-data="pipe:aws s3 cp s3://mybucket/path/{laion{00000..xxxxx}.tar -" \ --train-num-samples 203666042 \ --dataset-type webdataset \ --precision amp_bfloat16 \ --beta2 0.98 \ --warmup 2000 \ --batch-size=256 \ --epochs=12 \ --dataset-resampled \ --aug-cfg use_timm=True scale='(0.5, 1.0)' re_prob=0.4 \ --clip-grad-norm 5.0 \ --lr 5e-5 \ --workers=6 \ --model "convnext_large_d_320" \ --seed 0 \ --ddp-static-graph \ --local-loss \ --gather-with-grad \ --grad-checkpointing ``` # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. ## Results The models achieve between 75.9 and 76.9 top-1 zero-shot accuracy on ImageNet-1k. Zero-shot curve of origina from-scratch 256x256 training: ![](convnext_large_zero_shot.png) An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) for compute used to train this model. # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenCLIP software ```bibtex @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` OpenAI CLIP paper ```bibtex @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` ```bibtex @Article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ```

smilingface88/xlm-roberta-base-finetuned-panx-fr

smilingface88

xlm-roberta

10

0

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

# xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2676 - F1: 0.8449 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5915 | 1.0 | 191 | 0.3285 | 0.7814 | | 0.2651 | 2.0 | 382 | 0.2707 | 0.8314 | | 0.174 | 3.0 | 573 | 0.2676 | 0.8449 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

nanashisan/Sherlock-Hound

nanashisan

null

7

0

null

0

null

false

null

['ja']

null

0

[]

false

true

782

Mrs.Hudson - keyword:hudson - Sample Prompt:best quality,furry female, hudson, 1girl, solo, apron, smile, door, dress, white apron, long sleeves, indoors, pink dress, looking at viewer, dog girl, closed mouth, short hair, tail, bangs, furry, maid <lora:hudson-epoch08:1> - Negative Prompt: (worst quality,low quality:1.4),bad anatomy,3d,(animal tail,dog tail:1.4) - ![Sample_image](https://huggingface.co/nanashisan/Sherlock-Hound/resolve/main/hudson/hudson-epoch08.png) - ![Sample_image](https://huggingface.co/nanashisan/Sherlock-Hound/resolve/main/hudson/hudson_sampkle_xy.png) Model - hudson-epoch08.safetensors - 推奨：適切な学習でサンプルはこのバージョンで出力 - hudson-epoch06.safetensors - 学習不足：犬顔にならず人間顔が表示される事がある - hudson-epoch10.safetensors - 過学習：画質がやや劣化した

smilingface88/xlm-roberta-base-finetuned-panx-it

smilingface88

xlm-roberta

10

0

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

# xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2467 - F1: 0.8206 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7897 | 1.0 | 70 | 0.3096 | 0.7519 | | 0.2819 | 2.0 | 140 | 0.2603 | 0.8093 | | 0.1818 | 3.0 | 210 | 0.2467 | 0.8206 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

antonellaavad/pablo-pictures

antonellaavad

null

71

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

504

# LoRA DreamBooth - pablo-pic These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "pablo" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: picture of pablo ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)

smilingface88/xlm-roberta-base-finetuned-panx-en

smilingface88

xlm-roberta

10

0

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

# xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4028 - F1: 0.6869 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1396 | 1.0 | 50 | 0.5670 | 0.5101 | | 0.5289 | 2.0 | 100 | 0.4594 | 0.6358 | | 0.3838 | 3.0 | 150 | 0.4028 | 0.6869 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

frncscp/Patacotron

frncscp

null

13

0

keras

0

image-classification

false

mit

null

0

['One-Class Image Classification']

false

true

3,743

# Patacotrón Artificial Intelligence capable of patacón recognition # Details Here are hosted different models in cronological order, they were trained based on a Positive-Negative class dataset with custom architechtures and transfer learning. ## Description This series of models are part of an investigation of "One-Class Image Classification with AI Algorithms", and are able to recognize patacones on images. - **Developed by:** https://github.com/frncscp - **Model type:** Convolutional Neural Network, DNN - **License:** mit - **Finetuned from model (the ones that were trained with transfer learning):** DenseNet, Xception ## Sources [optional] - **Repository:** https://github.com/frncscp/ptctrn - **Paper:** Still on investigation - **Demo:** https://huggingface.co/spaces/frncscp/Patacotron # Uses The models that were taken from transfer learning now have custom weights, but all models can be used with Fine-Tuning for Image Classification tasks. ## Direct Use Patacognition (Patacón Recognition) # Bias, Risks, and Limitations Early versions can have a strong bias to color, most of the models could problems if the texture is too similar, like with deep fried foods. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Resize the images to 224x224, with RGB channels A clear, good definition photo will always be preferable The least things appearing on the image, the better A patacón is a prediction of 80% or more ## How to Get Started with Patacotrón Import the required modules, and with this four lines you're good to go: img = cv2.imread(image_dir) resize = tf.image.resize(cv2.cvtColor(img, cv2.COLOR_BGR2RGB),(IMAGE_WIDTH, IMAGE_HEIGHT)) ptctrn = load_model(model_dir) y_gorrito = ptctrn.predict(np.expand_dims(resize/255, 0)) ## Training Data A custom handmade dataset was used. ### Preprocessing Images were resized to 224x224 ### Speeds, Sizes, Times It largely depends on the model, but epoch's speed could range from 15 to 30 minutes, and training from several hours to a day and a half. # Evaluation A novel formula of efficiency was made, which uses average prediction, score on an image dataset, and weights to each variable. It is as follows: -For positive classes: $$E = \frac{(S * {S}')+(P * {P}')}{{S}'+{P}'}$$ -For negative classes: $$E = \frac{(S * {S}')+((1-P) * {P}')}{{S}'+{P}'}$$ Where: S represents normalized score (+1 for each image correctly classified, -1 if viceversa) from 0 to 1 P represents average prediction S´ and P´ are each variable biases Each score and average prediction is extracted from a folder with all positive or all negative class images. The results were taken from 4 folders (half positives, half negatives) with +15k files, a threshold of 80%, and a prediction bias of 1.2 ### Testing Data Custom handmade dataset. ### Metrics Efficiency, Binary Cross Entropy loss, Accuracy, AUC, Binary Accuracy (with a threshold of 0.8 on later versions) ## Results For efficiency: 'ptctrn_v1.1.h5': 0.4805495065119731 'ptctrn_v1.2.h5': 0.4238817456329115 'ptctrn_v1.3.h5': 0.5343622414829778 'ptctrn_v1.4.h5': 0.6059606705949329 'ptctrn_v1.5.h5': 0.5040440155920757 'ptctrn_v1.6.h5': 0.6889029338405537 'ptctrn_v1.7.h5': 0.7112169071407513 'ptctrn_v1.8.h5': 0.7181276230324835 'ptctrn_v1.9.h5': 0.6869660637054289 'ptctrn_v1.9.1.h5': 0.6810481039373982 'ptctrn_v1.10.h5': 0.6350583567300264 ### Summary Further training needs to be made, but some versions (1.5, 1.6, 1.7, 1.8) can be efficient against similar images, model ensembling is recommended. ### Hardware Ryzen 5 3500U (Laptop CPU) 20GB 2400mhz RAM

smilingface88/xlm-roberta-base-finetuned-panx-all

smilingface88

xlm-roberta

10

0

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,319

# xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1712 - F1: 0.8537 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3049 | 1.0 | 835 | 0.1862 | 0.8051 | | 0.1618 | 2.0 | 1670 | 0.1778 | 0.8385 | | 0.1063 | 3.0 | 2505 | 0.1712 | 0.8537 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

arun-shankar/GPT2-RLHF-covid

arun-shankar

gpt2

12

3

transformers

2

text-generation

true

false

apache-2.0

null

0

[]

false

true

760

## GPT2 fine-tuned with COVID-19 question and answer pairs using Reinforcement Learning with Human Feedback (RLHF) and Proximal Policy Optimization (PPO). Uses PPO and TRL library to align the response based on BERTScore towards the expected response. You can ask the model any question related to COVID-19 in this format: **question: should i wear a mask at home?\nanswer:** You can also add a CTRL token as a special prefix token in front of your prompt to align it to your preference. For example, you either add a [good] or [bad] token as a prefix. **[good]question: should i wear a mask at school?\nanswer:** Good and bad here refers to the quality of the response. Being more aligned to the expected (ground truth) response is good and not is bad.

corbt/roberta-lora-2

corbt

roberta

8

4

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

8,586

# roberta-lora-2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5790 - Mse: 0.5790 - Mae: 0.5751 - R2: 0.5572 - Accuracy: 0.5465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 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 | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:--------:| | 0.9268 | 0.02 | 2500 | 0.7467 | 0.7467 | 0.6737 | 0.4290 | 0.4621 | | 0.7651 | 0.05 | 5000 | 0.7631 | 0.7631 | 0.6773 | 0.4164 | 0.4582 | | 0.7399 | 0.07 | 7500 | 0.9654 | 0.9654 | 0.7675 | 0.2616 | 0.4104 | | 0.7249 | 0.1 | 10000 | 0.7259 | 0.7259 | 0.6579 | 0.4449 | 0.4763 | | 0.7122 | 0.12 | 12500 | 0.7292 | 0.7292 | 0.6596 | 0.4423 | 0.4753 | | 0.7035 | 0.15 | 15000 | 0.7039 | 0.7039 | 0.6425 | 0.4616 | 0.4889 | | 0.6992 | 0.17 | 17500 | 0.8192 | 0.8192 | 0.7018 | 0.3735 | 0.4485 | | 0.6885 | 0.2 | 20000 | 0.8312 | 0.8312 | 0.7040 | 0.3643 | 0.4480 | | 0.6974 | 0.22 | 22500 | 0.6822 | 0.6822 | 0.6317 | 0.4782 | 0.4987 | | 0.6933 | 0.25 | 25000 | 0.7079 | 0.7079 | 0.6426 | 0.4586 | 0.4936 | | 0.6972 | 0.27 | 27500 | 0.7470 | 0.7470 | 0.6638 | 0.4287 | 0.4768 | | 0.6838 | 0.29 | 30000 | 0.6918 | 0.6918 | 0.6362 | 0.4709 | 0.5009 | | 0.6766 | 0.32 | 32500 | 0.6597 | 0.6597 | 0.6199 | 0.4955 | 0.5035 | | 0.6746 | 0.34 | 35000 | 0.7049 | 0.7049 | 0.6431 | 0.4609 | 0.4897 | | 0.6742 | 0.37 | 37500 | 0.6701 | 0.6701 | 0.6240 | 0.4875 | 0.5096 | | 0.6772 | 0.39 | 40000 | 0.6616 | 0.6616 | 0.6176 | 0.4940 | 0.5120 | | 0.6717 | 0.42 | 42500 | 0.6548 | 0.6548 | 0.6187 | 0.4992 | 0.5072 | | 0.6849 | 0.44 | 45000 | 0.6486 | 0.6486 | 0.6157 | 0.5039 | 0.5087 | | 0.6727 | 0.47 | 47500 | 0.6829 | 0.6829 | 0.6294 | 0.4777 | 0.5030 | | 0.7081 | 0.49 | 50000 | 0.6777 | 0.6777 | 0.6299 | 0.4817 | 0.5037 | | 0.6692 | 0.52 | 52500 | 0.6634 | 0.6634 | 0.6206 | 0.4927 | 0.5078 | | 0.6676 | 0.54 | 55000 | 0.6760 | 0.6760 | 0.6261 | 0.4830 | 0.5068 | | 0.6575 | 0.56 | 57500 | 0.6301 | 0.6301 | 0.6060 | 0.5181 | 0.5172 | | 0.6661 | 0.59 | 60000 | 0.6626 | 0.6626 | 0.6168 | 0.4933 | 0.5153 | | 0.653 | 0.61 | 62500 | 0.6516 | 0.6516 | 0.6176 | 0.5017 | 0.5106 | | 0.6583 | 0.64 | 65000 | 0.7014 | 0.7014 | 0.6400 | 0.4636 | 0.4951 | | 0.6617 | 0.66 | 67500 | 0.6620 | 0.6620 | 0.6207 | 0.4937 | 0.5090 | | 0.6475 | 0.69 | 70000 | 0.6286 | 0.6286 | 0.6037 | 0.5193 | 0.5223 | | 0.6455 | 0.71 | 72500 | 0.7304 | 0.7304 | 0.6545 | 0.4414 | 0.4863 | | 0.6464 | 0.74 | 75000 | 0.6246 | 0.6246 | 0.6006 | 0.5223 | 0.5199 | | 0.646 | 0.76 | 77500 | 0.6414 | 0.6414 | 0.6124 | 0.5095 | 0.5126 | | 0.6502 | 0.79 | 80000 | 0.6131 | 0.6131 | 0.5988 | 0.5311 | 0.5245 | | 0.6443 | 0.81 | 82500 | 0.6376 | 0.6376 | 0.6064 | 0.5123 | 0.5229 | | 0.641 | 0.83 | 85000 | 0.6399 | 0.6399 | 0.6096 | 0.5106 | 0.5163 | | 0.6495 | 0.86 | 87500 | 0.6709 | 0.6709 | 0.6239 | 0.4869 | 0.5093 | | 0.642 | 0.88 | 90000 | 0.6025 | 0.6025 | 0.5952 | 0.5392 | 0.5212 | | 0.636 | 0.91 | 92500 | 0.6870 | 0.6870 | 0.6317 | 0.4746 | 0.5006 | | 0.633 | 0.93 | 95000 | 0.6190 | 0.6190 | 0.5949 | 0.5266 | 0.5270 | | 0.6316 | 0.96 | 97500 | 0.6053 | 0.6053 | 0.5926 | 0.5371 | 0.5280 | | 0.6224 | 0.98 | 100000 | 0.6098 | 0.6098 | 0.5956 | 0.5336 | 0.5217 | | 0.6304 | 1.01 | 102500 | 0.6124 | 0.6124 | 0.5949 | 0.5317 | 0.5280 | | 0.6238 | 1.03 | 105000 | 0.6138 | 0.6138 | 0.5950 | 0.5306 | 0.5313 | | 0.6228 | 1.06 | 107500 | 0.6302 | 0.6302 | 0.6038 | 0.5180 | 0.5189 | | 0.6218 | 1.08 | 110000 | 0.6198 | 0.6198 | 0.5958 | 0.5260 | 0.5274 | | 0.6164 | 1.1 | 112500 | 0.6045 | 0.6045 | 0.5895 | 0.5377 | 0.5327 | | 0.6295 | 1.13 | 115000 | 0.6040 | 0.6040 | 0.5884 | 0.5381 | 0.5352 | | 0.614 | 1.15 | 117500 | 0.5956 | 0.5956 | 0.5863 | 0.5445 | 0.5346 | | 0.6016 | 1.18 | 120000 | 0.6208 | 0.6208 | 0.5994 | 0.5252 | 0.5246 | | 0.6103 | 1.2 | 122500 | 0.6060 | 0.6060 | 0.5888 | 0.5366 | 0.5343 | | 0.614 | 1.23 | 125000 | 0.6198 | 0.6198 | 0.5995 | 0.5259 | 0.5293 | | 0.6113 | 1.25 | 127500 | 0.6010 | 0.6010 | 0.5874 | 0.5403 | 0.5340 | | 0.6131 | 1.28 | 130000 | 0.6118 | 0.6118 | 0.5926 | 0.5321 | 0.5303 | | 0.6069 | 1.3 | 132500 | 0.5914 | 0.5914 | 0.5815 | 0.5477 | 0.5406 | | 0.6016 | 1.33 | 135000 | 0.5908 | 0.5908 | 0.5825 | 0.5482 | 0.5417 | | 0.6053 | 1.35 | 137500 | 0.6166 | 0.6166 | 0.5939 | 0.5285 | 0.5317 | | 0.5927 | 1.37 | 140000 | 0.5910 | 0.5910 | 0.5840 | 0.5480 | 0.5392 | | 0.5942 | 1.4 | 142500 | 0.5965 | 0.5965 | 0.5856 | 0.5438 | 0.5387 | | 0.5966 | 1.42 | 145000 | 0.6121 | 0.6121 | 0.5923 | 0.5319 | 0.5358 | | 0.5941 | 1.45 | 147500 | 0.5889 | 0.5889 | 0.5814 | 0.5496 | 0.5373 | | 0.6007 | 1.47 | 150000 | 0.5833 | 0.5833 | 0.5770 | 0.5539 | 0.5436 | | 0.6024 | 1.5 | 152500 | 0.5862 | 0.5862 | 0.5786 | 0.5517 | 0.5423 | | 0.5896 | 1.52 | 155000 | 0.5913 | 0.5913 | 0.5813 | 0.5478 | 0.5429 | | 0.5906 | 1.55 | 157500 | 0.5944 | 0.5944 | 0.5854 | 0.5454 | 0.5373 | | 0.5847 | 1.57 | 160000 | 0.5989 | 0.5989 | 0.5845 | 0.5419 | 0.5398 | | 0.5837 | 1.6 | 162500 | 0.5914 | 0.5914 | 0.5822 | 0.5477 | 0.5394 | | 0.5928 | 1.62 | 165000 | 0.5888 | 0.5888 | 0.5798 | 0.5497 | 0.5424 | | 0.585 | 1.64 | 167500 | 0.5952 | 0.5952 | 0.5829 | 0.5448 | 0.5391 | | 0.5929 | 1.67 | 170000 | 0.5829 | 0.5829 | 0.5768 | 0.5542 | 0.5440 | | 0.5886 | 1.69 | 172500 | 0.5831 | 0.5831 | 0.5783 | 0.5540 | 0.5428 | | 0.5793 | 1.72 | 175000 | 0.5857 | 0.5857 | 0.5776 | 0.5520 | 0.5453 | | 0.5805 | 1.74 | 177500 | 0.5746 | 0.5746 | 0.5727 | 0.5606 | 0.5489 | | 0.5875 | 1.77 | 180000 | 0.5798 | 0.5798 | 0.5739 | 0.5566 | 0.5487 | | 0.5898 | 1.79 | 182500 | 0.5818 | 0.5818 | 0.5746 | 0.5550 | 0.5475 | | 0.5884 | 1.82 | 185000 | 0.5736 | 0.5736 | 0.5722 | 0.5613 | 0.5496 | | 0.5757 | 1.84 | 187500 | 0.5816 | 0.5816 | 0.5756 | 0.5552 | 0.5464 | | 0.5789 | 1.87 | 190000 | 0.5846 | 0.5846 | 0.5774 | 0.5529 | 0.5448 | | 0.575 | 1.89 | 192500 | 0.5866 | 0.5866 | 0.5779 | 0.5513 | 0.5443 | | 0.5836 | 1.91 | 195000 | 0.5815 | 0.5815 | 0.5764 | 0.5552 | 0.5470 | | 0.573 | 1.94 | 197500 | 0.5805 | 0.5805 | 0.5749 | 0.5561 | 0.5493 | | 0.5728 | 1.96 | 200000 | 0.5808 | 0.5808 | 0.5757 | 0.5558 | 0.5474 | | 0.5711 | 1.99 | 202500 | 0.5790 | 0.5790 | 0.5751 | 0.5572 | 0.5465 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.13.2

lora-library/walter-white-dreambooth

lora-library

null

71

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

504

# LoRA DreamBooth - walter-white These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "break bad" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: break bad ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)

dougtrajano/toxic-comment-classification

dougtrajano

bert

10

5

transformers

0

text-classification

true

false

apache-2.0

['pt']

null

0

['toxicity', 'portuguese', 'hate speech', 'offensive language', 'generated_from_trainer']

true

2,254

# dougtrajano/toxic-comment-classification Toxic Comment Classification is a model that detects if the text is toxic or not. This BERT model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the [OLID-BR dataset](https://huggingface.co/datasets/dougtrajano/olid-br). ## Overview **Input:** Text in Brazilian Portuguese **Output:** Binary classification (toxic or not toxic) ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dougtrajano/toxic-comment-classification") model = AutoModelForSequenceClassification.from_pretrained("dougtrajano/toxic-comment-classification") ``` ## Limitations and bias The following factors may degrade the model’s performance. **Text Language**: The model was trained on Brazilian Portuguese texts, so it may not work well with Portuguese dialects. **Text Origin**: The model was trained on texts from social media and a few texts from other sources, so it may not work well on other types of texts. ## Trade-offs Sometimes models exhibit performance issues under particular circumstances. In this section, we'll discuss situations in which you might discover that the model performs less than optimally, and should plan accordingly. **Text Length**: The model was fine-tuned on texts with a word count between 1 and 178 words (average of 18 words). It may give poor results on texts with a word count outside this range. ## Performance The model was evaluated on the test set of the [OLID-BR](https://dougtrajano.github.io/olid-br/) dataset. **Accuracy:** 0.8578 **Precision:** 0.8594 **Recall:** 0.8578 **F1-Score:** 0.8580 | Class | Precision | Recall | F1-Score | Support | | :---: | :-------: | :----: | :------: | :-----: | | `NOT-OFFENSIVE` | 0.8886 | 0.8490 | 0.8683 | 1,775 | | `OFFENSIVE` | 0.8233 | 0.8686 | 0.8453 | 1,438 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.255788747459486e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1993 - optimizer: Adam with betas=(0.8445637934160373,0.8338816842140165) and epsilon=2.527092625455385e-08 - lr_scheduler_type: linear - num_epochs: 30 - label_smoothing_factor: 0.07158711257743958 ### Framework versions - Transformers 4.26.0 - Pytorch 1.10.2+cu113 - Datasets 2.9.0 - Tokenizers 0.13.2 ## Provide Feedback If you have any feedback on this model, please [open an issue](https://github.com/DougTrajano/ToChiquinho/issues/new) on GitHub.

mqy/mt5-small-finetuned-11feb-1

mqy

mt5

14

5

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

1,593

# mt5-small-finetuned-11feb-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5110 - Rouge1: 17.54 - Rouge2: 5.46 - Rougel: 17.42 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 5.4156 | 1.0 | 311 | 2.6628 | 15.35 | 5.18 | 15.25 | | 3.366 | 2.0 | 622 | 2.5576 | 16.92 | 5.18 | 16.8 | | 3.1718 | 3.0 | 933 | 2.5174 | 16.96 | 5.48 | 16.83 | | 3.0648 | 4.0 | 1244 | 2.5021 | 17.32 | 5.34 | 17.12 | | 3.0095 | 5.0 | 1555 | 2.5110 | 17.54 | 5.46 | 17.42 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

AngelUrq/ppo-Huggy

AngelUrq

null

32

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

819

# **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: AngelUrq/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ziyu600601/etreyrt

ziyu600601

null

16

8

diffusers

0

text-to-image

false

creativeml-openrail-m

['en']

null

0

['stable-diffusion', 'text-to-image', 'image-to-image', 'diffusers']

false

true

4,567

# Diffusion model This model is trained with high quality and detailed anime images. ## Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI run EimisAnimeDiffusion_1.0v: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/EimisAnimeDiffusion_1.0v) # Sample generations This model works well on anime and landscape generations.<br> Anime:<br> There are some sample generations:<br> ``` Positive:a girl, Phoenix girl, fluffy hair, war, a hell on earth, Beautiful and detailed explosion, Cold machine, Fire in eyes, burning, Metal texture, Exquisite cloth, Metal carving, volume, best quality, normal hands, Metal details, Metal scratch, Metal defects, masterpiece, best quality, best quality, illustration, highres, masterpiece, contour deepening, illustration,(beautiful detailed girl),beautiful detailed glow Negative:lowres, bad anatomy, ((bad hands)), text, error, ((missing fingers)), cropped, jpeg artifacts, worst quality, low quality, signature, watermark, blurry, deformed, extra ears, deformed, disfigured, mutation, censored, ((multiple_girls)) Steps: 20, Sampler: DPM++ 2S a, CFG scale: 8, Seed: 4186044705/4186044707, Size: 704x896 ``` <img src=https://imgur.com/2U295w3.png width=75% height=75%> <img src=https://imgur.com/2jtF376.png width=75% height=75%> ``` Positive:(1girl), cute, walking in the park, (night), full moon, north star, blue shirt, red skirt, detailed shirt, jewelry, autumn, dark blue hair, shirt hair, (magic:1.5), beautiful blue eyes Negative: lowres, bad anatomy, ((bad hands)), text, error, ((missing fingers)), cropped, jpeg artifacts, worst quality, low quality, signature, watermark, blurry, deformed, extra ears, deformed, disfigured, mutation, censored, ((multiple_girls)) Steps: 35, Sampler: Euler a, CFG scale: 9, Seed: 296195494, Size: 768x960 ``` <img src=https://imgur.com/gudKxQe.png width=75% height=75%> ``` Positive:night , ((1 girl)), alone, masterpiece, 8k wallpaper, highres, absurdres, high quality background, short hair, black hair, multicolor hair, beautiful frozen village, (full bright moon), blue dress, detailed dress, jewelry dress, (magic:1.2), blue fire, blue eyes, glowing eyes, fire, ice goddess, (blue detailed beautiful crown), electricity, blue electricity, blue light particles Negative: lowres, bad anatomy, ((bad hands)), text, error, ((missing fingers)), cropped, jpeg artifacts, worst quality, low quality, signature, watermark, blurry, deformed, extra ears, deformed, disfigured, mutation, censored, ((multiple_girls)) Steps: 20, Sampler: DPM++ 2S a Karras, CFG scale: 9, Seed: 2118767319, Size: 768x832 ``` <img src=https://imgur.com/lJL4CJL.png width=75% height=75%> Want to generate some amazing backgrounds? No problem: ``` Positive: above clouds, mountains, (night), full moon, castle, huge forest, forest between mountains, beautiful, masterpiece Negative: lowres, bad anatomy, ((bad hands)), text, error, ((missing fingers)), cropped, jpeg artifacts, worst quality, low quality, signature, watermark, blurry, deformed, extra ears, deformed, disfigured, mutation, censored, ((multiple_girls)) Steps: 20, Sampler: DPM++ 2S a Karras, CFG scale: 9, Seed: 83644543, Size: 896x640 ``` <img src=https://imgur.com/XfxAx0S.png width=75% height=75%> ## Disclaimer Some prompts might not work perfectly (mainly colors), so add some more prompts for it to work, or try these -->(). Usually they help. Also works well with img2img if you want to add detail. ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

Seungjun/t5-small-finetuned-t5-Thor4

Seungjun

t5

12

7

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,592

# t5-small-finetuned-t5-Thor4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5607 - Rouge1: 30.1917 - Rouge2: 17.6334 - Rougel: 26.8513 - Rougelsum: 28.7606 - Gen Len: 18.9881 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9251 | 1.0 | 675 | 1.6082 | 29.3372 | 16.9607 | 26.1096 | 27.9357 | 18.9874 | | 1.763 | 2.0 | 1350 | 1.5696 | 30.1869 | 17.5627 | 26.8425 | 28.7413 | 18.9881 | | 1.7139 | 3.0 | 2025 | 1.5607 | 30.1917 | 17.6334 | 26.8513 | 28.7606 | 18.9881 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

figfig/local_test_model_with_local_dataset

figfig

whisper

14

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,469

# local_test_model_with_local_dataset This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5566 - Wer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 5 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 10.0 | 10 | 3.4660 | 85.7143 | | No log | 20.0 | 20 | 0.7373 | 10.7143 | | 3.3998 | 30.0 | 30 | 0.5920 | 0.0 | | 3.3998 | 40.0 | 40 | 0.5566 | 0.0 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ghproducts/ppo-LunarLander-v2

ghproducts

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **PP0** Agent playing **LunarLander-v2** This is a trained model of a **PP0** 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 ... ```

figfig/restaurant_test_at_local

figfig

whisper

20

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,458

# restaurant_test_at_local This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5402 - Wer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 5 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 10.0 | 10 | 3.7743 | 100.0 | | No log | 20.0 | 20 | 0.6750 | 52.6316 | | 3.6042 | 30.0 | 30 | 0.5724 | 0.0 | | 3.6042 | 40.0 | 40 | 0.5402 | 0.0 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.11.0+cu115 - Datasets 2.9.0 - Tokenizers 0.13.2

surprisedPikachu007/crop_prediction

surprisedPikachu007

null

3

0

null

0

null

false

null

0

[]

false

true

4,907

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

paulkm/autotrain-lottery_prod_v3-3409393337

paulkm

bert

8

7

transformers

0

text-classification

true

false

null

['zh']

['paulkm/autotrain-data-lottery_prod_v3']

{'emissions': 3.67386840637788}

0

['autotrain', 'text-classification']

false

true

946

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3409393337 - CO2 Emissions (in grams): 3.6739 ## Validation Metrics - Loss: 0.244 - Accuracy: 0.909 - Precision: 0.922 - Recall: 0.875 - AUC: 0.953 - F1: 0.898 ## 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_prod_v3-3409393337 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("paulkm/autotrain-lottery_prod_v3-3409393337", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("paulkm/autotrain-lottery_prod_v3-3409393337", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

yizhangliu/poca-SoccerTwos-v9

yizhangliu

null

24

63

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

847

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: yizhangliu/poca-SoccerTwos-v9 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

shields/whisper-medium-catalan

shields

whisper

18

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['hi']

['mozilla-foundation/common_voice_11_0']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,293

# Whisper Medium Catalan This model is a fine-tuned version of [openai/whisper-Medium](https://huggingface.co/openai/whisper-Medium) on the 10 hrs of Catalan Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 4.9217 - eval_wer: 132.1947 - eval_runtime: 3848.0596 - eval_samples_per_second: 0.78 - eval_steps_per_second: 0.78 - epoch: 1.14 - 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: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

kuhs/desci2

kuhs

null

4

0

sklearn

0

tabular-classification

false

null

0

['sklearn', 'skops', 'tabular-classification']

false

true

6,147

# Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------|---------| | ccp_alpha | 0.0 | | class_weight | | | criterion | gini | | max_depth | | | max_features | | | max_leaf_nodes | | | min_impurity_decrease | 0.0 | | min_samples_leaf | 1 | | min_samples_split | 2 | | min_weight_fraction_leaf | 0.0 | | random_state | | | splitter | best | </details> ### Model Plot The model plot is below. <style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div> ## Evaluation Results [More Information Needed] # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # model_description some random description

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

c-q

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

392

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="c-q/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Duskfallcrew/isometric-dreams

Duskfallcrew

null

21

diffusers

1

text-to-image

false

creativeml-openrail-m

null

1

0

1

0

['text-to-image']

false

true

1,194

[![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Duskfallcrew/isometric-dreams) ### Isometric Dreams Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! All samples and info are here: https://civitai.com/user/duskfallcrew If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk duskametrik (use that on your prompt)

FredZhang7/danbooru-tag-generator

FredZhang7

gpt2

5

0

transformers

0

text-generation

true

false

apache-2.0

['en']

['FredZhang7/anime-prompts-180K']

null

0

['stable-diffusion', 'anime', 'art']

false

true

595

## Disclaimer Danbooru stores millions of tagged anime images, but it doesn't have a way to filter out NSFW content. This model was trained on 100,000 of these tags with up_score ≥ 3 for 3 epochs, so it's possible that some tags might contain NSFW descriptions. So, just be mindful of that. Thank you for your cooperation. ## The Safe Version For details on data preprocessing, prompt engineering, and more, please see [Fast Anime PromptGen](https://huggingface.co/FredZhang7/anime-anything-promptgen-v2). I used a very similar approach to train the Danbooru version.

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

kaliputra

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

398

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="kaliputra/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

c-q/Taxi-v3

c-q

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

369

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="c-q/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

kaliputra/q-Taxi-v3-v1

kaliputra

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

368

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="kaliputra/q-Taxi-v3-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

pittawat/Reinforce-pixelcopter

pittawat

null

6

0

null

0

reinforcement-learning

false

null

0

['Pixelcopter-PLE-v0', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

300

# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Apes07/my_awesome_qa_model

Apes07

distilbert

36

2

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

908

# my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

kuhs/my-awesome-model

kuhs

null

5

0

sklearn

0

tabular-classification

false

mit

null

0

['sklearn', 'skops', 'tabular-classification']

false

true

6,777

# Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------|---------| | ccp_alpha | 0.0 | | class_weight | | | criterion | gini | | max_depth | | | max_features | | | max_leaf_nodes | | | min_impurity_decrease | 0.0 | | min_samples_leaf | 1 | | min_samples_split | 2 | | min_weight_fraction_leaf | 0.0 | | random_state | | | splitter | best | </details> ### Model Plot The model plot is below. <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.912281 | | f1 score | 0.912281 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # citation_bibtex bibtex @inproceedings{...,year={2020}} # get_started_code import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) # model_card_authors skops_user # limitations This model is not ready to be used in production. # model_description This is a DecisionTreeClassifier model trained on breast cancer dataset. # eval_method The model is evaluated using test split, on accuracy and F1 score with macro average. # confusion_matrix ![confusion_matrix](confusion_matrix.png)

pittawat/ppo-SnowballTarget

pittawat

null

30

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

855

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

kaliputra/q-Taxi-v3-v2

kaliputra

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

368

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="kaliputra/q-Taxi-v3-v2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Sjdan/finetuning11

Sjdan

wav2vec2

12

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,122

# finetuning11 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 0.0 | 0.31 | 500 | nan | 1.0 | | 0.0 | 0.61 | 1000 | nan | 1.0 | | 0.0 | 0.92 | 1500 | nan | 1.0 | | 0.0 | 1.23 | 2000 | nan | 1.0 | | 0.0 | 1.54 | 2500 | nan | 1.0 | | 0.0 | 1.84 | 3000 | nan | 1.0 | | 0.0 | 2.15 | 3500 | nan | 1.0 | | 0.0 | 2.46 | 4000 | nan | 1.0 | | 0.0 | 2.77 | 4500 | nan | 1.0 | | 0.0 | 3.07 | 5000 | nan | 1.0 | | 0.0 | 3.38 | 5500 | nan | 1.0 | | 0.0 | 3.69 | 6000 | nan | 1.0 | | 0.0 | 4.0 | 6500 | nan | 1.0 | | 0.0 | 4.3 | 7000 | nan | 1.0 | | 0.0 | 4.61 | 7500 | nan | 1.0 | | 0.0 | 4.92 | 8000 | nan | 1.0 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

erniechiew/a2c-PandaReachDense-v2

erniechiew

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

enankobh1/whisper-small-ASR

enankobh1

whisper

26

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,467

# whisper-small-ASR This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7176 - Wer: 112.3086 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0201 | 6.8 | 1000 | 0.4782 | 24.0338 | | 0.0006 | 13.61 | 2000 | 0.6535 | 76.6110 | | 0.0002 | 20.41 | 3000 | 0.7004 | 102.1109 | | 0.0002 | 27.21 | 4000 | 0.7176 | 112.3086 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

pittawat/ppo-PyramidsTraining1

pittawat

null

16

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

840

# **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: pittawat/ppo-PyramidsTraining1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

chist/poca-SoccerTwos

chist

null

22

56

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

839

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

Sabbir29/distilbert-base-uncased-finetuned-squad

Sabbir29

distilbert

22

2

transformers

0

question-answering

true

false

apache-2.0

null

['squad_bn']

null

0

['generated_from_trainer']

true

1,174

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_bn dataset. It achieves the following results on the evaluation set: - Loss: 1.4896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2709 | 1.0 | 8703 | 1.4896 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

mrizalf7/FirstTextClassification

mrizalf7

distilbert

12

6

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,274

# FirstTextClassification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2312 - Accuracy: 0.9313 ## 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.231 | 1.0 | 1563 | 0.1894 | 0.9268 | | 0.1514 | 2.0 | 3126 | 0.2312 | 0.9313 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Rubywong123/q-FrozenLake-v1-4x4-noSlippery

Rubywong123

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

400

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Rubywong123/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Rubywong123/q-Taxi-v3

Rubywong123

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

367

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Rubywong123/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

kubasvehla/distilbert-base-uncased-finetuned-emotion

kubasvehla

distilbert

14

5

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,344

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2288 - Accuracy: 0.9225 - F1: 0.9226 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8577 | 1.0 | 250 | 0.3264 | 0.903 | 0.8992 | | 0.2559 | 2.0 | 500 | 0.2288 | 0.9225 | 0.9226 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Sjdan/finetuning12

Sjdan

wav2vec2

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,562

# finetuning12 This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 0.0 | 0.31 | 500 | nan | 1.0 | | 0.0 | 0.61 | 1000 | nan | 1.0 | | 0.0 | 0.92 | 1500 | nan | 1.0 | | 0.0 | 1.23 | 2000 | nan | 1.0 | | 0.0 | 1.54 | 2500 | nan | 1.0 | | 0.0 | 1.84 | 3000 | nan | 1.0 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

LucianoDeben/Reinforce-cartpole-v3

LucianoDeben

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

lilDenden/ppo-LunarLander-v2

lilDenden

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

pittawat/a2c-AntBulletEnv-v0

pittawat

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

352

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

taron88/CCCmix

taron88

null

3

0

null

0

null

false

null

0

[]

false

true

341

公開されているモデルを単純マージしたモデルです。 7th v3.0 CをベースにCinnamonmixとCounterfeit-V2.5をマージしました。 7th v3.0 CをAに配置してB,CにCinnamonとcounterfeitを配置。設定はWeighted sumの0.5だったと思います。 7thCのアニメ寄りのイラストはそのままにCinnamonの塗りと雰囲気、counterfeitの背景の精度を目指しました。 https://s3.amazonaws.com/moonup/production/uploads/1676112658952-6315eee0e06cb6c5c424344d.jpeg --- license: other ---

hanselgm/autotrain-nlp-exercise-3413793400

hanselgm

bert

8

30

transformers

0

text-classification

true

false

null

['en']

['hanselgm/autotrain-data-nlp-exercise']

{'emissions': 7.21478572426289}

0

['autotrain', 'text-classification']

false

true

1,093

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3413793400 - CO2 Emissions (in grams): 7.2148 ## Validation Metrics - Loss: 0.311 - Accuracy: 0.896 - Macro F1: 0.861 - Micro F1: 0.896 - Weighted F1: 0.892 - Macro Precision: 0.912 - Micro Precision: 0.896 - Weighted Precision: 0.898 - Macro Recall: 0.828 - Micro Recall: 0.896 - Weighted Recall: 0.896 ## 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/hanselgm/autotrain-nlp-exercise-3413793400 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("hanselgm/autotrain-nlp-exercise-3413793400", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("hanselgm/autotrain-nlp-exercise-3413793400", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

Sa1i/gakki-mix-512-young

Sa1i

null

22

2

diffusers

1

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion', 'gakki']

false

true

529

# VAE Highly recommended for use with VAE # legal & risk ⚠️⚠ It is prohibited to use this model for commercial purposes and any scenarios of illegal acts and purposes. Sample pictures of this concept: ![0](https://huggingface.co/Sa1i/gakki-mix/resolve/main/sample_images/00986-2977967196.png) ![1](https://huggingface.co/Sa1i/gakki-mix/resolve/main/sample_images/00997-2275133157.png) ![2](https://huggingface.co/Sa1i/gakki-mix/resolve/main/sample_images/01002-3229456781.png)

deprem-ml/deprem-keras-satellite-semantic-mapping

deprem-ml

null

3

0

keras

0

image-segmentation

false

apache-2.0

null

0

['Segments', 'mapping', 'keras', 'object-segmentation']

false

true

272

Kaggle Notebook: https://www.kaggle.com/code/kmader/segmenting-buildings-in-satellite-images Dataset: https://www.kaggle.com/datasets/kmader/synthetic-word-ocr Hugging Face Space: https://huggingface.co/spaces/deprem-ml/deprem_keras-satellite_semantic_mapping-challange

jojoUla/bert-large-cased-sigir-support-refute-no-label-40

jojoUla

bert

14

0

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,247

# bert-large-cased-sigir-support-refute-no-label-40 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8371 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 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: 40.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.4511 | 1.0 | 252 | 2.0790 | | 2.0373 | 2.0 | 504 | 1.8538 | | 1.8052 | 3.0 | 756 | 1.6633 | | 1.6663 | 4.0 | 1008 | 1.5591 | | 1.5556 | 5.0 | 1260 | 1.4441 | | 1.4505 | 6.0 | 1512 | 1.3836 | | 1.3619 | 7.0 | 1764 | 1.3255 | | 1.2968 | 8.0 | 2016 | 1.2505 | | 1.2332 | 9.0 | 2268 | 1.2165 | | 1.1788 | 10.0 | 2520 | 1.1517 | | 1.1408 | 11.0 | 2772 | 1.1446 | | 1.0992 | 12.0 | 3024 | 1.1512 | | 1.0578 | 13.0 | 3276 | 1.1058 | | 1.0277 | 14.0 | 3528 | 1.0662 | | 1.0036 | 15.0 | 3780 | 1.0270 | | 0.9655 | 16.0 | 4032 | 1.0207 | | 0.9364 | 17.0 | 4284 | 1.0220 | | 0.9085 | 18.0 | 4536 | 0.9874 | | 0.8897 | 19.0 | 4788 | 0.9658 | | 0.8661 | 20.0 | 5040 | 0.9603 | | 0.8434 | 21.0 | 5292 | 0.9754 | | 0.8248 | 22.0 | 5544 | 0.9406 | | 0.8052 | 23.0 | 5796 | 0.9154 | | 0.7975 | 24.0 | 6048 | 0.8760 | | 0.7854 | 25.0 | 6300 | 0.8688 | | 0.7673 | 26.0 | 6552 | 0.8536 | | 0.7463 | 27.0 | 6804 | 0.8544 | | 0.7412 | 28.0 | 7056 | 0.8514 | | 0.7319 | 29.0 | 7308 | 0.8356 | | 0.7143 | 30.0 | 7560 | 0.8832 | | 0.7081 | 31.0 | 7812 | 0.8421 | | 0.7026 | 32.0 | 8064 | 0.8295 | | 0.687 | 33.0 | 8316 | 0.8401 | | 0.6882 | 34.0 | 8568 | 0.8053 | | 0.679 | 35.0 | 8820 | 0.8438 | | 0.6672 | 36.0 | 9072 | 0.8450 | | 0.6669 | 37.0 | 9324 | 0.8231 | | 0.6665 | 38.0 | 9576 | 0.8410 | | 0.6596 | 39.0 | 9828 | 0.7909 | | 0.6556 | 40.0 | 10080 | 0.8019 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

bluepen5805/blue_pencil

bluepen5805

null

9

0

null

11

text-to-image

false

creativeml-openrail-m

['ja']

null

0

['stable-diffusion', 'text-to-image']

false

true

3,071

# blue_pencil <strong>blue_pencil</strong> は、様々なモデルを適当な配合でマージしたモデルです。有名なモデルをいくつか思い浮かべてください。あなたが思い浮かべたモデルは、恐らくこのモデルに含まれています。このマージモデルの特徴はわかりません。いろいろなモデルをマージしてみることが目的なので、質も高くありません。全てのモデルは [stable-diffusion-webui-model-tookit](https://github.com/arenatemp/stable-diffusion-webui-model-toolkit) を用いて `fp16` にしています。 --- <details open><summary><h2 style="display: inline;"><code>blue_pencil-v2b</code> <small>(<code>@20230219</code>)</small></h2></summary> `blue_pencil-v2` の [Balor-V2](https://huggingface.co/ploughB660/Balor-V2) の代わりに [Balor-V3](https://huggingface.co/ploughB660/Balor-V3) を階層マージしたモデルです表現力が上がっている気がします ### 推奨設定 * VAE: [vae-ft-mse-840000-ema-pruned](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) * Negative Embedding: [EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative) ### 出力例 ``` girl, berlin, scenery Negative prompt: EasyNegative Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 3164975857 Size: 768x768, Clip skip: 2 Denoising strength: 0.65, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ``` ![blue_pencil-v2b_1](../../resolve/main/images/blue_pencil-v2b/1.png) </details> <details open><summary><h2 style="display: inline;"><code>blue_pencil-v2</code> <small>(<code>@20230217</code>)</small></h2></summary> [AbyssOrangeMix3A1](https://huggingface.co/WarriorMama777/OrangeMixs) をベースに配合しなおしたモデルです。気持ち程度に自由度が上がっている気がします（同時に破綻率も上がってる気がする）基本的には v1 系と同じ雰囲気の絵になる気がします。以下のモデルが含まれています（順不同） <details><summary>マージ元モデル一覧</summary> * [AbyssOrangeMix3A1](https://huggingface.co/WarriorMama777/OrangeMixs) * AnythingV3.0 * ChilloutMix * GAPE60 * Counterfeit2.5 * Kenshi * [Evt_M](https://huggingface.co/haor/Evt_M) * Evt_V4 * ACertainty * [GingerMixR](https://huggingface.co/Hemlok/GingerMix) * LimeMixV2 * [Elysium_Anime_V3](https://huggingface.co/hesw23168/SD-Elysium-Model) * [VaLJMix](https://huggingface.co/Hemlok/VaLMix) * pastel-mix * ACertainThing * basil_mix * Counterfeit-V2.5 * openjourney * [HD-22](https://www.cognitionai.org/hdhowtogetstarted) * [7th_anime_v3_testA](https://huggingface.co/syaimu/7th_test) * [AniReal](https://huggingface.co/Hosioka/AniReal) * [atwcustom_V4](https://huggingface.co/atsuwo/ATW-custom) * [Nabylon-v1.2](https://huggingface.co/NegiInNattoMaki/Nabylon-v1.0) * AbyssOrangeMix2 * LonganMix * and more * [TriPhaze_B](https://huggingface.co/Lucetepolis/TriPhaze) * ultracolor.v4 * Counterfeit-V2.5 * Treebark * [Balor-V2](https://huggingface.co/ploughB660/Balor-V2) </details> ### 推奨設定 * VAE: [vae-ft-mse-840000-ema-pruned](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) * Negative Embedding: [EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative) ### 出力例 ``` girl, tokyo, scenery Negative prompt: EasyNegative Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 205537258 Size: 768x768, Clip skip: 2 Denoising strength: 0.65, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ``` ![blue_pencil-v2_1](../../resolve/main/images/blue_pencil-v2/1.png) ``` girl, spacesuit, beautiful earth, scenery, on the moon Negative prompt: EasyNegative Steps: 50, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 1069444343 Size: 960x640, Clip skip: 2 Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ``` ![blue_pencil-v2_2](../../resolve/main/images/blue_pencil-v2/2.png) </details> <details><summary><h2 style="display: inline;"><code>blue_pencil-v1b</code> <small>(<code>@20230212</code>)</small></h2></summary> `blue_pencil-v1` の [Amalgam_Mix](https://civitai.com/models/4758/amalgammix) の代わりに [Balor-V2](https://huggingface.co/ploughB660/Balor-V2) を階層マージしたモデルです v1 とはちょっと傾向が違います ### 推奨設定 * VAE: [vae-ft-mse-840000-ema-pruned](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) * Negative Embedding: [EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative) ### 出力例 ``` girl, tokyo, scenery Negative prompt: EasyNegative Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 205537258 Size: 768x768, Clip skip: 2 Denoising strength: 0.65, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ``` ![blue_pencil-v1b_1](../../resolve/main/images/blue_pencil-v1b/1.png) </details> <details><summary><h2 style="display: inline;"><code>blue_pencil-v1</code> <small>(<code>@20230211</code>)</small></h2></summary> 以下のモデルが含まれています（順不同） <details><summary>マージ元モデル一覧</summary> * [Defmix-v1.1](https://huggingface.co/Defpoint/Defmix-v1.0) * Counterfeit v1.0 * Counterfeit v2.0 * Basil Mix * Anything v4.0 * [PastelRainier](https://huggingface.co/Hemlok/RainierMix) * ACertainThing * Anything-V4.5 * Counterfeit-V2.0 * Evt_V4-preview * basil_mix * pastel-mix * [GingerMixR](https://huggingface.co/Hemlok/GingerMix) * LimeMixV2 * [Elysium_Anime_V3](https://huggingface.co/hesw23168/SD-Elysium-Model) * [SukiyakiMix-v1.0](https://huggingface.co/Vsukiyaki/SukiyakiMix-v1.0) * pastel-mix * AbyssOrangeMix2 * [HD-20](https://www.cognitionai.org/hdhowtogetstarted) * [7th_anime_v3_testA](https://huggingface.co/syaimu/7th_test) * [AniReal](https://huggingface.co/Hosioka/AniReal) * [TriPhaze_B](https://huggingface.co/Lucetepolis/TriPhaze) * ultracolor.v4 * Counterfeit-V2.5 * Treebark * [Nabylon-v1.2](https://huggingface.co/NegiInNattoMaki/Nabylon-v1.0) * AbyssOrangeMix2 * LonganMix * and more * [atwcustom_V4](https://huggingface.co/atsuwo/ATW-custom) * [Amalgam_Mix](https://civitai.com/models/4758/amalgammix) </details> ### 推奨設定 * VAE: [vae-ft-mse-840000-ema-pruned](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) * Negative Embedding: [EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative) ### 出力例 #### 1 ``` girl, tokyo, scenery Negative prompt: EasyNegative Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 2526423076 Size: 768x768, Clip skip: 2 ``` ![blue_pencil-v1_1-1](../../resolve/main/images/blue_pencil-v1/1-1.png) ##### Hires. fix ``` Denoising strength: 0.6, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ``` ![blue_pencil-v1_1-2](../../resolve/main/images/blue_pencil-v1/1-2.png) #### 2 ``` girl, early teen, kimono, sakura, particles Negative prompt: EasyNegative Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 4036639388, Size: 512x768, Clip skip: 2 ``` ![blue_pencil-v1_2-1](../../resolve/main/images/blue_pencil-v1/2-1.png) ##### Hires. fix ``` Denoising strength: 0.62, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ``` ![blue_pencil-v1_2-2](../../resolve/main/images/blue_pencil-v1/2-2.png) #### 3 ``` girl, early teen, t-shirt, pants, from behind, landscape, scenery, apocalyptic Negative prompt: EasyNegative Steps: 40, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 748447692, Size: 768x512, Clip skip: 2 ``` ![blue_pencil-v1_3](../../resolve/main/images/blue_pencil-v1/3.png) </details>

pittawat/a2c-PandaReachDense-v2

pittawat

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

atorre/poca-SoccerTwos-50M

atorre

null

26

50

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

844

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: atorre/poca-SoccerTwos-50M 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

deprem-ml/deprem_satellite_semantic_whu

deprem-ml

segformer

9

272

transformers

0

null

true

false

null

0

['generated_from_trainer']

true

1,233

# deprem_satellite_semantic_whu This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0692 - eval_mean_iou: 0.8739 - eval_mean_accuracy: 0.9277 - eval_overall_accuracy: 0.9786 - eval_accuracy_background: 0.9888 - eval_accuracy_building: 0.8665 - eval_iou_background: 0.9770 - eval_iou_building: 0.7708 - eval_runtime: 124.6705 - eval_samples_per_second: 4.011 - eval_steps_per_second: 4.011 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ritesh27gole/ppo-LunarLander-v2

ritesh27gole

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

MerlinTK/ppo-Huggy

MerlinTK

null

32

3

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

819

# **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: MerlinTK/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ploughB660/Balor-V2

ploughB660

null

7

0

null

9

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'text-to-image']

false

true

1,671

Balor-V2は他のモデルとマージすることで描画を向上させることを目標に作成されたマージモデルです。 AIアートにおいて、服装や背景の書き込みが増えるとキャラクターの目の描写があいまいになることが多くあります。このモデルでは、特にこの目の描写の質を保つことを目標としています。 Balor-V2 is a merged model created with the goal of improving rendering by merging with other models. In AI art, the depiction of a character's eyes often becomes fuzzy as more clothing and backgrounds are written. This model specifically aims to maintain the quality of the depiction of these eyes. <img src="https://i.imgur.com/QC5223V.jpg" width="450" height=""> <img src="https://i.imgur.com/e3Bj3fq.jpg" width="450" height=""> <img src="https://i.imgur.com/pM6KZYI.png" width="450" height=""> Balor-V2は以下の配分でほかの自由に選んだモデルModelAと階層マージを行うことで、そのモデルにBalor-V2の要素を与えることが可能です。 Balor-V2 can be hierarchically merged with another freely chosen model ModelA using the following distribution to give that model the elements of BalorV2. | Model: A | Model: B | Weight | Base alpha | Merge Name | | --- | --- | --- | --- | --- | | ModelA | BalorV2 | 1,1,1,0,0,0,0,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1 | 1 | BalorV2featModelA | 配布しているものはそれぞれAbyssOrangeMix2_sfw、Counterfeit-V2.5、Evt_MをModelAに置いています。すべての要素のマージ比率が1のため、他のモデルModelBに対してBalorV2featModelAを使用して、BalorV2featModelBを作成することが可能です。流行のマージモデルなども使用することができます。 The distributions are placed in ModelA with AbyssOrangeMix2_sfw, Counterfeit-V2.5, and Evt_M, respectively. Since the merge ratio of all elements is 1, it is possible to create a BalorV2featModelB using BalorV2featModelA for the other model ModelB. マージ比率の記述にあたりWarriorMama777/OrangeMixsのモデルカードを参考としました。感謝申し上げます。 I used the WarriorMama777/OrangeMixes model card as a reference to describe the merge ratio.

0RisingStar0/LiveArcaMix

0RisingStar0

null

11

0

diffusers

7

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'text-to-image', 'diffusers']

false

true

1,615

<p align="center"><img src="https://huggingface.co/0RisingStar0/LiveArcaMix/resolve/main/00122-1387049655-(masterpiece%2C%20best%20quality%2C%20excellent%20quality)%2C%20((1girl%2C%20solo%2C%20cowboy%20shot))%2C%20street%2C%20apartment%2C%20building%2C%20pavement%2C%20trees%2C%20flow.png"> <img src="https://huggingface.co/0RisingStar0/LiveArcaMix/resolve/main/00123-3601874250-(masterpiece%2C%20best%20quality%2C%20excellent%20quality)%2C%20((1girl%2C%20solo%2C%20cowboy%20shot))%2C%20street%2C%20apartment%2C%20building%2C%20pavement%2C%20trees%2C%20flow.png"> <img src="https://huggingface.co/0RisingStar0/LiveArcaMix/resolve/main/00128-3652958527-(masterpiece%2C%20best%20quality%2C%20excellent%20quality)%2C%20((1girl%2C%20solo%2C%20cowboy%20shot))%2C%20city%2C%20(skyscrapers)%2C%20sky%2C%20wide%20street%2C%20pavement%2C%20t.png"> <img src="https://huggingface.co/0RisingStar0/LiveArcaMix/resolve/main/00135-2463548988-(masterpiece%2C%20best%20quality%2C%20excellent%20quality)%2C%20((1girl%2C%20solo%2C%20cowboy%20shot))%2C%20city%2C%20(skyscrapers)%2C%20sky%2C%20wide%20street%2C%20pavement%2C%20t.png"></p> <center><b>LiveArcaMix for channel competition.</b></center> 1. AikimiXPv1.0 + CounterfeitV2.0 - Preset GRAD_A base_alpha : 0 => AModel 2. AModel + CounterfeitV2.5 - IN09, IN10, OUT01, OUT02 : 1, else : 0 base_alpha : 0 => BModel 3. BModel + BasilMixFixed - Preset MID12_50, base_alpha : 1 => CModel 4. CModel + Anything V4.5 - Preset FLAT_75 base_alpha : 0 => DModel 5. CounterfeitV2.5 + AikimiXPv1.0 - Preset FLAT_25 => EModel 6. DModel + EModel - Preset RING10_5 => Result(LiveArcaMix)

kf1022/Crowbox-Vol.1

kf1022

null

10

0

null

1

text-to-image

false

creativeml-openrail-m

['en']

null

0

['stable-diffusion', 'text-to-image']

false

true

6,627

![00255-3381954399-(masterpiece), (best quality), (illustration), (beautiful detailed), (highres), 1girl, (in box_1.2), _cardboard box, full body,.png](https://s3.amazonaws.com/moonup/production/uploads/1676151918929-6352f15a02da56b12ed42369.png) based on Anything V4, Made by several Loras, and block merge with community models Using these negative prompt have actually resulted in significant improvements https://huggingface.co/datasets/gsdf/EasyNegative https://civitai.com/models/4629/deep-negative-v1x Although I tried to reduce negative prompt, but gave up because the difference was difficult to evaluate. Maybe this is enough. ``` (EasyNegative:1.4), (NG_DeepNegative:1.4), (worst quality:1.4), (low quality:1.4) , (monochrome:1.1) ``` ![grid-2105.png](https://s3.amazonaws.com/moonup/production/uploads/1676177700433-6352f15a02da56b12ed42369.png) ``` (masterpiece), (best quality), (illustration), (beautiful detailed), (highres), 1girl, solo, looking at viewer, sitting, sunset, (school uniform), long hair, red eyes, low twintails, ribbon, white shirt, (blue) skirt, black thighhighs, smile, blush, indoors, window, [building], classroom, table, chair, (coffee:0.5) Negative prompt: (EasyNegative:1.4), (NG_DeepNegative:1.4), (worst quality:1.4), (low quality:1.4) , (monochrome:1.1), lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, normal quality, jpeg artifacts, (signature, watermark, username:1.4), blurry, bad feet, multiple breasts, (mutated hands and fingers:1.5 ), (long body :1.3), (mutation, poorly drawn :1.2) , black-white, liquid body, liquid tongue, disfigured, malformed, mutated, anatomical nonsense, text font ui, malformed hands, long neck, blurred, lowers, bad proportions, bad shadow, uncoordinated body, unnatural body, fused breasts, bad breasts, huge breasts, poorly drawn breasts, extra breasts, liquid breasts, heavy breasts, missing breasts, huge haunch, huge thighs, huge calf, fused hand, missing hand, (holding) Steps: 20, Sampler: Euler a, CFG scale: 8.5, Seed: 2085854603, Size: 384x640, Model hash: 79fb704aeb, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent, Eta: 0.67 ``` ![grid-2109.png](https://s3.amazonaws.com/moonup/production/uploads/1676178427319-6352f15a02da56b12ed42369.png) ``` (masterpiece), (best quality), (illustration), (beautiful detailed), (highres), 1girl, solo, (Middle Ages), outdoors, (rooftop), sitting on roof, phantom thieves, night, short hair, silver hair, blue eyes, curly hair, (top hat:1.2), (white cape), gloves, black pantyhose, smirk, street, horizon, castle, moon Negative prompt: (EasyNegative:1.4), (NG_DeepNegative:1.4), (worst quality:1.4), (low quality:1.4) , (monochrome:1.1), lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, normal quality, jpeg artifacts, (signature, watermark, username:1.4), blurry, bad feet, multiple breasts, (mutated hands and fingers:1.5 ), (long body :1.3), (mutation, poorly drawn :1.2) , black-white, liquid body, liquid tongue, disfigured, malformed, mutated, anatomical nonsense, text font ui, malformed hands, long neck, blurred, lowers, bad proportions, bad shadow, uncoordinated body, unnatural body, fused breasts, bad breasts, huge breasts, poorly drawn breasts, extra breasts, liquid breasts, heavy breasts, missing breasts, huge haunch, huge thighs, huge calf, fused hand, missing hand, car, kneeing Steps: 20, Sampler: Euler a, CFG scale: 8.5, Seed: 3008135755, Size: 768x512, Model hash: 79fb704aeb, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent, Eta: 0.67 ``` ![grid-2112.png](https://s3.amazonaws.com/moonup/production/uploads/1676178971035-6352f15a02da56b12ed42369.png) ``` (masterpiece), (best quality), (illustration), (beautiful detailed), (highres), (1girl), (solo), sitting, upper body, (outdoors), afternoon tea, table, street, Middle Ages, gloves, (silver:1.2) long hair, (pink dress), beige hat, smile, upper teeth, see-through sleeves, castle, street, garden, (flower:1.1), dessert, (cake) Negative prompt: (EasyNegative:1.4), (NG_DeepNegative:1.4), (worst quality:1.4), (low quality:1.4) , (monochrome:1.1), lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, normal quality, jpeg artifacts, (signature, watermark, username:1.4), blurry, bad feet, multiple breasts, (mutated hands and fingers:1.5 ), (long body :1.3), (mutation, poorly drawn :1.2) , black-white, liquid body, liquid tongue, disfigured, malformed, mutated, anatomical nonsense, text font ui, malformed hands, long neck, blurred, lowers, bad proportions, bad shadow, uncoordinated body, unnatural body, fused breasts, bad breasts, huge breasts, poorly drawn breasts, extra breasts, liquid breasts, heavy breasts, missing breasts, huge haunch, huge thighs, huge calf, fused hand, missing hand Steps: 20, Sampler: Euler a, CFG scale: 8.5, Seed: 4194623658, Size: 512x704, Model hash: 79fb704aeb, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 2, Hires steps: 20, Hires upscaler: lollypop, Eta: 0.67 ``` ![grid-2116.png](https://s3.amazonaws.com/moonup/production/uploads/1676180972038-6352f15a02da56b12ed42369.png) ``` (masterpiece), (best quality), (illustration), (beautiful detailed), (highres), 1girl, solo, hugging own legs, underwater, bubble, water, full body, long hair, silver hair, purple eyes, smile, hair ribbon, bare legs, sleeveless dress, frilled dress, barefoot, one eye closed Negative prompt: (EasyNegative:1.4), (NG_DeepNegative:1.4), (worst quality:1.4), (low quality:1.4) , (monochrome:1.1), lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, normal quality, jpeg artifacts, (signature, watermark, username:1.4), blurry, bad feet, multiple breasts, (mutated hands and fingers:1.5 ), (long body :1.3), (mutation, poorly drawn :1.2) , black-white, liquid body, liquid tongue, disfigured, malformed, mutated, anatomical nonsense, text font ui, malformed hands, long neck, blurred, lowers, bad proportions, bad shadow, uncoordinated body, unnatural body, fused breasts, bad breasts, huge breasts, poorly drawn breasts, extra breasts, liquid breasts, heavy breasts, missing breasts, huge haunch, huge thighs, huge calf, fused hand, missing hand, black hair, fish, animal, creatue Steps: 20, Sampler: Euler a, CFG scale: 8.5, Seed: 1169234669, Size: 512x512, Model hash: 79fb704aeb, Denoising strength: 0.65, Clip skip: 2, Hires upscale: 2, Hires steps: 20, Hires upscaler: lollypop, Eta: 0.67 ```

OliP/a2c-AntBulletEnv-v0

OliP

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

352

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Duskfallcrew/duskfall-crew-visual-art-style-1-5

Duskfallcrew

null

21

17

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

930

### Duskfall Crew Visual Art Style 1.5 Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Trained on our own art DO NOT SELL YOUR MERGES DO NOT RESELL THIS MODEL PLEASE GIVE CREDIT WHEN USING OR MERGING IDGAF BEYOND THAT If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk dskyart1 (use that on your prompt)

Mithul/Reinforce-PixelCopter

Mithul

null

6

0

null

0

reinforcement-learning

false

null

0

['Pixelcopter-PLE-v0', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

300

# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

pittawat/a2c-PandaReachDense-v2-2

pittawat

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

gubartz/flan-t5-base7

gubartz

t5

15

10

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['text2text-generation', 'generated_from_trainer']

true

3,173

# flan-t5-base7 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5607 - Rouge1: 70.524 - Rouge2: 51.8406 - Rougel: 68.8374 - Rougelsum: 68.7883 ## 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: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 6.6376 | 1.0 | 52 | 1.2371 | 24.2463 | 14.2174 | 24.334 | 24.2896 | | 1.0438 | 2.0 | 104 | 0.6731 | 62.1063 | 45.7876 | 60.653 | 60.6363 | | 0.7045 | 3.0 | 156 | 0.5891 | 67.5005 | 49.6879 | 65.7653 | 65.7592 | | 0.6175 | 4.0 | 208 | 0.5638 | 67.1457 | 49.5298 | 65.6249 | 65.6293 | | 0.5804 | 5.0 | 260 | 0.5450 | 67.9682 | 49.7911 | 66.3619 | 66.4208 | | 0.5633 | 6.0 | 312 | 0.5612 | 66.2747 | 48.739 | 64.8014 | 64.8095 | | 0.5414 | 7.0 | 364 | 0.5626 | 67.3805 | 49.4668 | 65.7599 | 65.7622 | | 0.5255 | 8.0 | 416 | 0.5948 | 65.0301 | 47.8636 | 63.4687 | 63.4516 | | 0.5227 | 9.0 | 468 | 0.5307 | 67.7462 | 49.6922 | 66.2863 | 66.2771 | | 0.5074 | 10.0 | 520 | 0.5547 | 69.0972 | 50.9602 | 67.5046 | 67.5282 | | 0.4939 | 11.0 | 572 | 0.5843 | 67.6059 | 49.376 | 66.0482 | 66.1111 | | 0.4803 | 12.0 | 624 | 0.5369 | 67.7427 | 49.8254 | 66.2864 | 66.2938 | | 0.4869 | 13.0 | 676 | 0.5271 | 71.3421 | 53.1967 | 69.7099 | 69.7405 | | 0.475 | 14.0 | 728 | 0.6614 | 67.9896 | 49.8985 | 66.3579 | 66.3518 | | 0.479 | 15.0 | 780 | 0.5576 | 68.446 | 50.1408 | 66.942 | 66.9502 | | 0.4647 | 16.0 | 832 | 0.5501 | 70.5046 | 51.7277 | 68.836 | 68.8619 | | 0.4637 | 17.0 | 884 | 0.6093 | 69.8509 | 50.9488 | 68.1152 | 68.0849 | | 0.4588 | 18.0 | 936 | 0.5773 | 69.8538 | 51.1648 | 68.1531 | 68.13 | | 0.4606 | 19.0 | 988 | 0.5621 | 70.5416 | 51.9142 | 68.8009 | 68.7511 | | 0.4586 | 20.0 | 1040 | 0.5607 | 70.524 | 51.8406 | 68.8374 | 68.7883 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Celal11/resnet-50-4-32

Celal11

resnet

9

0

transformers

0

image-classification

true

false

apache-2.0

null

['image_folder']

null

0

['generated_from_trainer']

true

1,479

# resnet-50-4-32 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9705 - Accuracy: 0.6410 ## 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: 0.005 - 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3833 | 1.0 | 224 | 1.2683 | 0.5134 | | 1.2404 | 2.0 | 448 | 1.1342 | 0.5659 | | 1.1492 | 3.0 | 672 | 1.0359 | 0.6087 | | 1.1433 | 4.0 | 896 | 0.9705 | 0.6410 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

OliP/a2c-PandaReachDense-v2

OliP

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jmete/tweet_instruct_detect

jmete

bert

12

9

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,453

# tweet_instruct_detect This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on an dataset combining manually labelled tweets into either instructions or spam, and pre-processed instructions from the flan dataset that are less than 250 characters long to be used as positive instructions. It achieves the following results on the evaluation set: - Loss: 0.1300 - Accuracy: 0.9761 ## Model description This model is trained to help determine if tweets are useful instructions. This can be used to filter the large corpus of tweet data online into useful instruction datasets for instruction fine-tuning. ## Intended uses & limitations Intended to be used to determine if tweets are useful instructions. The model will be biased towards english data, and maybe be biased towards certain ways of phrasing "instructions". Instructions in this case may also be questions. Current version of the model is very basic and can get confused by simple things. For example, simply adding a ? character will bias it heavily towards an instruction, even if using the same sentence so it is highly sensitive to certain characters and ways of phrasing things. This can hopefully be fixed by better training data or model tuning. ## Training and evaluation data Model was fine-tuned on a relatively small number of tweets and instructions. Train data: 749 examples Test data: 251 examples Out of the total number of examples, 526 of them were manually labelled tweets, most of which were spam due to the high noise ratio in tweets. Spam in this case can refer to actual spam, gibberish, or also statements that are generally fine but not useful as an instruction or question. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 47 | 0.3832 | 0.9562 | | No log | 2.0 | 94 | 0.2004 | 0.9681 | | No log | 3.0 | 141 | 0.1501 | 0.9721 | | No log | 4.0 | 188 | 0.1362 | 0.9721 | | No log | 5.0 | 235 | 0.1300 | 0.9761 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

mili7522/Reinforce-CartPole-v1

mili7522

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

akghxhs55/poca-SoccerTwos

akghxhs55

null

20

49

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

843

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: akghxhs55/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Celal11/resnet-50-0.007

Celal11

resnet

9

0

transformers

0

image-classification

true

false

apache-2.0

null

['image_folder']

null

0

['generated_from_trainer']

true

1,480

# resnet-50-0.007 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9735 - Accuracy: 0.6296 ## 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: 0.007 - 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4221 | 1.0 | 224 | 1.2410 | 0.5274 | | 1.2521 | 2.0 | 448 | 1.1716 | 0.5499 | | 1.1609 | 3.0 | 672 | 1.0495 | 0.5968 | | 1.1457 | 4.0 | 896 | 0.9735 | 0.6296 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

ammr/ppo-Huggy

ammr

null

32

4

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

815

# **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: ammr/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

iammartian0/a2c-AntBulletEnv-v0

iammartian0

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

352

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

tiagoblima/punctuation-tedtalk2012-t5-base

tiagoblima

t5

15

10

transformers

0

text2text-generation

true

false

mit

null

['tiagoblima/punctuation-tedtalk2012-t5']

null

0

['generated_from_trainer']

true

1,232

# punctuation-tedtalk2012-t5-base This model is a fine-tuned version of [unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) on the tiagoblima/punctuation-tedtalk2012-t5 dataset. It achieves the following results on the evaluation set: - Loss: 0.0399 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0348 | 1.0 | 77894 | 0.0399 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.9.0 - Tokenizers 0.13.2

GesturingMan/Reinforce-CartPole-v1

GesturingMan

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

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

jmcneves

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

397

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="jmcneves/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

davanstrien/autotrain-ia_covers-3416193421

davanstrien

vit

5

9

transformers

0

image-classification

true

false

null

['davanstrien/autotrain-data-ia_covers']

{'emissions': 1.69724123660189}

0

1

0

['autotrain', 'vision', 'image-classification']

false

true

245

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3416193421 - CO2 Emissions (in grams): 1.6972 ## Validation Metrics - Loss: 0.213 - Accuracy: 0.904 - Precision: 0.714 - Recall: 0.875 - AUC: 0.948 - F1: 0.787

Deysi/clasificador-muchocine

Deysi

electra

10

4

transformers

0

text-classification

true

false

null

0

['classification', 'generated_from_trainer']

true

1,367

# clasificador-muchocine This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3029 - Accuracy: 0.4645 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 388 | 1.5140 | 0.3161 | | 1.496 | 2.0 | 776 | 1.2868 | 0.4194 | | 1.1622 | 3.0 | 1164 | 1.3029 | 0.4645 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

jmcneves/q-Taxi-v3

jmcneves

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

364

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="jmcneves/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

akgeni/poca-SoccerTwos5

akgeni

null

7

43

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

841

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: akgeni/poca-SoccerTwos5 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

iammartian0/a2c-PandaReachDense-v2

iammartian0

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

LarryAIDraw/lenaeightysix-000030

LarryAIDraw

null

3

0

null

0

null

false

creativeml-openrail-m

null

0

[]

false

true

271

my trained lora use masterpiece,best quality,art by lenaeightysix,1girl,ahoge,very long hair,silver hair, long sleeves,hair between eyes, bangs,medium breasts, buttons,belt,thighhighs,military uniform,pantyhose,looking at viewer more steps lora see my dataset. suggest 10

hectorjelly/Bert_and_Ernie_2

hectorjelly

null

25

41

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

846

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: hectorjelly/Bert_and_Ernie_2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

sohm/a2c-AntBulletEnv-v0

sohm

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

352

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

pfunk/Pong-v4-DQPN_p100-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

1,955

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p100.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p100]" python -m cleanrl_utils.enjoy --exp-name DQPN_p100 --env-id Pong-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p100 --start-policy-f 100000 --end-policy-f 100000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000 ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'end_policy_f': 100000, 'env_id': 'Pong-v4', 'evaluation_fraction': 1.0, 'exp_name': 'DQPN_p100', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'start_policy_f': 100000, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```