davanstrien/pytorchmodelmetadata · Datasets at Hugging Face

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huggan/pix2pix-edge2shoes	c33cecfe57658244f753611943fdff043fd08e39	2022-04-15T04:28:29.000Z	[ "pytorch", "dataset:huggan/edge2shoes", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]	null	false	huggan	null	huggan/pix2pix-edge2shoes	null	null	36,800	--- tags: - huggan - gan datasets: - huggan/edge2shoes # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. ## Intended uses & limitations: Used for reconstruction of images from edges #### How to use ```python from torchvision.transforms import Compose, Resize, ToTensor, Normalize from PIL import Image from torchvision.utils import save_image import cv2 from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet transform = Compose( [ Resize((256, 256), Image.BICUBIC), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) model = GeneratorUNet.from_pretrained('huggan/pix2pix-edge2shoes) def predict_fn(img): inp = transform(img).unsqueeze(0) out = model(inp) save_image(out, 'out.png', normalize=True) return 'out.png' predict_fn(img) ``` #### Limitations and bias * Gives unrealistic colors in the image * Patterns in the edge drawing are not recognize properly ## Training data * [edges2shoes](https://huggingface.co/datasets/huggan/edges2shoes) ## Training procedure ``` # clone the repository git clone https://github.com/huggingface/community-events.git pip install . # change directory cd community-events/huggan/pytorch/pix2pix/ # define config accelerate config # launch training with required parameters accelerate launch train.py --checkpoint_interval 10 --dataset huggan/edges2shoes --push_to_hub --model_name pix2pix-edge2shoes --batch_size 128 --n_epochs 100 ``` ## Generated Images Here, * First Image Row: Input Sketch * Second Image Row: Generated Image * Third Image Row: Target Image ![image1](34000.png) ![image2](35000.png) ### BibTeX entry and citation info ```bibtex @article{pix2pix2017, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={CVPR}, year={2017} } ```
timhbach/Team_Gryffindor_NER	01e38cc5491a0a9c77c65ca6db7662aa38cc47d6	2022-04-29T21:13:30.000Z	[ "pytorch", "distilbert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	timhbach	null	timhbach/Team_Gryffindor_NER	null	transformers	36,801	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: Team_Gryffindor_NER results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Team-Gryffindor-distilbert-base-finetuned-NER-creditcardcontract-100epoch This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Credit card agreement dataset. It achieves the following results on the evaluation set: - Loss: 0.0470 - Precision: 0.7319 - Recall: 0.7064 - F1: 0.7190 - Accuracy: 0.9920 ## 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: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0113 \| 0.33 \| 500 \| 0.0443 \| 0.6547 \| 0.7028 \| 0.6779 \| 0.9908 \| \| 0.0118 \| 0.67 \| 1000 \| 0.0435 \| 0.7207 \| 0.6440 \| 0.6802 \| 0.9916 \| \| 0.013 \| 1.0 \| 1500 \| 0.0449 \| 0.7113 \| 0.6826 \| 0.6966 \| 0.9918 \| \| 0.0113 \| 1.34 \| 2000 \| 0.0434 \| 0.7213 \| 0.6697 \| 0.6946 \| 0.9915 \| \| 0.0121 \| 1.67 \| 2500 \| 0.0467 \| 0.6955 \| 0.6789 \| 0.6871 \| 0.9914 \| \| 0.0125 \| 2.01 \| 3000 \| 0.0417 \| 0.7095 \| 0.6991 \| 0.7043 \| 0.9920 \| \| 0.0106 \| 2.34 \| 3500 \| 0.0437 \| 0.7191 \| 0.6624 \| 0.6896 \| 0.9918 \| \| 0.0114 \| 2.68 \| 4000 \| 0.0468 \| 0.7165 \| 0.6679 \| 0.6914 \| 0.9920 \| \| 0.0125 \| 3.01 \| 4500 \| 0.0431 \| 0.6888 \| 0.6862 \| 0.6875 \| 0.9917 \| \| 0.0107 \| 3.35 \| 5000 \| 0.0446 \| 0.7184 \| 0.6459 \| 0.6802 \| 0.9913 \| \| 0.0096 \| 3.68 \| 5500 \| 0.0485 \| 0.6926 \| 0.6532 \| 0.6723 \| 0.9912 \| \| 0.013 \| 4.02 \| 6000 \| 0.0448 \| 0.6134 \| 0.6697 \| 0.6404 \| 0.9907 \| \| 0.0102 \| 4.35 \| 6500 \| 0.0497 \| 0.6895 \| 0.6642 \| 0.6766 \| 0.9913 \| \| 0.0112 \| 4.69 \| 7000 \| 0.0464 \| 0.6759 \| 0.6697 \| 0.6728 \| 0.9910 \| \| 0.0117 \| 5.02 \| 7500 \| 0.0484 \| 0.7451 \| 0.6275 \| 0.6813 \| 0.9916 \| \| 0.0114 \| 5.36 \| 8000 \| 0.0411 \| 0.7086 \| 0.6826 \| 0.6953 \| 0.9919 \| \| 0.0108 \| 5.69 \| 8500 \| 0.0443 \| 0.7041 \| 0.6679 \| 0.6855 \| 0.9916 \| \| 0.0109 \| 6.03 \| 9000 \| 0.0470 \| 0.7228 \| 0.6697 \| 0.6952 \| 0.9916 \| \| 0.0099 \| 6.36 \| 9500 \| 0.0471 \| 0.7253 \| 0.6881 \| 0.7062 \| 0.9913 \| \| 0.0103 \| 6.7 \| 10000 \| 0.0430 \| 0.6986 \| 0.7101 \| 0.7043 \| 0.9914 \| \| 0.0117 \| 7.03 \| 10500 \| 0.0462 \| 0.7327 \| 0.6991 \| 0.7155 \| 0.9918 \| \| 0.0098 \| 7.37 \| 11000 \| 0.0483 \| 0.6910 \| 0.6771 \| 0.6840 \| 0.9914 \| \| 0.0107 \| 7.7 \| 11500 \| 0.0468 \| 0.7189 \| 0.6899 \| 0.7041 \| 0.9916 \| \| 0.0119 \| 8.04 \| 12000 \| 0.0434 \| 0.6970 \| 0.6881 \| 0.6925 \| 0.9918 \| \| 0.0112 \| 8.37 \| 12500 \| 0.0469 \| 0.7007 \| 0.6917 \| 0.6962 \| 0.9918 \| \| 0.011 \| 8.71 \| 13000 \| 0.0469 \| 0.6736 \| 0.6514 \| 0.6623 \| 0.9914 \| \| 0.0101 \| 9.04 \| 13500 \| 0.0451 \| 0.6691 \| 0.6606 \| 0.6648 \| 0.9913 \| \| 0.0099 \| 9.38 \| 14000 \| 0.0462 \| 0.7006 \| 0.6826 \| 0.6914 \| 0.9918 \| \| 0.0107 \| 9.71 \| 14500 \| 0.0444 \| 0.6840 \| 0.6752 \| 0.6796 \| 0.9915 \| \| 0.0118 \| 10.05 \| 15000 \| 0.0457 \| 0.7015 \| 0.6771 \| 0.6891 \| 0.9918 \| \| 0.0102 \| 10.38 \| 15500 \| 0.0500 \| 0.7413 \| 0.6679 \| 0.7027 \| 0.9919 \| \| 0.0107 \| 10.72 \| 16000 \| 0.0470 \| 0.7319 \| 0.7064 \| 0.7190 \| 0.9920 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggan/fastgan-few-shot-shells	698843e8a0c88fb7384898ca5f09f82b55b26a93	2022-05-06T22:32:48.000Z	[ "pytorch", "dataset:huggan/few-shot-shells", "arxiv:2101.04775", "huggan", "gan", "unconditional-image-generation", "license:mit" ]	unconditional-image-generation	false	huggan	null	huggan/fastgan-few-shot-shells	null	null	36,802	--- tags: - huggan - gan - unconditional-image-generation datasets: - huggan/few-shot-shells # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # Generate shell image using FastGAN ## Model description [FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets. This model was trained on a dataset of 64 high-quality images of Shells. #### How to use ```python # Clone this model git clone https://huggingface.co/huggan/fastgan-few-shot-shells/ def load_generator(model_name_or_path): generator = Generator(in_channels=256, out_channels=3) generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3) _ = generator.eval() return generator def _denormalize(input: torch.Tensor) -> torch.Tensor: return (input * 127.5) + 127.5 # Load generator generator = load_generator("huggan/fastgan-few-shot-shells") # Generate a random noise image noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0) with torch.no_grad(): gan_images, _ = generator(noise) gan_images = _denormalize(gan_images.detach()) save_image(gan_images, "sample.png", nrow=1, normalize=True) ``` #### Limitations and bias * Converge faster and better with small datasets (less than 1000 samples) ## Training data [fastgan-few-shot-shells](https://huggingface.co/datasets/huggan/few-shot-shells) ## Generated Images ![Example image](example.png) ### BibTeX entry and citation info ```bibtex @article{FastGAN, title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis}, author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal}, journal={ICLR}, year={2021} } ```
yogi/autotrain-amazon_text_sum-730222226	6562d546d0ab9d7b5484e755a88b0bd2b7e78f22	2022-04-12T09:08:15.000Z	[ "pytorch", "t5", "text2text-generation", "en", "dataset:yogi/autotrain-data-amazon_text_sum", "transformers", "autotrain", "co2_eq_emissions", "autotrain_compatible" ]	text2text-generation	false	yogi	null	yogi/autotrain-amazon_text_sum-730222226	null	transformers	36,803	--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - yogi/autotrain-data-amazon_text_sum co2_eq_emissions: 2986.6520132805163 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 730222226 - CO2 Emissions (in grams): 2986.6520132805163 ## Validation Metrics - Loss: 2.682709217071533 - Rouge1: 19.6069 - Rouge2: 7.3367 - RougeL: 19.2706 - RougeLsum: 19.286 - Gen Len: 5.5731 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/yogi/autotrain-amazon_text_sum-730222226 ```
maesneako/gpt2-en-maptask-finetuned	90982e36897723928853d8b84b855bb00678b840	2022-04-11T16:24:18.000Z	[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	text-generation	false	maesneako	null	maesneako/gpt2-en-maptask-finetuned	null	transformers	36,804	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-en-maptask-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-en-maptask-finetuned This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - 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: 100 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
maveriq/mybert-base-32k	e32e57cb0b128c3576d97db67d3f8212adfee131	2022-04-11T17:35:29.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	maveriq	null	maveriq/mybert-base-32k	null	transformers	36,805	Entry not found
tonyalves/ft-pt-br-local	19ebdfb27cc4049ab271241aa46c3ad2ef8f5faf	2022-04-11T20:39:07.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	tonyalves	null	tonyalves/ft-pt-br-local	null	transformers	36,806	--- license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer model-index: - name: ft-pt-br-local results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ft-pt-br-local This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.1+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6
maveriq/mybert-mini-60k	4e2290e60b2b9d2e456f03213017739ed8af3702	2022-04-11T18:18:51.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	maveriq	null	maveriq/mybert-mini-60k	null	transformers	36,807	Entry not found
maveriq/lingbert-mini-60k	6fda7bd34a8e004d1d28fa6ba6152c88a4ef83c4	2022-04-11T18:21:22.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	maveriq	null	maveriq/lingbert-mini-60k	null	transformers	36,808	Entry not found
maveriq/mybert-mini-172k	452439b80ef78cea8f510d6626218073dd352994	2022-04-11T18:58:35.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	maveriq	null	maveriq/mybert-mini-172k	null	transformers	36,809	Entry not found
irenelizihui/MarianMT_UFAL_en_es	711f4c9569ed0f44ad622075f63cbcd79c799524	2022-04-11T23:04:22.000Z	[ "pytorch", "marian", "text2text-generation", "transformers", "license:wtfpl", "autotrain_compatible" ]	text2text-generation	false	irenelizihui	null	irenelizihui/MarianMT_UFAL_en_es	1	transformers	36,810	--- license: wtfpl --- MarianMT trained on the UFAL dataset: English to Spanish Machine Translation model.
irenelizihui/MarianMT_UFAL_en_ro	57a6309a36d6f39f6341631c4d6f25e7c83e0e2d	2022-04-11T23:03:23.000Z	[ "pytorch", "marian", "text2text-generation", "transformers", "license:wtfpl", "autotrain_compatible" ]	text2text-generation	false	irenelizihui	null	irenelizihui/MarianMT_UFAL_en_ro	1	transformers	36,811	--- license: wtfpl --- UFAL English to Romainian Machine Translation Model based on MarianMT model.
huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings	7894ac79f51f6fa3db4130c4954307ae0f593384	2022-04-11T22:44:40.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings	null	transformers	36,812	--- language: en thumbnail: http://www.huggingtweets.com/angrymemorys-oldandtoothless-sadboi666_-witheredstrings/1649717075201/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506323689456947207/xBvvxyQr_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511852580216967169/b1Aiv2t3_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000610482331/8808c2f408b97fe3646f2dca86441506_400x400.jpeg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">makeouthill & VacuumF & Jason Hendricks & Angry Memories</div> <div style="text-align: center; font-size: 14px;">@angrymemorys-oldandtoothless-sadboi666_-witheredstrings</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from makeouthill & VacuumF & Jason Hendricks & Angry Memories. \| Data \| makeouthill \| VacuumF \| Jason Hendricks \| Angry Memories \| \| --- \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 321 \| 425 \| 3250 \| 3199 \| \| Retweets \| 34 \| 0 \| 0 \| 941 \| \| Short tweets \| 49 \| 31 \| 0 \| 1110 \| \| Tweets kept \| 238 \| 394 \| 3250 \| 1148 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nh2rd94/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @angrymemorys-oldandtoothless-sadboi666_-witheredstrings's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nates/LER-roberta	bc3607faa3cf871f02f36f3d6fa65f2e326b3a58	2022-04-12T03:28:11.000Z	[ "pytorch", "roberta", "token-classification", "transformers", "license:cc-by-4.0", "autotrain_compatible" ]	token-classification	false	nates	null	nates/LER-roberta	null	transformers	36,813	--- license: cc-by-4.0 widget: - text: This house was let out in tiny tenements and was inhabited by working people of all kinds--tailors, locksmiths, cooks, Germans ofsorts, girls picking up a living as best they could, petty clerks, etc. example_title: "Crime and Punishment" - text: Quixote having got on his back and the duke mounted a fine horse, they placed the duchess in the middle and set out for the castle. example_title: "Don Quixote" - text: The noble carriage of this gentleman, for whom he believed himself to be engaged, had won Planchet—that was the name of the Picard. example_title: "The Three Musketeers" --- ### Description A `roberta-base` model which has been fine tuned for token classification on the [LitBank](https://github.com/dbamman/litbank) dataset. ### Intended Use This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from [ACE 2005](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) - Person (PER) - ORG - GPE - LOC - VEH - FAC Due to the fine-tuning domain, it is expected to work best with literary sentences.
junaidamk/mlner-mlwptok-muril	aed40ea46acad1f57295092187f1dc9810d64779	2022-04-12T08:57:48.000Z	[ "pytorch", "bert", "token-classification", "dataset:mlner2021", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	junaidamk	null	junaidamk/mlner-mlwptok-muril	null	transformers	36,814	--- license: apache-2.0 tags: - generated_from_trainer datasets: - mlner2021 metrics: - precision - recall - f1 - accuracy model-index: - name: mlner-mlwptok-muril results: - task: name: Token Classification type: token-classification dataset: name: mlner2021 type: mlner2021 args: MLNER2021 metrics: - name: Precision type: precision value: 0.0 - name: Recall type: recall value: 0.0 - name: F1 type: f1 value: 0.0 - name: Accuracy type: accuracy value: 0.8112759262826688 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mlner-mlwptok-muril This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the mlner2021 dataset. It achieves the following results on the evaluation set: - Loss: 0.8331 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8113 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| 1.447 \| 1.0 \| 1389 \| 0.9396 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.898 \| 2.0 \| 2778 \| 0.8883 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.859 \| 3.0 \| 4167 \| 0.8721 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.8302 \| 4.0 \| 5556 \| 0.8666 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.8165 \| 5.0 \| 6945 \| 0.8403 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.8143 \| 6.0 \| 8334 \| 0.8376 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.8034 \| 7.0 \| 9723 \| 0.8393 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.7766 \| 8.0 \| 11112 \| 0.8383 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.794 \| 9.0 \| 12501 \| 0.8346 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| \| 0.7858 \| 10.0 \| 13890 \| 0.8331 \| 0.0 \| 0.0 \| 0.0 \| 0.8113 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
nnair25/Gram-Vaani-Harveen-Chadda-Fine-Tuning	e3fe71a121f576da1c402418e825d13403b5d330	2022-04-13T05:52:25.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	automatic-speech-recognition	false	nnair25	null	nnair25/Gram-Vaani-Harveen-Chadda-Fine-Tuning	null	transformers	36,815	--- license: mit tags: - generated_from_trainer model-index: name: Gram-Vaani-Harveen-Chadda-Fine-Tuning --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gram-Vaani-Harveen-Chadda-Fine-Tuning This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8934 - Wer: 0.359 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----:\| \| 4.5558 \| 21.05 \| 400 \| 0.8934 \| 0.359 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
johnowhitaker/mingpt-char-shakespeare	0687519135b70307e60ed5369419e1894660c49c	2022-04-12T07:56:59.000Z	[ "pytorch" ]	null	false	johnowhitaker	null	johnowhitaker/mingpt-char-shakespeare	null	null	36,816	Entry not found
mimi/book_data	9a4ec64cd99027097b9bac609664a53cdb72a5ac	2022-05-12T01:50:23.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	mimi	null	mimi/book_data	null	transformers	36,817	Entry not found
Chris1/CycleGAN_punk2apes	7db44664d29b484eff8f9dc1094b7e82e8132384	2022-04-12T08:10:37.000Z	[ "pytorch", "huggan", "gan", "license:mit" ]	null	false	Chris1	null	Chris1/CycleGAN_punk2apes	null	null	36,818	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
theojolliffe/opus-mt-en-ro-finetuned-en-to-cy	2392a76de863b287ce99d91cac93c75b479d01e9	2022-04-13T11:10:10.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	theojolliffe	null	theojolliffe/opus-mt-en-ro-finetuned-en-to-cy	null	transformers	36,819	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: opus-mt-en-ro-finetuned-en-to-cy results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ro-finetuned-en-to-cy This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/nv1t	1db943b3f55a42605435fed5f7e017ffc23039c1	2022-04-12T10:24:30.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/nv1t	null	transformers	36,820	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/521651470832136193/8-XdhaC7_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">nuit</div> <div style="text-align: center; font-size: 14px;">@nv1t</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from nuit. \| Data \| nuit \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 520 \| \| Short tweets \| 127 \| \| Tweets kept \| 2597 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3aj0ls3h/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nv1t's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n9kpomf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n9kpomf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nv1t') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Chris1/ape2punk_epoch80	f42c99e06db78f53a9460695268c1ad9ee52b137	2022-04-12T11:21:48.000Z	[ "pytorch", "huggan", "gan", "license:mit" ]	null	false	Chris1	null	Chris1/ape2punk_epoch80	null	null	36,821	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
huggingnft/cryptopunks__2__bored-apes-yacht-club	aa592734173938947c65191aca14a719332ab0dd	2022-04-25T16:05:53.000Z	[ "pytorch", "arxiv:1703.10593", "huggan", "gan", "image-to-image", "huggingnft", "nft", "image", "images", "license:mit" ]	image-to-image	false	huggingnft	null	huggingnft/cryptopunks__2__bored-apes-yacht-club	1	null	36,822	--- tags: - huggan - gan - image-to-image - huggingnft - nft - image - images # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # CycleGAN for unpaired image-to-image translation. ## Model description CycleGAN for unpaired image-to-image translation. Given two image domains A and B, the following components are trained end2end to translate between such domains: - A generator A to B, named G_AB conditioned on an image from A - A generator B to A, named G_BA conditioned on an image from B - A domain classifier D_A, associated with G_AB - A domain classifier D_B, associated with G_BA At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A. In the general setting, this technique provides style transfer functionalities between the selected image domains A and B. This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA. Under these framework, these aspects have been used to perform style transfer between NFT collections. A collection is selected as domain A, another one as domain B and the CycleGAN provides forward and backward translation between A and B. This has showed to allows high quality translation even in absence of paired sample-ground-truth data. In particular, the model performs well with stationary backgrounds (no drastic texture changes in the appearance of backgrounds) as it is capable of recognizing the attributes of each of the elements of an NFT collections. An attribute can be a variation in type of dressed fashion items such as sunglasses, earrings, clothes and also face or body attributes with respect to a common template model of the given NFT collection). ## Intended uses & limitations #### How to use ```python import torch from PIL import Image from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet from torchvision import transforms as T from torchvision.transforms import Compose, Resize, ToTensor, Normalize from torchvision.utils import make_grid from huggingface_hub import hf_hub_download, file_download from accelerate import Accelerator import json def load_lightweight_model(model_name): file_path = file_download.hf_hub_download( repo_id=model_name, filename="config.json" ) config = json.loads(open(file_path).read()) organization_name, name = model_name.split("/") model = Trainer(**config, organization_name=organization_name, name=name) model.load(use_cpu=True) model.accelerator = Accelerator() return model def get_concat_h(im1, im2): dst = Image.new('RGB', (im1.width + im2.width, im1.height)) dst.paste(im1, (0, 0)) dst.paste(im2, (im1.width, 0)) return dst n_channels = 3 image_size = 256 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) # load the translation model from source to target images: source will be generated by a separate Lightweight GAN, w # while the target images are the result of the translation applied by the GeneratorResnet to the generated source images. # Hence, given the source domain A and target domain B, # B = Translator(GAN(A)) translator = GeneratorResNet.from_pretrained(f'huggingnft/{model_name}', input_shape=(n_channels, image_size, image_size), num_residual_blocks=9) # sample noise that is used to generate source images by the z = torch.randn(nrows, 100, 1, 1) # load the GAN generator of source images that will be translated by the translation model model = load_lightweight_model(f"huggingnft/{model_name.split('__2__')[0]}") collectionA = model.generate_app( num=timestamped_filename(), nrow=nrows, checkpoint=-1, types="default" )[1] # resize to translator model input shape resize = T.Resize((256, 256)) input = resize(collectionA) # translate the resized collectionA to collectionB collectionB = translator(input) out_transform = T.ToPILImage() results = [] for collA_image, collB_image in zip(input, collectionB): results.append( get_concat_h(out_transform(make_grid(collA_image, nrow=1, normalize=True)), out_transform(make_grid(collB_image, nrow=1, normalize=True))) ) ``` #### Limitations and bias Translation between collections provides exceptional output images in the case of NFT collections that portray subjects in the same way. If the backgrounds vary too much within either of the collections, performance degrades or many more training iterations re required to achieve acceptable results. ## Training data The CycleGAN model is trained on an unpaired dataset of samples from two selected NFT collections: colle tionA and collectionB. To this end, two collections are loaded by means of the function load_dataset in the huggingface library, as follows. A list of all available collections is available at [huggingNFT](https://huggingface.co/huggingnft) ```python from datasets import load_dataset collectionA = load_dataset("huggingnft/COLLECTION_A") collectionB = load_dataset("huggingnft/COLLECTION_B") ``` ## Training procedure #### Preprocessing The following transformations are applied to each input sample of collectionA and collectionB. The input size is fixed to RGB images of height, width = 256, 256 ```python n_channels = 3 image_size = 256 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) ``` #### Hardware The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs. #### Hyperparameters The following configuration has been kept fixed for all translation models: - learning rate 0.0002 - number of epochs 200 - learning rate decay activation at epoch 80 - number of residual blocks of the cyclegan 9 - cycle loss weight 10.0 - identity loss weight 5.0 - optimizer ADAM with beta1 0.5 and beta2 0.999 - batch size 8 - NO mixed precision training ## Eval results #### Training reports [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/CycleGAN-training-report--VmlldzoxODUxNzQz?accessToken=vueurpbhd2i8n347j880yakggs0sqdf7u0hpz3bpfsbrxcmk1jk4obg18f6wfk9w) [Boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/CycleGAN-training-report--VmlldzoxODUxNzg4?accessToken=jpyviwn7kdf5216ycrthwp6l8t3heb0lt8djt7dz12guu64qnpdh3ekecfcnoahu) #### Generated Images In the provided images, row0 and row2 represent real images from the respective collections. Row1 is the translation of the immediate above images in row0 by means of the G_AB translation model. Row3 is the translation of the immediate above images in row2 by means of the G_BA translation model. Visualization over the training iterations for [boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/Shared-panel-22-04-15-08-04-99--VmlldzoxODQ0MDI3?accessToken=45m3kxex5m3rpev3s6vmrv69k3u9p9uxcsp2k90wvbxwxzlqbqjqlnmgpl9265c0) Visualization over the training iterations for [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/Shared-panel-22-04-17-11-04-83--VmlldzoxODUxNjk5?accessToken=o25si6nflp2xst649vt6ayt56bnb95mxmngt1ieso091j2oazmqnwaf4h78vc2tu) ### References ```bibtex @misc{https://doi.org/10.48550/arxiv.1703.10593, doi = {10.48550/ARXIV.1703.10593}, url = {https://arxiv.org/abs/1703.10593}, author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks}, publisher = {arXiv}, year = {2017}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk, Christian Cancedda} year=2022 } ```
Chris1/real2sim	e14a0dac96ae97890973a5223a9d46ebe882e411	2022-04-12T11:33:32.000Z	[ "pytorch", "huggan", "gan", "license:mit" ]	null	false	Chris1	null	Chris1/real2sim	null	null	36,823	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
huggan/sim2real_cyclegan	0a60c15105b38055c6897be6d91f142fd9a903e1	2022-05-05T10:55:19.000Z	[ "pytorch", "arxiv:2104.13395", "arxiv:1703.10593", "conditional-image-generation", "image-to-image", "gan", "cyclegan", "license:mit" ]	image-to-image	false	huggan	null	huggan/sim2real_cyclegan	1	null	36,824	--- tags: - conditional-image-generation - image-to-image - gan - cyclegan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # CycleGAN for unpaired image-to-image translation. ## Model description CycleGAN for unpaired image-to-image translation. Given two image domains A and B, the following components are trained end2end to translate between such domains: - A generator A to B, named G_AB conditioned on an image from A - A generator B to A, named G_BA conditioned on an image from B - A domain classifier D_A, associated with G_AB - A domain classifier D_B, associated with G_BA At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A. In the general setting, this technique provides style transfer functionalities between the selected image domains A and B. This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA. Under these framework, these aspects have been used to perform style transfer between synthetic data obtained from a simulated driving dataset, GTA5, and the real driving data from Cityscapes. This is of paramount importance to develop autonomous driving perception deep learning models, as this allows to generate synthetic data with automatic annotations which resembles real world images, without requiring the intervention of a human annotator. This is fundamental because a manual annotator has been shown to require 1.5 to 3.3 hours to create semantic and instance segmentation masks for a single images. These have been provided in the original [cityscapes paper (Cordts et al 2016)](https://arxiv.org/abs/2104.13395) and the [adverse condition dataset (Sakaridis et al. 2021)](https://arxiv.org/abs/2104.13395) paper. Hence the CycleGAN provides forward and backward translation between synthetic and real world data. This has showed to allows high quality translation even in absence of paired sample-ground-truth data. The idea behind such model is that as the synthetic data distribution gets closer to the real world one, deep models do not suffer from degraded performance due to the domain shift issue. A broad literature is available on the minimization of the domain shift, under the research branch of domain adaptation and transfer learning, of which image translation models provide an alternative approach ## Intended uses & limitations #### How to use ```python import os from PIL import Image from torchvision import transforms as T from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip from torchvision.utils import make_grid from torch.utils.data import DataLoader from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet import torch.nn as nn import torch import gradio as gr import glob def pred_pipeline(img, transforms): orig_shape = img.shape input = transforms(img) input = input.unsqueeze(0) output = model(input) out_img = make_grid(output,#.detach().cpu(), nrow=1, normalize=True) out_transform = Compose([ T.Resize(orig_shape[:2]), T.ToPILImage() ]) return out_transform(out_img) n_channels = 3 image_size = 512 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) model = GeneratorResNet.from_pretrained('Chris1/sim2real', input_shape=(n_channels, image_size, image_size), num_residual_blocks=9) real_images = model(synthetic_images) ``` #### Limitations and bias Due to the absence of paired data, some background parts of the synthetic images are seldom wrongly translated, e.g. sky is translated to vegetation. Additional pretext tasks in parallel to the discriminative classifier of fake and real samples could improve the result. One easy improvement is the use of an additional parallel branch that performs semantic segmentation on the synthetic data, in order to learn features which are common to sky and vegetation, thus disentangling their representations as separate classes. ## Training data The CycleGAN model is trained on an unpaired dataset of samples from synthetic and real driving data, respectively from the GTA5 and Cityscapes datasets. To this end, the synthetic-to-real dataset can be loaded by means of the function load_dataset in the huggingface library, as follows. ```python from datasets import load_dataset unpaired_dataset = load_dataset("huggan/sim2real_gta5_to_cityscapes") ``` This dataset contains two columns, imageA and imageB representing respectively the GTA5 and Cityscapes data. Due to the fact that the two columns have to be of the same length, GTA5 is subsampled in order to reach the same number of samples provided by the Cityscapes train split (2975) ## Training procedure #### Preprocessing The following transformations are applied to each input sample of synthetic and real data. The input size is fixed to RGB images of height, width = 512, 512. This choice has been made in order to limit the impact of upsampling the translated images to higher resolutions. ```python n_channels = 3 image_size = 512 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) ``` #### Hardware The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs. #### Hyperparameters The following configuration has been kept fixed for all translation models: - learning rate 0.0002 - number of epochs 200 - learning rate decay activation at epoch 100 - number of residual blocks of the cyclegan 9 - image size 512x512 - number of channels=3 - cycle loss weight 10.0 - identity loss weight 5.0 - optimizer ADAM with beta1 0.5 and beta2 0.999 - batch size 8 - NO mixed precision training ## Eval results #### Generated Images In the provided images, row0 and row2 represent the synthetic and real images from the respective datasets. Row1 is the translation of the immediate above images in row0(synthetic) by means of the G_AB translation model, to the real world style. Row3 is the translation of the immediate above images in row2(real) by means of the G_BA translation model, to the synthetic world style. Visualization over the training iterations for [synthetic (GTA5) to real (Cityscapes) translation](https://wandb.ai/chris1nexus/experiments_cyclegan_s2r_hp_opt--10/reports/CycleGAN-sim2real-training-results--VmlldzoxODUyNTk4?accessToken=tow3v4vp02aurzodedrdht15ig1cx69v5mited4dm8bgnup0z192wri0xtftaeqj) ### References ```bibtex @misc{https://doi.org/10.48550/arxiv.1703.10593, doi = {10.48550/ARXIV.1703.10593}, url = {https://arxiv.org/abs/1703.10593}, author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks}, publisher = {arXiv}, year = {2017}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
sanchit-gandhi/tiny-random-bart	31143b0f16b739cbadec03b5d2a0025fba90918c	2022-04-12T13:32:12.000Z	[ "pytorch", "jax", "bart", "feature-extraction", "transformers" ]	feature-extraction	false	sanchit-gandhi	null	sanchit-gandhi/tiny-random-bart	null	transformers	36,825	Entry not found
Guldeniz/pix2pix_maps	a8ef56fd47c2d8f9b245daa1bd71eceb4c7ce391	2022-04-12T17:44:34.000Z	[ "pytorch", "huggan", "gan", "license:mit" ]	null	false	Guldeniz	null	Guldeniz/pix2pix_maps	null	null	36,826	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
huggan/pix2pix-maps-test	39acb13bdb610983eeb8c10c5edbc8bc2d700a13	2022-04-12T15:57:14.000Z	[ "pytorch", "huggan", "gan", "license:mit" ]	null	false	huggan	null	huggan/pix2pix-maps-test	null	null	36,827	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
nielsr/test	7717b62c60f0e314a56fb96fe9d98975826176a5	2022-04-12T15:20:09.000Z	[ "pytorch" ]	null	false	nielsr	null	nielsr/test	null	null	36,828	Entry not found
rajat99/SSL-Harveen-Chadda-Fine-Tuning	37d5efecb32d113c24e8eb9b2263efda5b8aa737	2022-04-12T22:07:20.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	automatic-speech-recognition	false	rajat99	null	rajat99/SSL-Harveen-Chadda-Fine-Tuning	null	transformers	36,829	--- license: mit tags: - generated_from_trainer model-index: name: SSL-Harveen-Chadda-Fine-Tuning --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SSL-Harveen-Chadda-Fine-Tuning This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0032 - Wer: 0.1008 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 4.3793 \| 4.17 \| 400 \| 0.5347 \| 0.2584 \| \| 0.2137 \| 8.33 \| 800 \| 0.8339 \| 0.2664 \| \| 0.1282 \| 12.5 \| 1200 \| 0.1785 \| 0.1414 \| \| 0.0698 \| 16.66 \| 1600 \| 0.0135 \| 0.1066 \| \| 0.0354 \| 20.83 \| 2000 \| 0.0351 \| 0.1179 \| \| 0.0212 \| 24.99 \| 2400 \| 0.0104 \| 0.1035 \| \| 0.0066 \| 29.17 \| 2800 \| 0.0032 \| 0.1008 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
huggan/pix2pix-uavid-15	bfa54b692d5d897bf7cb4181db1535e5ce303049	2022-04-15T13:45:13.000Z	[ "pytorch", "dataset:arakesh/uavid-15-hq-mixedres", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]	null	false	huggan	null	huggan/pix2pix-uavid-15	null	null	36,830	--- tags: - huggan - gan datasets: - arakesh/uavid-15-hq-mixedres # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. ## Intended uses & limitations: Used for reconstruction of images from edges #### How to use ```python from torchvision.transforms import Compose, Resize, ToTensor, Normalize from PIL import Image from torchvision.utils import save_image import cv2 from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet transform = Compose( [ Resize((256, 256), Image.BICUBIC), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) model = GeneratorUNet.from_pretrained('huggan/pix2pix-uavid-15) def predict_fn(img): inp = transform(img).unsqueeze(0) out = model(inp) save_image(out, 'out.png', normalize=True) return 'out.png' predict_fn(img) ``` #### Limitations and bias * Gives unrealistic colors in the image ## Training data * [edges2shoes](https://huggingface.co/datasets/huggan/edges2shoes) ## Training procedure ``` # clone the repository git clone https://github.com/huggingface/community-events.git pip install . # change directory cd community-events/huggan/pytorch/pix2pix/ # define config accelerate config # launch training with required parameters accelerate launch train.py --checkpoint_interval 1 --dataset arakesh/uavid-15-hq-mixedres --push_to_hub --model_name pix2pix-uavid-15 --batch_size 2 --n_epochs 50 --image_size 1024 --sample_interval 500 ``` ## Generated Images Here, * First Image Row: Input Image * Second Image Row: Generated Image * Third Image Row: Target Image ![image1](34000.png) ![image2](35000.png) ### BibTeX entry and citation info ```bibtex @article{pix2pix2017, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={CVPR}, year={2017} } ```
lilitket/20220412-203254	7988a597cdefd4bdcf9dadaae38071a50cc43f42	2022-04-13T05:04:25.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	lilitket	null	lilitket/20220412-203254	null	transformers	36,831	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: 20220412-203254 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 20220412-203254 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 5.0428 - Wer: 1.0019 ## 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-06 - train_batch_size: 1 - 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: 2000 - num_epochs: 1200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:------:\|:---------------:\|:------:\| \| 16.9455 \| 1.5 \| 200 \| 16.4676 \| 1.2534 \| \| 15.444 \| 3.01 \| 400 \| 14.1207 \| 1.0 \| \| 9.5452 \| 4.51 \| 600 \| 8.4030 \| 1.0 \| \| 6.2565 \| 6.02 \| 800 \| 6.5233 \| 1.0 \| \| 5.2827 \| 7.52 \| 1000 \| 5.6058 \| 1.0 \| \| 4.7652 \| 9.02 \| 1200 \| 4.9765 \| 1.0 \| \| 4.3803 \| 10.53 \| 1400 \| 4.4565 \| 1.0 \| \| 4.0005 \| 12.03 \| 1600 \| 4.0224 \| 1.0 \| \| 3.7041 \| 13.53 \| 1800 \| 3.6903 \| 1.0 \| \| 3.4991 \| 15.04 \| 2000 \| 3.4642 \| 1.0 \| \| 3.34 \| 16.54 \| 2200 \| 3.3425 \| 1.0 \| \| 3.2352 \| 18.05 \| 2400 \| 3.2617 \| 1.0 \| \| 3.1867 \| 19.55 \| 2600 \| 3.2358 \| 1.0 \| \| 3.161 \| 21.05 \| 2800 \| 3.2289 \| 1.0 \| \| 3.145 \| 22.56 \| 3000 \| 3.2023 \| 1.0 \| \| 3.1203 \| 24.06 \| 3200 \| 3.1964 \| 1.0 \| \| 3.1109 \| 25.56 \| 3400 \| 3.1844 \| 1.0 \| \| 3.0958 \| 27.07 \| 3600 \| 3.1839 \| 1.0 \| \| 3.0732 \| 28.57 \| 3800 \| 3.2058 \| 1.0 \| \| 3.0535 \| 30.08 \| 4000 \| 3.1843 \| 1.0 \| \| 3.0243 \| 31.58 \| 4200 \| 3.1992 \| 1.0 \| \| 2.9829 \| 33.08 \| 4400 \| 3.2019 \| 1.0 \| \| 2.9219 \| 34.59 \| 4600 \| 3.2346 \| 1.0 \| \| 2.8313 \| 36.09 \| 4800 \| 3.2781 \| 1.0 \| \| 2.7186 \| 37.59 \| 5000 \| 3.3056 \| 1.0 \| \| 2.5745 \| 39.1 \| 5200 \| 3.3554 \| 1.0 \| \| 2.4028 \| 40.6 \| 5400 \| 3.4331 \| 1.0 \| \| 2.2645 \| 42.11 \| 5600 \| 3.4418 \| 1.0 \| \| 2.1303 \| 43.61 \| 5800 \| 3.5584 \| 1.0 \| \| 2.0257 \| 45.11 \| 6000 \| 3.5943 \| 1.0 \| \| 1.9223 \| 46.62 \| 6200 \| 3.6767 \| 1.0 \| \| 1.8344 \| 48.12 \| 6400 \| 3.7363 \| 1.0 \| \| 1.7574 \| 49.62 \| 6600 \| 3.8921 \| 1.0 \| \| 1.67 \| 51.13 \| 6800 \| 3.9054 \| 1.0 \| \| 1.6118 \| 52.63 \| 7000 \| 4.0352 \| 1.0 \| \| 1.5372 \| 54.14 \| 7200 \| 3.9742 \| 1.0 \| \| 1.4846 \| 55.64 \| 7400 \| 4.1078 \| 1.0 \| \| 1.4093 \| 57.14 \| 7600 \| 4.1705 \| 1.0 \| \| 1.3379 \| 58.65 \| 7800 \| 4.2737 \| 1.0 \| \| 1.28 \| 60.15 \| 8000 \| 4.3662 \| 1.0 \| \| 1.2268 \| 61.65 \| 8200 \| 4.4278 \| 1.0 \| \| 1.1641 \| 63.16 \| 8400 \| 4.4831 \| 1.0 \| \| 1.1058 \| 64.66 \| 8600 \| 4.5354 \| 1.0 \| \| 1.0596 \| 66.17 \| 8800 \| 4.5983 \| 1.0 \| \| 0.9953 \| 67.67 \| 9000 \| 4.7143 \| 1.0 \| \| 0.9406 \| 69.17 \| 9200 \| 4.8536 \| 1.0 \| \| 0.9022 \| 70.68 \| 9400 \| 4.7732 \| 1.0 \| \| 0.8551 \| 72.18 \| 9600 \| 4.8929 \| 1.0 \| \| 0.8103 \| 73.68 \| 9800 \| 4.9513 \| 1.0 \| \| 0.7665 \| 75.19 \| 10000 \| 4.9530 \| 1.0 \| \| 0.7215 \| 76.69 \| 10200 \| 5.1471 \| 1.0 \| \| 0.6906 \| 78.2 \| 10400 \| 5.2295 \| 1.0 \| \| 0.6354 \| 79.7 \| 10600 \| 5.1287 \| 1.0 \| \| 0.6196 \| 81.2 \| 10800 \| 5.2081 \| 1.0 \| \| 0.6026 \| 82.71 \| 11000 \| 5.4323 \| 1.0 \| \| 0.5726 \| 84.21 \| 11200 \| 5.3907 \| 1.0 \| \| 0.5348 \| 85.71 \| 11400 \| 5.5669 \| 1.0 \| \| 0.5344 \| 87.22 \| 11600 \| 5.5685 \| 1.0 \| \| 0.4849 \| 88.72 \| 11800 \| 5.5814 \| 1.0 \| \| 0.4689 \| 90.23 \| 12000 \| 5.6186 \| 1.0 \| \| 0.4646 \| 91.73 \| 12200 \| 5.4834 \| 1.0 \| \| 0.4266 \| 93.23 \| 12400 \| 5.6463 \| 1.0 \| \| 0.4424 \| 94.74 \| 12600 \| 5.6562 \| 1.0 \| \| 0.3865 \| 96.24 \| 12800 \| 5.7463 \| 1.0 \| \| 0.3914 \| 97.74 \| 13000 \| 5.7014 \| 1.0 \| \| 0.3661 \| 99.25 \| 13200 \| 5.7543 \| 1.0 \| \| 0.3582 \| 100.75 \| 13400 \| 5.9172 \| 1.0 \| \| 0.3571 \| 102.26 \| 13600 \| 5.5968 \| 1.0 \| \| 0.3343 \| 103.76 \| 13800 \| 5.3691 \| 1.0 \| \| 0.3123 \| 105.26 \| 14000 \| 5.8917 \| 1.0 \| \| 0.3089 \| 106.77 \| 14200 \| 5.8054 \| 1.0 \| \| 0.3078 \| 108.27 \| 14400 \| 5.9066 \| 1.0 \| \| 0.3076 \| 109.77 \| 14600 \| 5.7379 \| 1.0 \| \| 0.2924 \| 111.28 \| 14800 \| 5.7931 \| 1.0 \| \| 0.2925 \| 112.78 \| 15000 \| 5.9529 \| 1.0 \| \| 0.2839 \| 114.29 \| 15200 \| 5.9881 \| 1.0 \| \| 0.2599 \| 115.79 \| 15400 \| 6.0081 \| 1.0 \| \| 0.2685 \| 117.29 \| 15600 \| 6.1049 \| 1.0 \| \| 0.2557 \| 118.8 \| 15800 \| 6.1154 \| 1.0 \| \| 0.2688 \| 120.3 \| 16000 \| 5.9336 \| 1.0 \| \| 0.2422 \| 121.8 \| 16200 \| 6.0492 \| 1.0 \| \| 0.2408 \| 123.31 \| 16400 \| 6.3155 \| 1.0 \| \| 0.2423 \| 124.81 \| 16600 \| 6.3437 \| 1.0 \| \| 0.2421 \| 126.32 \| 16800 \| 6.0979 \| 1.0 \| \| 0.2212 \| 127.82 \| 17000 \| 5.5551 \| 1.0 \| \| 0.2239 \| 129.32 \| 17200 \| 5.9007 \| 1.0 \| \| 0.2101 \| 130.83 \| 17400 \| 6.0142 \| 1.0 \| \| 0.2097 \| 132.33 \| 17600 \| 5.8984 \| 1.0 \| \| 0.2064 \| 133.83 \| 17800 \| 5.9705 \| 1.0 \| \| 0.1898 \| 135.34 \| 18000 \| 5.9915 \| 1.0 \| \| 0.2053 \| 136.84 \| 18200 \| 6.1079 \| 1.0 \| \| 0.1822 \| 138.35 \| 18400 \| 6.1324 \| 1.0 \| \| 0.1867 \| 139.85 \| 18600 \| 6.1122 \| 1.0 \| \| 0.1831 \| 141.35 \| 18800 \| 6.1476 \| 1.0 \| \| 0.1935 \| 142.86 \| 19000 \| 5.7248 \| 1.0 \| \| 0.1983 \| 144.36 \| 19200 \| 6.1466 \| 1.0 \| \| 0.176 \| 145.86 \| 19400 \| 5.9555 \| 1.0 \| \| 0.1778 \| 147.37 \| 19600 \| 6.1434 \| 1.0 \| \| 0.1758 \| 148.87 \| 19800 \| 6.2104 \| 1.0 \| \| 0.1799 \| 150.38 \| 20000 \| 6.0933 \| 1.0 \| \| 0.1674 \| 151.88 \| 20200 \| 6.0476 \| 1.0 \| \| 0.1777 \| 153.38 \| 20400 \| 5.8937 \| 1.0 \| \| 0.1616 \| 154.89 \| 20600 \| 6.4417 \| 1.0 \| \| 0.1498 \| 156.39 \| 20800 \| 6.3136 \| 1.0 \| \| 0.1607 \| 157.89 \| 21000 \| 5.9295 \| 1.0 \| \| 0.1445 \| 159.4 \| 21200 \| 6.2741 \| 1.0 \| \| 0.1636 \| 160.9 \| 21400 \| 6.1931 \| 1.0 \| \| 0.1488 \| 162.41 \| 21600 \| 6.0089 \| 1.0 \| \| 0.1549 \| 163.91 \| 21800 \| 5.6184 \| 1.0 \| \| 0.1532 \| 165.41 \| 22000 \| 6.1250 \| 1.0 \| \| 0.1581 \| 166.92 \| 22200 \| 6.2635 \| 1.0 \| \| 0.146 \| 168.42 \| 22400 \| 6.0498 \| 1.0 \| \| 0.148 \| 169.92 \| 22600 \| 6.3486 \| 1.0 \| \| 0.1489 \| 171.43 \| 22800 \| 6.1659 \| 1.0 \| \| 0.1464 \| 172.93 \| 23000 \| 6.2259 \| 1.0 \| \| 0.139 \| 174.44 \| 23200 \| 6.2796 \| 1.0 \| \| 0.1357 \| 175.94 \| 23400 \| 6.2119 \| 1.0 \| \| 0.1435 \| 177.44 \| 23600 \| 6.5722 \| 1.0 \| \| 0.1172 \| 178.95 \| 23800 \| 6.4221 \| 1.0 \| \| 0.1539 \| 180.45 \| 24000 \| 6.3963 \| 1.0 \| \| 0.1389 \| 181.95 \| 24200 \| 6.2367 \| 1.0 \| \| 0.1274 \| 183.46 \| 24400 \| 6.3693 \| 1.0 \| \| 0.1295 \| 184.96 \| 24600 \| 6.0819 \| 1.0 \| \| 0.1337 \| 186.47 \| 24800 \| 6.1525 \| 1.0 \| \| 0.1303 \| 187.97 \| 25000 \| 6.2520 \| 1.0 \| \| 0.141 \| 189.47 \| 25200 \| 6.5302 \| 1.0 \| \| 0.1322 \| 190.98 \| 25400 \| 6.3731 \| 1.0 \| \| 0.1313 \| 192.48 \| 25600 \| 6.3570 \| 1.0 \| \| 0.1178 \| 193.98 \| 25800 \| 6.1667 \| 1.0 \| \| 0.1277 \| 195.49 \| 26000 \| 6.1352 \| 1.0 \| \| 0.1169 \| 196.99 \| 26200 \| 6.3132 \| 1.0 \| \| 0.1199 \| 198.5 \| 26400 \| 6.6116 \| 1.0 \| \| 0.1138 \| 200.0 \| 26600 \| 6.4862 \| 1.0 \| \| 0.1129 \| 201.5 \| 26800 \| 6.3442 \| 1.0 \| \| 0.1142 \| 203.01 \| 27000 \| 6.5077 \| 1.0 \| \| 0.1169 \| 204.51 \| 27200 \| 6.5710 \| 1.0 \| \| 0.111 \| 206.02 \| 27400 \| 6.0623 \| 1.0 \| \| 0.1198 \| 207.52 \| 27600 \| 6.4331 \| 1.0 \| \| 0.1108 \| 209.02 \| 27800 \| 5.9192 \| 1.0 \| \| 0.1121 \| 210.53 \| 28000 \| 6.0724 \| 1.0 \| \| 0.1171 \| 212.03 \| 28200 \| 6.3363 \| 1.0 \| \| 0.1188 \| 213.53 \| 28400 \| 6.3704 \| 1.0 \| \| 0.104 \| 215.04 \| 28600 \| 6.5802 \| 1.0 \| \| 0.1125 \| 216.54 \| 28800 \| 5.4428 \| 1.0 \| \| 0.1115 \| 218.05 \| 29000 \| 6.4286 \| 1.0 \| \| 0.1109 \| 219.55 \| 29200 \| 6.6998 \| 1.0 \| \| 0.1061 \| 221.05 \| 29400 \| 6.3761 \| 1.0 \| \| 0.1161 \| 222.56 \| 29600 \| 5.8712 \| 1.0 \| \| 0.1091 \| 224.06 \| 29800 \| 6.1844 \| 1.0 \| \| 0.0947 \| 225.56 \| 30000 \| 6.5670 \| 1.0 \| \| 0.1004 \| 227.07 \| 30200 \| 6.2302 \| 1.0 \| \| 0.1099 \| 228.57 \| 30400 \| 6.4218 \| 1.0 \| \| 0.1154 \| 230.08 \| 30600 \| 6.4911 \| 1.0 \| \| 0.0999 \| 231.58 \| 30800 \| 6.4390 \| 1.0 \| \| 0.1068 \| 233.08 \| 31000 \| 6.2367 \| 1.0 \| \| 0.1015 \| 234.59 \| 31200 \| 6.0790 \| 1.0 \| \| 0.0958 \| 236.09 \| 31400 \| 5.9926 \| 1.0 \| \| 0.1183 \| 237.59 \| 31600 \| 6.3400 \| 1.0 \| \| 0.0833 \| 239.1 \| 31800 \| 6.4481 \| 1.0 \| \| 0.0874 \| 240.6 \| 32000 \| 6.4535 \| 1.0 \| \| 0.0958 \| 242.11 \| 32200 \| 6.0597 \| 1.0 \| \| 0.1075 \| 243.61 \| 32400 \| 6.3403 \| 1.0 \| \| 0.0909 \| 245.11 \| 32600 \| 6.1297 \| 1.0 \| \| 0.1093 \| 246.62 \| 32800 \| 6.2232 \| 1.0 \| \| 0.0995 \| 248.12 \| 33000 \| 6.7110 \| 1.0 \| \| 0.1061 \| 249.62 \| 33200 \| 5.8551 \| 1.0 \| \| 0.0872 \| 251.13 \| 33400 \| 6.0338 \| 1.0 \| \| 0.109 \| 252.63 \| 33600 \| 6.2880 \| 1.0 \| \| 0.0976 \| 254.14 \| 33800 \| 5.9304 \| 1.0 \| \| 0.0977 \| 255.64 \| 34000 \| 6.4527 \| 1.0 \| \| 0.0895 \| 257.14 \| 34200 \| 6.3178 \| 1.0 \| \| 0.0951 \| 258.65 \| 34400 \| 6.3646 \| 1.0 \| \| 0.0942 \| 260.15 \| 34600 \| 6.4405 \| 1.0 \| \| 0.0876 \| 261.65 \| 34800 \| 5.8373 \| 1.0 \| \| 0.0877 \| 263.16 \| 35000 \| 6.5296 \| 1.0 \| \| 0.0896 \| 264.66 \| 35200 \| 6.6644 \| 1.0 \| \| 0.0938 \| 266.17 \| 35400 \| 6.4721 \| 1.0 \| \| 0.0864 \| 267.67 \| 35600 \| 7.0132 \| 1.0 \| \| 0.0897 \| 269.17 \| 35800 \| 6.3767 \| 1.0 \| \| 0.094 \| 270.68 \| 36000 \| 6.1663 \| 1.0 \| \| 0.0782 \| 272.18 \| 36200 \| 5.7325 \| 1.0 \| \| 0.0878 \| 273.68 \| 36400 \| 6.0681 \| 1.0 \| \| 0.0877 \| 275.19 \| 36600 \| 6.2621 \| 1.0 \| \| 0.0827 \| 276.69 \| 36800 \| 5.9692 \| 1.0 \| \| 0.0929 \| 278.2 \| 37000 \| 6.0207 \| 1.0 \| \| 0.0899 \| 279.7 \| 37200 \| 5.4185 \| 1.0 \| \| 0.0841 \| 281.2 \| 37400 \| 5.9206 \| 1.0 \| \| 0.0924 \| 282.71 \| 37600 \| 6.1820 \| 1.0 \| \| 0.0844 \| 284.21 \| 37800 \| 6.1505 \| 1.0 \| \| 0.0824 \| 285.71 \| 38000 \| 6.1564 \| 1.0 \| \| 0.0842 \| 287.22 \| 38200 \| 5.9483 \| 1.0 \| \| 0.0863 \| 288.72 \| 38400 \| 5.9305 \| 1.0 \| \| 0.0851 \| 290.23 \| 38600 \| 5.8416 \| 1.0 \| \| 0.079 \| 291.73 \| 38800 \| 5.7345 \| 1.0 \| \| 0.081 \| 293.23 \| 39000 \| 5.7323 \| 1.0 \| \| 0.0873 \| 294.74 \| 39200 \| 5.9131 \| 1.0 \| \| 0.0836 \| 296.24 \| 39400 \| 6.1722 \| 1.0 \| \| 0.0774 \| 297.74 \| 39600 \| 5.9523 \| 1.0 \| \| 0.0902 \| 299.25 \| 39800 \| 5.8769 \| 1.0 \| \| 0.0766 \| 300.75 \| 40000 \| 6.2435 \| 1.0 \| \| 0.0766 \| 302.26 \| 40200 \| 5.7556 \| 1.0 \| \| 0.0723 \| 303.76 \| 40400 \| 5.4647 \| 1.0 \| \| 0.0724 \| 305.26 \| 40600 \| 6.0184 \| 1.0 \| \| 0.0834 \| 306.77 \| 40800 \| 5.8434 \| 1.0 \| \| 0.0846 \| 308.27 \| 41000 \| 6.0586 \| 1.0 \| \| 0.0878 \| 309.77 \| 41200 \| 5.7270 \| 1.0 \| \| 0.0761 \| 311.28 \| 41400 \| 5.7259 \| 1.0 \| \| 0.0639 \| 312.78 \| 41600 \| 6.0848 \| 1.0 \| \| 0.0733 \| 314.29 \| 41800 \| 6.0474 \| 1.0 \| \| 0.0734 \| 315.79 \| 42000 \| 5.9387 \| 1.0 \| \| 0.0779 \| 317.29 \| 42200 \| 5.6040 \| 1.0 \| \| 0.0713 \| 318.8 \| 42400 \| 6.3136 \| 1.0 \| \| 0.0756 \| 320.3 \| 42600 \| 5.8936 \| 1.0 \| \| 0.0758 \| 321.8 \| 42800 \| 6.3659 \| 1.0 \| \| 0.0662 \| 323.31 \| 43000 \| 5.8035 \| 1.0 \| \| 0.0714 \| 324.81 \| 43200 \| 5.3194 \| 1.0 \| \| 0.0782 \| 326.32 \| 43400 \| 6.0054 \| 1.0 \| \| 0.0775 \| 327.82 \| 43600 \| 5.8471 \| 1.0 \| \| 0.0653 \| 329.32 \| 43800 \| 5.4054 \| 1.0 \| \| 0.0739 \| 330.83 \| 44000 \| 6.0978 \| 1.0 \| \| 0.0634 \| 332.33 \| 44200 \| 6.0081 \| 1.0 \| \| 0.075 \| 333.83 \| 44400 \| 6.0761 \| 1.0 \| \| 0.0609 \| 335.34 \| 44600 \| 5.8444 \| 1.0 \| \| 0.0622 \| 336.84 \| 44800 \| 6.2485 \| 1.0 \| \| 0.0757 \| 338.35 \| 45000 \| 6.0131 \| 1.0 \| \| 0.0758 \| 339.85 \| 45200 \| 5.9577 \| 1.0 \| \| 0.0617 \| 341.35 \| 45400 \| 5.7657 \| 1.0 \| \| 0.0694 \| 342.86 \| 45600 \| 5.7509 \| 1.0 \| \| 0.0646 \| 344.36 \| 45800 \| 5.5593 \| 1.0 \| \| 0.0548 \| 345.86 \| 46000 \| 5.9096 \| 1.0 \| \| 0.0604 \| 347.37 \| 46200 \| 6.2313 \| 1.0 \| \| 0.0505 \| 348.87 \| 46400 \| 5.4780 \| 1.0 \| \| 0.0631 \| 350.38 \| 46600 \| 6.0868 \| 1.0 \| \| 0.0622 \| 351.88 \| 46800 \| 5.8833 \| 1.0 \| \| 0.0605 \| 353.38 \| 47000 \| 5.5888 \| 1.0 \| \| 0.0632 \| 354.89 \| 47200 \| 5.7510 \| 1.0 \| \| 0.0658 \| 356.39 \| 47400 \| 5.2321 \| 1.0 \| \| 0.0561 \| 357.89 \| 47600 \| 5.6745 \| 1.0 \| \| 0.0737 \| 359.4 \| 47800 \| 6.0472 \| 1.0 \| \| 0.0561 \| 360.9 \| 48000 \| 6.2185 \| 1.0 \| \| 0.0564 \| 362.41 \| 48200 \| 6.0749 \| 1.0 \| \| 0.0626 \| 363.91 \| 48400 \| 5.6136 \| 1.0 \| \| 0.0725 \| 365.41 \| 48600 \| 5.7983 \| 1.0 \| \| 0.0602 \| 366.92 \| 48800 \| 5.5020 \| 1.0 \| \| 0.0599 \| 368.42 \| 49000 \| 6.0626 \| 1.0 \| \| 0.0728 \| 369.92 \| 49200 \| 6.3407 \| 1.0 \| \| 0.0561 \| 371.43 \| 49400 \| 6.2899 \| 1.0 \| \| 0.0611 \| 372.93 \| 49600 \| 6.5780 \| 1.0 \| \| 0.065 \| 374.44 \| 49800 \| 6.4685 \| 1.0 \| \| 0.0561 \| 375.94 \| 50000 \| 5.5252 \| 1.0 \| \| 0.0482 \| 377.44 \| 50200 \| 5.3905 \| 1.0 \| \| 0.0575 \| 378.95 \| 50400 \| 5.5660 \| 1.0 \| \| 0.0673 \| 380.45 \| 50600 \| 6.3424 \| 1.0 \| \| 0.0588 \| 381.95 \| 50800 \| 6.5294 \| 1.0 \| \| 0.0563 \| 383.46 \| 51000 \| 5.2974 \| 1.0 \| \| 0.0702 \| 384.96 \| 51200 \| 5.8705 \| 1.0 \| \| 0.0517 \| 386.47 \| 51400 \| 5.7488 \| 1.0 \| \| 0.0629 \| 387.97 \| 51600 \| 5.8414 \| 1.0 \| \| 0.0569 \| 389.47 \| 51800 \| 5.3303 \| 1.0 \| \| 0.0586 \| 390.98 \| 52000 \| 5.1755 \| 1.0 \| \| 0.0581 \| 392.48 \| 52200 \| 6.0030 \| 1.0 \| \| 0.0673 \| 393.98 \| 52400 \| 5.9972 \| 1.0 \| \| 0.0533 \| 395.49 \| 52600 \| 6.1624 \| 1.0 \| \| 0.0597 \| 396.99 \| 52800 \| 5.6803 \| 1.0 \| \| 0.0494 \| 398.5 \| 53000 \| 5.4154 \| 1.0 \| \| 0.0526 \| 400.0 \| 53200 \| 5.5855 \| 1.0 \| \| 0.0578 \| 401.5 \| 53400 \| 5.9491 \| 1.0 \| \| 0.0546 \| 403.01 \| 53600 \| 5.9381 \| 1.0 \| \| 0.0575 \| 404.51 \| 53800 \| 5.9629 \| 1.0 \| \| 0.0592 \| 406.02 \| 54000 \| 5.8617 \| 1.0 \| \| 0.0444 \| 407.52 \| 54200 \| 5.5513 \| 1.0 \| \| 0.0467 \| 409.02 \| 54400 \| 5.2998 \| 1.0 \| \| 0.0654 \| 410.53 \| 54600 \| 5.3034 \| 1.0 \| \| 0.0546 \| 412.03 \| 54800 \| 5.3077 \| 1.0 \| \| 0.0567 \| 413.53 \| 55000 \| 5.0215 \| 1.0 \| \| 0.0564 \| 415.04 \| 55200 \| 5.4569 \| 1.0 \| \| 0.0494 \| 416.54 \| 55400 \| 5.7311 \| 1.0 \| \| 0.0448 \| 418.05 \| 55600 \| 5.6774 \| 1.0 \| \| 0.0695 \| 419.55 \| 55800 \| 5.5563 \| 1.0 \| \| 0.0451 \| 421.05 \| 56000 \| 6.0087 \| 1.0 \| \| 0.0514 \| 422.56 \| 56200 \| 5.4969 \| 1.0 \| \| 0.0504 \| 424.06 \| 56400 \| 6.0321 \| 1.0 \| \| 0.0429 \| 425.56 \| 56600 \| 5.6021 \| 1.0 \| \| 0.0503 \| 427.07 \| 56800 \| 5.8039 \| 1.0 \| \| 0.0528 \| 428.57 \| 57000 \| 5.9237 \| 1.0 \| \| 0.0508 \| 430.08 \| 57200 \| 5.7653 \| 1.0 \| \| 0.0533 \| 431.58 \| 57400 \| 6.2778 \| 1.0 \| \| 0.048 \| 433.08 \| 57600 \| 6.0965 \| 1.0 \| \| 0.0492 \| 434.59 \| 57800 \| 5.3128 \| 1.0 \| \| 0.0438 \| 436.09 \| 58000 \| 5.7699 \| 1.0 \| \| 0.0525 \| 437.59 \| 58200 \| 5.1163 \| 1.0 \| \| 0.0573 \| 439.1 \| 58400 \| 5.4089 \| 1.0 \| \| 0.0587 \| 440.6 \| 58600 \| 5.2019 \| 1.0 \| \| 0.049 \| 442.11 \| 58800 \| 5.4515 \| 1.0 \| \| 0.0435 \| 443.61 \| 59000 \| 5.5448 \| 1.0 \| \| 0.0378 \| 445.11 \| 59200 \| 5.8339 \| 1.0 \| \| 0.0498 \| 446.62 \| 59400 \| 5.4560 \| 1.0 \| \| 0.0361 \| 448.12 \| 59600 \| 5.5045 \| 1.0 \| \| 0.0438 \| 449.62 \| 59800 \| 4.8485 \| 1.0 \| \| 0.0512 \| 451.13 \| 60000 \| 4.9816 \| 1.0 \| \| 0.0464 \| 452.63 \| 60200 \| 5.2011 \| 1.0 \| \| 0.0437 \| 454.14 \| 60400 \| 5.6679 \| 1.0 \| \| 0.0457 \| 455.64 \| 60600 \| 5.7678 \| 1.0 \| \| 0.0397 \| 457.14 \| 60800 \| 5.6997 \| 1.0 \| \| 0.0397 \| 458.65 \| 61000 \| 5.2398 \| 1.0 \| \| 0.0412 \| 460.15 \| 61200 \| 5.3039 \| 1.0 \| \| 0.0472 \| 461.65 \| 61400 \| 5.9334 \| 1.0 \| \| 0.0389 \| 463.16 \| 61600 \| 5.6996 \| 1.0 \| \| 0.043 \| 464.66 \| 61800 \| 5.4419 \| 1.0 \| \| 0.0407 \| 466.17 \| 62000 \| 5.2868 \| 1.0 \| \| 0.0511 \| 467.67 \| 62200 \| 5.2794 \| 1.0 \| \| 0.0456 \| 469.17 \| 62400 \| 5.6698 \| 1.0 \| \| 0.0462 \| 470.68 \| 62600 \| 5.4807 \| 1.0 \| \| 0.0402 \| 472.18 \| 62800 \| 4.9668 \| 1.0 \| \| 0.0459 \| 473.68 \| 63000 \| 5.1433 \| 1.0 \| \| 0.0416 \| 475.19 \| 63200 \| 5.1973 \| 1.0 \| \| 0.0468 \| 476.69 \| 63400 \| 4.5775 \| 1.0 \| \| 0.0382 \| 478.2 \| 63600 \| 5.4247 \| 1.0 \| \| 0.04 \| 479.7 \| 63800 \| 5.3787 \| 1.0 \| \| 0.0446 \| 481.2 \| 64000 \| 5.8649 \| 1.0 \| \| 0.0478 \| 482.71 \| 64200 \| 5.5780 \| 1.0 \| \| 0.0468 \| 484.21 \| 64400 \| 5.6468 \| 1.0 \| \| 0.0358 \| 485.71 \| 64600 \| 5.5878 \| 1.0 \| \| 0.0375 \| 487.22 \| 64800 \| 6.0500 \| 1.0 \| \| 0.0418 \| 488.72 \| 65000 \| 5.6539 \| 1.0 \| \| 0.0419 \| 490.23 \| 65200 \| 6.0848 \| 1.0 \| \| 0.0381 \| 491.73 \| 65400 \| 5.7511 \| 1.0 \| \| 0.0476 \| 493.23 \| 65600 \| 5.7423 \| 1.0 \| \| 0.0492 \| 494.74 \| 65800 \| 5.3481 \| 1.0 \| \| 0.0373 \| 496.24 \| 66000 \| 6.2950 \| 1.0 \| \| 0.0451 \| 497.74 \| 66200 \| 5.4381 \| 1.0 \| \| 0.0457 \| 499.25 \| 66400 \| 5.5962 \| 1.0 \| \| 0.0384 \| 500.75 \| 66600 \| 5.6821 \| 1.0 \| \| 0.037 \| 502.26 \| 66800 \| 5.8399 \| 1.0 \| \| 0.0378 \| 503.76 \| 67000 \| 5.4415 \| 1.0 \| \| 0.038 \| 505.26 \| 67200 \| 5.6578 \| 1.0 \| \| 0.0371 \| 506.77 \| 67400 \| 5.6192 \| 1.0 \| \| 0.0314 \| 508.27 \| 67600 \| 6.0258 \| 1.0 \| \| 0.0345 \| 509.77 \| 67800 \| 6.4183 \| 1.0 \| \| 0.039 \| 511.28 \| 68000 \| 5.1909 \| 1.0 \| \| 0.0337 \| 512.78 \| 68200 \| 5.3650 \| 1.0 \| \| 0.0463 \| 514.29 \| 68400 \| 5.5936 \| 1.0 \| \| 0.05 \| 515.79 \| 68600 \| 5.6765 \| 1.0 \| \| 0.0374 \| 517.29 \| 68800 \| 5.3339 \| 1.0 \| \| 0.0374 \| 518.8 \| 69000 \| 5.7343 \| 1.0 \| \| 0.0406 \| 520.3 \| 69200 \| 5.6398 \| 1.0 \| \| 0.0397 \| 521.8 \| 69400 \| 5.4041 \| 1.0 \| \| 0.0391 \| 523.31 \| 69600 \| 5.8327 \| 1.0 \| \| 0.0332 \| 524.81 \| 69800 \| 5.6610 \| 1.0 \| \| 0.0325 \| 526.32 \| 70000 \| 5.4675 \| 1.0 \| \| 0.0438 \| 527.82 \| 70200 \| 5.4601 \| 1.0 \| \| 0.0281 \| 529.32 \| 70400 \| 5.3118 \| 1.0 \| \| 0.0314 \| 530.83 \| 70600 \| 5.6760 \| 1.0 \| \| 0.039 \| 532.33 \| 70800 \| 5.2432 \| 1.0 \| \| 0.0293 \| 533.83 \| 71000 \| 5.4250 \| 1.0 \| \| 0.0334 \| 535.34 \| 71200 \| 4.9681 \| 1.0 \| \| 0.0341 \| 536.84 \| 71400 \| 5.2753 \| 1.0 \| \| 0.0368 \| 538.35 \| 71600 \| 4.7242 \| 1.0 \| \| 0.0339 \| 539.85 \| 71800 \| 5.0283 \| 1.0 \| \| 0.0285 \| 541.35 \| 72000 \| 4.9008 \| 1.0 \| \| 0.0323 \| 542.86 \| 72200 \| 5.0817 \| 1.0 \| \| 0.0391 \| 544.36 \| 72400 \| 5.2062 \| 1.0 \| \| 0.0337 \| 545.86 \| 72600 \| 5.4544 \| 1.0 \| \| 0.0352 \| 547.37 \| 72800 \| 5.0674 \| 1.0 \| \| 0.0419 \| 548.87 \| 73000 \| 5.2037 \| 1.0 \| \| 0.0314 \| 550.38 \| 73200 \| 4.8665 \| 1.0 \| \| 0.0324 \| 551.88 \| 73400 \| 5.2775 \| 1.0 \| \| 0.0329 \| 553.38 \| 73600 \| 4.7535 \| 1.0 \| \| 0.0304 \| 554.89 \| 73800 \| 4.8733 \| 0.9994 \| \| 0.0388 \| 556.39 \| 74000 \| 5.2259 \| 1.0 \| \| 0.0423 \| 557.89 \| 74200 \| 5.4575 \| 1.0 \| \| 0.034 \| 559.4 \| 74400 \| 4.9118 \| 1.0 \| \| 0.0289 \| 560.9 \| 74600 \| 4.9018 \| 1.0 \| \| 0.0317 \| 562.41 \| 74800 \| 5.0265 \| 1.0 \| \| 0.0346 \| 563.91 \| 75000 \| 4.7586 \| 1.0 \| \| 0.0238 \| 565.41 \| 75200 \| 5.0713 \| 1.0 \| \| 0.0275 \| 566.92 \| 75400 \| 4.8972 \| 1.0 \| \| 0.0291 \| 568.42 \| 75600 \| 4.8198 \| 1.0 \| \| 0.0371 \| 569.92 \| 75800 \| 4.3204 \| 1.0 \| \| 0.033 \| 571.43 \| 76000 \| 4.5274 \| 1.0 \| \| 0.0334 \| 572.93 \| 76200 \| 4.8907 \| 1.0 \| \| 0.0375 \| 574.44 \| 76400 \| 4.6204 \| 1.0 \| \| 0.0332 \| 575.94 \| 76600 \| 4.4255 \| 1.0 \| \| 0.0359 \| 577.44 \| 76800 \| 4.8451 \| 1.0 \| \| 0.0273 \| 578.95 \| 77000 \| 5.8480 \| 1.0 \| \| 0.0273 \| 580.45 \| 77200 \| 5.2345 \| 1.0 \| \| 0.0296 \| 581.95 \| 77400 \| 5.3281 \| 0.9994 \| \| 0.0332 \| 583.46 \| 77600 \| 5.6027 \| 0.9994 \| \| 0.0251 \| 584.96 \| 77800 \| 5.3038 \| 0.9994 \| \| 0.0275 \| 586.47 \| 78000 \| 5.0464 \| 1.0 \| \| 0.0193 \| 587.97 \| 78200 \| 4.9974 \| 0.9994 \| \| 0.0326 \| 589.47 \| 78400 \| 4.6220 \| 1.0 \| \| 0.033 \| 590.98 \| 78600 \| 4.9772 \| 1.0 \| \| 0.0258 \| 592.48 \| 78800 \| 4.6183 \| 1.0 \| \| 0.0326 \| 593.98 \| 79000 \| 5.2420 \| 1.0 \| \| 0.0311 \| 595.49 \| 79200 \| 4.6937 \| 1.0 \| \| 0.0257 \| 596.99 \| 79400 \| 4.9505 \| 1.0 \| \| 0.0295 \| 598.5 \| 79600 \| 5.0226 \| 1.0 \| \| 0.0309 \| 600.0 \| 79800 \| 4.8904 \| 1.0 \| \| 0.0239 \| 601.5 \| 80000 \| 5.4780 \| 1.0 \| \| 0.0238 \| 603.01 \| 80200 \| 5.4525 \| 1.0 \| \| 0.0373 \| 604.51 \| 80400 \| 4.8573 \| 0.9994 \| \| 0.0308 \| 606.02 \| 80600 \| 5.1780 \| 1.0 \| \| 0.0262 \| 607.52 \| 80800 \| 5.4216 \| 1.0 \| \| 0.0227 \| 609.02 \| 81000 \| 4.9209 \| 1.0 \| \| 0.0222 \| 610.53 \| 81200 \| 5.4042 \| 1.0 \| \| 0.0332 \| 612.03 \| 81400 \| 5.2453 \| 1.0 \| \| 0.0261 \| 613.53 \| 81600 \| 5.3043 \| 1.0 \| \| 0.0254 \| 615.04 \| 81800 \| 5.7649 \| 1.0 \| \| 0.0276 \| 616.54 \| 82000 \| 5.1942 \| 1.0 \| \| 0.0291 \| 618.05 \| 82200 \| 4.9448 \| 0.9994 \| \| 0.0275 \| 619.55 \| 82400 \| 5.0157 \| 1.0006 \| \| 0.0278 \| 621.05 \| 82600 \| 4.7635 \| 1.0006 \| \| 0.0232 \| 622.56 \| 82800 \| 5.4406 \| 1.0 \| \| 0.0311 \| 624.06 \| 83000 \| 5.3966 \| 1.0 \| \| 0.0206 \| 625.56 \| 83200 \| 4.8876 \| 1.0013 \| \| 0.0274 \| 627.07 \| 83400 \| 5.4422 \| 1.0 \| \| 0.0199 \| 628.57 \| 83600 \| 4.9966 \| 1.0006 \| \| 0.0253 \| 630.08 \| 83800 \| 5.1257 \| 1.0006 \| \| 0.027 \| 631.58 \| 84000 \| 5.1748 \| 1.0 \| \| 0.0312 \| 633.08 \| 84200 \| 5.0923 \| 1.0006 \| \| 0.0288 \| 634.59 \| 84400 \| 5.1963 \| 1.0 \| \| 0.0261 \| 636.09 \| 84600 \| 4.8105 \| 1.0013 \| \| 0.0259 \| 637.59 \| 84800 \| 4.9715 \| 1.0 \| \| 0.03 \| 639.1 \| 85000 \| 5.2808 \| 1.0 \| \| 0.0215 \| 640.6 \| 85200 \| 5.3155 \| 1.0 \| \| 0.028 \| 642.11 \| 85400 \| 5.1311 \| 1.0 \| \| 0.0268 \| 643.61 \| 85600 \| 5.1429 \| 1.0 \| \| 0.0238 \| 645.11 \| 85800 \| 5.0456 \| 1.0 \| \| 0.0273 \| 646.62 \| 86000 \| 5.2772 \| 1.0 \| \| 0.0196 \| 648.12 \| 86200 \| 5.2186 \| 1.0 \| \| 0.0212 \| 649.62 \| 86400 \| 4.8386 \| 0.9994 \| \| 0.0243 \| 651.13 \| 86600 \| 5.1898 \| 1.0 \| \| 0.0255 \| 652.63 \| 86800 \| 5.5885 \| 1.0 \| \| 0.0192 \| 654.14 \| 87000 \| 5.7636 \| 1.0 \| \| 0.0272 \| 655.64 \| 87200 \| 5.5897 \| 1.0 \| \| 0.0254 \| 657.14 \| 87400 \| 5.3412 \| 1.0 \| \| 0.0389 \| 658.65 \| 87600 \| 5.2754 \| 1.0 \| \| 0.025 \| 660.15 \| 87800 \| 4.6929 \| 1.0 \| \| 0.0268 \| 661.65 \| 88000 \| 4.7299 \| 1.0 \| \| 0.0303 \| 663.16 \| 88200 \| 5.1038 \| 1.0 \| \| 0.019 \| 664.66 \| 88400 \| 5.4165 \| 1.0 \| \| 0.0197 \| 666.17 \| 88600 \| 5.3144 \| 1.0 \| \| 0.024 \| 667.67 \| 88800 \| 4.7525 \| 1.0 \| \| 0.0272 \| 669.17 \| 89000 \| 5.4154 \| 1.0 \| \| 0.0267 \| 670.68 \| 89200 \| 5.1149 \| 0.9994 \| \| 0.0231 \| 672.18 \| 89400 \| 5.3135 \| 1.0 \| \| 0.034 \| 673.68 \| 89600 \| 4.8940 \| 1.0 \| \| 0.0205 \| 675.19 \| 89800 \| 5.4456 \| 1.0 \| \| 0.0236 \| 676.69 \| 90000 \| 5.1145 \| 1.0 \| \| 0.0276 \| 678.2 \| 90200 \| 5.3905 \| 1.0 \| \| 0.0316 \| 679.7 \| 90400 \| 5.4222 \| 1.0 \| \| 0.0253 \| 681.2 \| 90600 \| 5.1456 \| 1.0006 \| \| 0.0255 \| 682.71 \| 90800 \| 5.3852 \| 1.0 \| \| 0.0185 \| 684.21 \| 91000 \| 4.9756 \| 1.0 \| \| 0.019 \| 685.71 \| 91200 \| 5.9086 \| 1.0 \| \| 0.0232 \| 687.22 \| 91400 \| 5.1278 \| 1.0 \| \| 0.0281 \| 688.72 \| 91600 \| 4.9946 \| 1.0 \| \| 0.0188 \| 690.23 \| 91800 \| 5.0910 \| 1.0 \| \| 0.0189 \| 691.73 \| 92000 \| 4.8426 \| 1.0 \| \| 0.0276 \| 693.23 \| 92200 \| 5.0411 \| 1.0 \| \| 0.021 \| 694.74 \| 92400 \| 4.8377 \| 1.0 \| \| 0.0237 \| 696.24 \| 92600 \| 4.9047 \| 1.0 \| \| 0.0209 \| 697.74 \| 92800 \| 4.3727 \| 1.0 \| \| 0.0212 \| 699.25 \| 93000 \| 4.4921 \| 1.0006 \| \| 0.0228 \| 700.75 \| 93200 \| 4.7233 \| 1.0006 \| \| 0.0187 \| 702.26 \| 93400 \| 4.7441 \| 1.0006 \| \| 0.0217 \| 703.76 \| 93600 \| 4.7464 \| 1.0 \| \| 0.0254 \| 705.26 \| 93800 \| 4.8955 \| 0.9994 \| \| 0.0268 \| 706.77 \| 94000 \| 5.2548 \| 1.0 \| \| 0.0236 \| 708.27 \| 94200 \| 5.2691 \| 1.0 \| \| 0.0174 \| 709.77 \| 94400 \| 4.8898 \| 1.0 \| \| 0.0262 \| 711.28 \| 94600 \| 4.7713 \| 1.0 \| \| 0.0226 \| 712.78 \| 94800 \| 4.8464 \| 1.0 \| \| 0.0193 \| 714.29 \| 95000 \| 5.0019 \| 1.0 \| \| 0.0229 \| 715.79 \| 95200 \| 5.2043 \| 1.0 \| \| 0.014 \| 717.29 \| 95400 \| 4.9124 \| 0.9994 \| \| 0.0274 \| 718.8 \| 95600 \| 4.9160 \| 1.0 \| \| 0.0198 \| 720.3 \| 95800 \| 5.1003 \| 1.0 \| \| 0.0222 \| 721.8 \| 96000 \| 4.9865 \| 1.0 \| \| 0.0133 \| 723.31 \| 96200 \| 5.0203 \| 1.0 \| \| 0.0162 \| 724.81 \| 96400 \| 4.9140 \| 1.0 \| \| 0.024 \| 726.32 \| 96600 \| 4.9667 \| 1.0006 \| \| 0.0197 \| 727.82 \| 96800 \| 5.1605 \| 1.0 \| \| 0.0169 \| 729.32 \| 97000 \| 5.4196 \| 1.0 \| \| 0.0238 \| 730.83 \| 97200 \| 5.3665 \| 1.0 \| \| 0.0182 \| 732.33 \| 97400 \| 5.0020 \| 1.0013 \| \| 0.0212 \| 733.83 \| 97600 \| 5.1674 \| 1.0006 \| \| 0.0165 \| 735.34 \| 97800 \| 4.9132 \| 1.0013 \| \| 0.0242 \| 736.84 \| 98000 \| 4.7047 \| 1.0013 \| \| 0.022 \| 738.35 \| 98200 \| 5.1006 \| 1.0 \| \| 0.022 \| 739.85 \| 98400 \| 4.7582 \| 0.9994 \| \| 0.0178 \| 741.35 \| 98600 \| 5.0911 \| 1.0 \| \| 0.0197 \| 742.86 \| 98800 \| 4.9315 \| 1.0 \| \| 0.022 \| 744.36 \| 99000 \| 4.4003 \| 1.0019 \| \| 0.0114 \| 745.86 \| 99200 \| 4.9068 \| 1.0 \| \| 0.0184 \| 747.37 \| 99400 \| 4.7211 \| 1.0 \| \| 0.0227 \| 748.87 \| 99600 \| 4.8788 \| 1.0 \| \| 0.0127 \| 750.38 \| 99800 \| 5.0337 \| 1.0 \| \| 0.0193 \| 751.88 \| 100000 \| 5.0193 \| 1.0 \| \| 0.0167 \| 753.38 \| 100200 \| 5.1066 \| 1.0019 \| \| 0.0149 \| 754.89 \| 100400 \| 5.2865 \| 1.0 \| \| 0.0219 \| 756.39 \| 100600 \| 5.2765 \| 1.0 \| \| 0.0185 \| 757.89 \| 100800 \| 5.3224 \| 1.0 \| \| 0.0159 \| 759.4 \| 101000 \| 5.1665 \| 1.0 \| \| 0.0178 \| 760.9 \| 101200 \| 5.4050 \| 0.9994 \| \| 0.0119 \| 762.41 \| 101400 \| 5.1887 \| 0.9994 \| \| 0.022 \| 763.91 \| 101600 \| 4.7725 \| 1.0 \| \| 0.0214 \| 765.41 \| 101800 \| 4.9794 \| 1.0 \| \| 0.0151 \| 766.92 \| 102000 \| 4.7806 \| 1.0013 \| \| 0.0181 \| 768.42 \| 102200 \| 4.3024 \| 1.0006 \| \| 0.0166 \| 769.92 \| 102400 \| 4.8876 \| 1.0 \| \| 0.0211 \| 771.43 \| 102600 \| 4.5744 \| 1.0 \| \| 0.0184 \| 772.93 \| 102800 \| 4.8648 \| 0.9994 \| \| 0.02 \| 774.44 \| 103000 \| 4.7831 \| 0.9994 \| \| 0.0188 \| 775.94 \| 103200 \| 4.9471 \| 0.9994 \| \| 0.0201 \| 777.44 \| 103400 \| 4.9495 \| 1.0006 \| \| 0.0169 \| 778.95 \| 103600 \| 5.2604 \| 1.0 \| \| 0.0145 \| 780.45 \| 103800 \| 5.5133 \| 1.0 \| \| 0.0176 \| 781.95 \| 104000 \| 5.1844 \| 0.9994 \| \| 0.0149 \| 783.46 \| 104200 \| 4.6289 \| 1.0 \| \| 0.0181 \| 784.96 \| 104400 \| 5.2480 \| 1.0 \| \| 0.0132 \| 786.47 \| 104600 \| 4.7374 \| 0.9994 \| \| 0.0115 \| 787.97 \| 104800 \| 5.6450 \| 1.0 \| \| 0.014 \| 789.47 \| 105000 \| 5.0672 \| 1.0 \| \| 0.0181 \| 790.98 \| 105200 \| 5.1088 \| 1.0 \| \| 0.0181 \| 792.48 \| 105400 \| 5.0798 \| 1.0 \| \| 0.0146 \| 793.98 \| 105600 \| 5.1835 \| 1.0 \| \| 0.0219 \| 795.49 \| 105800 \| 5.3850 \| 1.0 \| \| 0.0222 \| 796.99 \| 106000 \| 4.9615 \| 1.0 \| \| 0.0176 \| 798.5 \| 106200 \| 4.6814 \| 1.0 \| \| 0.017 \| 800.0 \| 106400 \| 4.9264 \| 1.0 \| \| 0.0134 \| 801.5 \| 106600 \| 5.0058 \| 1.0 \| \| 0.0139 \| 803.01 \| 106800 \| 5.1207 \| 1.0 \| \| 0.0146 \| 804.51 \| 107000 \| 4.8640 \| 1.0006 \| \| 0.018 \| 806.02 \| 107200 \| 4.6173 \| 1.0 \| \| 0.0183 \| 807.52 \| 107400 \| 4.8846 \| 1.0 \| \| 0.0169 \| 809.02 \| 107600 \| 5.2157 \| 1.0006 \| \| 0.0128 \| 810.53 \| 107800 \| 5.1951 \| 1.0006 \| \| 0.0181 \| 812.03 \| 108000 \| 4.9453 \| 1.0013 \| \| 0.0107 \| 813.53 \| 108200 \| 5.0670 \| 1.0013 \| \| 0.0171 \| 815.04 \| 108400 \| 4.8881 \| 1.0006 \| \| 0.0144 \| 816.54 \| 108600 \| 4.8071 \| 1.0013 \| \| 0.0133 \| 818.05 \| 108800 \| 4.9399 \| 1.0013 \| \| 0.0138 \| 819.55 \| 109000 \| 4.5266 \| 1.0038 \| \| 0.0159 \| 821.05 \| 109200 \| 4.9887 \| 1.0025 \| \| 0.0157 \| 822.56 \| 109400 \| 4.8344 \| 1.0019 \| \| 0.0134 \| 824.06 \| 109600 \| 5.1275 \| 1.0019 \| \| 0.0142 \| 825.56 \| 109800 \| 4.7311 \| 1.0025 \| \| 0.0207 \| 827.07 \| 110000 \| 4.5203 \| 1.0050 \| \| 0.0125 \| 828.57 \| 110200 \| 4.5815 \| 1.0044 \| \| 0.0161 \| 830.08 \| 110400 \| 4.7650 \| 1.0031 \| \| 0.0091 \| 831.58 \| 110600 \| 4.9839 \| 1.0025 \| \| 0.0154 \| 833.08 \| 110800 \| 4.9781 \| 1.0013 \| \| 0.012 \| 834.59 \| 111000 \| 5.0515 \| 1.0025 \| \| 0.0182 \| 836.09 \| 111200 \| 5.1645 \| 1.0025 \| \| 0.0135 \| 837.59 \| 111400 \| 5.1050 \| 1.0013 \| \| 0.0171 \| 839.1 \| 111600 \| 4.8125 \| 1.0025 \| \| 0.0152 \| 840.6 \| 111800 \| 5.4213 \| 1.0038 \| \| 0.0103 \| 842.11 \| 112000 \| 5.0478 \| 1.0031 \| \| 0.0112 \| 843.61 \| 112200 \| 4.9343 \| 1.0019 \| \| 0.0156 \| 845.11 \| 112400 \| 5.2666 \| 1.0019 \| \| 0.0122 \| 846.62 \| 112600 \| 5.0991 \| 1.0031 \| \| 0.0178 \| 848.12 \| 112800 \| 5.1477 \| 1.0006 \| \| 0.0174 \| 849.62 \| 113000 \| 5.1716 \| 1.0031 \| \| 0.0136 \| 851.13 \| 113200 \| 5.3554 \| 1.0013 \| \| 0.0123 \| 852.63 \| 113400 \| 4.8209 \| 1.0019 \| \| 0.0095 \| 854.14 \| 113600 \| 4.9493 \| 1.0019 \| \| 0.0183 \| 855.64 \| 113800 \| 4.8060 \| 1.0038 \| \| 0.0111 \| 857.14 \| 114000 \| 4.8071 \| 1.0006 \| \| 0.0121 \| 858.65 \| 114200 \| 4.7184 \| 1.0006 \| \| 0.0079 \| 860.15 \| 114400 \| 4.7966 \| 1.0019 \| \| 0.0129 \| 861.65 \| 114600 \| 4.8338 \| 1.0013 \| \| 0.0103 \| 863.16 \| 114800 \| 4.5879 \| 1.0019 \| \| 0.0117 \| 864.66 \| 115000 \| 4.7938 \| 1.0025 \| \| 0.018 \| 866.17 \| 115200 \| 4.6562 \| 1.0 \| \| 0.0121 \| 867.67 \| 115400 \| 4.6558 \| 1.0 \| \| 0.0184 \| 869.17 \| 115600 \| 4.7076 \| 1.0 \| \| 0.0135 \| 870.68 \| 115800 \| 5.1430 \| 1.0 \| \| 0.0163 \| 872.18 \| 116000 \| 5.2987 \| 1.0019 \| \| 0.014 \| 873.68 \| 116200 \| 5.0680 \| 1.0019 \| \| 0.0114 \| 875.19 \| 116400 \| 4.9600 \| 1.0013 \| \| 0.0145 \| 876.69 \| 116600 \| 5.0292 \| 1.0025 \| \| 0.0125 \| 878.2 \| 116800 \| 4.6149 \| 1.0038 \| \| 0.008 \| 879.7 \| 117000 \| 4.9819 \| 1.0019 \| \| 0.0122 \| 881.2 \| 117200 \| 5.0868 \| 1.0019 \| \| 0.0157 \| 882.71 \| 117400 \| 4.8522 \| 1.0031 \| \| 0.0146 \| 884.21 \| 117600 \| 4.6680 \| 1.0044 \| \| 0.014 \| 885.71 \| 117800 \| 4.4612 \| 1.0038 \| \| 0.0123 \| 887.22 \| 118000 \| 4.4058 \| 1.0044 \| \| 0.0122 \| 888.72 \| 118200 \| 4.6614 \| 1.0038 \| \| 0.012 \| 890.23 \| 118400 \| 4.7230 \| 1.0019 \| \| 0.0159 \| 891.73 \| 118600 \| 5.0094 \| 1.0006 \| \| 0.0087 \| 893.23 \| 118800 \| 4.8522 \| 1.0006 \| \| 0.013 \| 894.74 \| 119000 \| 4.9456 \| 1.0013 \| \| 0.0166 \| 896.24 \| 119200 \| 4.9068 \| 1.0019 \| \| 0.0129 \| 897.74 \| 119400 \| 4.9066 \| 1.0019 \| \| 0.0136 \| 899.25 \| 119600 \| 4.6458 \| 1.0019 \| \| 0.0208 \| 900.75 \| 119800 \| 4.6729 \| 1.0013 \| \| 0.0158 \| 902.26 \| 120000 \| 4.6674 \| 1.0019 \| \| 0.0125 \| 903.76 \| 120200 \| 4.5242 \| 1.0025 \| \| 0.0098 \| 905.26 \| 120400 \| 4.7753 \| 1.0025 \| \| 0.0078 \| 906.77 \| 120600 \| 4.8435 \| 1.0013 \| \| 0.0155 \| 908.27 \| 120800 \| 4.6419 \| 1.0006 \| \| 0.0142 \| 909.77 \| 121000 \| 4.7051 \| 1.0013 \| \| 0.0098 \| 911.28 \| 121200 \| 5.0023 \| 1.0 \| \| 0.0097 \| 912.78 \| 121400 \| 5.2775 \| 1.0 \| \| 0.0103 \| 914.29 \| 121600 \| 5.1593 \| 1.0 \| \| 0.0115 \| 915.79 \| 121800 \| 5.0693 \| 1.0 \| \| 0.0129 \| 917.29 \| 122000 \| 5.4565 \| 1.0 \| \| 0.0123 \| 918.8 \| 122200 \| 4.9698 \| 1.0 \| \| 0.0176 \| 920.3 \| 122400 \| 4.8110 \| 1.0019 \| \| 0.0106 \| 921.8 \| 122600 \| 4.7044 \| 1.0031 \| \| 0.0098 \| 923.31 \| 122800 \| 4.7205 \| 1.0031 \| \| 0.0132 \| 924.81 \| 123000 \| 4.5947 \| 1.0013 \| \| 0.0122 \| 926.32 \| 123200 \| 4.9378 \| 1.0 \| \| 0.0101 \| 927.82 \| 123400 \| 5.0470 \| 1.0006 \| \| 0.0151 \| 929.32 \| 123600 \| 4.7670 \| 1.0 \| \| 0.0089 \| 930.83 \| 123800 \| 4.9127 \| 1.0019 \| \| 0.0107 \| 932.33 \| 124000 \| 4.9719 \| 1.0031 \| \| 0.0084 \| 933.83 \| 124200 \| 5.0971 \| 1.0025 \| \| 0.0111 \| 935.34 \| 124400 \| 4.6700 \| 1.0031 \| \| 0.0121 \| 936.84 \| 124600 \| 4.7236 \| 1.0031 \| \| 0.0104 \| 938.35 \| 124800 \| 4.7380 \| 1.0019 \| \| 0.0101 \| 939.85 \| 125000 \| 4.8754 \| 1.0025 \| \| 0.0145 \| 941.35 \| 125200 \| 5.0708 \| 1.0031 \| \| 0.0096 \| 942.86 \| 125400 \| 4.8973 \| 1.0031 \| \| 0.0158 \| 944.36 \| 125600 \| 5.1670 \| 1.0019 \| \| 0.0106 \| 945.86 \| 125800 \| 5.0989 \| 1.0044 \| \| 0.0157 \| 947.37 \| 126000 \| 5.0135 \| 1.0044 \| \| 0.0092 \| 948.87 \| 126200 \| 4.7069 \| 1.0025 \| \| 0.0121 \| 950.38 \| 126400 \| 4.6865 \| 1.0031 \| \| 0.0117 \| 951.88 \| 126600 \| 4.6496 \| 1.0050 \| \| 0.0107 \| 953.38 \| 126800 \| 5.0900 \| 1.0025 \| \| 0.0107 \| 954.89 \| 127000 \| 4.8722 \| 1.0025 \| \| 0.0137 \| 956.39 \| 127200 \| 4.7971 \| 1.0013 \| \| 0.0106 \| 957.89 \| 127400 \| 5.0268 \| 1.0025 \| \| 0.0138 \| 959.4 \| 127600 \| 5.1737 \| 1.0019 \| \| 0.01 \| 960.9 \| 127800 \| 5.2657 \| 1.0025 \| \| 0.0101 \| 962.41 \| 128000 \| 5.0265 \| 1.0019 \| \| 0.0092 \| 963.91 \| 128200 \| 5.4657 \| 1.0019 \| \| 0.0109 \| 965.41 \| 128400 \| 5.5858 \| 1.0019 \| \| 0.0078 \| 966.92 \| 128600 \| 5.2281 \| 1.0031 \| \| 0.0111 \| 968.42 \| 128800 \| 4.9028 \| 1.0013 \| \| 0.0068 \| 969.92 \| 129000 \| 4.8840 \| 1.0019 \| \| 0.008 \| 971.43 \| 129200 \| 5.3572 \| 1.0013 \| \| 0.0108 \| 972.93 \| 129400 \| 5.0857 \| 1.0025 \| \| 0.0128 \| 974.44 \| 129600 \| 5.1988 \| 1.0019 \| \| 0.0088 \| 975.94 \| 129800 \| 4.9510 \| 1.0025 \| \| 0.013 \| 977.44 \| 130000 \| 5.3014 \| 1.0019 \| \| 0.0108 \| 978.95 \| 130200 \| 4.9356 \| 1.0019 \| \| 0.0112 \| 980.45 \| 130400 \| 4.8090 \| 1.0025 \| \| 0.0115 \| 981.95 \| 130600 \| 5.0231 \| 1.0038 \| \| 0.0166 \| 983.46 \| 130800 \| 4.8263 \| 1.0019 \| \| 0.0061 \| 984.96 \| 131000 \| 4.7797 \| 1.0025 \| \| 0.0097 \| 986.47 \| 131200 \| 4.8421 \| 1.0019 \| \| 0.0115 \| 987.97 \| 131400 \| 4.9492 \| 1.0031 \| \| 0.0058 \| 989.47 \| 131600 \| 4.9278 \| 1.0013 \| \| 0.0089 \| 990.98 \| 131800 \| 4.8451 \| 1.0013 \| \| 0.0133 \| 992.48 \| 132000 \| 4.8694 \| 1.0013 \| \| 0.0074 \| 993.98 \| 132200 \| 5.0419 \| 1.0006 \| \| 0.0088 \| 995.49 \| 132400 \| 5.2701 \| 1.0013 \| \| 0.0116 \| 996.99 \| 132600 \| 5.2893 \| 1.0006 \| \| 0.0068 \| 998.5 \| 132800 \| 4.9237 \| 1.0019 \| \| 0.0096 \| 1000.0 \| 133000 \| 5.0376 \| 1.0013 \| \| 0.0097 \| 1001.5 \| 133200 \| 5.0571 \| 1.0025 \| \| 0.009 \| 1003.01 \| 133400 \| 5.0585 \| 1.0019 \| \| 0.0077 \| 1004.51 \| 133600 \| 4.9175 \| 1.0013 \| \| 0.0099 \| 1006.02 \| 133800 \| 5.0497 \| 1.0013 \| \| 0.0042 \| 1007.52 \| 134000 \| 4.9861 \| 1.0013 \| \| 0.0114 \| 1009.02 \| 134200 \| 5.0604 \| 1.0 \| \| 0.0079 \| 1010.53 \| 134400 \| 4.9738 \| 1.0 \| \| 0.0039 \| 1012.03 \| 134600 \| 5.2350 \| 1.0006 \| \| 0.0081 \| 1013.53 \| 134800 \| 4.8783 \| 1.0006 \| \| 0.0072 \| 1015.04 \| 135000 \| 5.0616 \| 1.0019 \| \| 0.0059 \| 1016.54 \| 135200 \| 4.9421 \| 1.0006 \| \| 0.0052 \| 1018.05 \| 135400 \| 5.2076 \| 1.0006 \| \| 0.0072 \| 1019.55 \| 135600 \| 4.7459 \| 1.0019 \| \| 0.0137 \| 1021.05 \| 135800 \| 4.4156 \| 1.0025 \| \| 0.0086 \| 1022.56 \| 136000 \| 4.7582 \| 1.0038 \| \| 0.006 \| 1024.06 \| 136200 \| 4.6716 \| 1.0013 \| \| 0.0086 \| 1025.56 \| 136400 \| 4.9067 \| 1.0006 \| \| 0.0074 \| 1027.07 \| 136600 \| 5.1224 \| 1.0 \| \| 0.0099 \| 1028.57 \| 136800 \| 4.8046 \| 1.0006 \| \| 0.0129 \| 1030.08 \| 137000 \| 5.0482 \| 1.0 \| \| 0.011 \| 1031.58 \| 137200 \| 4.9666 \| 1.0019 \| \| 0.0071 \| 1033.08 \| 137400 \| 5.0899 \| 1.0013 \| \| 0.0067 \| 1034.59 \| 137600 \| 5.0229 \| 1.0006 \| \| 0.0044 \| 1036.09 \| 137800 \| 5.0371 \| 1.0013 \| \| 0.0101 \| 1037.59 \| 138000 \| 4.9919 \| 0.9994 \| \| 0.006 \| 1039.1 \| 138200 \| 5.1368 \| 0.9994 \| \| 0.0105 \| 1040.6 \| 138400 \| 4.8836 \| 0.9987 \| \| 0.0084 \| 1042.11 \| 138600 \| 4.7600 \| 1.0025 \| \| 0.0089 \| 1043.61 \| 138800 \| 4.9488 \| 1.0019 \| \| 0.0075 \| 1045.11 \| 139000 \| 5.0581 \| 1.0006 \| \| 0.0109 \| 1046.62 \| 139200 \| 5.1216 \| 1.0006 \| \| 0.0028 \| 1048.12 \| 139400 \| 5.0811 \| 0.9981 \| \| 0.0044 \| 1049.62 \| 139600 \| 4.8405 \| 1.0013 \| \| 0.0067 \| 1051.13 \| 139800 \| 5.0062 \| 1.0013 \| \| 0.0057 \| 1052.63 \| 140000 \| 5.0492 \| 1.0006 \| \| 0.0062 \| 1054.14 \| 140200 \| 5.0783 \| 1.0 \| \| 0.0076 \| 1055.64 \| 140400 \| 5.1404 \| 1.0 \| \| 0.0132 \| 1057.14 \| 140600 \| 5.1925 \| 1.0006 \| \| 0.0043 \| 1058.65 \| 140800 \| 5.0225 \| 1.0019 \| \| 0.0032 \| 1060.15 \| 141000 \| 4.9694 \| 1.0019 \| \| 0.004 \| 1061.65 \| 141200 \| 5.1353 \| 1.0006 \| \| 0.0092 \| 1063.16 \| 141400 \| 5.2068 \| 1.0013 \| \| 0.0092 \| 1064.66 \| 141600 \| 5.0825 \| 1.0013 \| \| 0.0057 \| 1066.17 \| 141800 \| 5.0087 \| 1.0013 \| \| 0.0048 \| 1067.67 \| 142000 \| 4.9626 \| 1.0025 \| \| 0.0052 \| 1069.17 \| 142200 \| 5.0154 \| 1.0006 \| \| 0.0094 \| 1070.68 \| 142400 \| 5.0147 \| 1.0019 \| \| 0.008 \| 1072.18 \| 142600 \| 4.9181 \| 1.0006 \| \| 0.0073 \| 1073.68 \| 142800 \| 4.9787 \| 1.0019 \| \| 0.0063 \| 1075.19 \| 143000 \| 4.9458 \| 1.0025 \| \| 0.0069 \| 1076.69 \| 143200 \| 5.0951 \| 1.0019 \| \| 0.005 \| 1078.2 \| 143400 \| 4.8268 \| 1.0019 \| \| 0.008 \| 1079.7 \| 143600 \| 4.8822 \| 1.0019 \| \| 0.0047 \| 1081.2 \| 143800 \| 5.1148 \| 1.0019 \| \| 0.0138 \| 1082.71 \| 144000 \| 4.9464 \| 1.0 \| \| 0.0054 \| 1084.21 \| 144200 \| 4.9410 \| 1.0013 \| \| 0.0061 \| 1085.71 \| 144400 \| 5.1612 \| 1.0006 \| \| 0.0095 \| 1087.22 \| 144600 \| 5.0962 \| 1.0006 \| \| 0.0069 \| 1088.72 \| 144800 \| 5.0092 \| 1.0 \| \| 0.0041 \| 1090.23 \| 145000 \| 5.0815 \| 1.0 \| \| 0.0104 \| 1091.73 \| 145200 \| 5.0631 \| 1.0006 \| \| 0.0025 \| 1093.23 \| 145400 \| 5.1077 \| 1.0006 \| \| 0.0068 \| 1094.74 \| 145600 \| 4.9715 \| 1.0019 \| \| 0.006 \| 1096.24 \| 145800 \| 5.0608 \| 1.0013 \| \| 0.0081 \| 1097.74 \| 146000 \| 5.1091 \| 1.0013 \| \| 0.0068 \| 1099.25 \| 146200 \| 5.1470 \| 1.0013 \| \| 0.0047 \| 1100.75 \| 146400 \| 5.1790 \| 1.0006 \| \| 0.0054 \| 1102.26 \| 146600 \| 5.2646 \| 1.0 \| \| 0.0092 \| 1103.76 \| 146800 \| 5.2853 \| 1.0013 \| \| 0.0066 \| 1105.26 \| 147000 \| 5.0318 \| 1.0013 \| \| 0.008 \| 1106.77 \| 147200 \| 5.0337 \| 1.0013 \| \| 0.0034 \| 1108.27 \| 147400 \| 5.0388 \| 1.0013 \| \| 0.011 \| 1109.77 \| 147600 \| 5.0256 \| 1.0019 \| \| 0.0072 \| 1111.28 \| 147800 \| 5.0236 \| 1.0019 \| \| 0.0129 \| 1112.78 \| 148000 \| 5.0792 \| 1.0019 \| \| 0.0063 \| 1114.29 \| 148200 \| 5.1229 \| 1.0 \| \| 0.0045 \| 1115.79 \| 148400 \| 5.1730 \| 1.0013 \| \| 0.0049 \| 1117.29 \| 148600 \| 5.1491 \| 1.0006 \| \| 0.0039 \| 1118.8 \| 148800 \| 5.1083 \| 1.0013 \| \| 0.008 \| 1120.3 \| 149000 \| 5.0877 \| 1.0019 \| \| 0.0058 \| 1121.8 \| 149200 \| 5.0018 \| 1.0019 \| \| 0.0046 \| 1123.31 \| 149400 \| 5.0775 \| 1.0013 \| \| 0.0057 \| 1124.81 \| 149600 \| 5.0081 \| 1.0006 \| \| 0.0048 \| 1126.32 \| 149800 \| 5.0912 \| 1.0013 \| \| 0.0031 \| 1127.82 \| 150000 \| 5.0987 \| 1.0019 \| \| 0.0059 \| 1129.32 \| 150200 \| 5.0811 \| 1.0013 \| \| 0.0043 \| 1130.83 \| 150400 \| 5.1520 \| 1.0019 \| \| 0.0071 \| 1132.33 \| 150600 \| 5.2432 \| 1.0019 \| \| 0.0115 \| 1133.83 \| 150800 \| 5.1358 \| 1.0019 \| \| 0.0061 \| 1135.34 \| 151000 \| 5.0458 \| 1.0019 \| \| 0.0068 \| 1136.84 \| 151200 \| 5.1654 \| 1.0019 \| \| 0.0107 \| 1138.35 \| 151400 \| 5.0748 \| 1.0019 \| \| 0.0053 \| 1139.85 \| 151600 \| 5.1501 \| 1.0019 \| \| 0.0058 \| 1141.35 \| 151800 \| 5.2146 \| 1.0019 \| \| 0.0053 \| 1142.86 \| 152000 \| 5.2234 \| 1.0013 \| \| 0.0034 \| 1144.36 \| 152200 \| 5.2226 \| 1.0019 \| \| 0.0058 \| 1145.86 \| 152400 \| 5.2033 \| 1.0019 \| \| 0.0039 \| 1147.37 \| 152600 \| 5.2151 \| 1.0019 \| \| 0.0111 \| 1148.87 \| 152800 \| 5.1126 \| 1.0019 \| \| 0.0054 \| 1150.38 \| 153000 \| 5.2183 \| 1.0019 \| \| 0.0068 \| 1151.88 \| 153200 \| 5.2718 \| 1.0019 \| \| 0.0074 \| 1153.38 \| 153400 \| 5.1996 \| 1.0019 \| \| 0.0064 \| 1154.89 \| 153600 \| 5.1910 \| 1.0019 \| \| 0.0058 \| 1156.39 \| 153800 \| 5.1924 \| 1.0019 \| \| 0.004 \| 1157.89 \| 154000 \| 5.2096 \| 1.0019 \| \| 0.0046 \| 1159.4 \| 154200 \| 5.1493 \| 1.0019 \| \| 0.0066 \| 1160.9 \| 154400 \| 5.1710 \| 1.0019 \| \| 0.0055 \| 1162.41 \| 154600 \| 5.1339 \| 1.0019 \| \| 0.0076 \| 1163.91 \| 154800 \| 5.1114 \| 1.0019 \| \| 0.0078 \| 1165.41 \| 155000 \| 5.1069 \| 1.0019 \| \| 0.0039 \| 1166.92 \| 155200 \| 5.1307 \| 1.0019 \| \| 0.0029 \| 1168.42 \| 155400 \| 5.1418 \| 1.0025 \| \| 0.0033 \| 1169.92 \| 155600 \| 5.0614 \| 1.0025 \| \| 0.005 \| 1171.43 \| 155800 \| 5.0568 \| 1.0025 \| \| 0.0048 \| 1172.93 \| 156000 \| 5.0617 \| 1.0025 \| \| 0.005 \| 1174.44 \| 156200 \| 5.0957 \| 1.0025 \| \| 0.0072 \| 1175.94 \| 156400 \| 5.0974 \| 1.0025 \| \| 0.0052 \| 1177.44 \| 156600 \| 5.0740 \| 1.0025 \| \| 0.0042 \| 1178.95 \| 156800 \| 5.0641 \| 1.0019 \| \| 0.0057 \| 1180.45 \| 157000 \| 5.0398 \| 1.0013 \| \| 0.0063 \| 1181.95 \| 157200 \| 4.9869 \| 1.0019 \| \| 0.0046 \| 1183.46 \| 157400 \| 5.0102 \| 1.0019 \| \| 0.005 \| 1184.96 \| 157600 \| 4.9957 \| 1.0019 \| \| 0.0044 \| 1186.47 \| 157800 \| 5.0196 \| 1.0019 \| \| 0.0041 \| 1187.97 \| 158000 \| 5.0451 \| 1.0019 \| \| 0.0053 \| 1189.47 \| 158200 \| 5.0466 \| 1.0019 \| \| 0.0078 \| 1190.98 \| 158400 \| 5.0443 \| 1.0019 \| \| 0.0061 \| 1192.48 \| 158600 \| 5.0306 \| 1.0019 \| \| 0.0069 \| 1193.98 \| 158800 \| 5.0399 \| 1.0019 \| \| 0.0114 \| 1195.49 \| 159000 \| 5.0391 \| 1.0019 \| \| 0.0126 \| 1196.99 \| 159200 \| 5.0403 \| 1.0019 \| \| 0.0067 \| 1198.5 \| 159400 \| 5.0430 \| 1.0019 \| \| 0.0044 \| 1200.0 \| 159600 \| 5.0428 \| 1.0019 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.6
markn/adalm-bio-small	22824f560e494e077e6e898a5c22497d3931e521	2022-04-12T21:12:35.000Z	[ "pytorch", "bert", "transformers", "license:mit" ]	null	false	markn	null	markn/adalm-bio-small	null	transformers	36,832	--- license: mit ---
huggan/fastgan-few-shot-aurora	8234db31eec47eb4720a814f42455ed30b89b6c0	2022-05-06T22:30:43.000Z	[ "pytorch", "dataset:huggan/few-shot-aurora", "arxiv:2101.04775", "huggan", "gan", "unconditional-image-generation", "license:mit" ]	unconditional-image-generation	false	huggan	null	huggan/fastgan-few-shot-aurora	null	null	36,833	--- tags: - huggan - gan - unconditional-image-generation datasets: - huggan/few-shot-aurora # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # Generate aurora image using FastGAN ## Model description [FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets. This model was trained on a dataset of 272 high-quality images of aurora. #### How to use ```python # Clone this model git clone https://huggingface.co/huggan/fastgan-few-shot-aurora/ def load_generator(model_name_or_path): generator = Generator(in_channels=256, out_channels=3) generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3) _ = generator.eval() return generator def _denormalize(input: torch.Tensor) -> torch.Tensor: return (input * 127.5) + 127.5 # Load generator generator = load_generator("huggan/fastgan-few-shot-aurora") # Generate a random noise image noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0) with torch.no_grad(): gan_images, _ = generator(noise) gan_images = _denormalize(gan_images.detach()) save_image(gan_images, "sample.png", nrow=1, normalize=True) ``` #### Limitations and bias * Converge faster and better with small datasets (less than 1000 samples) ## Training data [few-shot-aurora](https://huggingface.co/datasets/huggan/few-shot-aurora) ## Generated Images ![Example image](example.png) ### BibTeX entry and citation info ```bibtex @article{FastGAN, title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis}, author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal}, journal={ICLR}, year={2021} } ```
mimicheng/codeparrot-ds-sample-1ep-12apr	74d9d6d5c5bed469b64e79b2909849c6d58a4044	2022-04-13T07:16:11.000Z	[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	text-generation	false	mimicheng	null	mimicheng/codeparrot-ds-sample-1ep-12apr	null	transformers	36,834	--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-1ep-12apr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds-sample-1ep-12apr This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9947 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8723 \| 0.37 \| 1000 \| 2.5340 \| \| 2.1776 \| 0.74 \| 2000 \| 1.9947 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/radfemman	8e54c05acd96a0f147b5c263888801474421829f	2022-04-13T06:22:23.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/radfemman	null	transformers	36,835	--- language: en thumbnail: http://www.huggingtweets.com/radfemman/1649830938917/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1428572680882688005/rqGxWIRJ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Radfem Ally 🇺🇸</div> <div style="text-align: center; font-size: 14px;">@radfemman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Radfem Ally 🇺🇸. \| Data \| Radfem Ally 🇺🇸 \| \| --- \| --- \| \| Tweets downloaded \| 227 \| \| Retweets \| 33 \| \| Short tweets \| 14 \| \| Tweets kept \| 180 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29ku9tl5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @radfemman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33qza7xp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33qza7xp/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/radfemman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggan/pix2pix-maps	29b247f1f7559f1667e3c516666c631ebfd785a1	2022-04-13T16:25:52.000Z	[ "pytorch", "dataset:huggan/maps", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]	null	false	huggan	null	huggan/pix2pix-maps	null	null	36,836	--- tags: - huggan - gan datasets: - huggan/maps # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # Pix2Pix trained on the maps dataset ## Model description This model is a [Pix2Pix](https://arxiv.org/abs/1611.07004) model trained on the [huggan/maps](https://huggingface.co/datasets/huggan/maps) dataset. The goal for the model is to turn a satellite map into a geographic map à la Google Maps, and the other way around. The model was trained using the [example script](https://github.com/huggingface/community-events/tree/main/huggan/pytorch/pix2pix) provided by HuggingFace as part of the [HugGAN sprint](https://github.com/huggingface/community-events/tree/main/huggan). ## Intended uses & limitations #### How to use ```python from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet from PIL import Image from torchvision.utils import save_image image = Image.open("...") generator = GeneratorUNet.from_pretrained("huggan/pix2pix-maps") pixel_values = transform(image).unsqueeze(0) output = generator(pixel_values) save_image(output, 'output.png', normalize=True) ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data The data used was huggan/maps. ## Training procedure The following command was used: ```bash accelerate launch train.py --dataset huggan/maps --push_to_hub --model_name pix2pix-maps --checkpoint_interval 1 ``` ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/IsolaZZE16, author = {Phillip Isola and Jun{-}Yan Zhu and Tinghui Zhou and Alexei A. Efros}, title = {Image-to-Image Translation with Conditional Adversarial Networks}, journal = {CoRR}, volume = {abs/1611.07004}, year = {2016}, url = {http://arxiv.org/abs/1611.07004}, eprinttype = {arXiv}, eprint = {1611.07004}, timestamp = {Mon, 13 Aug 2018 16:49:05 +0200}, biburl = {https://dblp.org/rec/journals/corr/IsolaZZE16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
obokkkk/kobert-finetuned-klue-v2	ef5f1945d614548e889146ce106b4d581af5e79e	2022-04-13T11:24:54.000Z	[ "pytorch", "bert", "question-answering", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]	question-answering	false	obokkkk	null	obokkkk/kobert-finetuned-klue-v2	null	transformers	36,837	--- tags: - generated_from_trainer model-index: - name: kobert-finetuned-klue-v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kobert-finetuned-klue-v2 This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 5.6289 \| 0.54 \| 500 \| 5.3024 \| \| 5.3083 \| 1.08 \| 1000 \| 5.3707 \| \| 5.3518 \| 1.62 \| 1500 \| 5.3039 \| \| 5.2912 \| 2.16 \| 2000 \| 5.2800 \| \| 5.2282 \| 2.7 \| 2500 \| 5.2301 \| \| 5.1498 \| 3.24 \| 3000 \| 5.2435 \| \| 5.079 \| 3.78 \| 3500 \| 5.1997 \| \| 4.8886 \| 4.32 \| 4000 \| 5.1350 \| \| 4.8166 \| 4.86 \| 4500 \| 5.1441 \| \| 4.5615 \| 5.4 \| 5000 \| 5.1485 \| \| 4.4183 \| 5.94 \| 5500 \| 5.0775 \| \| 4.1282 \| 6.48 \| 6000 \| 5.0402 \| \| 4.1214 \| 7.02 \| 6500 \| 5.1331 \| \| 3.7792 \| 7.56 \| 7000 \| 5.1314 \| \| 3.7455 \| 8.1 \| 7500 \| 5.1936 \| \| 3.5369 \| 8.64 \| 8000 \| 5.1931 \| \| 3.4832 \| 9.18 \| 8500 \| 5.2678 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
vabadeh213/autotrain-wikihow-737822494	c22a2d5b572f10ea0fd77e1a1cde9c2bf6bd8cf8	2022-04-13T13:07:34.000Z	[ "pytorch", "mt5", "text2text-generation", "ja", "dataset:vabadeh213/autotrain-data-wikihow", "transformers", "autotrain", "co2_eq_emissions", "autotrain_compatible" ]	text2text-generation	false	vabadeh213	null	vabadeh213/autotrain-wikihow-737822494	null	transformers	36,838	--- tags: autotrain language: ja widget: - text: "脅威を感じた蛇は再び襲いかかります。したがって、噛まれた際は速やかに蛇の攻撃範囲から離れましょう。少なくとも6mは間合いを取りましょう。できる限り速やかに医療処置を求めることが大切です。ほとんどの病院は、毒蛇用の抗毒素（血清）を用意しています。病院に到着する前の応急手当だけでは、あまり症状の改善にはつながりません。被害現場からすぐさま救急サービスに通報できれば不幸中の幸いです。救急車を呼べない場合は、何としても助けを求め、みなさんまたは被害者を最寄りの病院へ搬送しなければなりません。みなさんに噛みついた蛇がガラガラヘビかどうかが分からない場合でも、すぐに病院へ直行しましょう。実際に毒が体に回り、症状が出始めたとしても、病院にいれば安心できるでしょう。噛まれた箇所を心臓よりも上に置くと、毒を含んだ血液が猛スピードで心臓に流れ込みます。救助が来るまでの間、できれば被害者の体を静止させましょう。体を動かすと血流が増大し、あっという間に毒が回ります。したがって、毒蛇に噛まれた際は体の動きを最小限に抑えて安静にすることが大切です。もちろん、みなさんの周りに誰もいなければ、じっとしている場合ではありません。すぐに助けを求めましょう。" - text: "噛み傷の周囲は大きく腫れ上がります。傷口の周りの衣類はすべて取り除きましょう。また、患部に付けているアクセサリー類も取り外しましょう。アクセサリーを付けたままにすると、患部が腫れた際に血管を締め付けてしまうため、場合によっては大切なアクセサリーを切断する羽目になります。30秒ほど噛み傷からそのまま出血させましょう。出血とともに、ある程度は毒が傷口から排出されるでしょう。毒を少しでも吸引できればそれに越したことはありません。ただし、必ず毒蛇用の吸引器を使いましょう。吸引ポンプには詳しい取扱説明書が付属しているはずですが、基本的にポンプを直接噛み傷に当てて毒を吸い出します。傷口を水で洗ってはいけません。皮膚を洗い流してしまうと、後で毒の種類を特定しにくくなります。医療者は皮膚に残った毒からガラガラヘビの種類を特定し、みなさんの症状に最適な治療法を決定します。添え木または三角巾で固定すれば、患部の血流を抑えることができます。できるだけ血流を抑えて毒の回りを遅らせましょう。腕を吊るす場合は、衣類を三角形に折り畳むか、または三角形に切りましょう。肘を中心にして腕を三角巾で包みます。みなさんまたは被害者の肘を布の形に合わせて曲げましょう。三角巾の端同士を肩口で結び合わせます。肘から先を三角形の底辺で固定したら、手を外に出します。板切れや丸めた新聞紙を添え木にして腕や脚を支えましょう。それらが手元になければ、衣類を丸めて使いましょう。添え木を腕や脚の側面に当て、傷口の上下の関節を伸ばして固定します。ベルト、テープ、包帯といった手元にあるものを使って添え木をしっかりと縛り付けましょう。ただし、直接傷口の上から縛ってはいけません。傷口の上下いずれかの側で縛りましょう。患部の腫れが酷い場合は、添え木の圧迫を緩める必要があります。" - text: "被害者に話しかけましょう。次々に質問を投げかけ、できる限り被害者の意識を噛み傷から逸らしましょう。不安やパニックは心拍を上昇させ、毒の回りを速めます。みなさん自身が噛まれた場合は、とにかく落ち着きましょう。何度かゆっくりと深く息をして緊張をほぐしましょう。アメリカに滞在中のみなさんは、できれば救急車を待つ間、アメリカ中毒情報センター（1-800-222-1222）に連絡を取りましょう。噛まれた箇所が腫れ上がれば、一目でそれは毒蛇によるものと判断できます。それと同時に皮膚の変色が起こるでしょう。また、一連の細かい刺傷が残る無毒蛇の噛み傷とは違い、毒蛇の噛み傷は1カ所ないしは2カ所に目立った刺傷が残るのが特徴です。これは毒牙の発達に伴い、歯が退化しているためです。さらに、めまい、患部の激しい痛み、視覚障害、他の箇所のチクチクした痛み、そして極度の発汗といった症状も、毒蛇による噛み傷のサインです。皮膚が青ざめるのはショック症状の典型です。他のサインとして、心拍の上昇、過呼吸、吐き気、めまいなどがあります。また、被害者の瞳孔が拡大していないかをチェックしましょう。被害者がショック症状を起こしつつある場合は、仰向けに寝かせて足を30cmほど浮かせましょう。さらに、毛布や上着などで体を包んで温めましょう。呼吸、咳、または体の動きといった生体反応が見られない場合は、直ちに心肺蘇生が必要です。これらの物質は毒の回りを加速させます。したがって、ガラガラヘビに噛まれた直後にアルコールまたはカフェイン飲料で水分補給をするのは禁物です。" datasets: - vabadeh213/autotrain-data-wikihow co2_eq_emissions: 361.800665798794 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 737822494 - CO2 Emissions (in grams): 361.800665798794 ## Validation Metrics - Loss: 2.326287031173706 - Rouge1: 5.2053 - Rouge2: 1.8535 - RougeL: 5.2419 - RougeLsum: 5.228 - Gen Len: 18.3677 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/vabadeh213/autotrain-wikihow-737822494 ```
melnikoff-oleg/bart-end-to-end	55ab6562174cca944643de6a1e86875068b41031	2022-04-13T11:59:46.000Z	[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	melnikoff-oleg	null	melnikoff-oleg/bart-end-to-end	null	transformers	36,839	Entry not found
huggingtweets/elonmusk-jeffbezos-sweatystartup	94780eada23add9a26e08095981a5bb9fea2170a	2022-04-13T13:47:00.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/elonmusk-jeffbezos-sweatystartup	null	transformers	36,840	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/669103856106668033/UF3cgUk4_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1504792567031992320/7EflpzpQ_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Jeff Bezos & Nick Huber</div> <div style="text-align: center; font-size: 14px;">@elonmusk-jeffbezos-sweatystartup</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Jeff Bezos & Nick Huber. \| Data \| Elon Musk \| Jeff Bezos \| Nick Huber \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 221 \| 294 \| 3250 \| \| Retweets \| 15 \| 22 \| 44 \| \| Short tweets \| 67 \| 14 \| 339 \| \| Tweets kept \| 139 \| 258 \| 2867 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hd9nbhx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-jeffbezos-sweatystartup's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2f15ydkr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2f15ydkr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-jeffbezos-sweatystartup') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
frozenwalker/SciFive_pubmedqa_question_generation	ea678c0cf37b4426457a003fcf0f1de018f991a6	2022-04-19T11:42:33.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	frozenwalker	null	frozenwalker/SciFive_pubmedqa_question_generation	null	transformers	36,841	Entry not found
masakhane/afrimt5_bbj_fr_news	e924d50eb15911e45941520dcf7066ece7782f16	2022-04-13T18:28:32.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afrimt5_bbj_fr_news	null	transformers	36,842	--- license: afl-3.0 ---
masakhane/afrimbart_bbj_fr_news	d5ec07e79eeb4b46749070963f446a55fad66aef	2022-04-13T18:28:39.000Z	[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afrimbart_bbj_fr_news	null	transformers	36,843	--- license: afl-3.0 ---
masakhane/afribyt5_bbj_fr_news	1ed1883a4e0e479b3e7a7b70b08c802a23f7b4ea	2022-04-13T19:29:55.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afribyt5_bbj_fr_news	null	transformers	36,844	--- license: afl-3.0 ---
masakhane/byt5_fr_bbj_news	5ba81fa10eeeb493e04df914bf6434e0858df3a2	2022-04-13T19:29:58.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/byt5_fr_bbj_news	null	transformers	36,845	--- license: afl-3.0 ---
masakhane/byt5_bbj_fr_news	dc62009e7fd9f773157e06571fe054f931fda04d	2022-04-13T19:30:01.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/byt5_bbj_fr_news	null	transformers	36,846	--- license: afl-3.0 ---
masakhane/mt5_bbj_fr_news	2abaff5c158f0feacdd135f8e747e747a3e5c522	2022-04-13T20:41:14.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/mt5_bbj_fr_news	null	transformers	36,847	--- license: afl-3.0 ---
masakhane/mt5_fr_bbj_news	547173268338375fca6258a3200692b87225ee9e	2022-04-13T20:41:07.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/mt5_fr_bbj_news	null	transformers	36,848	--- license: afl-3.0 ---
masakhane/mbart50_bbj_fr_news	e07fdf1eb0cc81155de359bc23c376512ebd9cc0	2022-04-13T20:41:18.000Z	[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/mbart50_bbj_fr_news	null	transformers	36,849	--- license: afl-3.0 ---
masakhane/m2m100_418M_fr_bbj_news	46570681958c9b8e59ee1b8e0bc7f8842598decf	2022-04-13T21:40:15.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_fr_bbj_news	null	transformers	36,850	--- license: afl-3.0 ---
masakhane/m2m100_418M_fr_bbj_rel_news	579c9a135b62a74aad340cf55af9caf01507c151	2022-04-13T21:40:19.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_fr_bbj_rel_news	null	transformers	36,851	--- license: afl-3.0 ---
masakhane/m2m100_418M_bbj_fr_rel_news	40949ff2d525d1a3985c783f637e1b836094b526	2022-04-13T21:40:24.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_bbj_fr_rel_news	null	transformers	36,852	--- license: afl-3.0 ---
masakhane/m2m100_418M_fr_bbj_rel_news_ft	0b00157684120bdf5d7a5ce07897443df73b0a42	2022-04-14T08:42:45.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_fr_bbj_rel_news_ft	null	transformers	36,853	--- license: afl-3.0 ---
masakhane/m2m100_418M_fr_bbj_rel	8635f78e4017ff014116d6f0b031a0b14c62109d	2022-04-14T08:42:48.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_fr_bbj_rel	null	transformers	36,854	--- license: afl-3.0 ---
masakhane/m2m100_418M_fr_bbj_rel_ft	144e9b6b42351bb0ce3735abc4b7cdd1b3252b7d	2022-04-14T10:00:19.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_fr_bbj_rel_ft	null	transformers	36,855	--- license: afl-3.0 ---
masakhane/m2m100_418M_bbj_fr_rel_ft	1f99875257572a5a807d696773a8e775d5bab245	2022-04-14T10:00:22.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_bbj_fr_rel_ft	null	transformers	36,856	--- license: afl-3.0 ---
ntoldalagi/nick_asr_fresh	136346b60c1781725fa77427cc32c17b1ee7d2f7	2022-04-15T01:04:08.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	ntoldalagi	null	ntoldalagi/nick_asr_fresh	null	transformers	36,857	Entry not found
lilitket/20220413-210552	bdac66b4f511f417c2380db3409241edf37a6baa	2022-04-14T05:44:32.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	lilitket	null	lilitket/20220413-210552	null	transformers	36,858	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: 20220413-210552 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 20220413-210552 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.0348 - Wer: 1.0006 ## 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-06 - train_batch_size: 1 - 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: 2000 - num_epochs: 1200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:------:\|:---------------:\|:------:\| \| 17.1111 \| 1.5 \| 200 \| 16.6792 \| 1.0 \| \| 16.0992 \| 3.01 \| 400 \| 15.3947 \| 1.0 \| \| 10.7668 \| 4.51 \| 600 \| 10.3625 \| 1.0 \| \| 6.2652 \| 6.02 \| 800 \| 7.6849 \| 1.0 \| \| 5.1417 \| 7.52 \| 1000 \| 6.0307 \| 1.0 \| \| 4.6159 \| 9.02 \| 1200 \| 5.0891 \| 1.0 \| \| 4.2444 \| 10.53 \| 1400 \| 4.4120 \| 1.0 \| \| 3.8935 \| 12.03 \| 1600 \| 3.9570 \| 1.0 \| \| 3.6292 \| 13.53 \| 1800 \| 3.6405 \| 1.0 \| \| 3.4535 \| 15.04 \| 2000 \| 3.4523 \| 1.0 \| \| 3.3175 \| 16.54 \| 2200 \| 3.3589 \| 1.0 \| \| 3.2266 \| 18.05 \| 2400 \| 3.2966 \| 1.0 \| \| 3.1825 \| 19.55 \| 2600 \| 3.2658 \| 1.0 \| \| 3.1604 \| 21.05 \| 2800 \| 3.2534 \| 1.0 \| \| 3.1438 \| 22.56 \| 3000 \| 3.2437 \| 1.0 \| \| 3.1176 \| 24.06 \| 3200 \| 3.2169 \| 1.0 \| \| 3.1088 \| 25.56 \| 3400 \| 3.2102 \| 1.0 \| \| 3.0955 \| 27.07 \| 3600 \| 3.1983 \| 1.0 \| \| 3.0763 \| 28.57 \| 3800 \| 3.2092 \| 1.0 \| \| 3.0599 \| 30.08 \| 4000 \| 3.2092 \| 1.0 \| \| 3.0385 \| 31.58 \| 4200 \| 3.2154 \| 1.0 \| \| 2.9996 \| 33.08 \| 4400 \| 3.2120 \| 1.0 \| \| 2.9207 \| 34.59 \| 4600 \| 3.2146 \| 1.0 \| \| 2.8071 \| 36.09 \| 4800 \| 3.2093 \| 1.0 \| \| 2.6412 \| 37.59 \| 5000 \| 3.2282 \| 1.0 \| \| 2.4594 \| 39.1 \| 5200 \| 3.2442 \| 1.0 \| \| 2.2708 \| 40.6 \| 5400 \| 3.2944 \| 1.0 \| \| 2.1279 \| 42.11 \| 5600 \| 3.3260 \| 1.0 \| \| 1.9985 \| 43.61 \| 5800 \| 3.3586 \| 1.0 \| \| 1.8979 \| 45.11 \| 6000 \| 3.3945 \| 1.0 \| \| 1.7838 \| 46.62 \| 6200 \| 3.4761 \| 1.0 \| \| 1.6774 \| 48.12 \| 6400 \| 3.4886 \| 1.0 \| \| 1.5958 \| 49.62 \| 6600 \| 3.6208 \| 1.0 \| \| 1.4957 \| 51.13 \| 6800 \| 3.6501 \| 1.0 \| \| 1.4202 \| 52.63 \| 7000 \| 3.6492 \| 1.0 \| \| 1.3377 \| 54.14 \| 7200 \| 3.7392 \| 1.0 \| \| 1.2872 \| 55.64 \| 7400 \| 3.8624 \| 1.0 \| \| 1.1992 \| 57.14 \| 7600 \| 3.8511 \| 1.0 \| \| 1.1238 \| 58.65 \| 7800 \| 3.9662 \| 1.0 \| \| 1.0775 \| 60.15 \| 8000 \| 3.9267 \| 1.0 \| \| 1.011 \| 61.65 \| 8200 \| 4.0933 \| 1.0 \| \| 0.962 \| 63.16 \| 8400 \| 4.0941 \| 1.0 \| \| 0.9041 \| 64.66 \| 8600 \| 4.1163 \| 1.0 \| \| 0.8552 \| 66.17 \| 8800 \| 4.1937 \| 1.0 \| \| 0.8054 \| 67.67 \| 9000 \| 4.2277 \| 1.0 \| \| 0.7457 \| 69.17 \| 9200 \| 4.3899 \| 1.0 \| \| 0.7292 \| 70.68 \| 9400 \| 4.3621 \| 1.0 \| \| 0.6635 \| 72.18 \| 9600 \| 4.4706 \| 1.0 \| \| 0.6333 \| 73.68 \| 9800 \| 4.4571 \| 1.0 \| \| 0.6109 \| 75.19 \| 10000 \| 4.4594 \| 1.0 \| \| 0.5611 \| 76.69 \| 10200 \| 4.5672 \| 1.0 \| \| 0.5286 \| 78.2 \| 10400 \| 4.4957 \| 1.0 \| \| 0.4894 \| 79.7 \| 10600 \| 4.5278 \| 1.0 \| \| 0.4831 \| 81.2 \| 10800 \| 4.4604 \| 1.0 \| \| 0.4575 \| 82.71 \| 11000 \| 4.7439 \| 1.0 \| \| 0.4418 \| 84.21 \| 11200 \| 4.6511 \| 1.0 \| \| 0.4085 \| 85.71 \| 11400 \| 4.5008 \| 1.0 \| \| 0.4011 \| 87.22 \| 11600 \| 4.7690 \| 1.0 \| \| 0.3791 \| 88.72 \| 11800 \| 4.8675 \| 1.0 \| \| 0.3487 \| 90.23 \| 12000 \| 5.0327 \| 1.0 \| \| 0.3661 \| 91.73 \| 12200 \| 4.8084 \| 1.0 \| \| 0.3306 \| 93.23 \| 12400 \| 4.9095 \| 1.0 \| \| 0.3449 \| 94.74 \| 12600 \| 4.8223 \| 1.0 \| \| 0.2949 \| 96.24 \| 12800 \| 4.8245 \| 1.0 \| \| 0.2987 \| 97.74 \| 13000 \| 5.0803 \| 1.0 \| \| 0.2896 \| 99.25 \| 13200 \| 5.2074 \| 1.0 \| \| 0.2731 \| 100.75 \| 13400 \| 5.1951 \| 1.0 \| \| 0.2749 \| 102.26 \| 13600 \| 5.2071 \| 1.0 \| \| 0.2554 \| 103.76 \| 13800 \| 5.0861 \| 1.0 \| \| 0.2436 \| 105.26 \| 14000 \| 5.0851 \| 1.0 \| \| 0.2494 \| 106.77 \| 14200 \| 4.8623 \| 1.0 \| \| 0.23 \| 108.27 \| 14400 \| 5.0466 \| 1.0 \| \| 0.2345 \| 109.77 \| 14600 \| 5.2474 \| 1.0 \| \| 0.2233 \| 111.28 \| 14800 \| 4.9394 \| 1.0 \| \| 0.2231 \| 112.78 \| 15000 \| 4.9572 \| 1.0 \| \| 0.213 \| 114.29 \| 15200 \| 5.3215 \| 1.0 \| \| 0.2002 \| 115.79 \| 15400 \| 5.3042 \| 1.0 \| \| 0.2023 \| 117.29 \| 15600 \| 5.0279 \| 1.0 \| \| 0.2074 \| 118.8 \| 15800 \| 4.9727 \| 1.0 \| \| 0.2071 \| 120.3 \| 16000 \| 4.6775 \| 1.0 \| \| 0.1915 \| 121.8 \| 16200 \| 4.8386 \| 1.0 \| \| 0.1899 \| 123.31 \| 16400 \| 4.7898 \| 1.0 \| \| 0.1821 \| 124.81 \| 16600 \| 5.3147 \| 1.0 \| \| 0.1908 \| 126.32 \| 16800 \| 5.6218 \| 1.0 \| \| 0.1712 \| 127.82 \| 17000 \| 4.6083 \| 1.0 \| \| 0.1705 \| 129.32 \| 17200 \| 5.2468 \| 1.0 \| \| 0.1664 \| 130.83 \| 17400 \| 5.0412 \| 1.0 \| \| 0.167 \| 132.33 \| 17600 \| 5.0116 \| 1.0 \| \| 0.162 \| 133.83 \| 17800 \| 5.2799 \| 1.0 \| \| 0.1561 \| 135.34 \| 18000 \| 5.2485 \| 1.0 \| \| 0.1501 \| 136.84 \| 18200 \| 5.1109 \| 1.0 \| \| 0.14 \| 138.35 \| 18400 \| 5.2310 \| 1.0 \| \| 0.1576 \| 139.85 \| 18600 \| 5.1631 \| 1.0 \| \| 0.1433 \| 141.35 \| 18800 \| 5.3497 \| 1.0 \| \| 0.148 \| 142.86 \| 19000 \| 4.8892 \| 1.0 \| \| 0.1525 \| 144.36 \| 19200 \| 4.8522 \| 1.0 \| \| 0.1517 \| 145.86 \| 19400 \| 4.7830 \| 1.0 \| \| 0.139 \| 147.37 \| 19600 \| 5.2041 \| 1.0 \| \| 0.1392 \| 148.87 \| 19800 \| 4.7968 \| 1.0 \| \| 0.1351 \| 150.38 \| 20000 \| 5.0326 \| 1.0 \| \| 0.1355 \| 151.88 \| 20200 \| 5.0474 \| 1.0 \| \| 0.138 \| 153.38 \| 20400 \| 4.7491 \| 1.0006 \| \| 0.1332 \| 154.89 \| 20600 \| 5.3905 \| 1.0 \| \| 0.1252 \| 156.39 \| 20800 \| 4.9815 \| 1.0 \| \| 0.1179 \| 157.89 \| 21000 \| 5.3281 \| 1.0 \| \| 0.1228 \| 159.4 \| 21200 \| 5.1108 \| 1.0006 \| \| 0.1311 \| 160.9 \| 21400 \| 4.8016 \| 1.0 \| \| 0.1278 \| 162.41 \| 21600 \| 4.8306 \| 1.0 \| \| 0.1209 \| 163.91 \| 21800 \| 4.6413 \| 1.0 \| \| 0.1199 \| 165.41 \| 22000 \| 4.6375 \| 1.0 \| \| 0.1172 \| 166.92 \| 22200 \| 4.9108 \| 1.0 \| \| 0.1247 \| 168.42 \| 22400 \| 4.6139 \| 1.0006 \| \| 0.1121 \| 169.92 \| 22600 \| 4.4765 \| 1.0006 \| \| 0.125 \| 171.43 \| 22800 \| 4.6819 \| 1.0006 \| \| 0.1259 \| 172.93 \| 23000 \| 4.9577 \| 1.0 \| \| 0.1044 \| 174.44 \| 23200 \| 5.2993 \| 1.0006 \| \| 0.1107 \| 175.94 \| 23400 \| 4.3140 \| 1.0 \| \| 0.1142 \| 177.44 \| 23600 \| 4.5850 \| 1.0 \| \| 0.0971 \| 178.95 \| 23800 \| 4.8177 \| 1.0006 \| \| 0.1186 \| 180.45 \| 24000 \| 4.9972 \| 1.0 \| \| 0.1164 \| 181.95 \| 24200 \| 4.5840 \| 1.0 \| \| 0.1014 \| 183.46 \| 24400 \| 4.9117 \| 0.9994 \| \| 0.1087 \| 184.96 \| 24600 \| 4.5646 \| 1.0006 \| \| 0.1075 \| 186.47 \| 24800 \| 4.6995 \| 1.0 \| \| 0.1111 \| 187.97 \| 25000 \| 4.7877 \| 1.0 \| \| 0.1079 \| 189.47 \| 25200 \| 4.8420 \| 1.0 \| \| 0.1053 \| 190.98 \| 25400 \| 5.1083 \| 1.0 \| \| 0.1048 \| 192.48 \| 25600 \| 4.2876 \| 1.0 \| \| 0.0974 \| 193.98 \| 25800 \| 4.6699 \| 1.0006 \| \| 0.0983 \| 195.49 \| 26000 \| 4.6522 \| 1.0 \| \| 0.0935 \| 196.99 \| 26200 \| 4.9879 \| 1.0 \| \| 0.0948 \| 198.5 \| 26400 \| 4.4146 \| 1.0 \| \| 0.0867 \| 200.0 \| 26600 \| 5.1909 \| 1.0 \| \| 0.0932 \| 201.5 \| 26800 \| 5.2019 \| 1.0 \| \| 0.0951 \| 203.01 \| 27000 \| 3.6893 \| 1.0 \| \| 0.085 \| 204.51 \| 27200 \| 4.3071 \| 1.0006 \| \| 0.0912 \| 206.02 \| 27400 \| 4.4651 \| 1.0 \| \| 0.092 \| 207.52 \| 27600 \| 4.4218 \| 1.0 \| \| 0.0936 \| 209.02 \| 27800 \| 5.1391 \| 1.0 \| \| 0.0989 \| 210.53 \| 28000 \| 4.8787 \| 1.0006 \| \| 0.0898 \| 212.03 \| 28200 \| 4.1418 \| 1.0013 \| \| 0.0943 \| 213.53 \| 28400 \| 4.1857 \| 1.0 \| \| 0.0834 \| 215.04 \| 28600 \| 4.3519 \| 1.0 \| \| 0.0851 \| 216.54 \| 28800 \| 4.3612 \| 1.0006 \| \| 0.0932 \| 218.05 \| 29000 \| 4.2200 \| 1.0006 \| \| 0.0848 \| 219.55 \| 29200 \| 4.2054 \| 1.0 \| \| 0.0873 \| 221.05 \| 29400 \| 4.4815 \| 1.0 \| \| 0.0949 \| 222.56 \| 29600 \| 3.9426 \| 1.0 \| \| 0.0856 \| 224.06 \| 29800 \| 3.7650 \| 1.0 \| \| 0.0768 \| 225.56 \| 30000 \| 3.9774 \| 1.0 \| \| 0.0823 \| 227.07 \| 30200 \| 4.3728 \| 1.0 \| \| 0.0913 \| 228.57 \| 30400 \| 3.7813 \| 1.0 \| \| 0.0951 \| 230.08 \| 30600 \| 4.1581 \| 1.0 \| \| 0.0843 \| 231.58 \| 30800 \| 4.6891 \| 1.0 \| \| 0.0879 \| 233.08 \| 31000 \| 4.2984 \| 1.0 \| \| 0.0807 \| 234.59 \| 31200 \| 3.9511 \| 1.0 \| \| 0.0765 \| 236.09 \| 31400 \| 3.8094 \| 1.0 \| \| 0.0861 \| 237.59 \| 31600 \| 4.3118 \| 1.0 \| \| 0.0596 \| 239.1 \| 31800 \| 4.0774 \| 1.0006 \| \| 0.0752 \| 240.6 \| 32000 \| 3.6005 \| 1.0 \| \| 0.0729 \| 242.11 \| 32200 \| 4.8616 \| 1.0 \| \| 0.0783 \| 243.61 \| 32400 \| 3.9858 \| 1.0 \| \| 0.0759 \| 245.11 \| 32600 \| 4.1231 \| 1.0 \| \| 0.08 \| 246.62 \| 32800 \| 4.5182 \| 1.0 \| \| 0.0782 \| 248.12 \| 33000 \| 3.7721 \| 1.0 \| \| 0.0914 \| 249.62 \| 33200 \| 3.5902 \| 1.0 \| \| 0.0668 \| 251.13 \| 33400 \| 3.9673 \| 1.0 \| \| 0.0798 \| 252.63 \| 33600 \| 3.8693 \| 1.0 \| \| 0.0814 \| 254.14 \| 33800 \| 3.9804 \| 1.0006 \| \| 0.0775 \| 255.64 \| 34000 \| 3.9483 \| 1.0 \| \| 0.0721 \| 257.14 \| 34200 \| 4.6892 \| 1.0 \| \| 0.0722 \| 258.65 \| 34400 \| 4.1972 \| 1.0 \| \| 0.0755 \| 260.15 \| 34600 \| 4.4523 \| 1.0 \| \| 0.0683 \| 261.65 \| 34800 \| 4.1090 \| 1.0 \| \| 0.0698 \| 263.16 \| 35000 \| 4.0634 \| 1.0 \| \| 0.0712 \| 264.66 \| 35200 \| 4.0469 \| 1.0006 \| \| 0.0754 \| 266.17 \| 35400 \| 4.0113 \| 1.0006 \| \| 0.0709 \| 267.67 \| 35600 \| 4.0592 \| 1.0 \| \| 0.0637 \| 269.17 \| 35800 \| 3.7540 \| 1.0 \| \| 0.0688 \| 270.68 \| 36000 \| 3.9645 \| 1.0 \| \| 0.0592 \| 272.18 \| 36200 \| 3.7443 \| 1.0 \| \| 0.0585 \| 273.68 \| 36400 \| 3.8287 \| 1.0 \| \| 0.0734 \| 275.19 \| 36600 \| 3.6780 \| 1.0 \| \| 0.058 \| 276.69 \| 36800 \| 4.0194 \| 1.0 \| \| 0.0707 \| 278.2 \| 37000 \| 3.6663 \| 1.0006 \| \| 0.0728 \| 279.7 \| 37200 \| 3.8640 \| 1.0 \| \| 0.064 \| 281.2 \| 37400 \| 4.5473 \| 1.0 \| \| 0.0583 \| 282.71 \| 37600 \| 4.1813 \| 1.0 \| \| 0.0634 \| 284.21 \| 37800 \| 3.8821 \| 1.0 \| \| 0.0565 \| 285.71 \| 38000 \| 3.9566 \| 1.0006 \| \| 0.0735 \| 287.22 \| 38200 \| 4.5317 \| 1.0 \| \| 0.0797 \| 288.72 \| 38400 \| 3.8040 \| 1.0 \| \| 0.0601 \| 290.23 \| 38600 \| 4.0956 \| 1.0 \| \| 0.0599 \| 291.73 \| 38800 \| 4.0592 \| 1.0 \| \| 0.0517 \| 293.23 \| 39000 \| 3.5204 \| 1.0006 \| \| 0.0622 \| 294.74 \| 39200 \| 4.1739 \| 1.0 \| \| 0.0705 \| 296.24 \| 39400 \| 4.0262 \| 1.0 \| \| 0.0589 \| 297.74 \| 39600 \| 4.2476 \| 1.0 \| \| 0.0606 \| 299.25 \| 39800 \| 3.7931 \| 1.0 \| \| 0.0603 \| 300.75 \| 40000 \| 4.0540 \| 0.9994 \| \| 0.0568 \| 302.26 \| 40200 \| 3.5900 \| 1.0 \| \| 0.0583 \| 303.76 \| 40400 \| 3.8095 \| 1.0 \| \| 0.0513 \| 305.26 \| 40600 \| 3.8949 \| 1.0 \| \| 0.0637 \| 306.77 \| 40800 \| 3.8085 \| 1.0 \| \| 0.0659 \| 308.27 \| 41000 \| 4.2311 \| 1.0 \| \| 0.068 \| 309.77 \| 41200 \| 3.4876 \| 1.0006 \| \| 0.0616 \| 311.28 \| 41400 \| 3.7634 \| 1.0 \| \| 0.0515 \| 312.78 \| 41600 \| 3.8762 \| 1.0 \| \| 0.0584 \| 314.29 \| 41800 \| 4.2070 \| 1.0 \| \| 0.054 \| 315.79 \| 42000 \| 3.9088 \| 1.0 \| \| 0.0571 \| 317.29 \| 42200 \| 3.9679 \| 1.0006 \| \| 0.0497 \| 318.8 \| 42400 \| 3.8443 \| 1.0 \| \| 0.0507 \| 320.3 \| 42600 \| 4.2397 \| 1.0 \| \| 0.0612 \| 321.8 \| 42800 \| 4.2228 \| 1.0 \| \| 0.0467 \| 323.31 \| 43000 \| 3.6684 \| 1.0 \| \| 0.0586 \| 324.81 \| 43200 \| 3.8685 \| 1.0013 \| \| 0.0557 \| 326.32 \| 43400 \| 4.3341 \| 1.0006 \| \| 0.0584 \| 327.82 \| 43600 \| 3.6683 \| 1.0 \| \| 0.0575 \| 329.32 \| 43800 \| 3.9005 \| 1.0 \| \| 0.0571 \| 330.83 \| 44000 \| 3.8594 \| 1.0 \| \| 0.0471 \| 332.33 \| 44200 \| 3.6871 \| 1.0 \| \| 0.055 \| 333.83 \| 44400 \| 4.0402 \| 1.0 \| \| 0.0422 \| 335.34 \| 44600 \| 4.0226 \| 1.0 \| \| 0.0422 \| 336.84 \| 44800 \| 3.5907 \| 1.0 \| \| 0.0513 \| 338.35 \| 45000 \| 3.7380 \| 1.0 \| \| 0.0593 \| 339.85 \| 45200 \| 3.8530 \| 1.0 \| \| 0.0446 \| 341.35 \| 45400 \| 4.0879 \| 1.0 \| \| 0.0492 \| 342.86 \| 45600 \| 3.8984 \| 1.0 \| \| 0.0422 \| 344.36 \| 45800 \| 4.2423 \| 1.0 \| \| 0.0478 \| 345.86 \| 46000 \| 3.6391 \| 1.0 \| \| 0.0425 \| 347.37 \| 46200 \| 4.2352 \| 1.0 \| \| 0.0426 \| 348.87 \| 46400 \| 4.0004 \| 1.0 \| \| 0.0566 \| 350.38 \| 46600 \| 4.0957 \| 1.0006 \| \| 0.0522 \| 351.88 \| 46800 \| 3.9436 \| 0.9994 \| \| 0.0503 \| 353.38 \| 47000 \| 4.3325 \| 1.0 \| \| 0.0513 \| 354.89 \| 47200 \| 3.5738 \| 0.9994 \| \| 0.0428 \| 356.39 \| 47400 \| 3.8233 \| 0.9994 \| \| 0.0402 \| 357.89 \| 47600 \| 3.6210 \| 1.0006 \| \| 0.0575 \| 359.4 \| 47800 \| 3.6991 \| 1.0 \| \| 0.0459 \| 360.9 \| 48000 \| 3.8384 \| 1.0 \| \| 0.0423 \| 362.41 \| 48200 \| 4.2164 \| 1.0 \| \| 0.0401 \| 363.91 \| 48400 \| 4.0001 \| 1.0 \| \| 0.0612 \| 365.41 \| 48600 \| 4.1363 \| 0.9994 \| \| 0.0492 \| 366.92 \| 48800 \| 4.0748 \| 0.9994 \| \| 0.0467 \| 368.42 \| 49000 \| 3.6856 \| 0.9994 \| \| 0.0565 \| 369.92 \| 49200 \| 4.1829 \| 1.0 \| \| 0.0351 \| 371.43 \| 49400 \| 3.9579 \| 1.0006 \| \| 0.0499 \| 372.93 \| 49600 \| 3.8893 \| 1.0 \| \| 0.0477 \| 374.44 \| 49800 \| 3.5199 \| 1.0 \| \| 0.0471 \| 375.94 \| 50000 \| 4.0405 \| 1.0013 \| \| 0.037 \| 377.44 \| 50200 \| 3.8785 \| 1.0006 \| \| 0.0382 \| 378.95 \| 50400 \| 4.2958 \| 1.0013 \| \| 0.0553 \| 380.45 \| 50600 \| 4.3845 \| 1.0006 \| \| 0.0389 \| 381.95 \| 50800 \| 3.7282 \| 1.0013 \| \| 0.0373 \| 383.46 \| 51000 \| 4.0840 \| 1.0006 \| \| 0.0597 \| 384.96 \| 51200 \| 4.0250 \| 1.0006 \| \| 0.0404 \| 386.47 \| 51400 \| 3.6077 \| 0.9994 \| \| 0.0501 \| 387.97 \| 51600 \| 3.5198 \| 0.9994 \| \| 0.0432 \| 389.47 \| 51800 \| 3.7678 \| 1.0 \| \| 0.0462 \| 390.98 \| 52000 \| 3.6467 \| 1.0 \| \| 0.0444 \| 392.48 \| 52200 \| 3.8941 \| 1.0006 \| \| 0.0494 \| 393.98 \| 52400 \| 3.6969 \| 1.0 \| \| 0.0402 \| 395.49 \| 52600 \| 3.5324 \| 1.0006 \| \| 0.0402 \| 396.99 \| 52800 \| 3.8874 \| 1.0006 \| \| 0.0395 \| 398.5 \| 53000 \| 3.3793 \| 1.0006 \| \| 0.0367 \| 400.0 \| 53200 \| 3.6539 \| 1.0013 \| \| 0.0374 \| 401.5 \| 53400 \| 3.4230 \| 1.0 \| \| 0.0407 \| 403.01 \| 53600 \| 3.6068 \| 1.0 \| \| 0.0378 \| 404.51 \| 53800 \| 3.5291 \| 1.0013 \| \| 0.0471 \| 406.02 \| 54000 \| 3.3563 \| 1.0006 \| \| 0.0336 \| 407.52 \| 54200 \| 2.9932 \| 1.0044 \| \| 0.032 \| 409.02 \| 54400 \| 3.3549 \| 1.0006 \| \| 0.0474 \| 410.53 \| 54600 \| 3.1140 \| 1.0013 \| \| 0.0402 \| 412.03 \| 54800 \| 2.8515 \| 1.0075 \| \| 0.0366 \| 413.53 \| 55000 \| 3.1142 \| 1.0050 \| \| 0.041 \| 415.04 \| 55200 \| 3.0917 \| 1.0056 \| \| 0.0358 \| 416.54 \| 55400 \| 3.3401 \| 1.0006 \| \| 0.0299 \| 418.05 \| 55600 \| 3.5304 \| 1.0 \| \| 0.0512 \| 419.55 \| 55800 \| 3.4768 \| 1.0 \| \| 0.0374 \| 421.05 \| 56000 \| 3.2792 \| 0.9994 \| \| 0.037 \| 422.56 \| 56200 \| 3.0088 \| 1.0013 \| \| 0.0395 \| 424.06 \| 56400 \| 3.0185 \| 0.9994 \| \| 0.039 \| 425.56 \| 56600 \| 3.0249 \| 1.0 \| \| 0.0345 \| 427.07 \| 56800 \| 3.3437 \| 1.0 \| \| 0.0438 \| 428.57 \| 57000 \| 3.0905 \| 1.0 \| \| 0.0373 \| 430.08 \| 57200 \| 3.4256 \| 1.0019 \| \| 0.0362 \| 431.58 \| 57400 \| 3.4747 \| 1.0006 \| \| 0.0353 \| 433.08 \| 57600 \| 3.3952 \| 1.0019 \| \| 0.0363 \| 434.59 \| 57800 \| 3.3967 \| 1.0006 \| \| 0.0234 \| 436.09 \| 58000 \| 3.5076 \| 0.9994 \| \| 0.0347 \| 437.59 \| 58200 \| 3.4128 \| 0.9987 \| \| 0.039 \| 439.1 \| 58400 \| 3.5784 \| 1.0013 \| \| 0.041 \| 440.6 \| 58600 \| 3.6292 \| 1.0013 \| \| 0.0348 \| 442.11 \| 58800 \| 3.3197 \| 0.9981 \| \| 0.0285 \| 443.61 \| 59000 \| 3.0545 \| 0.9981 \| \| 0.0293 \| 445.11 \| 59200 \| 3.1784 \| 1.0013 \| \| 0.0394 \| 446.62 \| 59400 \| 2.8197 \| 0.9975 \| \| 0.0282 \| 448.12 \| 59600 \| 3.1450 \| 1.0 \| \| 0.0387 \| 449.62 \| 59800 \| 2.8260 \| 0.9987 \| \| 0.0348 \| 451.13 \| 60000 \| 3.0389 \| 0.9987 \| \| 0.03 \| 452.63 \| 60200 \| 3.1857 \| 0.9981 \| \| 0.029 \| 454.14 \| 60400 \| 3.1893 \| 1.0013 \| \| 0.0333 \| 455.64 \| 60600 \| 3.1428 \| 0.9981 \| \| 0.027 \| 457.14 \| 60800 \| 3.5792 \| 0.9994 \| \| 0.037 \| 458.65 \| 61000 \| 3.4338 \| 1.0 \| \| 0.0327 \| 460.15 \| 61200 \| 2.9592 \| 1.0056 \| \| 0.0283 \| 461.65 \| 61400 \| 3.3657 \| 1.0031 \| \| 0.0337 \| 463.16 \| 61600 \| 3.1392 \| 1.0025 \| \| 0.0275 \| 464.66 \| 61800 \| 3.2706 \| 1.0019 \| \| 0.0304 \| 466.17 \| 62000 \| 3.5289 \| 1.0019 \| \| 0.042 \| 467.67 \| 62200 \| 3.2222 \| 1.0019 \| \| 0.035 \| 469.17 \| 62400 \| 3.3533 \| 1.0038 \| \| 0.038 \| 470.68 \| 62600 \| 3.7162 \| 1.0006 \| \| 0.0297 \| 472.18 \| 62800 \| 3.2382 \| 1.0025 \| \| 0.0314 \| 473.68 \| 63000 \| 3.2555 \| 1.0006 \| \| 0.0288 \| 475.19 \| 63200 \| 3.8791 \| 1.0013 \| \| 0.0336 \| 476.69 \| 63400 \| 3.1988 \| 1.0006 \| \| 0.0371 \| 478.2 \| 63600 \| 3.1025 \| 1.0006 \| \| 0.0288 \| 479.7 \| 63800 \| 3.2712 \| 1.0006 \| \| 0.0374 \| 481.2 \| 64000 \| 3.4452 \| 0.9987 \| \| 0.0329 \| 482.71 \| 64200 \| 3.3956 \| 1.0 \| \| 0.036 \| 484.21 \| 64400 \| 3.4541 \| 1.0006 \| \| 0.03 \| 485.71 \| 64600 \| 3.2153 \| 1.0 \| \| 0.0319 \| 487.22 \| 64800 \| 2.9109 \| 0.9962 \| \| 0.0276 \| 488.72 \| 65000 \| 3.0832 \| 0.9975 \| \| 0.0315 \| 490.23 \| 65200 \| 3.4669 \| 1.0 \| \| 0.0244 \| 491.73 \| 65400 \| 3.4678 \| 0.9994 \| \| 0.0306 \| 493.23 \| 65600 \| 3.6775 \| 0.9987 \| \| 0.0266 \| 494.74 \| 65800 \| 3.3509 \| 1.0 \| \| 0.0247 \| 496.24 \| 66000 \| 3.8882 \| 1.0006 \| \| 0.0322 \| 497.74 \| 66200 \| 3.5801 \| 0.9994 \| \| 0.04 \| 499.25 \| 66400 \| 3.6454 \| 1.0006 \| \| 0.0313 \| 500.75 \| 66600 \| 3.9949 \| 1.0013 \| \| 0.0222 \| 502.26 \| 66800 \| 3.5645 \| 0.9987 \| \| 0.0257 \| 503.76 \| 67000 \| 3.7539 \| 0.9994 \| \| 0.028 \| 505.26 \| 67200 \| 3.6815 \| 1.0006 \| \| 0.0256 \| 506.77 \| 67400 \| 3.3799 \| 1.0013 \| \| 0.0257 \| 508.27 \| 67600 \| 3.6969 \| 1.0013 \| \| 0.0271 \| 509.77 \| 67800 \| 3.7083 \| 0.9994 \| \| 0.0265 \| 511.28 \| 68000 \| 3.3015 \| 0.9994 \| \| 0.0289 \| 512.78 \| 68200 \| 3.7103 \| 1.0006 \| \| 0.0337 \| 514.29 \| 68400 \| 3.1877 \| 1.0006 \| \| 0.0396 \| 515.79 \| 68600 \| 3.7914 \| 1.0 \| \| 0.0316 \| 517.29 \| 68800 \| 3.4757 \| 0.9981 \| \| 0.0303 \| 518.8 \| 69000 \| 3.4129 \| 0.9987 \| \| 0.0298 \| 520.3 \| 69200 \| 3.1453 \| 1.0 \| \| 0.0257 \| 521.8 \| 69400 \| 3.4649 \| 1.0 \| \| 0.0252 \| 523.31 \| 69600 \| 3.2707 \| 0.9994 \| \| 0.0254 \| 524.81 \| 69800 \| 3.0304 \| 1.0056 \| \| 0.0199 \| 526.32 \| 70000 \| 3.4323 \| 1.0019 \| \| 0.0333 \| 527.82 \| 70200 \| 3.4081 \| 1.0006 \| \| 0.0205 \| 529.32 \| 70400 \| 2.9900 \| 1.0188 \| \| 0.0223 \| 530.83 \| 70600 \| 3.3499 \| 1.0031 \| \| 0.0261 \| 532.33 \| 70800 \| 3.2094 \| 0.9975 \| \| 0.0198 \| 533.83 \| 71000 \| 3.2793 \| 1.0063 \| \| 0.021 \| 535.34 \| 71200 \| 3.2266 \| 0.9975 \| \| 0.022 \| 536.84 \| 71400 \| 3.1391 \| 0.9994 \| \| 0.0215 \| 538.35 \| 71600 \| 3.4587 \| 0.9994 \| \| 0.0269 \| 539.85 \| 71800 \| 3.2814 \| 0.9994 \| \| 0.0226 \| 541.35 \| 72000 \| 3.2583 \| 0.9981 \| \| 0.0257 \| 542.86 \| 72200 \| 3.7259 \| 0.9987 \| \| 0.0259 \| 544.36 \| 72400 \| 3.5004 \| 0.9994 \| \| 0.0223 \| 545.86 \| 72600 \| 3.3689 \| 0.9975 \| \| 0.0307 \| 547.37 \| 72800 \| 3.0706 \| 0.9950 \| \| 0.0234 \| 548.87 \| 73000 \| 3.0892 \| 0.9969 \| \| 0.0243 \| 550.38 \| 73200 \| 3.4028 \| 0.9987 \| \| 0.0215 \| 551.88 \| 73400 \| 2.9474 \| 0.9981 \| \| 0.0224 \| 553.38 \| 73600 \| 3.3028 \| 0.9981 \| \| 0.0282 \| 554.89 \| 73800 \| 3.5360 \| 0.9994 \| \| 0.0282 \| 556.39 \| 74000 \| 3.3730 \| 0.9981 \| \| 0.0278 \| 557.89 \| 74200 \| 3.2750 \| 1.0 \| \| 0.0198 \| 559.4 \| 74400 \| 3.0983 \| 1.0025 \| \| 0.0229 \| 560.9 \| 74600 \| 3.2628 \| 1.0006 \| \| 0.0234 \| 562.41 \| 74800 \| 2.9351 \| 0.9987 \| \| 0.0255 \| 563.91 \| 75000 \| 2.9952 \| 0.9962 \| \| 0.0267 \| 565.41 \| 75200 \| 3.1814 \| 0.9969 \| \| 0.0217 \| 566.92 \| 75400 \| 3.1355 \| 1.0006 \| \| 0.0236 \| 568.42 \| 75600 \| 3.5030 \| 1.0019 \| \| 0.0249 \| 569.92 \| 75800 \| 2.8262 \| 1.0006 \| \| 0.0192 \| 571.43 \| 76000 \| 3.0427 \| 1.0006 \| \| 0.0194 \| 572.93 \| 76200 \| 3.0055 \| 0.9981 \| \| 0.0257 \| 574.44 \| 76400 \| 3.2506 \| 0.9987 \| \| 0.0214 \| 575.94 \| 76600 \| 3.2980 \| 0.9981 \| \| 0.0314 \| 577.44 \| 76800 \| 3.4304 \| 0.9981 \| \| 0.0186 \| 578.95 \| 77000 \| 3.2306 \| 0.9987 \| \| 0.0219 \| 580.45 \| 77200 \| 3.4410 \| 0.9994 \| \| 0.0244 \| 581.95 \| 77400 \| 3.3982 \| 0.9994 \| \| 0.0232 \| 583.46 \| 77600 \| 3.0487 \| 0.9962 \| \| 0.026 \| 584.96 \| 77800 \| 2.9061 \| 1.0 \| \| 0.0206 \| 586.47 \| 78000 \| 2.8331 \| 1.0006 \| \| 0.0202 \| 587.97 \| 78200 \| 2.8493 \| 0.9956 \| \| 0.0251 \| 589.47 \| 78400 \| 3.0203 \| 0.9956 \| \| 0.0231 \| 590.98 \| 78600 \| 3.0142 \| 0.9969 \| \| 0.0183 \| 592.48 \| 78800 \| 3.1480 \| 0.9969 \| \| 0.022 \| 593.98 \| 79000 \| 3.0777 \| 0.9975 \| \| 0.0225 \| 595.49 \| 79200 \| 3.3248 \| 0.9975 \| \| 0.0186 \| 596.99 \| 79400 \| 3.3309 \| 0.9994 \| \| 0.0184 \| 598.5 \| 79600 \| 3.4888 \| 0.9981 \| \| 0.0181 \| 600.0 \| 79800 \| 3.2628 \| 0.9962 \| \| 0.0158 \| 601.5 \| 80000 \| 3.4862 \| 0.9975 \| \| 0.0197 \| 603.01 \| 80200 \| 3.2335 \| 0.9962 \| \| 0.0259 \| 604.51 \| 80400 \| 3.1643 \| 0.9987 \| \| 0.0218 \| 606.02 \| 80600 \| 3.6256 \| 0.9969 \| \| 0.0196 \| 607.52 \| 80800 \| 3.3183 \| 1.0 \| \| 0.0127 \| 609.02 \| 81000 \| 3.7223 \| 0.9987 \| \| 0.0182 \| 610.53 \| 81200 \| 3.2768 \| 0.9994 \| \| 0.0234 \| 612.03 \| 81400 \| 3.3749 \| 1.0 \| \| 0.0195 \| 613.53 \| 81600 \| 3.2097 \| 0.9987 \| \| 0.0259 \| 615.04 \| 81800 \| 3.2090 \| 0.9987 \| \| 0.0217 \| 616.54 \| 82000 \| 3.3893 \| 0.9981 \| \| 0.0181 \| 618.05 \| 82200 \| 3.2223 \| 0.9994 \| \| 0.0155 \| 619.55 \| 82400 \| 3.1307 \| 0.9969 \| \| 0.0192 \| 621.05 \| 82600 \| 3.0695 \| 0.9975 \| \| 0.0155 \| 622.56 \| 82800 \| 3.3623 \| 0.9975 \| \| 0.0266 \| 624.06 \| 83000 \| 3.0329 \| 0.9937 \| \| 0.017 \| 625.56 \| 83200 \| 2.8108 \| 0.9956 \| \| 0.0224 \| 627.07 \| 83400 \| 3.2747 \| 0.9981 \| \| 0.0202 \| 628.57 \| 83600 \| 3.1392 \| 0.9981 \| \| 0.0157 \| 630.08 \| 83800 \| 2.9493 \| 0.9975 \| \| 0.0187 \| 631.58 \| 84000 \| 2.9518 \| 0.9969 \| \| 0.0234 \| 633.08 \| 84200 \| 3.3938 \| 0.9981 \| \| 0.0205 \| 634.59 \| 84400 \| 2.9594 \| 0.9937 \| \| 0.0161 \| 636.09 \| 84600 \| 2.7611 \| 0.9944 \| \| 0.0187 \| 637.59 \| 84800 \| 2.8704 \| 0.9969 \| \| 0.0285 \| 639.1 \| 85000 \| 3.1030 \| 0.9987 \| \| 0.0163 \| 640.6 \| 85200 \| 3.2914 \| 0.9975 \| \| 0.0216 \| 642.11 \| 85400 \| 2.9154 \| 0.9969 \| \| 0.0233 \| 643.61 \| 85600 \| 2.7749 \| 0.9975 \| \| 0.0218 \| 645.11 \| 85800 \| 2.8473 \| 1.0 \| \| 0.0196 \| 646.62 \| 86000 \| 2.5928 \| 1.0038 \| \| 0.0158 \| 648.12 \| 86200 \| 2.8972 \| 0.9962 \| \| 0.0156 \| 649.62 \| 86400 \| 2.9890 \| 0.9981 \| \| 0.0131 \| 651.13 \| 86600 \| 2.7676 \| 0.9987 \| \| 0.0206 \| 652.63 \| 86800 \| 2.8754 \| 0.9944 \| \| 0.0197 \| 654.14 \| 87000 \| 3.0768 \| 0.9962 \| \| 0.0139 \| 655.64 \| 87200 \| 2.6567 \| 0.9975 \| \| 0.0139 \| 657.14 \| 87400 \| 3.1492 \| 0.9987 \| \| 0.0226 \| 658.65 \| 87600 \| 2.7989 \| 0.9975 \| \| 0.0182 \| 660.15 \| 87800 \| 3.0076 \| 1.0013 \| \| 0.0164 \| 661.65 \| 88000 \| 2.9625 \| 0.9975 \| \| 0.0221 \| 663.16 \| 88200 \| 2.9768 \| 0.9987 \| \| 0.0144 \| 664.66 \| 88400 \| 3.0221 \| 0.9962 \| \| 0.0178 \| 666.17 \| 88600 \| 2.9881 \| 0.9981 \| \| 0.0214 \| 667.67 \| 88800 \| 3.3127 \| 0.9994 \| \| 0.0193 \| 669.17 \| 89000 \| 3.0776 \| 0.9969 \| \| 0.0211 \| 670.68 \| 89200 \| 2.8097 \| 0.9975 \| \| 0.0223 \| 672.18 \| 89400 \| 3.1911 \| 0.9975 \| \| 0.0222 \| 673.68 \| 89600 \| 3.0041 \| 0.9969 \| \| 0.0209 \| 675.19 \| 89800 \| 2.9853 \| 0.9975 \| \| 0.0116 \| 676.69 \| 90000 \| 2.9112 \| 0.9994 \| \| 0.0159 \| 678.2 \| 90200 \| 2.8274 \| 0.9969 \| \| 0.0185 \| 679.7 \| 90400 \| 2.7524 \| 0.9975 \| \| 0.0123 \| 681.2 \| 90600 \| 2.9673 \| 0.9981 \| \| 0.0152 \| 682.71 \| 90800 \| 3.0797 \| 1.0 \| \| 0.0117 \| 684.21 \| 91000 \| 3.0703 \| 0.9994 \| \| 0.0154 \| 685.71 \| 91200 \| 2.7031 \| 0.9987 \| \| 0.0148 \| 687.22 \| 91400 \| 2.9449 \| 0.9994 \| \| 0.0196 \| 688.72 \| 91600 \| 2.7991 \| 0.9981 \| \| 0.0168 \| 690.23 \| 91800 \| 2.8938 \| 0.9969 \| \| 0.0155 \| 691.73 \| 92000 \| 2.9104 \| 0.9975 \| \| 0.0167 \| 693.23 \| 92200 \| 2.7905 \| 0.9975 \| \| 0.0175 \| 694.74 \| 92400 \| 3.0061 \| 0.9962 \| \| 0.0134 \| 696.24 \| 92600 \| 3.1880 \| 0.9975 \| \| 0.0145 \| 697.74 \| 92800 \| 3.1567 \| 0.9975 \| \| 0.0191 \| 699.25 \| 93000 \| 3.2099 \| 0.9981 \| \| 0.0192 \| 700.75 \| 93200 \| 3.0210 \| 0.9962 \| \| 0.0143 \| 702.26 \| 93400 \| 3.0640 \| 0.9962 \| \| 0.0145 \| 703.76 \| 93600 \| 2.9882 \| 0.9956 \| \| 0.0151 \| 705.26 \| 93800 \| 2.9178 \| 0.9969 \| \| 0.0192 \| 706.77 \| 94000 \| 2.9631 \| 0.9962 \| \| 0.016 \| 708.27 \| 94200 \| 2.8724 \| 0.9950 \| \| 0.0185 \| 709.77 \| 94400 \| 2.8253 \| 1.0013 \| \| 0.0186 \| 711.28 \| 94600 \| 2.8386 \| 0.9950 \| \| 0.015 \| 712.78 \| 94800 \| 2.8134 \| 0.9987 \| \| 0.0137 \| 714.29 \| 95000 \| 3.1884 \| 0.9969 \| \| 0.0168 \| 715.79 \| 95200 \| 2.9308 \| 0.9969 \| \| 0.0092 \| 717.29 \| 95400 \| 3.4363 \| 0.9981 \| \| 0.0167 \| 718.8 \| 95600 \| 3.2090 \| 0.9981 \| \| 0.0095 \| 720.3 \| 95800 \| 3.1477 \| 0.9981 \| \| 0.0166 \| 721.8 \| 96000 \| 2.9676 \| 0.9981 \| \| 0.0162 \| 723.31 \| 96200 \| 3.0809 \| 0.9969 \| \| 0.0214 \| 724.81 \| 96400 \| 3.3432 \| 0.9987 \| \| 0.0153 \| 726.32 \| 96600 \| 3.0991 \| 0.9981 \| \| 0.0126 \| 727.82 \| 96800 \| 3.0080 \| 0.9975 \| \| 0.009 \| 729.32 \| 97000 \| 2.7920 \| 0.9981 \| \| 0.0194 \| 730.83 \| 97200 \| 2.9781 \| 0.9950 \| \| 0.0159 \| 732.33 \| 97400 \| 3.0364 \| 0.9969 \| \| 0.0175 \| 733.83 \| 97600 \| 2.8637 \| 0.9975 \| \| 0.0131 \| 735.34 \| 97800 \| 3.0862 \| 0.9956 \| \| 0.0222 \| 736.84 \| 98000 \| 2.7875 \| 0.9987 \| \| 0.0107 \| 738.35 \| 98200 \| 2.9837 \| 0.9962 \| \| 0.0171 \| 739.85 \| 98400 \| 2.9214 \| 0.9981 \| \| 0.0162 \| 741.35 \| 98600 \| 2.9194 \| 0.9956 \| \| 0.0133 \| 742.86 \| 98800 \| 2.7002 \| 1.0069 \| \| 0.0201 \| 744.36 \| 99000 \| 2.9725 \| 0.9962 \| \| 0.0137 \| 745.86 \| 99200 \| 2.8473 \| 0.9975 \| \| 0.0081 \| 747.37 \| 99400 \| 2.9228 \| 0.9981 \| \| 0.0155 \| 748.87 \| 99600 \| 2.7624 \| 0.9981 \| \| 0.0127 \| 750.38 \| 99800 \| 2.8617 \| 1.0019 \| \| 0.0158 \| 751.88 \| 100000 \| 2.6715 \| 1.0044 \| \| 0.0146 \| 753.38 \| 100200 \| 2.7292 \| 1.0019 \| \| 0.0136 \| 754.89 \| 100400 \| 2.7341 \| 0.9987 \| \| 0.0177 \| 756.39 \| 100600 \| 2.8756 \| 1.0 \| \| 0.0166 \| 757.89 \| 100800 \| 2.7004 \| 0.9981 \| \| 0.0125 \| 759.4 \| 101000 \| 2.6595 \| 1.0013 \| \| 0.012 \| 760.9 \| 101200 \| 2.9655 \| 1.0031 \| \| 0.0075 \| 762.41 \| 101400 \| 2.8493 \| 0.9969 \| \| 0.0161 \| 763.91 \| 101600 \| 3.0451 \| 0.9931 \| \| 0.0159 \| 765.41 \| 101800 \| 2.8029 \| 0.9956 \| \| 0.0179 \| 766.92 \| 102000 \| 2.7643 \| 0.9975 \| \| 0.0123 \| 768.42 \| 102200 \| 2.7163 \| 0.9962 \| \| 0.0131 \| 769.92 \| 102400 \| 2.7235 \| 1.0 \| \| 0.0134 \| 771.43 \| 102600 \| 2.7199 \| 0.9944 \| \| 0.0148 \| 772.93 \| 102800 \| 3.0776 \| 0.9962 \| \| 0.0132 \| 774.44 \| 103000 \| 2.8314 \| 0.9987 \| \| 0.0104 \| 775.94 \| 103200 \| 2.8094 \| 0.9969 \| \| 0.0166 \| 777.44 \| 103400 \| 2.8420 \| 0.9956 \| \| 0.0104 \| 778.95 \| 103600 \| 2.7902 \| 0.9944 \| \| 0.0065 \| 780.45 \| 103800 \| 3.0951 \| 0.9962 \| \| 0.0138 \| 781.95 \| 104000 \| 2.9704 \| 0.9956 \| \| 0.0113 \| 783.46 \| 104200 \| 2.7171 \| 0.9994 \| \| 0.0111 \| 784.96 \| 104400 \| 3.2024 \| 0.9975 \| \| 0.0123 \| 786.47 \| 104600 \| 3.1505 \| 0.9956 \| \| 0.0111 \| 787.97 \| 104800 \| 3.1629 \| 0.9969 \| \| 0.0105 \| 789.47 \| 105000 \| 3.4622 \| 0.9975 \| \| 0.0083 \| 790.98 \| 105200 \| 3.0705 \| 0.9987 \| \| 0.0123 \| 792.48 \| 105400 \| 3.1194 \| 1.0 \| \| 0.0156 \| 793.98 \| 105600 \| 2.9915 \| 0.9969 \| \| 0.0171 \| 795.49 \| 105800 \| 2.9393 \| 0.9969 \| \| 0.0144 \| 796.99 \| 106000 \| 3.1108 \| 0.9981 \| \| 0.0126 \| 798.5 \| 106200 \| 3.1683 \| 0.9994 \| \| 0.0101 \| 800.0 \| 106400 \| 2.8538 \| 1.0019 \| \| 0.0166 \| 801.5 \| 106600 \| 2.8343 \| 0.9987 \| \| 0.014 \| 803.01 \| 106800 \| 3.2088 \| 0.9969 \| \| 0.0121 \| 804.51 \| 107000 \| 2.9219 \| 0.9987 \| \| 0.012 \| 806.02 \| 107200 \| 3.1733 \| 0.9994 \| \| 0.0116 \| 807.52 \| 107400 \| 3.1237 \| 1.0 \| \| 0.0088 \| 809.02 \| 107600 \| 2.9925 \| 1.0 \| \| 0.0119 \| 810.53 \| 107800 \| 2.8498 \| 1.0019 \| \| 0.0128 \| 812.03 \| 108000 \| 2.7667 \| 0.9950 \| \| 0.0099 \| 813.53 \| 108200 \| 2.8768 \| 0.9975 \| \| 0.0135 \| 815.04 \| 108400 \| 3.0824 \| 0.9994 \| \| 0.01 \| 816.54 \| 108600 \| 3.0932 \| 1.0006 \| \| 0.0106 \| 818.05 \| 108800 \| 3.1329 \| 0.9975 \| \| 0.0127 \| 819.55 \| 109000 \| 3.0865 \| 0.9962 \| \| 0.01 \| 821.05 \| 109200 \| 2.8887 \| 0.9975 \| \| 0.0156 \| 822.56 \| 109400 \| 3.1740 \| 0.9962 \| \| 0.0131 \| 824.06 \| 109600 \| 3.2385 \| 0.9981 \| \| 0.0081 \| 825.56 \| 109800 \| 3.3594 \| 0.9981 \| \| 0.0138 \| 827.07 \| 110000 \| 3.4093 \| 0.9981 \| \| 0.0103 \| 828.57 \| 110200 \| 3.2142 \| 0.9987 \| \| 0.009 \| 830.08 \| 110400 \| 3.1570 \| 0.9981 \| \| 0.0065 \| 831.58 \| 110600 \| 3.0934 \| 0.9987 \| \| 0.0101 \| 833.08 \| 110800 \| 2.7369 \| 0.9975 \| \| 0.0084 \| 834.59 \| 111000 \| 2.9523 \| 0.9994 \| \| 0.0123 \| 836.09 \| 111200 \| 2.9521 \| 0.9987 \| \| 0.0061 \| 837.59 \| 111400 \| 2.9804 \| 0.9962 \| \| 0.0098 \| 839.1 \| 111600 \| 3.0332 \| 0.9969 \| \| 0.0086 \| 840.6 \| 111800 \| 2.9643 \| 0.9956 \| \| 0.008 \| 842.11 \| 112000 \| 2.9080 \| 0.9956 \| \| 0.0074 \| 843.61 \| 112200 \| 2.9878 \| 0.9962 \| \| 0.0125 \| 845.11 \| 112400 \| 3.1683 \| 0.9956 \| \| 0.0112 \| 846.62 \| 112600 \| 3.1810 \| 1.0019 \| \| 0.0093 \| 848.12 \| 112800 \| 3.1701 \| 0.9994 \| \| 0.0133 \| 849.62 \| 113000 \| 3.1736 \| 1.0019 \| \| 0.0093 \| 851.13 \| 113200 \| 3.0622 \| 0.9950 \| \| 0.0085 \| 852.63 \| 113400 \| 3.0084 \| 0.9994 \| \| 0.0119 \| 854.14 \| 113600 \| 3.0510 \| 1.0038 \| \| 0.0132 \| 855.64 \| 113800 \| 3.0081 \| 0.9975 \| \| 0.0068 \| 857.14 \| 114000 \| 3.1519 \| 0.9975 \| \| 0.0103 \| 858.65 \| 114200 \| 3.0568 \| 0.9950 \| \| 0.0116 \| 860.15 \| 114400 \| 2.9386 \| 0.9969 \| \| 0.0098 \| 861.65 \| 114600 \| 2.9005 \| 0.9975 \| \| 0.0102 \| 863.16 \| 114800 \| 2.8415 \| 0.9981 \| \| 0.0061 \| 864.66 \| 115000 \| 3.0904 \| 0.9969 \| \| 0.0111 \| 866.17 \| 115200 \| 2.7751 \| 1.0 \| \| 0.011 \| 867.67 \| 115400 \| 2.9156 \| 1.0006 \| \| 0.0159 \| 869.17 \| 115600 \| 2.7727 \| 1.0019 \| \| 0.0093 \| 870.68 \| 115800 \| 2.9028 \| 1.0013 \| \| 0.009 \| 872.18 \| 116000 \| 2.9202 \| 1.0 \| \| 0.012 \| 873.68 \| 116200 \| 3.0764 \| 1.0006 \| \| 0.0126 \| 875.19 \| 116400 \| 3.0276 \| 1.0031 \| \| 0.0091 \| 876.69 \| 116600 \| 3.1787 \| 0.9987 \| \| 0.0105 \| 878.2 \| 116800 \| 3.0780 \| 1.0 \| \| 0.0088 \| 879.7 \| 117000 \| 3.1704 \| 1.0056 \| \| 0.0075 \| 881.2 \| 117200 \| 2.9814 \| 1.0 \| \| 0.0111 \| 882.71 \| 117400 \| 2.9560 \| 0.9987 \| \| 0.0086 \| 884.21 \| 117600 \| 2.9376 \| 0.9994 \| \| 0.0137 \| 885.71 \| 117800 \| 3.1393 \| 0.9981 \| \| 0.0093 \| 887.22 \| 118000 \| 3.0911 \| 0.9994 \| \| 0.0087 \| 888.72 \| 118200 \| 3.1769 \| 0.9987 \| \| 0.0103 \| 890.23 \| 118400 \| 3.2035 \| 0.9969 \| \| 0.0093 \| 891.73 \| 118600 \| 3.0411 \| 0.9981 \| \| 0.0099 \| 893.23 \| 118800 \| 3.1438 \| 0.9975 \| \| 0.0082 \| 894.74 \| 119000 \| 3.0581 \| 0.9975 \| \| 0.0079 \| 896.24 \| 119200 \| 3.2609 \| 0.9981 \| \| 0.0134 \| 897.74 \| 119400 \| 3.0753 \| 0.9975 \| \| 0.012 \| 899.25 \| 119600 \| 3.0611 \| 0.9981 \| \| 0.0127 \| 900.75 \| 119800 \| 3.0978 \| 0.9987 \| \| 0.0092 \| 902.26 \| 120000 \| 3.0483 \| 0.9981 \| \| 0.0107 \| 903.76 \| 120200 \| 3.1183 \| 0.9969 \| \| 0.0104 \| 905.26 \| 120400 \| 3.0339 \| 1.0006 \| \| 0.0066 \| 906.77 \| 120600 \| 3.0664 \| 1.0 \| \| 0.0136 \| 908.27 \| 120800 \| 3.2616 \| 0.9987 \| \| 0.0075 \| 909.77 \| 121000 \| 3.2563 \| 0.9994 \| \| 0.0096 \| 911.28 \| 121200 \| 3.2538 \| 1.0019 \| \| 0.0094 \| 912.78 \| 121400 \| 3.4125 \| 0.9994 \| \| 0.0059 \| 914.29 \| 121600 \| 3.2795 \| 0.9981 \| \| 0.011 \| 915.79 \| 121800 \| 3.0787 \| 0.9981 \| \| 0.01 \| 917.29 \| 122000 \| 3.0978 \| 0.9962 \| \| 0.0105 \| 918.8 \| 122200 \| 3.3389 \| 1.0006 \| \| 0.0116 \| 920.3 \| 122400 \| 3.3616 \| 1.0006 \| \| 0.0098 \| 921.8 \| 122600 \| 3.3360 \| 1.0 \| \| 0.0091 \| 923.31 \| 122800 \| 3.0145 \| 1.0006 \| \| 0.0134 \| 924.81 \| 123000 \| 3.2691 \| 0.9987 \| \| 0.0124 \| 926.32 \| 123200 \| 2.9881 \| 1.0038 \| \| 0.0093 \| 927.82 \| 123400 \| 3.2461 \| 0.9981 \| \| 0.0077 \| 929.32 \| 123600 \| 3.2159 \| 0.9975 \| \| 0.0088 \| 930.83 \| 123800 \| 2.9951 \| 1.0 \| \| 0.0047 \| 932.33 \| 124000 \| 3.1991 \| 1.0 \| \| 0.0078 \| 933.83 \| 124200 \| 3.1098 \| 0.9975 \| \| 0.0066 \| 935.34 \| 124400 \| 3.0344 \| 0.9969 \| \| 0.0108 \| 936.84 \| 124600 \| 3.1163 \| 0.9981 \| \| 0.0086 \| 938.35 \| 124800 \| 3.0668 \| 0.9981 \| \| 0.0071 \| 939.85 \| 125000 \| 2.9860 \| 0.9981 \| \| 0.0096 \| 941.35 \| 125200 \| 3.0639 \| 0.9981 \| \| 0.0066 \| 942.86 \| 125400 \| 3.0273 \| 0.9987 \| \| 0.0071 \| 944.36 \| 125600 \| 2.9351 \| 0.9981 \| \| 0.0084 \| 945.86 \| 125800 \| 3.2054 \| 0.9981 \| \| 0.0099 \| 947.37 \| 126000 \| 3.0305 \| 1.0006 \| \| 0.0084 \| 948.87 \| 126200 \| 3.0412 \| 1.0 \| \| 0.0085 \| 950.38 \| 126400 \| 2.9697 \| 0.9994 \| \| 0.0072 \| 951.88 \| 126600 \| 2.9454 \| 0.9956 \| \| 0.007 \| 953.38 \| 126800 \| 2.9225 \| 0.9981 \| \| 0.0068 \| 954.89 \| 127000 \| 2.9063 \| 0.9962 \| \| 0.0121 \| 956.39 \| 127200 \| 2.7681 \| 0.9975 \| \| 0.0063 \| 957.89 \| 127400 \| 3.0608 \| 0.9975 \| \| 0.0155 \| 959.4 \| 127600 \| 2.9355 \| 0.9994 \| \| 0.0098 \| 960.9 \| 127800 \| 3.0073 \| 0.9987 \| \| 0.0083 \| 962.41 \| 128000 \| 3.0921 \| 0.9987 \| \| 0.0042 \| 963.91 \| 128200 \| 3.2363 \| 0.9981 \| \| 0.0083 \| 965.41 \| 128400 \| 3.0678 \| 0.9987 \| \| 0.0097 \| 966.92 \| 128600 \| 2.8499 \| 1.0019 \| \| 0.0095 \| 968.42 \| 128800 \| 2.9329 \| 1.0038 \| \| 0.0058 \| 969.92 \| 129000 \| 2.7835 \| 1.0038 \| \| 0.0052 \| 971.43 \| 129200 \| 2.9134 \| 1.0019 \| \| 0.0081 \| 972.93 \| 129400 \| 2.9436 \| 1.0031 \| \| 0.0084 \| 974.44 \| 129600 \| 2.8237 \| 0.9994 \| \| 0.0069 \| 975.94 \| 129800 \| 2.9216 \| 0.9994 \| \| 0.0089 \| 977.44 \| 130000 \| 2.8717 \| 0.9994 \| \| 0.0074 \| 978.95 \| 130200 \| 3.0350 \| 0.9994 \| \| 0.0072 \| 980.45 \| 130400 \| 2.8856 \| 1.0013 \| \| 0.0103 \| 981.95 \| 130600 \| 2.9572 \| 1.0 \| \| 0.01 \| 983.46 \| 130800 \| 2.9522 \| 1.0031 \| \| 0.0071 \| 984.96 \| 131000 \| 2.9940 \| 1.0031 \| \| 0.0042 \| 986.47 \| 131200 \| 3.0343 \| 1.0019 \| \| 0.0048 \| 987.97 \| 131400 \| 2.9638 \| 1.0069 \| \| 0.0038 \| 989.47 \| 131600 \| 2.9672 \| 1.0063 \| \| 0.0043 \| 990.98 \| 131800 \| 2.9115 \| 1.0063 \| \| 0.0129 \| 992.48 \| 132000 \| 2.7689 \| 1.0006 \| \| 0.0059 \| 993.98 \| 132200 \| 2.9322 \| 1.0038 \| \| 0.0098 \| 995.49 \| 132400 \| 2.8063 \| 1.0013 \| \| 0.0083 \| 996.99 \| 132600 \| 3.0642 \| 0.9981 \| \| 0.0055 \| 998.5 \| 132800 \| 2.7167 \| 1.0056 \| \| 0.0073 \| 1000.0 \| 133000 \| 2.7715 \| 1.0075 \| \| 0.0048 \| 1001.5 \| 133200 \| 2.9235 \| 1.0019 \| \| 0.0148 \| 1003.01 \| 133400 \| 3.0357 \| 1.0031 \| \| 0.0059 \| 1004.51 \| 133600 \| 2.9261 \| 1.0044 \| \| 0.008 \| 1006.02 \| 133800 \| 2.9927 \| 0.9994 \| \| 0.0093 \| 1007.52 \| 134000 \| 2.9374 \| 1.0019 \| \| 0.0102 \| 1009.02 \| 134200 \| 2.9598 \| 0.9987 \| \| 0.0045 \| 1010.53 \| 134400 \| 3.0381 \| 1.0 \| \| 0.0058 \| 1012.03 \| 134600 \| 3.0556 \| 1.0 \| \| 0.0089 \| 1013.53 \| 134800 \| 3.1323 \| 1.0006 \| \| 0.0058 \| 1015.04 \| 135000 \| 3.0611 \| 1.0013 \| \| 0.0043 \| 1016.54 \| 135200 \| 3.0465 \| 0.9981 \| \| 0.004 \| 1018.05 \| 135400 \| 3.1712 \| 0.9962 \| \| 0.0056 \| 1019.55 \| 135600 \| 2.8690 \| 0.9956 \| \| 0.0098 \| 1021.05 \| 135800 \| 3.0515 \| 0.9987 \| \| 0.0055 \| 1022.56 \| 136000 \| 3.1504 \| 0.9987 \| \| 0.0054 \| 1024.06 \| 136200 \| 3.0969 \| 0.9969 \| \| 0.005 \| 1025.56 \| 136400 \| 3.0669 \| 0.9969 \| \| 0.0073 \| 1027.07 \| 136600 \| 3.0097 \| 0.9975 \| \| 0.005 \| 1028.57 \| 136800 \| 3.0517 \| 0.9994 \| \| 0.0076 \| 1030.08 \| 137000 \| 3.0154 \| 1.0 \| \| 0.0084 \| 1031.58 \| 137200 \| 2.9610 \| 1.0025 \| \| 0.0044 \| 1033.08 \| 137400 \| 2.9468 \| 0.9994 \| \| 0.0063 \| 1034.59 \| 137600 \| 3.0337 \| 1.0006 \| \| 0.0031 \| 1036.09 \| 137800 \| 3.1654 \| 1.0006 \| \| 0.0066 \| 1037.59 \| 138000 \| 3.0546 \| 0.9987 \| \| 0.0053 \| 1039.1 \| 138200 \| 3.0273 \| 1.0006 \| \| 0.0086 \| 1040.6 \| 138400 \| 3.0024 \| 0.9962 \| \| 0.0061 \| 1042.11 \| 138600 \| 2.9791 \| 0.9994 \| \| 0.0057 \| 1043.61 \| 138800 \| 3.1378 \| 0.9994 \| \| 0.0052 \| 1045.11 \| 139000 \| 3.0045 \| 0.9987 \| \| 0.0094 \| 1046.62 \| 139200 \| 2.9606 \| 0.9994 \| \| 0.0027 \| 1048.12 \| 139400 \| 3.1745 \| 0.9981 \| \| 0.0024 \| 1049.62 \| 139600 \| 3.1448 \| 0.9981 \| \| 0.0045 \| 1051.13 \| 139800 \| 3.0968 \| 1.0 \| \| 0.0048 \| 1052.63 \| 140000 \| 3.1278 \| 0.9975 \| \| 0.0079 \| 1054.14 \| 140200 \| 3.2618 \| 0.9969 \| \| 0.0078 \| 1055.64 \| 140400 \| 3.1333 \| 0.9969 \| \| 0.0077 \| 1057.14 \| 140600 \| 3.0945 \| 0.9987 \| \| 0.0029 \| 1058.65 \| 140800 \| 3.1253 \| 0.9969 \| \| 0.0022 \| 1060.15 \| 141000 \| 3.0270 \| 0.9969 \| \| 0.0054 \| 1061.65 \| 141200 \| 3.1937 \| 0.9975 \| \| 0.0097 \| 1063.16 \| 141400 \| 2.9620 \| 0.9987 \| \| 0.006 \| 1064.66 \| 141600 \| 2.9298 \| 0.9994 \| \| 0.0041 \| 1066.17 \| 141800 \| 2.9171 \| 1.0013 \| \| 0.006 \| 1067.67 \| 142000 \| 3.0870 \| 0.9994 \| \| 0.0059 \| 1069.17 \| 142200 \| 2.9893 \| 0.9987 \| \| 0.0054 \| 1070.68 \| 142400 \| 3.0031 \| 0.9975 \| \| 0.0076 \| 1072.18 \| 142600 \| 2.9808 \| 0.9969 \| \| 0.0091 \| 1073.68 \| 142800 \| 3.0710 \| 0.9975 \| \| 0.0081 \| 1075.19 \| 143000 \| 3.2000 \| 0.9956 \| \| 0.0037 \| 1076.69 \| 143200 \| 3.1321 \| 0.9956 \| \| 0.0042 \| 1078.2 \| 143400 \| 3.0672 \| 0.9950 \| \| 0.0031 \| 1079.7 \| 143600 \| 3.0094 \| 0.9969 \| \| 0.003 \| 1081.2 \| 143800 \| 3.0956 \| 0.9956 \| \| 0.0087 \| 1082.71 \| 144000 \| 3.0842 \| 0.9969 \| \| 0.0039 \| 1084.21 \| 144200 \| 2.9722 \| 0.9962 \| \| 0.0059 \| 1085.71 \| 144400 \| 3.0395 \| 0.9962 \| \| 0.0088 \| 1087.22 \| 144600 \| 2.9895 \| 0.9950 \| \| 0.0056 \| 1088.72 \| 144800 \| 2.9265 \| 0.9956 \| \| 0.0031 \| 1090.23 \| 145000 \| 2.9205 \| 0.9969 \| \| 0.0089 \| 1091.73 \| 145200 \| 2.9462 \| 0.9969 \| \| 0.0037 \| 1093.23 \| 145400 \| 2.9021 \| 0.9975 \| \| 0.0047 \| 1094.74 \| 145600 \| 2.9209 \| 0.9987 \| \| 0.0058 \| 1096.24 \| 145800 \| 2.9616 \| 0.9975 \| \| 0.0068 \| 1097.74 \| 146000 \| 2.9260 \| 0.9981 \| \| 0.0051 \| 1099.25 \| 146200 \| 2.9815 \| 0.9969 \| \| 0.003 \| 1100.75 \| 146400 \| 3.0032 \| 0.9969 \| \| 0.0055 \| 1102.26 \| 146600 \| 2.9972 \| 0.9975 \| \| 0.0064 \| 1103.76 \| 146800 \| 3.0282 \| 0.9969 \| \| 0.0045 \| 1105.26 \| 147000 \| 3.0201 \| 0.9969 \| \| 0.0075 \| 1106.77 \| 147200 \| 3.1340 \| 0.9956 \| \| 0.0052 \| 1108.27 \| 147400 \| 3.1120 \| 0.9956 \| \| 0.0075 \| 1109.77 \| 147600 \| 3.1448 \| 0.9969 \| \| 0.0053 \| 1111.28 \| 147800 \| 3.1039 \| 0.9981 \| \| 0.0061 \| 1112.78 \| 148000 \| 3.2061 \| 1.0006 \| \| 0.0105 \| 1114.29 \| 148200 \| 3.1274 \| 0.9994 \| \| 0.0071 \| 1115.79 \| 148400 \| 3.0449 \| 1.0013 \| \| 0.0042 \| 1117.29 \| 148600 \| 3.1285 \| 0.9994 \| \| 0.0017 \| 1118.8 \| 148800 \| 3.0261 \| 0.9975 \| \| 0.0083 \| 1120.3 \| 149000 \| 3.0042 \| 0.9944 \| \| 0.0026 \| 1121.8 \| 149200 \| 2.9851 \| 0.9950 \| \| 0.003 \| 1123.31 \| 149400 \| 2.9439 \| 0.9956 \| \| 0.005 \| 1124.81 \| 149600 \| 2.9231 \| 0.9944 \| \| 0.0039 \| 1126.32 \| 149800 \| 2.9187 \| 0.9944 \| \| 0.0031 \| 1127.82 \| 150000 \| 2.9250 \| 0.9950 \| \| 0.002 \| 1129.32 \| 150200 \| 2.9452 \| 0.9969 \| \| 0.0057 \| 1130.83 \| 150400 \| 2.9566 \| 0.9944 \| \| 0.0046 \| 1132.33 \| 150600 \| 3.0111 \| 0.9975 \| \| 0.0076 \| 1133.83 \| 150800 \| 2.9526 \| 0.9962 \| \| 0.0048 \| 1135.34 \| 151000 \| 2.9313 \| 0.9969 \| \| 0.0052 \| 1136.84 \| 151200 \| 2.9782 \| 0.9975 \| \| 0.0086 \| 1138.35 \| 151400 \| 3.0282 \| 0.9975 \| \| 0.0018 \| 1139.85 \| 151600 \| 3.0765 \| 0.9962 \| \| 0.0046 \| 1141.35 \| 151800 \| 3.0391 \| 0.9969 \| \| 0.0043 \| 1142.86 \| 152000 \| 3.0364 \| 0.9962 \| \| 0.006 \| 1144.36 \| 152200 \| 3.0741 \| 0.9975 \| \| 0.0059 \| 1145.86 \| 152400 \| 3.0201 \| 0.9975 \| \| 0.0038 \| 1147.37 \| 152600 \| 3.0556 \| 0.9969 \| \| 0.007 \| 1148.87 \| 152800 \| 2.9943 \| 0.9962 \| \| 0.0042 \| 1150.38 \| 153000 \| 2.9979 \| 0.9981 \| \| 0.006 \| 1151.88 \| 153200 \| 3.0304 \| 0.9981 \| \| 0.0063 \| 1153.38 \| 153400 \| 2.9844 \| 0.9981 \| \| 0.0067 \| 1154.89 \| 153600 \| 2.9773 \| 0.9994 \| \| 0.0037 \| 1156.39 \| 153800 \| 2.9956 \| 0.9994 \| \| 0.0029 \| 1157.89 \| 154000 \| 2.9977 \| 1.0 \| \| 0.0056 \| 1159.4 \| 154200 \| 2.9639 \| 0.9987 \| \| 0.0063 \| 1160.9 \| 154400 \| 2.9705 \| 0.9994 \| \| 0.0063 \| 1162.41 \| 154600 \| 3.0182 \| 0.9994 \| \| 0.0045 \| 1163.91 \| 154800 \| 3.0294 \| 0.9994 \| \| 0.0107 \| 1165.41 \| 155000 \| 2.9982 \| 1.0 \| \| 0.0047 \| 1166.92 \| 155200 \| 3.0119 \| 1.0 \| \| 0.0043 \| 1168.42 \| 155400 \| 3.0040 \| 1.0006 \| \| 0.0025 \| 1169.92 \| 155600 \| 3.0062 \| 1.0 \| \| 0.0044 \| 1171.43 \| 155800 \| 2.9886 \| 1.0006 \| \| 0.0032 \| 1172.93 \| 156000 \| 3.0112 \| 1.0006 \| \| 0.0056 \| 1174.44 \| 156200 \| 3.0036 \| 1.0006 \| \| 0.0074 \| 1175.94 \| 156400 \| 3.0185 \| 1.0 \| \| 0.0056 \| 1177.44 \| 156600 \| 3.0197 \| 1.0 \| \| 0.0051 \| 1178.95 \| 156800 \| 3.0232 \| 0.9994 \| \| 0.0034 \| 1180.45 \| 157000 \| 3.0373 \| 1.0 \| \| 0.0038 \| 1181.95 \| 157200 \| 3.0297 \| 1.0 \| \| 0.0058 \| 1183.46 \| 157400 \| 3.0453 \| 0.9994 \| \| 0.0052 \| 1184.96 \| 157600 \| 3.0235 \| 1.0006 \| \| 0.0023 \| 1186.47 \| 157800 \| 3.0338 \| 1.0006 \| \| 0.0035 \| 1187.97 \| 158000 \| 3.0402 \| 1.0 \| \| 0.0035 \| 1189.47 \| 158200 \| 3.0333 \| 1.0 \| \| 0.0042 \| 1190.98 \| 158400 \| 3.0572 \| 0.9994 \| \| 0.0049 \| 1192.48 \| 158600 \| 3.0431 \| 1.0 \| \| 0.0017 \| 1193.98 \| 158800 \| 3.0432 \| 1.0 \| \| 0.0089 \| 1195.49 \| 159000 \| 3.0372 \| 1.0 \| \| 0.0089 \| 1196.99 \| 159200 \| 3.0366 \| 1.0006 \| \| 0.0053 \| 1198.5 \| 159400 \| 3.0345 \| 1.0006 \| \| 0.0054 \| 1200.0 \| 159600 \| 3.0348 \| 1.0006 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.6
huggingtweets/kc_lyricbot	3506e514498e9bea6a8f66e7e959e481f57f8278	2022-04-13T21:14:37.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/kc_lyricbot	null	transformers	36,859	--- language: en thumbnail: http://www.huggingtweets.com/kc_lyricbot/1649884470723/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1448393533921112064/q3fCXTyu_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">King Crimson Lyric Bot</div> <div style="text-align: center; font-size: 14px;">@kc_lyricbot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from King Crimson Lyric Bot. \| Data \| King Crimson Lyric Bot \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 0 \| \| Short tweets \| 231 \| \| Tweets kept \| 3019 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yn81k4o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kc_lyricbot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15ndpk6d) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15ndpk6d/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/kc_lyricbot') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
kangela/Metaphor-FineTuned-BERT	a8be63c2a11e22afc7a455d43fa556bb9835da39	2022-05-19T17:44:32.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	kangela	null	kangela/Metaphor-FineTuned-BERT	null	transformers	36,860	Entry not found
huggan/projected_gan_color_field	8d2331fc46c18dcd0b043f5ef20d049f1c8b61af	2022-04-25T11:16:28.000Z	[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]	unconditional-image-generation	false	huggan	null	huggan/projected_gan_color_field	null	pytorch	36,861	--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
huggan/projected_gan_popart	fc975cd4031baccc2d6fb37f8a9fe74438fc5f9d	2022-04-25T11:16:48.000Z	[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]	unconditional-image-generation	false	huggan	null	huggan/projected_gan_popart	null	pytorch	36,862	--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
huggan/projected_gan_abstract_expressionism	477e0e9cac181ed43f328397c60f0e94c40109c8	2022-04-25T11:16:38.000Z	[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]	unconditional-image-generation	false	huggan	null	huggan/projected_gan_abstract_expressionism	null	pytorch	36,863	--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
obokkkk/bert-base-multilingual-cased-finetuned-klue	cd6c21273c9d61dcbbcc5882631a6796394f5358	2022-04-14T12:57:25.000Z	[ "pytorch", "bert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	question-answering	false	obokkkk	null	obokkkk/bert-base-multilingual-cased-finetuned-klue	null	transformers	36,864	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-multilingual-cased-finetuned-klue results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-multilingual-cased-finetuned-klue This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4197 ## 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 - gradient_accumulation_steps: 36 - total_train_batch_size: 288 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.6323 \| 5.0 \| 500 \| 1.6799 \| \| 1.3765 \| 10.0 \| 1000 \| 1.3027 \| \| 0.8433 \| 15.0 \| 1500 \| 1.2946 \| \| 0.5224 \| 20.0 \| 2000 \| 1.4197 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.1
lisdarr/fuck_shanghai_covid19	3dae4b2416f259632eb5d0ce2242b91defc50287	2022-04-14T04:27:57.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	false	lisdarr	null	lisdarr/fuck_shanghai_covid19	0	transformers	36,865	--- license: apache-2.0 ---
pitiwat/argument_wangchanberta	06248b420a1bb15c048dd0a1b1a204d88a678e54	2022-04-14T11:35:08.000Z	[ "pytorch", "camembert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	false	pitiwat	null	pitiwat/argument_wangchanberta	null	transformers	36,866	Used for extracting arguments from Thai text.
TalTechNLP/voxlingua107-xls-r-300m-wav2vec	a96a1c36191f8b7a5676fec8228279318b9057a3	2022-04-19T11:08:36.000Z	[ "wav2vec2", "multilingual", "dataset:voxlingua107", "speechbrain", "language-identification", "pytorch", "embeddings", "Language", "Identification", "audio-classification", "wav2vec2.0", "XLS-R-300M", "VoxLingua107", "license:cc-by-4.0" ]	audio-classification	false	TalTechNLP	null	TalTechNLP/voxlingua107-xls-r-300m-wav2vec	null	speechbrain	36,867	--- language: multilingual license: cc-by-4.0 tags: - language-identification - speechbrain - wav2vec2 - pytorch - embeddings - Language - Identification - audio-classification - wav2vec2.0 - XLS-R-300M - VoxLingua107 datasets: - voxlingua107 metrics: - Accuracy --- # VoxLingua107 Wav2Vec Spoken Language Identification Model ## Model description This is a spoken language identification model trained on the VoxLingua107 dataset using SpeechBrain. The model is trained using weights of pretrained [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model, Wav2Vec2.0 architecture and negative log likelihood loss. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese). ## Intended uses & limitations The model has two uses: - use 'as is' for spoken language recognition - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data The model is trained on automatically collected YouTube data. For more information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/). #### How to use ```python import torchaudio from speechbrain.pretrained.interfaces import foreign_class language_id = foreign_class(source="TalTechNLP/voxlingua107-xls-r-300m-wav2vec", pymodule_file="encoder_wav2vec_classifier.py", classname="EncoderWav2vecClassifier", hparams_file='inference_wav2vec.yaml', savedir="tmp") # Download Thai language sample from Omniglot and convert to suitable form wav_file = "https://omniglot.com/soundfiles/udhr/udhr_th.mp3" out_prob, score, index, text_lab = language_id.classify_file(wav_file) print("probability:", out_prob) print("label:", text_lab) print("score:", score) print("index:", index) probability: tensor([[[-2.2849e+01, -2.4349e+01, -2.3686e+01, -2.3632e+01, -2.0218e+01, -2.7241e+01, -2.6715e+01, -2.2301e+01, -2.6076e+01, -2.1716e+01, -1.9923e+01, -2.7303e+01, -2.1211e+01, -2.2998e+01, -2.4436e+01, -2.6437e+01, -2.2686e+01, -2.4244e+01, -2.0416e+01, -2.8329e+01, -1.7788e+01, -2.4829e+01, -2.4186e+01, -2.7036e+01, -2.5993e+01, -1.9677e+01, -2.2746e+01, -2.9192e+01, -2.4941e+01, -2.7135e+01, -2.6653e+01, -2.2791e+01, -2.4599e+01, -2.1066e+01, -2.4855e+01, -2.1874e+01, -2.2914e+01, -2.4174e+01, -2.0902e+01, -2.3197e+01, -2.6108e+01, -2.3941e+01, -2.3103e+01, -2.2363e+01, -2.8969e+01, -2.5302e+01, -2.4862e+01, -2.2392e+01, -2.4042e+01, -2.1221e+01, -2.3656e+01, -2.1286e+01, -1.9209e+01, -2.3254e+01, -2.8291e+01, -5.9105e+00, -2.4525e+01, -2.4937e+01, -2.8349e+01, -2.4420e+01, -2.7439e+01, -2.6329e+01, -2.3317e+01, -2.3842e+01, -2.2114e+01, -2.3637e+01, -1.7217e+01, -1.8342e+01, -2.4332e+01, -2.6090e+01, -2.5452e+01, -2.3854e+01, -2.6082e+01, -2.4992e+01, -2.0618e+01, -2.9351e+01, -2.4153e+01, -2.3156e+01, -2.6893e+01, -2.5314e+01, -2.8374e+01, -2.4009e+01, -2.3604e+01, -2.4063e+01, -2.3538e+01, -2.4953e+01, -2.5607e+01, -2.3960e+01, -2.6471e+01, -2.3348e+01, -2.1681e+01, -2.7610e+01, -2.5023e+01, -2.3585e+01, -2.7146e-03, -2.0338e+01, -1.8737e+01, -2.5158e+01, -2.7491e+01, -2.3623e+01, -2.5718e+01, -2.3465e+01, -1.8305e+01, -2.1064e+01, -2.9880e+01, -2.2809e+01, -1.9856e+01]]]) # The identified language ISO code is given in score[0][0] label: [['th']] score: tensor([[-0.0027]]) index: tensor([[94]]) # The scores in the out_prob tensor can be interpreted as log-likelihoods that # the given utterance belongs to the given language (i.e., the larger the better) # The linear-scale likelihood can be retrieved using the following: print(score.exp()) tensor([0.9973]) # Alternatively, use the utterance embedding extractor: signal, fs = torchaudio.load(wav_file) embeddings = language_id.encode_batch(signal) print(embeddings.shape) torch.Size([2, 1, 2048]) ``` #### Limitations and bias Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: - Probably it's accuracy on smaller languages is quite limited - Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) - Based on experiments, it performs satisfactory on accented speech - Probably it doesn't work well on children's speech and on persons with speech disorders ## Training data The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/). VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. ## Training procedure We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model. Training recipe will be published soon. ## Evaluation results \| Version \| Error Rate (%) \| \|-----------------------\|:------:\| \| 2022-04-14 \| 5.6 \| Error rate is calculated on VoxLingua107 development dataset. ### BibTeX entry and citation info ```bibtex @inproceedings{valk2021slt, title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, booktitle={Proc. IEEE SLT Workshop}, year={2021}, } ```
huggingtweets/elonmusk-joebiden	67e9ad350fbad49e9485da977f5b374eb8218c6c	2022-04-14T12:38:39.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/elonmusk-joebiden	null	transformers	36,868	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Joe Biden</div> <div style="text-align: center; font-size: 14px;">@elonmusk-joebiden</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Joe Biden. \| Data \| Elon Musk \| Joe Biden \| \| --- \| --- \| --- \| \| Tweets downloaded \| 200 \| 3249 \| \| Retweets \| 15 \| 571 \| \| Short tweets \| 60 \| 34 \| \| Tweets kept \| 125 \| 2644 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ne2s3c4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-joebiden's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ka86kb6l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ka86kb6l/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-joebiden') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggan/pix2pix-night2day	150e94d5ccdecfb61a98bceee3962c981d175d2e	2022-04-15T04:27:40.000Z	[ "pytorch", "dataset:huggan/night2day", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]	null	false	huggan	null	huggan/pix2pix-night2day	null	null	36,869	--- tags: - huggan - gan datasets: - huggan/night2day # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. ## Intended uses & limitations: Used for reconstruction of images from edges #### How to use ```python from torchvision.transforms import Compose, Resize, ToTensor, Normalize from PIL import Image from torchvision.utils import save_image import cv2 from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet transform = Compose( [ Resize((256, 256), Image.BICUBIC), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) model = GeneratorUNet.from_pretrained('huggan/pix2pix-night2day') def predict_fn(img): inp = transform(img).unsqueeze(0) out = model(inp) save_image(out, 'out.png', normalize=True) return 'out.png' predict_fn(img) ``` #### Limitations and bias * Gives unrealistic colors in the image * Gives Blurry image sometimes ## Training data * [night2day](https://huggingface.co/datasets/huggan/night2day) ## Training procedure ``` # clone the repository git clone https://github.com/huggingface/community-events.git pip install . # change directory cd community-events/huggan/pytorch/pix2pix/ # define config accelerate config # launch training with required parameters accelerate launch train.py --checkpoint_interval 5 --dataset huggan/night2day --push_to_hub --model_name pix2pix-night2day --batch_size 128 --n_epochs 50 ``` ## Generated Images Here, * First Image Row: Input Image * Second Image Row: Generated Image * Third Image Row: Target Image ![image1](7000.png) ![image2](6500.png) ### BibTeX entry and citation info ```bibtex @article{pix2pix2017, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={CVPR}, year={2017} } ```
lilitket/20220414-201056	5eff65cdccdf7e273cd2184df8cc210a9800439d	2022-04-14T21:41:58.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	lilitket	null	lilitket/20220414-201056	null	transformers	36,870	Entry not found
lilitket/20220414-210228	5a8fff0ed7110a8cbb35c22d8bfb5bda86df4e08	2022-04-15T10:40:24.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	lilitket	null	lilitket/20220414-210228	null	transformers	36,871	Entry not found
huggan/projected_gan_impressionism	87948de0e87971987fa5a0244884d6ae32f03ec4	2022-04-18T21:29:45.000Z	[ "pytorch" ]	null	false	huggan	null	huggan/projected_gan_impressionism	null	null	36,872	dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
huggan/projected_gan_Hana_Hanak	df86ca72afc7bbd8956769fe14ac105d5dfbb076	2022-04-25T11:17:23.000Z	[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]	unconditional-image-generation	false	huggan	null	huggan/projected_gan_Hana_Hanak	null	pytorch	36,873	--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
huggan/projected_gan_color_field_hana	7f952f515856c2dfd298cceeffd4f908c434aa10	2022-04-25T11:16:59.000Z	[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]	unconditional-image-generation	false	huggan	null	huggan/projected_gan_color_field_hana	null	pytorch	36,874	--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
huggan/projected_gan_abstract_expressionism_hana	e43eb18a4185a4ea0e084e100c97466528b53fb6	2022-04-25T11:17:09.000Z	[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]	unconditional-image-generation	false	huggan	null	huggan/projected_gan_abstract_expressionism_hana	null	pytorch	36,875	--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>
mT0/mt0_11B_t0_train_ckpt_1012500	91af79ba953686b099e9df3c4de7fc109984dcd8	2022-04-16T03:40:02.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	mT0	null	mT0/mt0_11B_t0_train_ckpt_1012500	null	transformers	36,876	Entry not found
ntoldalagi/nick_asr_LID	061feb844ed87dd44535a706e1a809bc480c6c22	2022-04-21T10:47:23.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]	automatic-speech-recognition	false	ntoldalagi	null	ntoldalagi/nick_asr_LID	null	transformers	36,877	--- tags: - generated_from_trainer model-index: - name: nick_asr_LID results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nick_asr_LID This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 1.0 - Cer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 50.7955 \| 1.0 \| 458 \| 54.9678 \| 1.0 \| 1.0 \| \| 29.3958 \| 2.0 \| 916 \| 37.1618 \| 0.9928 \| 0.9887 \| \| 27.1413 \| 3.0 \| 1374 \| 32.5933 \| 0.9856 \| 0.9854 \| \| 24.0847 \| 4.0 \| 1832 \| 34.2804 \| 0.9784 \| 0.9447 \| \| 492.7757 \| 5.0 \| 2290 \| nan \| 0.9736 \| 0.9428 \| \| 0.0 \| 6.0 \| 2748 \| nan \| 1.0 \| 1.0 \| \| 0.0 \| 7.0 \| 3206 \| nan \| 1.0 \| 1.0 \| \| 0.0 \| 8.0 \| 3664 \| nan \| 1.0 \| 1.0 \| \| 0.0 \| 9.0 \| 4122 \| nan \| 1.0 \| 1.0 \| \| 0.0 \| 10.0 \| 4580 \| nan \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
mimicheng/codeparrot-ds-sample-2ep-14apr	a68a7876fa2c3a8e046a85485293025dff44b9ec	2022-04-15T18:40:03.000Z	[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	text-generation	false	mimicheng	null	mimicheng/codeparrot-ds-sample-2ep-14apr	null	transformers	36,878	--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-2ep-14apr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds-sample-2ep-14apr This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.1367 \| 0.37 \| 1000 \| 2.5592 \| \| 2.2049 \| 0.74 \| 2000 \| 2.0419 \| \| 1.8302 \| 1.11 \| 3000 \| 1.8245 \| \| 1.6214 \| 1.49 \| 4000 \| 1.6913 \| \| 1.5125 \| 1.86 \| 5000 \| 1.6319 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1
ntoldalagi/nick_asr_COMBO	9af031677926de377eda1c7af586fc88f26b1794	2022-04-24T01:24:22.000Z	[ "pytorch", "tensorboard", "wav2vec2", "transformers", "generated_from_trainer", "model-index" ]	null	false	ntoldalagi	null	ntoldalagi/nick_asr_COMBO	null	transformers	36,879	--- tags: - generated_from_trainer model-index: - name: nick_asr_COMBO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nick_asr_COMBO This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4313 - Wer: 0.6723 - Cer: 0.2408 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 0.0857 \| 1.0 \| 687 \| 1.3883 \| 0.7082 \| 0.2576 \| \| 0.0627 \| 2.0 \| 1374 \| 1.4099 \| 0.7076 \| 0.2561 \| \| 0.0697 \| 3.0 \| 2061 \| 1.3864 \| 0.6906 \| 0.2486 \| \| 0.0575 \| 4.0 \| 2748 \| 1.4356 \| 0.6906 \| 0.2455 \| \| 0.0552 \| 5.0 \| 3435 \| 1.4061 \| 0.6778 \| 0.2440 \| \| 0.0631 \| 6.0 \| 4122 \| 1.4541 \| 0.6839 \| 0.2444 \| \| 0.0418 \| 7.0 \| 4809 \| 1.4258 \| 0.6930 \| 0.2465 \| \| 0.0407 \| 8.0 \| 5496 \| 1.4193 \| 0.6809 \| 0.2451 \| \| 0.0487 \| 9.0 \| 6183 \| 1.4261 \| 0.6778 \| 0.2424 \| \| 0.0371 \| 10.0 \| 6870 \| 1.4313 \| 0.6723 \| 0.2408 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
Cristianoo/nezha-large-zh	3dd8e95e7311ad9b8cbdb83f4ea04de1f63aa9c2	2022-04-15T07:22:08.000Z	[ "pytorch" ]	null	false	Cristianoo	null	Cristianoo/nezha-large-zh	null	null	36,880	Entry not found
tau/false_large_t5_single_mask_5_1024_0.3_epoch1	9287fecd54a3f903ebe1c89ff33d20a10dde3b13	2022-04-15T07:47:45.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	tau	null	tau/false_large_t5_single_mask_5_1024_0.3_epoch1	null	transformers	36,881	Entry not found
tau/false_large_random_paraNone_sentNone_span0_multi_masks_5_1024_0.3_epoch1	48b4581e39f8d6672827e3c54a0a70cbea16b1fa	2022-04-15T08:21:14.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	tau	null	tau/false_large_random_paraNone_sentNone_span0_multi_masks_5_1024_0.3_epoch1	null	transformers	36,882	Entry not found
masakhane/afrimt5_fr_ewe_news	cc8f256e3e6ca0aadc5f756993888350e6d9734c	2022-04-15T09:01:20.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afrimt5_fr_ewe_news	null	transformers	36,883	--- license: afl-3.0 ---
masakhane/afrimt5_ewe_fr_news	e684c873016b389e702c7eb6649111e74bd7f5a3	2022-04-15T09:01:24.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afrimt5_ewe_fr_news	null	transformers	36,884	--- license: afl-3.0 ---
masakhane/afrimbart_fr_ewe_news	bd4f6a98462ff632ffe131c86239ebff041ce6aa	2022-04-15T09:01:37.000Z	[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afrimbart_fr_ewe_news	null	transformers	36,885	--- license: afl-3.0 ---
masakhane/afribyt5_ewe_fr_news	b4cb039988fab88d399b322c0c9175001ea4af76	2022-04-15T10:06:29.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afribyt5_ewe_fr_news	null	transformers	36,886	--- license: afl-3.0 ---
masakhane/afribyt5_fr_ewe_news	b156c367f02e69be1ecf61df61cca4d3564d622c	2022-04-15T10:06:25.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/afribyt5_fr_ewe_news	null	transformers	36,887	--- license: afl-3.0 ---
masakhane/byt5_fr_ewe_news	0fa6efaa879d2a909e5f850a7335ee435877f15d	2022-04-15T10:06:33.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/byt5_fr_ewe_news	null	transformers	36,888	--- license: afl-3.0 ---
masakhane/byt5_ewe_fr_news	e9bb988159362cdcab353a392a4177d52a98c611	2022-04-15T10:06:37.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/byt5_ewe_fr_news	null	transformers	36,889	--- license: afl-3.0 ---
masakhane/mt5_fr_ewe_news	666d3974bddc414c547a345fbae0f768520fa35c	2022-04-15T11:19:46.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/mt5_fr_ewe_news	null	transformers	36,890	--- license: afl-3.0 ---
masakhane/mt5_ewe_fr_news	1996364930ac66520871602c9640fb8ddfe21f8e	2022-04-15T11:19:38.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/mt5_ewe_fr_news	null	transformers	36,891	--- license: afl-3.0 ---
masakhane/mbart50_fr_ewe_news	6e559e03c4f762b5189b77c1c99847ec4c91a622	2022-04-15T11:19:26.000Z	[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/mbart50_fr_ewe_news	null	transformers	36,892	--- license: afl-3.0 ---
masakhane/mbart50_ewe_fr_news	535139d1b9482217748159aeb1242b58d018e976	2022-04-15T11:19:33.000Z	[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/mbart50_ewe_fr_news	null	transformers	36,893	--- license: afl-3.0 ---
masakhane/m2m100_418M_ewe_fr_news	404bb64c3207a0a5377c1a08c484180aa94e10ab	2022-04-15T13:28:00.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_ewe_fr_news	null	transformers	36,894	--- license: afl-3.0 ---
masakhane/m2m100_418M_ewe_fr_rel_news	cbaeff3d677296bad068633176cabfba44c61a80	2022-04-15T13:28:09.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_ewe_fr_rel_news	null	transformers	36,895	--- license: afl-3.0 ---
masakhane/m2m100_418M_fr_ewe_rel_news_ft	43c7d60c1276258d5184f899d5dd6f5bbb5d251a	2022-04-15T16:28:07.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_fr_ewe_rel_news_ft	null	transformers	36,896	--- license: afl-3.0 ---
masakhane/m2m100_418M_ewe_fr_rel_news_ft	25080abbe934676a34fe86a0efd01408ca6c53ec	2022-04-15T16:27:42.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_ewe_fr_rel_news_ft	null	transformers	36,897	--- license: afl-3.0 ---
masakhane/m2m100_418M_ewe_fr_rel_ft	a08335a51e2e84fa37eae59410fd80d31a27bbf8	2022-04-15T16:28:00.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_ewe_fr_rel_ft	null	transformers	36,898	--- license: afl-3.0 ---
masakhane/m2m100_418M_ewe_fr_rel	36fc61609692fa8fd4a60502a12fdb7d8acfad90	2022-04-15T17:39:03.000Z	[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	masakhane	null	masakhane/m2m100_418M_ewe_fr_rel	null	transformers	36,899	--- license: afl-3.0 ---

huggan/pix2pix-edge2shoes

c33cecfe57658244f753611943fdff043fd08e39

2022-04-15T04:28:29.000Z

[ "pytorch", "dataset:huggan/edge2shoes", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]

null

false

huggan

null

huggan/pix2pix-edge2shoes

0

null

36,800

--- tags: - huggan - gan datasets: - huggan/edge2shoes # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. ## Intended uses & limitations: Used for reconstruction of images from edges #### How to use ```python from torchvision.transforms import Compose, Resize, ToTensor, Normalize from PIL import Image from torchvision.utils import save_image import cv2 from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet transform = Compose( [ Resize((256, 256), Image.BICUBIC), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) model = GeneratorUNet.from_pretrained('huggan/pix2pix-edge2shoes) def predict_fn(img): inp = transform(img).unsqueeze(0) out = model(inp) save_image(out, 'out.png', normalize=True) return 'out.png' predict_fn(img) ``` #### Limitations and bias * Gives unrealistic colors in the image * Patterns in the edge drawing are not recognize properly ## Training data * [edges2shoes](https://huggingface.co/datasets/huggan/edges2shoes) ## Training procedure ``` # clone the repository git clone https://github.com/huggingface/community-events.git pip install . # change directory cd community-events/huggan/pytorch/pix2pix/ # define config accelerate config # launch training with required parameters accelerate launch train.py --checkpoint_interval 10 --dataset huggan/edges2shoes --push_to_hub --model_name pix2pix-edge2shoes --batch_size 128 --n_epochs 100 ``` ## Generated Images Here, * First Image Row: Input Sketch * Second Image Row: Generated Image * Third Image Row: Target Image ![image1](34000.png) ![image2](35000.png) ### BibTeX entry and citation info ```bibtex @article{pix2pix2017, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={CVPR}, year={2017} } ```

timhbach/Team_Gryffindor_NER

01e38cc5491a0a9c77c65ca6db7662aa38cc47d6

2022-04-29T21:13:30.000Z

[ "pytorch", "distilbert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

timhbach

null

timhbach/Team_Gryffindor_NER

0

null

transformers

36,801

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: Team_Gryffindor_NER results: [] ---  # Team-Gryffindor-distilbert-base-finetuned-NER-creditcardcontract-100epoch This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Credit card agreement dataset. It achieves the following results on the evaluation set: - Loss: 0.0470 - Precision: 0.7319 - Recall: 0.7064 - F1: 0.7190 - Accuracy: 0.9920 ## 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: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0113 | 0.33 | 500 | 0.0443 | 0.6547 | 0.7028 | 0.6779 | 0.9908 | | 0.0118 | 0.67 | 1000 | 0.0435 | 0.7207 | 0.6440 | 0.6802 | 0.9916 | | 0.013 | 1.0 | 1500 | 0.0449 | 0.7113 | 0.6826 | 0.6966 | 0.9918 | | 0.0113 | 1.34 | 2000 | 0.0434 | 0.7213 | 0.6697 | 0.6946 | 0.9915 | | 0.0121 | 1.67 | 2500 | 0.0467 | 0.6955 | 0.6789 | 0.6871 | 0.9914 | | 0.0125 | 2.01 | 3000 | 0.0417 | 0.7095 | 0.6991 | 0.7043 | 0.9920 | | 0.0106 | 2.34 | 3500 | 0.0437 | 0.7191 | 0.6624 | 0.6896 | 0.9918 | | 0.0114 | 2.68 | 4000 | 0.0468 | 0.7165 | 0.6679 | 0.6914 | 0.9920 | | 0.0125 | 3.01 | 4500 | 0.0431 | 0.6888 | 0.6862 | 0.6875 | 0.9917 | | 0.0107 | 3.35 | 5000 | 0.0446 | 0.7184 | 0.6459 | 0.6802 | 0.9913 | | 0.0096 | 3.68 | 5500 | 0.0485 | 0.6926 | 0.6532 | 0.6723 | 0.9912 | | 0.013 | 4.02 | 6000 | 0.0448 | 0.6134 | 0.6697 | 0.6404 | 0.9907 | | 0.0102 | 4.35 | 6500 | 0.0497 | 0.6895 | 0.6642 | 0.6766 | 0.9913 | | 0.0112 | 4.69 | 7000 | 0.0464 | 0.6759 | 0.6697 | 0.6728 | 0.9910 | | 0.0117 | 5.02 | 7500 | 0.0484 | 0.7451 | 0.6275 | 0.6813 | 0.9916 | | 0.0114 | 5.36 | 8000 | 0.0411 | 0.7086 | 0.6826 | 0.6953 | 0.9919 | | 0.0108 | 5.69 | 8500 | 0.0443 | 0.7041 | 0.6679 | 0.6855 | 0.9916 | | 0.0109 | 6.03 | 9000 | 0.0470 | 0.7228 | 0.6697 | 0.6952 | 0.9916 | | 0.0099 | 6.36 | 9500 | 0.0471 | 0.7253 | 0.6881 | 0.7062 | 0.9913 | | 0.0103 | 6.7 | 10000 | 0.0430 | 0.6986 | 0.7101 | 0.7043 | 0.9914 | | 0.0117 | 7.03 | 10500 | 0.0462 | 0.7327 | 0.6991 | 0.7155 | 0.9918 | | 0.0098 | 7.37 | 11000 | 0.0483 | 0.6910 | 0.6771 | 0.6840 | 0.9914 | | 0.0107 | 7.7 | 11500 | 0.0468 | 0.7189 | 0.6899 | 0.7041 | 0.9916 | | 0.0119 | 8.04 | 12000 | 0.0434 | 0.6970 | 0.6881 | 0.6925 | 0.9918 | | 0.0112 | 8.37 | 12500 | 0.0469 | 0.7007 | 0.6917 | 0.6962 | 0.9918 | | 0.011 | 8.71 | 13000 | 0.0469 | 0.6736 | 0.6514 | 0.6623 | 0.9914 | | 0.0101 | 9.04 | 13500 | 0.0451 | 0.6691 | 0.6606 | 0.6648 | 0.9913 | | 0.0099 | 9.38 | 14000 | 0.0462 | 0.7006 | 0.6826 | 0.6914 | 0.9918 | | 0.0107 | 9.71 | 14500 | 0.0444 | 0.6840 | 0.6752 | 0.6796 | 0.9915 | | 0.0118 | 10.05 | 15000 | 0.0457 | 0.7015 | 0.6771 | 0.6891 | 0.9918 | | 0.0102 | 10.38 | 15500 | 0.0500 | 0.7413 | 0.6679 | 0.7027 | 0.9919 | | 0.0107 | 10.72 | 16000 | 0.0470 | 0.7319 | 0.7064 | 0.7190 | 0.9920 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

huggan/fastgan-few-shot-shells

698843e8a0c88fb7384898ca5f09f82b55b26a93

2022-05-06T22:32:48.000Z

[ "pytorch", "dataset:huggan/few-shot-shells", "arxiv:2101.04775", "huggan", "gan", "unconditional-image-generation", "license:mit" ]

unconditional-image-generation

false

huggan

null

huggan/fastgan-few-shot-shells

0

null

36,802

--- tags: - huggan - gan - unconditional-image-generation datasets: - huggan/few-shot-shells # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # Generate shell image using FastGAN ## Model description [FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets. This model was trained on a dataset of 64 high-quality images of Shells. #### How to use ```python # Clone this model git clone https://huggingface.co/huggan/fastgan-few-shot-shells/ def load_generator(model_name_or_path): generator = Generator(in_channels=256, out_channels=3) generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3) _ = generator.eval() return generator def _denormalize(input: torch.Tensor) -> torch.Tensor: return (input * 127.5) + 127.5 # Load generator generator = load_generator("huggan/fastgan-few-shot-shells") # Generate a random noise image noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0) with torch.no_grad(): gan_images, _ = generator(noise) gan_images = _denormalize(gan_images.detach()) save_image(gan_images, "sample.png", nrow=1, normalize=True) ``` #### Limitations and bias * Converge faster and better with small datasets (less than 1000 samples) ## Training data [fastgan-few-shot-shells](https://huggingface.co/datasets/huggan/few-shot-shells) ## Generated Images ![Example image](example.png) ### BibTeX entry and citation info ```bibtex @article{FastGAN, title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis}, author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal}, journal={ICLR}, year={2021} } ```

yogi/autotrain-amazon_text_sum-730222226

6562d546d0ab9d7b5484e755a88b0bd2b7e78f22

2022-04-12T09:08:15.000Z

[ "pytorch", "t5", "text2text-generation", "en", "dataset:yogi/autotrain-data-amazon_text_sum", "transformers", "autotrain", "co2_eq_emissions", "autotrain_compatible" ]

text2text-generation

false

yogi

null

yogi/autotrain-amazon_text_sum-730222226

0

null

transformers

36,803

--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - yogi/autotrain-data-amazon_text_sum co2_eq_emissions: 2986.6520132805163 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 730222226 - CO2 Emissions (in grams): 2986.6520132805163 ## Validation Metrics - Loss: 2.682709217071533 - Rouge1: 19.6069 - Rouge2: 7.3367 - RougeL: 19.2706 - RougeLsum: 19.286 - Gen Len: 5.5731 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/yogi/autotrain-amazon_text_sum-730222226 ```

maesneako/gpt2-en-maptask-finetuned

90982e36897723928853d8b84b855bb00678b840

2022-04-11T16:24:18.000Z

[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

text-generation

false

maesneako

null

maesneako/gpt2-en-maptask-finetuned

0

null

transformers

36,804

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-en-maptask-finetuned results: [] ---  # gpt2-en-maptask-finetuned This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - 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: 100 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

maveriq/mybert-base-32k

e32e57cb0b128c3576d97db67d3f8212adfee131

2022-04-11T17:35:29.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

maveriq

null

maveriq/mybert-base-32k

0

null

transformers

36,805

Entry not found

tonyalves/ft-pt-br-local

19ebdfb27cc4049ab271241aa46c3ad2ef8f5faf

2022-04-11T20:39:07.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

tonyalves

null

tonyalves/ft-pt-br-local

0

null

transformers

36,806

--- license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer model-index: - name: ft-pt-br-local results: [] ---  # ft-pt-br-local This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.1+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6

maveriq/mybert-mini-60k

4e2290e60b2b9d2e456f03213017739ed8af3702

2022-04-11T18:18:51.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

maveriq

null

maveriq/mybert-mini-60k

0

null

transformers

36,807

Entry not found

maveriq/lingbert-mini-60k

6fda7bd34a8e004d1d28fa6ba6152c88a4ef83c4

2022-04-11T18:21:22.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

maveriq

null

maveriq/lingbert-mini-60k

0

null

transformers

36,808

Entry not found

maveriq/mybert-mini-172k

452439b80ef78cea8f510d6626218073dd352994

2022-04-11T18:58:35.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

maveriq

null

maveriq/mybert-mini-172k

0

null

transformers

36,809

Entry not found

irenelizihui/MarianMT_UFAL_en_es

711f4c9569ed0f44ad622075f63cbcd79c799524

2022-04-11T23:04:22.000Z

[ "pytorch", "marian", "text2text-generation", "transformers", "license:wtfpl", "autotrain_compatible" ]

text2text-generation

false

irenelizihui

null

irenelizihui/MarianMT_UFAL_en_es

0

1

transformers

36,810

--- license: wtfpl --- MarianMT trained on the UFAL dataset: English to Spanish Machine Translation model.

irenelizihui/MarianMT_UFAL_en_ro

57a6309a36d6f39f6341631c4d6f25e7c83e0e2d

2022-04-11T23:03:23.000Z

[ "pytorch", "marian", "text2text-generation", "transformers", "license:wtfpl", "autotrain_compatible" ]

text2text-generation

false

irenelizihui

null

irenelizihui/MarianMT_UFAL_en_ro

0

1

transformers

36,811

--- license: wtfpl --- UFAL English to Romainian Machine Translation Model based on MarianMT model.

huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings

7894ac79f51f6fa3db4130c4954307ae0f593384

2022-04-11T22:44:40.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings

0

null

transformers

36,812

--- language: en thumbnail: http://www.huggingtweets.com/angrymemorys-oldandtoothless-sadboi666_-witheredstrings/1649717075201/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506323689456947207/xBvvxyQr_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511852580216967169/b1Aiv2t3_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000610482331/8808c2f408b97fe3646f2dca86441506_400x400.jpeg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">makeouthill & VacuumF & Jason Hendricks & Angry Memories</div> <div style="text-align: center; font-size: 14px;">@angrymemorys-oldandtoothless-sadboi666_-witheredstrings</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from makeouthill & VacuumF & Jason Hendricks & Angry Memories. | Data | makeouthill | VacuumF | Jason Hendricks | Angry Memories | | --- | --- | --- | --- | --- | | Tweets downloaded | 321 | 425 | 3250 | 3199 | | Retweets | 34 | 0 | 0 | 941 | | Short tweets | 49 | 31 | 0 | 1110 | | Tweets kept | 238 | 394 | 3250 | 1148 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nh2rd94/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @angrymemorys-oldandtoothless-sadboi666_-witheredstrings's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

nates/LER-roberta

bc3607faa3cf871f02f36f3d6fa65f2e326b3a58

2022-04-12T03:28:11.000Z

[ "pytorch", "roberta", "token-classification", "transformers", "license:cc-by-4.0", "autotrain_compatible" ]

token-classification

false

nates

null

nates/LER-roberta

0

null

transformers

36,813

--- license: cc-by-4.0 widget: - text: This house was let out in tiny tenements and was inhabited by working people of all kinds--tailors, locksmiths, cooks, Germans ofsorts, girls picking up a living as best they could, petty clerks, etc. example_title: "Crime and Punishment" - text: Quixote having got on his back and the duke mounted a fine horse, they placed the duchess in the middle and set out for the castle. example_title: "Don Quixote" - text: The noble carriage of this gentleman, for whom he believed himself to be engaged, had won Planchet—that was the name of the Picard. example_title: "The Three Musketeers" --- ### Description A `roberta-base` model which has been fine tuned for token classification on the [LitBank](https://github.com/dbamman/litbank) dataset. ### Intended Use This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from [ACE 2005](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) - Person (PER) - ORG - GPE - LOC - VEH - FAC Due to the fine-tuning domain, it is expected to work best with literary sentences.

junaidamk/mlner-mlwptok-muril

aed40ea46acad1f57295092187f1dc9810d64779

2022-04-12T08:57:48.000Z

[ "pytorch", "bert", "token-classification", "dataset:mlner2021", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

junaidamk

null

junaidamk/mlner-mlwptok-muril

0

null

transformers

36,814

--- license: apache-2.0 tags: - generated_from_trainer datasets: - mlner2021 metrics: - precision - recall - f1 - accuracy model-index: - name: mlner-mlwptok-muril results: - task: name: Token Classification type: token-classification dataset: name: mlner2021 type: mlner2021 args: MLNER2021 metrics: - name: Precision type: precision value: 0.0 - name: Recall type: recall value: 0.0 - name: F1 type: f1 value: 0.0 - name: Accuracy type: accuracy value: 0.8112759262826688 ---  # mlner-mlwptok-muril This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the mlner2021 dataset. It achieves the following results on the evaluation set: - Loss: 0.8331 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8113 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:| | 1.447 | 1.0 | 1389 | 0.9396 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.898 | 2.0 | 2778 | 0.8883 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.859 | 3.0 | 4167 | 0.8721 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.8302 | 4.0 | 5556 | 0.8666 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.8165 | 5.0 | 6945 | 0.8403 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.8143 | 6.0 | 8334 | 0.8376 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.8034 | 7.0 | 9723 | 0.8393 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.7766 | 8.0 | 11112 | 0.8383 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.794 | 9.0 | 12501 | 0.8346 | 0.0 | 0.0 | 0.0 | 0.8113 | | 0.7858 | 10.0 | 13890 | 0.8331 | 0.0 | 0.0 | 0.0 | 0.8113 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

nnair25/Gram-Vaani-Harveen-Chadda-Fine-Tuning

e3fe71a121f576da1c402418e825d13403b5d330

2022-04-13T05:52:25.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

automatic-speech-recognition

false

nnair25

null

nnair25/Gram-Vaani-Harveen-Chadda-Fine-Tuning

0

null

transformers

36,815

--- license: mit tags: - generated_from_trainer model-index: name: Gram-Vaani-Harveen-Chadda-Fine-Tuning ---  # Gram-Vaani-Harveen-Chadda-Fine-Tuning This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8934 - Wer: 0.359 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-----:| | 4.5558 | 21.05 | 400 | 0.8934 | 0.359 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

johnowhitaker/mingpt-char-shakespeare

0687519135b70307e60ed5369419e1894660c49c

2022-04-12T07:56:59.000Z

[ "pytorch" ]

null

false

johnowhitaker

null

johnowhitaker/mingpt-char-shakespeare

0

null

36,816

Entry not found

mimi/book_data

9a4ec64cd99027097b9bac609664a53cdb72a5ac

2022-05-12T01:50:23.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

mimi

null

mimi/book_data

0

null

transformers

36,817

Entry not found

Chris1/CycleGAN_punk2apes

7db44664d29b484eff8f9dc1094b7e82e8132384

2022-04-12T08:10:37.000Z

[ "pytorch", "huggan", "gan", "license:mit" ]

null

false

Chris1

null

Chris1/CycleGAN_punk2apes

0

null

36,818

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

theojolliffe/opus-mt-en-ro-finetuned-en-to-cy

2392a76de863b287ce99d91cac93c75b479d01e9

2022-04-13T11:10:10.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

theojolliffe

null

theojolliffe/opus-mt-en-ro-finetuned-en-to-cy

0

null

transformers

36,819

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: opus-mt-en-ro-finetuned-en-to-cy results: [] ---  # opus-mt-en-ro-finetuned-en-to-cy This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

huggingtweets/nv1t

1db943b3f55a42605435fed5f7e017ffc23039c1

2022-04-12T10:24:30.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/nv1t

0

null

transformers

36,820

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/521651470832136193/8-XdhaC7_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">nuit</div> <div style="text-align: center; font-size: 14px;">@nv1t</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from nuit. | Data | nuit | | --- | --- | | Tweets downloaded | 3244 | | Retweets | 520 | | Short tweets | 127 | | Tweets kept | 2597 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3aj0ls3h/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nv1t's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n9kpomf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n9kpomf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nv1t') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Chris1/ape2punk_epoch80

f42c99e06db78f53a9460695268c1ad9ee52b137

2022-04-12T11:21:48.000Z

[ "pytorch", "huggan", "gan", "license:mit" ]

null

false

Chris1

null

Chris1/ape2punk_epoch80

0

null

36,821

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

huggingnft/cryptopunks__2__bored-apes-yacht-club

aa592734173938947c65191aca14a719332ab0dd

2022-04-25T16:05:53.000Z

[ "pytorch", "arxiv:1703.10593", "huggan", "gan", "image-to-image", "huggingnft", "nft", "image", "images", "license:mit" ]

image-to-image

false

huggingnft

null

huggingnft/cryptopunks__2__bored-apes-yacht-club

0

1

null

36,822

--- tags: - huggan - gan - image-to-image - huggingnft - nft - image - images # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # CycleGAN for unpaired image-to-image translation. ## Model description CycleGAN for unpaired image-to-image translation. Given two image domains A and B, the following components are trained end2end to translate between such domains: - A generator A to B, named G_AB conditioned on an image from A - A generator B to A, named G_BA conditioned on an image from B - A domain classifier D_A, associated with G_AB - A domain classifier D_B, associated with G_BA At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A. In the general setting, this technique provides style transfer functionalities between the selected image domains A and B. This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA. Under these framework, these aspects have been used to perform style transfer between NFT collections. A collection is selected as domain A, another one as domain B and the CycleGAN provides forward and backward translation between A and B. This has showed to allows high quality translation even in absence of paired sample-ground-truth data. In particular, the model performs well with stationary backgrounds (no drastic texture changes in the appearance of backgrounds) as it is capable of recognizing the attributes of each of the elements of an NFT collections. An attribute can be a variation in type of dressed fashion items such as sunglasses, earrings, clothes and also face or body attributes with respect to a common template model of the given NFT collection). ## Intended uses & limitations #### How to use ```python import torch from PIL import Image from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet from torchvision import transforms as T from torchvision.transforms import Compose, Resize, ToTensor, Normalize from torchvision.utils import make_grid from huggingface_hub import hf_hub_download, file_download from accelerate import Accelerator import json def load_lightweight_model(model_name): file_path = file_download.hf_hub_download( repo_id=model_name, filename="config.json" ) config = json.loads(open(file_path).read()) organization_name, name = model_name.split("/") model = Trainer(**config, organization_name=organization_name, name=name) model.load(use_cpu=True) model.accelerator = Accelerator() return model def get_concat_h(im1, im2): dst = Image.new('RGB', (im1.width + im2.width, im1.height)) dst.paste(im1, (0, 0)) dst.paste(im2, (im1.width, 0)) return dst n_channels = 3 image_size = 256 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) # load the translation model from source to target images: source will be generated by a separate Lightweight GAN, w # while the target images are the result of the translation applied by the GeneratorResnet to the generated source images. # Hence, given the source domain A and target domain B, # B = Translator(GAN(A)) translator = GeneratorResNet.from_pretrained(f'huggingnft/{model_name}', input_shape=(n_channels, image_size, image_size), num_residual_blocks=9) # sample noise that is used to generate source images by the z = torch.randn(nrows, 100, 1, 1) # load the GAN generator of source images that will be translated by the translation model model = load_lightweight_model(f"huggingnft/{model_name.split('__2__')[0]}") collectionA = model.generate_app( num=timestamped_filename(), nrow=nrows, checkpoint=-1, types="default" )[1] # resize to translator model input shape resize = T.Resize((256, 256)) input = resize(collectionA) # translate the resized collectionA to collectionB collectionB = translator(input) out_transform = T.ToPILImage() results = [] for collA_image, collB_image in zip(input, collectionB): results.append( get_concat_h(out_transform(make_grid(collA_image, nrow=1, normalize=True)), out_transform(make_grid(collB_image, nrow=1, normalize=True))) ) ``` #### Limitations and bias Translation between collections provides exceptional output images in the case of NFT collections that portray subjects in the same way. If the backgrounds vary too much within either of the collections, performance degrades or many more training iterations re required to achieve acceptable results. ## Training data The CycleGAN model is trained on an unpaired dataset of samples from two selected NFT collections: colle tionA and collectionB. To this end, two collections are loaded by means of the function load_dataset in the huggingface library, as follows. A list of all available collections is available at [huggingNFT](https://huggingface.co/huggingnft) ```python from datasets import load_dataset collectionA = load_dataset("huggingnft/COLLECTION_A") collectionB = load_dataset("huggingnft/COLLECTION_B") ``` ## Training procedure #### Preprocessing The following transformations are applied to each input sample of collectionA and collectionB. The input size is fixed to RGB images of height, width = 256, 256 ```python n_channels = 3 image_size = 256 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) ``` #### Hardware The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs. #### Hyperparameters The following configuration has been kept fixed for all translation models: - learning rate 0.0002 - number of epochs 200 - learning rate decay activation at epoch 80 - number of residual blocks of the cyclegan 9 - cycle loss weight 10.0 - identity loss weight 5.0 - optimizer ADAM with beta1 0.5 and beta2 0.999 - batch size 8 - NO mixed precision training ## Eval results #### Training reports [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/CycleGAN-training-report--VmlldzoxODUxNzQz?accessToken=vueurpbhd2i8n347j880yakggs0sqdf7u0hpz3bpfsbrxcmk1jk4obg18f6wfk9w) [Boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/CycleGAN-training-report--VmlldzoxODUxNzg4?accessToken=jpyviwn7kdf5216ycrthwp6l8t3heb0lt8djt7dz12guu64qnpdh3ekecfcnoahu) #### Generated Images In the provided images, row0 and row2 represent real images from the respective collections. Row1 is the translation of the immediate above images in row0 by means of the G_AB translation model. Row3 is the translation of the immediate above images in row2 by means of the G_BA translation model. Visualization over the training iterations for [boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/Shared-panel-22-04-15-08-04-99--VmlldzoxODQ0MDI3?accessToken=45m3kxex5m3rpev3s6vmrv69k3u9p9uxcsp2k90wvbxwxzlqbqjqlnmgpl9265c0) Visualization over the training iterations for [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/Shared-panel-22-04-17-11-04-83--VmlldzoxODUxNjk5?accessToken=o25si6nflp2xst649vt6ayt56bnb95mxmngt1ieso091j2oazmqnwaf4h78vc2tu) ### References ```bibtex @misc{https://doi.org/10.48550/arxiv.1703.10593, doi = {10.48550/ARXIV.1703.10593}, url = {https://arxiv.org/abs/1703.10593}, author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks}, publisher = {arXiv}, year = {2017}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk, Christian Cancedda} year=2022 } ```

Chris1/real2sim

e14a0dac96ae97890973a5223a9d46ebe882e411

2022-04-12T11:33:32.000Z

[ "pytorch", "huggan", "gan", "license:mit" ]

null

false

Chris1

null

Chris1/real2sim

0

null

36,823

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

huggan/sim2real_cyclegan

0a60c15105b38055c6897be6d91f142fd9a903e1

2022-05-05T10:55:19.000Z

[ "pytorch", "arxiv:2104.13395", "arxiv:1703.10593", "conditional-image-generation", "image-to-image", "gan", "cyclegan", "license:mit" ]

image-to-image

false

huggan

null

huggan/sim2real_cyclegan

0

1

null

36,824

--- tags: - conditional-image-generation - image-to-image - gan - cyclegan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # CycleGAN for unpaired image-to-image translation. ## Model description CycleGAN for unpaired image-to-image translation. Given two image domains A and B, the following components are trained end2end to translate between such domains: - A generator A to B, named G_AB conditioned on an image from A - A generator B to A, named G_BA conditioned on an image from B - A domain classifier D_A, associated with G_AB - A domain classifier D_B, associated with G_BA At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A. In the general setting, this technique provides style transfer functionalities between the selected image domains A and B. This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA. Under these framework, these aspects have been used to perform style transfer between synthetic data obtained from a simulated driving dataset, GTA5, and the real driving data from Cityscapes. This is of paramount importance to develop autonomous driving perception deep learning models, as this allows to generate synthetic data with automatic annotations which resembles real world images, without requiring the intervention of a human annotator. This is fundamental because a manual annotator has been shown to require 1.5 to 3.3 hours to create semantic and instance segmentation masks for a single images. These have been provided in the original [cityscapes paper (Cordts et al 2016)](https://arxiv.org/abs/2104.13395) and the [adverse condition dataset (Sakaridis et al. 2021)](https://arxiv.org/abs/2104.13395) paper. Hence the CycleGAN provides forward and backward translation between synthetic and real world data. This has showed to allows high quality translation even in absence of paired sample-ground-truth data. The idea behind such model is that as the synthetic data distribution gets closer to the real world one, deep models do not suffer from degraded performance due to the domain shift issue. A broad literature is available on the minimization of the domain shift, under the research branch of domain adaptation and transfer learning, of which image translation models provide an alternative approach ## Intended uses & limitations #### How to use ```python import os from PIL import Image from torchvision import transforms as T from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip from torchvision.utils import make_grid from torch.utils.data import DataLoader from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet import torch.nn as nn import torch import gradio as gr import glob def pred_pipeline(img, transforms): orig_shape = img.shape input = transforms(img) input = input.unsqueeze(0) output = model(input) out_img = make_grid(output,#.detach().cpu(), nrow=1, normalize=True) out_transform = Compose([ T.Resize(orig_shape[:2]), T.ToPILImage() ]) return out_transform(out_img) n_channels = 3 image_size = 512 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) model = GeneratorResNet.from_pretrained('Chris1/sim2real', input_shape=(n_channels, image_size, image_size), num_residual_blocks=9) real_images = model(synthetic_images) ``` #### Limitations and bias Due to the absence of paired data, some background parts of the synthetic images are seldom wrongly translated, e.g. sky is translated to vegetation. Additional pretext tasks in parallel to the discriminative classifier of fake and real samples could improve the result. One easy improvement is the use of an additional parallel branch that performs semantic segmentation on the synthetic data, in order to learn features which are common to sky and vegetation, thus disentangling their representations as separate classes. ## Training data The CycleGAN model is trained on an unpaired dataset of samples from synthetic and real driving data, respectively from the GTA5 and Cityscapes datasets. To this end, the synthetic-to-real dataset can be loaded by means of the function load_dataset in the huggingface library, as follows. ```python from datasets import load_dataset unpaired_dataset = load_dataset("huggan/sim2real_gta5_to_cityscapes") ``` This dataset contains two columns, imageA and imageB representing respectively the GTA5 and Cityscapes data. Due to the fact that the two columns have to be of the same length, GTA5 is subsampled in order to reach the same number of samples provided by the Cityscapes train split (2975) ## Training procedure #### Preprocessing The following transformations are applied to each input sample of synthetic and real data. The input size is fixed to RGB images of height, width = 512, 512. This choice has been made in order to limit the impact of upsampling the translated images to higher resolutions. ```python n_channels = 3 image_size = 512 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) ``` #### Hardware The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs. #### Hyperparameters The following configuration has been kept fixed for all translation models: - learning rate 0.0002 - number of epochs 200 - learning rate decay activation at epoch 100 - number of residual blocks of the cyclegan 9 - image size 512x512 - number of channels=3 - cycle loss weight 10.0 - identity loss weight 5.0 - optimizer ADAM with beta1 0.5 and beta2 0.999 - batch size 8 - NO mixed precision training ## Eval results #### Generated Images In the provided images, row0 and row2 represent the synthetic and real images from the respective datasets. Row1 is the translation of the immediate above images in row0(synthetic) by means of the G_AB translation model, to the real world style. Row3 is the translation of the immediate above images in row2(real) by means of the G_BA translation model, to the synthetic world style. Visualization over the training iterations for [synthetic (GTA5) to real (Cityscapes) translation](https://wandb.ai/chris1nexus/experiments_cyclegan_s2r_hp_opt--10/reports/CycleGAN-sim2real-training-results--VmlldzoxODUyNTk4?accessToken=tow3v4vp02aurzodedrdht15ig1cx69v5mited4dm8bgnup0z192wri0xtftaeqj) ### References ```bibtex @misc{https://doi.org/10.48550/arxiv.1703.10593, doi = {10.48550/ARXIV.1703.10593}, url = {https://arxiv.org/abs/1703.10593}, author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks}, publisher = {arXiv}, year = {2017}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

sanchit-gandhi/tiny-random-bart

31143b0f16b739cbadec03b5d2a0025fba90918c

2022-04-12T13:32:12.000Z

[ "pytorch", "jax", "bart", "feature-extraction", "transformers" ]

feature-extraction

false

sanchit-gandhi

null

sanchit-gandhi/tiny-random-bart

0

null

transformers

36,825

Entry not found

Guldeniz/pix2pix_maps

a8ef56fd47c2d8f9b245daa1bd71eceb4c7ce391

2022-04-12T17:44:34.000Z

[ "pytorch", "huggan", "gan", "license:mit" ]

null

false

Guldeniz

null

Guldeniz/pix2pix_maps

0

null

36,826

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

huggan/pix2pix-maps-test

39acb13bdb610983eeb8c10c5edbc8bc2d700a13

2022-04-12T15:57:14.000Z

[ "pytorch", "huggan", "gan", "license:mit" ]

null

false

huggan

null

huggan/pix2pix-maps-test

0

null

36,827

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

nielsr/test

7717b62c60f0e314a56fb96fe9d98975826176a5

2022-04-12T15:20:09.000Z

[ "pytorch" ]

null

false

nielsr

null

nielsr/test

0

null

36,828

Entry not found

rajat99/SSL-Harveen-Chadda-Fine-Tuning

37d5efecb32d113c24e8eb9b2263efda5b8aa737

2022-04-12T22:07:20.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

automatic-speech-recognition

false

rajat99

null

rajat99/SSL-Harveen-Chadda-Fine-Tuning

0

null

transformers

36,829

--- license: mit tags: - generated_from_trainer model-index: name: SSL-Harveen-Chadda-Fine-Tuning ---  # SSL-Harveen-Chadda-Fine-Tuning This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0032 - Wer: 0.1008 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.3793 | 4.17 | 400 | 0.5347 | 0.2584 | | 0.2137 | 8.33 | 800 | 0.8339 | 0.2664 | | 0.1282 | 12.5 | 1200 | 0.1785 | 0.1414 | | 0.0698 | 16.66 | 1600 | 0.0135 | 0.1066 | | 0.0354 | 20.83 | 2000 | 0.0351 | 0.1179 | | 0.0212 | 24.99 | 2400 | 0.0104 | 0.1035 | | 0.0066 | 29.17 | 2800 | 0.0032 | 0.1008 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

huggan/pix2pix-uavid-15

bfa54b692d5d897bf7cb4181db1535e5ce303049

2022-04-15T13:45:13.000Z

[ "pytorch", "dataset:arakesh/uavid-15-hq-mixedres", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]

null

false

huggan

null

huggan/pix2pix-uavid-15

0

null

36,830

--- tags: - huggan - gan datasets: - arakesh/uavid-15-hq-mixedres # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. ## Intended uses & limitations: Used for reconstruction of images from edges #### How to use ```python from torchvision.transforms import Compose, Resize, ToTensor, Normalize from PIL import Image from torchvision.utils import save_image import cv2 from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet transform = Compose( [ Resize((256, 256), Image.BICUBIC), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) model = GeneratorUNet.from_pretrained('huggan/pix2pix-uavid-15) def predict_fn(img): inp = transform(img).unsqueeze(0) out = model(inp) save_image(out, 'out.png', normalize=True) return 'out.png' predict_fn(img) ``` #### Limitations and bias * Gives unrealistic colors in the image ## Training data * [edges2shoes](https://huggingface.co/datasets/huggan/edges2shoes) ## Training procedure ``` # clone the repository git clone https://github.com/huggingface/community-events.git pip install . # change directory cd community-events/huggan/pytorch/pix2pix/ # define config accelerate config # launch training with required parameters accelerate launch train.py --checkpoint_interval 1 --dataset arakesh/uavid-15-hq-mixedres --push_to_hub --model_name pix2pix-uavid-15 --batch_size 2 --n_epochs 50 --image_size 1024 --sample_interval 500 ``` ## Generated Images Here, * First Image Row: Input Image * Second Image Row: Generated Image * Third Image Row: Target Image ![image1](34000.png) ![image2](35000.png) ### BibTeX entry and citation info ```bibtex @article{pix2pix2017, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={CVPR}, year={2017} } ```

lilitket/20220412-203254

7988a597cdefd4bdcf9dadaae38071a50cc43f42

2022-04-13T05:04:25.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

lilitket

null

lilitket/20220412-203254

0

null

transformers

36,831

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: 20220412-203254 results: [] ---  # 20220412-203254 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 5.0428 - Wer: 1.0019 ## 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-06 - train_batch_size: 1 - 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: 2000 - num_epochs: 1200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:------:|:---------------:|:------:| | 16.9455 | 1.5 | 200 | 16.4676 | 1.2534 | | 15.444 | 3.01 | 400 | 14.1207 | 1.0 | | 9.5452 | 4.51 | 600 | 8.4030 | 1.0 | | 6.2565 | 6.02 | 800 | 6.5233 | 1.0 | | 5.2827 | 7.52 | 1000 | 5.6058 | 1.0 | | 4.7652 | 9.02 | 1200 | 4.9765 | 1.0 | | 4.3803 | 10.53 | 1400 | 4.4565 | 1.0 | | 4.0005 | 12.03 | 1600 | 4.0224 | 1.0 | | 3.7041 | 13.53 | 1800 | 3.6903 | 1.0 | | 3.4991 | 15.04 | 2000 | 3.4642 | 1.0 | | 3.34 | 16.54 | 2200 | 3.3425 | 1.0 | | 3.2352 | 18.05 | 2400 | 3.2617 | 1.0 | | 3.1867 | 19.55 | 2600 | 3.2358 | 1.0 | | 3.161 | 21.05 | 2800 | 3.2289 | 1.0 | | 3.145 | 22.56 | 3000 | 3.2023 | 1.0 | | 3.1203 | 24.06 | 3200 | 3.1964 | 1.0 | | 3.1109 | 25.56 | 3400 | 3.1844 | 1.0 | | 3.0958 | 27.07 | 3600 | 3.1839 | 1.0 | | 3.0732 | 28.57 | 3800 | 3.2058 | 1.0 | | 3.0535 | 30.08 | 4000 | 3.1843 | 1.0 | | 3.0243 | 31.58 | 4200 | 3.1992 | 1.0 | | 2.9829 | 33.08 | 4400 | 3.2019 | 1.0 | | 2.9219 | 34.59 | 4600 | 3.2346 | 1.0 | | 2.8313 | 36.09 | 4800 | 3.2781 | 1.0 | | 2.7186 | 37.59 | 5000 | 3.3056 | 1.0 | | 2.5745 | 39.1 | 5200 | 3.3554 | 1.0 | | 2.4028 | 40.6 | 5400 | 3.4331 | 1.0 | | 2.2645 | 42.11 | 5600 | 3.4418 | 1.0 | | 2.1303 | 43.61 | 5800 | 3.5584 | 1.0 | | 2.0257 | 45.11 | 6000 | 3.5943 | 1.0 | | 1.9223 | 46.62 | 6200 | 3.6767 | 1.0 | | 1.8344 | 48.12 | 6400 | 3.7363 | 1.0 | | 1.7574 | 49.62 | 6600 | 3.8921 | 1.0 | | 1.67 | 51.13 | 6800 | 3.9054 | 1.0 | | 1.6118 | 52.63 | 7000 | 4.0352 | 1.0 | | 1.5372 | 54.14 | 7200 | 3.9742 | 1.0 | | 1.4846 | 55.64 | 7400 | 4.1078 | 1.0 | | 1.4093 | 57.14 | 7600 | 4.1705 | 1.0 | | 1.3379 | 58.65 | 7800 | 4.2737 | 1.0 | | 1.28 | 60.15 | 8000 | 4.3662 | 1.0 | | 1.2268 | 61.65 | 8200 | 4.4278 | 1.0 | | 1.1641 | 63.16 | 8400 | 4.4831 | 1.0 | | 1.1058 | 64.66 | 8600 | 4.5354 | 1.0 | | 1.0596 | 66.17 | 8800 | 4.5983 | 1.0 | | 0.9953 | 67.67 | 9000 | 4.7143 | 1.0 | | 0.9406 | 69.17 | 9200 | 4.8536 | 1.0 | | 0.9022 | 70.68 | 9400 | 4.7732 | 1.0 | | 0.8551 | 72.18 | 9600 | 4.8929 | 1.0 | | 0.8103 | 73.68 | 9800 | 4.9513 | 1.0 | | 0.7665 | 75.19 | 10000 | 4.9530 | 1.0 | | 0.7215 | 76.69 | 10200 | 5.1471 | 1.0 | | 0.6906 | 78.2 | 10400 | 5.2295 | 1.0 | | 0.6354 | 79.7 | 10600 | 5.1287 | 1.0 | | 0.6196 | 81.2 | 10800 | 5.2081 | 1.0 | | 0.6026 | 82.71 | 11000 | 5.4323 | 1.0 | | 0.5726 | 84.21 | 11200 | 5.3907 | 1.0 | | 0.5348 | 85.71 | 11400 | 5.5669 | 1.0 | | 0.5344 | 87.22 | 11600 | 5.5685 | 1.0 | | 0.4849 | 88.72 | 11800 | 5.5814 | 1.0 | | 0.4689 | 90.23 | 12000 | 5.6186 | 1.0 | | 0.4646 | 91.73 | 12200 | 5.4834 | 1.0 | | 0.4266 | 93.23 | 12400 | 5.6463 | 1.0 | | 0.4424 | 94.74 | 12600 | 5.6562 | 1.0 | | 0.3865 | 96.24 | 12800 | 5.7463 | 1.0 | | 0.3914 | 97.74 | 13000 | 5.7014 | 1.0 | | 0.3661 | 99.25 | 13200 | 5.7543 | 1.0 | | 0.3582 | 100.75 | 13400 | 5.9172 | 1.0 | | 0.3571 | 102.26 | 13600 | 5.5968 | 1.0 | | 0.3343 | 103.76 | 13800 | 5.3691 | 1.0 | | 0.3123 | 105.26 | 14000 | 5.8917 | 1.0 | | 0.3089 | 106.77 | 14200 | 5.8054 | 1.0 | | 0.3078 | 108.27 | 14400 | 5.9066 | 1.0 | | 0.3076 | 109.77 | 14600 | 5.7379 | 1.0 | | 0.2924 | 111.28 | 14800 | 5.7931 | 1.0 | | 0.2925 | 112.78 | 15000 | 5.9529 | 1.0 | | 0.2839 | 114.29 | 15200 | 5.9881 | 1.0 | | 0.2599 | 115.79 | 15400 | 6.0081 | 1.0 | | 0.2685 | 117.29 | 15600 | 6.1049 | 1.0 | | 0.2557 | 118.8 | 15800 | 6.1154 | 1.0 | | 0.2688 | 120.3 | 16000 | 5.9336 | 1.0 | | 0.2422 | 121.8 | 16200 | 6.0492 | 1.0 | | 0.2408 | 123.31 | 16400 | 6.3155 | 1.0 | | 0.2423 | 124.81 | 16600 | 6.3437 | 1.0 | | 0.2421 | 126.32 | 16800 | 6.0979 | 1.0 | | 0.2212 | 127.82 | 17000 | 5.5551 | 1.0 | | 0.2239 | 129.32 | 17200 | 5.9007 | 1.0 | | 0.2101 | 130.83 | 17400 | 6.0142 | 1.0 | | 0.2097 | 132.33 | 17600 | 5.8984 | 1.0 | | 0.2064 | 133.83 | 17800 | 5.9705 | 1.0 | | 0.1898 | 135.34 | 18000 | 5.9915 | 1.0 | | 0.2053 | 136.84 | 18200 | 6.1079 | 1.0 | | 0.1822 | 138.35 | 18400 | 6.1324 | 1.0 | | 0.1867 | 139.85 | 18600 | 6.1122 | 1.0 | | 0.1831 | 141.35 | 18800 | 6.1476 | 1.0 | | 0.1935 | 142.86 | 19000 | 5.7248 | 1.0 | | 0.1983 | 144.36 | 19200 | 6.1466 | 1.0 | | 0.176 | 145.86 | 19400 | 5.9555 | 1.0 | | 0.1778 | 147.37 | 19600 | 6.1434 | 1.0 | | 0.1758 | 148.87 | 19800 | 6.2104 | 1.0 | | 0.1799 | 150.38 | 20000 | 6.0933 | 1.0 | | 0.1674 | 151.88 | 20200 | 6.0476 | 1.0 | | 0.1777 | 153.38 | 20400 | 5.8937 | 1.0 | | 0.1616 | 154.89 | 20600 | 6.4417 | 1.0 | | 0.1498 | 156.39 | 20800 | 6.3136 | 1.0 | | 0.1607 | 157.89 | 21000 | 5.9295 | 1.0 | | 0.1445 | 159.4 | 21200 | 6.2741 | 1.0 | | 0.1636 | 160.9 | 21400 | 6.1931 | 1.0 | | 0.1488 | 162.41 | 21600 | 6.0089 | 1.0 | | 0.1549 | 163.91 | 21800 | 5.6184 | 1.0 | | 0.1532 | 165.41 | 22000 | 6.1250 | 1.0 | | 0.1581 | 166.92 | 22200 | 6.2635 | 1.0 | | 0.146 | 168.42 | 22400 | 6.0498 | 1.0 | | 0.148 | 169.92 | 22600 | 6.3486 | 1.0 | | 0.1489 | 171.43 | 22800 | 6.1659 | 1.0 | | 0.1464 | 172.93 | 23000 | 6.2259 | 1.0 | | 0.139 | 174.44 | 23200 | 6.2796 | 1.0 | | 0.1357 | 175.94 | 23400 | 6.2119 | 1.0 | | 0.1435 | 177.44 | 23600 | 6.5722 | 1.0 | | 0.1172 | 178.95 | 23800 | 6.4221 | 1.0 | | 0.1539 | 180.45 | 24000 | 6.3963 | 1.0 | | 0.1389 | 181.95 | 24200 | 6.2367 | 1.0 | | 0.1274 | 183.46 | 24400 | 6.3693 | 1.0 | | 0.1295 | 184.96 | 24600 | 6.0819 | 1.0 | | 0.1337 | 186.47 | 24800 | 6.1525 | 1.0 | | 0.1303 | 187.97 | 25000 | 6.2520 | 1.0 | | 0.141 | 189.47 | 25200 | 6.5302 | 1.0 | | 0.1322 | 190.98 | 25400 | 6.3731 | 1.0 | | 0.1313 | 192.48 | 25600 | 6.3570 | 1.0 | | 0.1178 | 193.98 | 25800 | 6.1667 | 1.0 | | 0.1277 | 195.49 | 26000 | 6.1352 | 1.0 | | 0.1169 | 196.99 | 26200 | 6.3132 | 1.0 | | 0.1199 | 198.5 | 26400 | 6.6116 | 1.0 | | 0.1138 | 200.0 | 26600 | 6.4862 | 1.0 | | 0.1129 | 201.5 | 26800 | 6.3442 | 1.0 | | 0.1142 | 203.01 | 27000 | 6.5077 | 1.0 | | 0.1169 | 204.51 | 27200 | 6.5710 | 1.0 | | 0.111 | 206.02 | 27400 | 6.0623 | 1.0 | | 0.1198 | 207.52 | 27600 | 6.4331 | 1.0 | | 0.1108 | 209.02 | 27800 | 5.9192 | 1.0 | | 0.1121 | 210.53 | 28000 | 6.0724 | 1.0 | | 0.1171 | 212.03 | 28200 | 6.3363 | 1.0 | | 0.1188 | 213.53 | 28400 | 6.3704 | 1.0 | | 0.104 | 215.04 | 28600 | 6.5802 | 1.0 | | 0.1125 | 216.54 | 28800 | 5.4428 | 1.0 | | 0.1115 | 218.05 | 29000 | 6.4286 | 1.0 | | 0.1109 | 219.55 | 29200 | 6.6998 | 1.0 | | 0.1061 | 221.05 | 29400 | 6.3761 | 1.0 | | 0.1161 | 222.56 | 29600 | 5.8712 | 1.0 | | 0.1091 | 224.06 | 29800 | 6.1844 | 1.0 | | 0.0947 | 225.56 | 30000 | 6.5670 | 1.0 | | 0.1004 | 227.07 | 30200 | 6.2302 | 1.0 | | 0.1099 | 228.57 | 30400 | 6.4218 | 1.0 | | 0.1154 | 230.08 | 30600 | 6.4911 | 1.0 | | 0.0999 | 231.58 | 30800 | 6.4390 | 1.0 | | 0.1068 | 233.08 | 31000 | 6.2367 | 1.0 | | 0.1015 | 234.59 | 31200 | 6.0790 | 1.0 | | 0.0958 | 236.09 | 31400 | 5.9926 | 1.0 | | 0.1183 | 237.59 | 31600 | 6.3400 | 1.0 | | 0.0833 | 239.1 | 31800 | 6.4481 | 1.0 | | 0.0874 | 240.6 | 32000 | 6.4535 | 1.0 | | 0.0958 | 242.11 | 32200 | 6.0597 | 1.0 | | 0.1075 | 243.61 | 32400 | 6.3403 | 1.0 | | 0.0909 | 245.11 | 32600 | 6.1297 | 1.0 | | 0.1093 | 246.62 | 32800 | 6.2232 | 1.0 | | 0.0995 | 248.12 | 33000 | 6.7110 | 1.0 | | 0.1061 | 249.62 | 33200 | 5.8551 | 1.0 | | 0.0872 | 251.13 | 33400 | 6.0338 | 1.0 | | 0.109 | 252.63 | 33600 | 6.2880 | 1.0 | | 0.0976 | 254.14 | 33800 | 5.9304 | 1.0 | | 0.0977 | 255.64 | 34000 | 6.4527 | 1.0 | | 0.0895 | 257.14 | 34200 | 6.3178 | 1.0 | | 0.0951 | 258.65 | 34400 | 6.3646 | 1.0 | | 0.0942 | 260.15 | 34600 | 6.4405 | 1.0 | | 0.0876 | 261.65 | 34800 | 5.8373 | 1.0 | | 0.0877 | 263.16 | 35000 | 6.5296 | 1.0 | | 0.0896 | 264.66 | 35200 | 6.6644 | 1.0 | | 0.0938 | 266.17 | 35400 | 6.4721 | 1.0 | | 0.0864 | 267.67 | 35600 | 7.0132 | 1.0 | | 0.0897 | 269.17 | 35800 | 6.3767 | 1.0 | | 0.094 | 270.68 | 36000 | 6.1663 | 1.0 | | 0.0782 | 272.18 | 36200 | 5.7325 | 1.0 | | 0.0878 | 273.68 | 36400 | 6.0681 | 1.0 | | 0.0877 | 275.19 | 36600 | 6.2621 | 1.0 | | 0.0827 | 276.69 | 36800 | 5.9692 | 1.0 | | 0.0929 | 278.2 | 37000 | 6.0207 | 1.0 | | 0.0899 | 279.7 | 37200 | 5.4185 | 1.0 | | 0.0841 | 281.2 | 37400 | 5.9206 | 1.0 | | 0.0924 | 282.71 | 37600 | 6.1820 | 1.0 | | 0.0844 | 284.21 | 37800 | 6.1505 | 1.0 | | 0.0824 | 285.71 | 38000 | 6.1564 | 1.0 | | 0.0842 | 287.22 | 38200 | 5.9483 | 1.0 | | 0.0863 | 288.72 | 38400 | 5.9305 | 1.0 | | 0.0851 | 290.23 | 38600 | 5.8416 | 1.0 | | 0.079 | 291.73 | 38800 | 5.7345 | 1.0 | | 0.081 | 293.23 | 39000 | 5.7323 | 1.0 | | 0.0873 | 294.74 | 39200 | 5.9131 | 1.0 | | 0.0836 | 296.24 | 39400 | 6.1722 | 1.0 | | 0.0774 | 297.74 | 39600 | 5.9523 | 1.0 | | 0.0902 | 299.25 | 39800 | 5.8769 | 1.0 | | 0.0766 | 300.75 | 40000 | 6.2435 | 1.0 | | 0.0766 | 302.26 | 40200 | 5.7556 | 1.0 | | 0.0723 | 303.76 | 40400 | 5.4647 | 1.0 | | 0.0724 | 305.26 | 40600 | 6.0184 | 1.0 | | 0.0834 | 306.77 | 40800 | 5.8434 | 1.0 | | 0.0846 | 308.27 | 41000 | 6.0586 | 1.0 | | 0.0878 | 309.77 | 41200 | 5.7270 | 1.0 | | 0.0761 | 311.28 | 41400 | 5.7259 | 1.0 | | 0.0639 | 312.78 | 41600 | 6.0848 | 1.0 | | 0.0733 | 314.29 | 41800 | 6.0474 | 1.0 | | 0.0734 | 315.79 | 42000 | 5.9387 | 1.0 | | 0.0779 | 317.29 | 42200 | 5.6040 | 1.0 | | 0.0713 | 318.8 | 42400 | 6.3136 | 1.0 | | 0.0756 | 320.3 | 42600 | 5.8936 | 1.0 | | 0.0758 | 321.8 | 42800 | 6.3659 | 1.0 | | 0.0662 | 323.31 | 43000 | 5.8035 | 1.0 | | 0.0714 | 324.81 | 43200 | 5.3194 | 1.0 | | 0.0782 | 326.32 | 43400 | 6.0054 | 1.0 | | 0.0775 | 327.82 | 43600 | 5.8471 | 1.0 | | 0.0653 | 329.32 | 43800 | 5.4054 | 1.0 | | 0.0739 | 330.83 | 44000 | 6.0978 | 1.0 | | 0.0634 | 332.33 | 44200 | 6.0081 | 1.0 | | 0.075 | 333.83 | 44400 | 6.0761 | 1.0 | | 0.0609 | 335.34 | 44600 | 5.8444 | 1.0 | | 0.0622 | 336.84 | 44800 | 6.2485 | 1.0 | | 0.0757 | 338.35 | 45000 | 6.0131 | 1.0 | | 0.0758 | 339.85 | 45200 | 5.9577 | 1.0 | | 0.0617 | 341.35 | 45400 | 5.7657 | 1.0 | | 0.0694 | 342.86 | 45600 | 5.7509 | 1.0 | | 0.0646 | 344.36 | 45800 | 5.5593 | 1.0 | | 0.0548 | 345.86 | 46000 | 5.9096 | 1.0 | | 0.0604 | 347.37 | 46200 | 6.2313 | 1.0 | | 0.0505 | 348.87 | 46400 | 5.4780 | 1.0 | | 0.0631 | 350.38 | 46600 | 6.0868 | 1.0 | | 0.0622 | 351.88 | 46800 | 5.8833 | 1.0 | | 0.0605 | 353.38 | 47000 | 5.5888 | 1.0 | | 0.0632 | 354.89 | 47200 | 5.7510 | 1.0 | | 0.0658 | 356.39 | 47400 | 5.2321 | 1.0 | | 0.0561 | 357.89 | 47600 | 5.6745 | 1.0 | | 0.0737 | 359.4 | 47800 | 6.0472 | 1.0 | | 0.0561 | 360.9 | 48000 | 6.2185 | 1.0 | | 0.0564 | 362.41 | 48200 | 6.0749 | 1.0 | | 0.0626 | 363.91 | 48400 | 5.6136 | 1.0 | | 0.0725 | 365.41 | 48600 | 5.7983 | 1.0 | | 0.0602 | 366.92 | 48800 | 5.5020 | 1.0 | | 0.0599 | 368.42 | 49000 | 6.0626 | 1.0 | | 0.0728 | 369.92 | 49200 | 6.3407 | 1.0 | | 0.0561 | 371.43 | 49400 | 6.2899 | 1.0 | | 0.0611 | 372.93 | 49600 | 6.5780 | 1.0 | | 0.065 | 374.44 | 49800 | 6.4685 | 1.0 | | 0.0561 | 375.94 | 50000 | 5.5252 | 1.0 | | 0.0482 | 377.44 | 50200 | 5.3905 | 1.0 | | 0.0575 | 378.95 | 50400 | 5.5660 | 1.0 | | 0.0673 | 380.45 | 50600 | 6.3424 | 1.0 | | 0.0588 | 381.95 | 50800 | 6.5294 | 1.0 | | 0.0563 | 383.46 | 51000 | 5.2974 | 1.0 | | 0.0702 | 384.96 | 51200 | 5.8705 | 1.0 | | 0.0517 | 386.47 | 51400 | 5.7488 | 1.0 | | 0.0629 | 387.97 | 51600 | 5.8414 | 1.0 | | 0.0569 | 389.47 | 51800 | 5.3303 | 1.0 | | 0.0586 | 390.98 | 52000 | 5.1755 | 1.0 | | 0.0581 | 392.48 | 52200 | 6.0030 | 1.0 | | 0.0673 | 393.98 | 52400 | 5.9972 | 1.0 | | 0.0533 | 395.49 | 52600 | 6.1624 | 1.0 | | 0.0597 | 396.99 | 52800 | 5.6803 | 1.0 | | 0.0494 | 398.5 | 53000 | 5.4154 | 1.0 | | 0.0526 | 400.0 | 53200 | 5.5855 | 1.0 | | 0.0578 | 401.5 | 53400 | 5.9491 | 1.0 | | 0.0546 | 403.01 | 53600 | 5.9381 | 1.0 | | 0.0575 | 404.51 | 53800 | 5.9629 | 1.0 | | 0.0592 | 406.02 | 54000 | 5.8617 | 1.0 | | 0.0444 | 407.52 | 54200 | 5.5513 | 1.0 | | 0.0467 | 409.02 | 54400 | 5.2998 | 1.0 | | 0.0654 | 410.53 | 54600 | 5.3034 | 1.0 | | 0.0546 | 412.03 | 54800 | 5.3077 | 1.0 | | 0.0567 | 413.53 | 55000 | 5.0215 | 1.0 | | 0.0564 | 415.04 | 55200 | 5.4569 | 1.0 | | 0.0494 | 416.54 | 55400 | 5.7311 | 1.0 | | 0.0448 | 418.05 | 55600 | 5.6774 | 1.0 | | 0.0695 | 419.55 | 55800 | 5.5563 | 1.0 | | 0.0451 | 421.05 | 56000 | 6.0087 | 1.0 | | 0.0514 | 422.56 | 56200 | 5.4969 | 1.0 | | 0.0504 | 424.06 | 56400 | 6.0321 | 1.0 | | 0.0429 | 425.56 | 56600 | 5.6021 | 1.0 | | 0.0503 | 427.07 | 56800 | 5.8039 | 1.0 | | 0.0528 | 428.57 | 57000 | 5.9237 | 1.0 | | 0.0508 | 430.08 | 57200 | 5.7653 | 1.0 | | 0.0533 | 431.58 | 57400 | 6.2778 | 1.0 | | 0.048 | 433.08 | 57600 | 6.0965 | 1.0 | | 0.0492 | 434.59 | 57800 | 5.3128 | 1.0 | | 0.0438 | 436.09 | 58000 | 5.7699 | 1.0 | | 0.0525 | 437.59 | 58200 | 5.1163 | 1.0 | | 0.0573 | 439.1 | 58400 | 5.4089 | 1.0 | | 0.0587 | 440.6 | 58600 | 5.2019 | 1.0 | | 0.049 | 442.11 | 58800 | 5.4515 | 1.0 | | 0.0435 | 443.61 | 59000 | 5.5448 | 1.0 | | 0.0378 | 445.11 | 59200 | 5.8339 | 1.0 | | 0.0498 | 446.62 | 59400 | 5.4560 | 1.0 | | 0.0361 | 448.12 | 59600 | 5.5045 | 1.0 | | 0.0438 | 449.62 | 59800 | 4.8485 | 1.0 | | 0.0512 | 451.13 | 60000 | 4.9816 | 1.0 | | 0.0464 | 452.63 | 60200 | 5.2011 | 1.0 | | 0.0437 | 454.14 | 60400 | 5.6679 | 1.0 | | 0.0457 | 455.64 | 60600 | 5.7678 | 1.0 | | 0.0397 | 457.14 | 60800 | 5.6997 | 1.0 | | 0.0397 | 458.65 | 61000 | 5.2398 | 1.0 | | 0.0412 | 460.15 | 61200 | 5.3039 | 1.0 | | 0.0472 | 461.65 | 61400 | 5.9334 | 1.0 | | 0.0389 | 463.16 | 61600 | 5.6996 | 1.0 | | 0.043 | 464.66 | 61800 | 5.4419 | 1.0 | | 0.0407 | 466.17 | 62000 | 5.2868 | 1.0 | | 0.0511 | 467.67 | 62200 | 5.2794 | 1.0 | | 0.0456 | 469.17 | 62400 | 5.6698 | 1.0 | | 0.0462 | 470.68 | 62600 | 5.4807 | 1.0 | | 0.0402 | 472.18 | 62800 | 4.9668 | 1.0 | | 0.0459 | 473.68 | 63000 | 5.1433 | 1.0 | | 0.0416 | 475.19 | 63200 | 5.1973 | 1.0 | | 0.0468 | 476.69 | 63400 | 4.5775 | 1.0 | | 0.0382 | 478.2 | 63600 | 5.4247 | 1.0 | | 0.04 | 479.7 | 63800 | 5.3787 | 1.0 | | 0.0446 | 481.2 | 64000 | 5.8649 | 1.0 | | 0.0478 | 482.71 | 64200 | 5.5780 | 1.0 | | 0.0468 | 484.21 | 64400 | 5.6468 | 1.0 | | 0.0358 | 485.71 | 64600 | 5.5878 | 1.0 | | 0.0375 | 487.22 | 64800 | 6.0500 | 1.0 | | 0.0418 | 488.72 | 65000 | 5.6539 | 1.0 | | 0.0419 | 490.23 | 65200 | 6.0848 | 1.0 | | 0.0381 | 491.73 | 65400 | 5.7511 | 1.0 | | 0.0476 | 493.23 | 65600 | 5.7423 | 1.0 | | 0.0492 | 494.74 | 65800 | 5.3481 | 1.0 | | 0.0373 | 496.24 | 66000 | 6.2950 | 1.0 | | 0.0451 | 497.74 | 66200 | 5.4381 | 1.0 | | 0.0457 | 499.25 | 66400 | 5.5962 | 1.0 | | 0.0384 | 500.75 | 66600 | 5.6821 | 1.0 | | 0.037 | 502.26 | 66800 | 5.8399 | 1.0 | | 0.0378 | 503.76 | 67000 | 5.4415 | 1.0 | | 0.038 | 505.26 | 67200 | 5.6578 | 1.0 | | 0.0371 | 506.77 | 67400 | 5.6192 | 1.0 | | 0.0314 | 508.27 | 67600 | 6.0258 | 1.0 | | 0.0345 | 509.77 | 67800 | 6.4183 | 1.0 | | 0.039 | 511.28 | 68000 | 5.1909 | 1.0 | | 0.0337 | 512.78 | 68200 | 5.3650 | 1.0 | | 0.0463 | 514.29 | 68400 | 5.5936 | 1.0 | | 0.05 | 515.79 | 68600 | 5.6765 | 1.0 | | 0.0374 | 517.29 | 68800 | 5.3339 | 1.0 | | 0.0374 | 518.8 | 69000 | 5.7343 | 1.0 | | 0.0406 | 520.3 | 69200 | 5.6398 | 1.0 | | 0.0397 | 521.8 | 69400 | 5.4041 | 1.0 | | 0.0391 | 523.31 | 69600 | 5.8327 | 1.0 | | 0.0332 | 524.81 | 69800 | 5.6610 | 1.0 | | 0.0325 | 526.32 | 70000 | 5.4675 | 1.0 | | 0.0438 | 527.82 | 70200 | 5.4601 | 1.0 | | 0.0281 | 529.32 | 70400 | 5.3118 | 1.0 | | 0.0314 | 530.83 | 70600 | 5.6760 | 1.0 | | 0.039 | 532.33 | 70800 | 5.2432 | 1.0 | | 0.0293 | 533.83 | 71000 | 5.4250 | 1.0 | | 0.0334 | 535.34 | 71200 | 4.9681 | 1.0 | | 0.0341 | 536.84 | 71400 | 5.2753 | 1.0 | | 0.0368 | 538.35 | 71600 | 4.7242 | 1.0 | | 0.0339 | 539.85 | 71800 | 5.0283 | 1.0 | | 0.0285 | 541.35 | 72000 | 4.9008 | 1.0 | | 0.0323 | 542.86 | 72200 | 5.0817 | 1.0 | | 0.0391 | 544.36 | 72400 | 5.2062 | 1.0 | | 0.0337 | 545.86 | 72600 | 5.4544 | 1.0 | | 0.0352 | 547.37 | 72800 | 5.0674 | 1.0 | | 0.0419 | 548.87 | 73000 | 5.2037 | 1.0 | | 0.0314 | 550.38 | 73200 | 4.8665 | 1.0 | | 0.0324 | 551.88 | 73400 | 5.2775 | 1.0 | | 0.0329 | 553.38 | 73600 | 4.7535 | 1.0 | | 0.0304 | 554.89 | 73800 | 4.8733 | 0.9994 | | 0.0388 | 556.39 | 74000 | 5.2259 | 1.0 | | 0.0423 | 557.89 | 74200 | 5.4575 | 1.0 | | 0.034 | 559.4 | 74400 | 4.9118 | 1.0 | | 0.0289 | 560.9 | 74600 | 4.9018 | 1.0 | | 0.0317 | 562.41 | 74800 | 5.0265 | 1.0 | | 0.0346 | 563.91 | 75000 | 4.7586 | 1.0 | | 0.0238 | 565.41 | 75200 | 5.0713 | 1.0 | | 0.0275 | 566.92 | 75400 | 4.8972 | 1.0 | | 0.0291 | 568.42 | 75600 | 4.8198 | 1.0 | | 0.0371 | 569.92 | 75800 | 4.3204 | 1.0 | | 0.033 | 571.43 | 76000 | 4.5274 | 1.0 | | 0.0334 | 572.93 | 76200 | 4.8907 | 1.0 | | 0.0375 | 574.44 | 76400 | 4.6204 | 1.0 | | 0.0332 | 575.94 | 76600 | 4.4255 | 1.0 | | 0.0359 | 577.44 | 76800 | 4.8451 | 1.0 | | 0.0273 | 578.95 | 77000 | 5.8480 | 1.0 | | 0.0273 | 580.45 | 77200 | 5.2345 | 1.0 | | 0.0296 | 581.95 | 77400 | 5.3281 | 0.9994 | | 0.0332 | 583.46 | 77600 | 5.6027 | 0.9994 | | 0.0251 | 584.96 | 77800 | 5.3038 | 0.9994 | | 0.0275 | 586.47 | 78000 | 5.0464 | 1.0 | | 0.0193 | 587.97 | 78200 | 4.9974 | 0.9994 | | 0.0326 | 589.47 | 78400 | 4.6220 | 1.0 | | 0.033 | 590.98 | 78600 | 4.9772 | 1.0 | | 0.0258 | 592.48 | 78800 | 4.6183 | 1.0 | | 0.0326 | 593.98 | 79000 | 5.2420 | 1.0 | | 0.0311 | 595.49 | 79200 | 4.6937 | 1.0 | | 0.0257 | 596.99 | 79400 | 4.9505 | 1.0 | | 0.0295 | 598.5 | 79600 | 5.0226 | 1.0 | | 0.0309 | 600.0 | 79800 | 4.8904 | 1.0 | | 0.0239 | 601.5 | 80000 | 5.4780 | 1.0 | | 0.0238 | 603.01 | 80200 | 5.4525 | 1.0 | | 0.0373 | 604.51 | 80400 | 4.8573 | 0.9994 | | 0.0308 | 606.02 | 80600 | 5.1780 | 1.0 | | 0.0262 | 607.52 | 80800 | 5.4216 | 1.0 | | 0.0227 | 609.02 | 81000 | 4.9209 | 1.0 | | 0.0222 | 610.53 | 81200 | 5.4042 | 1.0 | | 0.0332 | 612.03 | 81400 | 5.2453 | 1.0 | | 0.0261 | 613.53 | 81600 | 5.3043 | 1.0 | | 0.0254 | 615.04 | 81800 | 5.7649 | 1.0 | | 0.0276 | 616.54 | 82000 | 5.1942 | 1.0 | | 0.0291 | 618.05 | 82200 | 4.9448 | 0.9994 | | 0.0275 | 619.55 | 82400 | 5.0157 | 1.0006 | | 0.0278 | 621.05 | 82600 | 4.7635 | 1.0006 | | 0.0232 | 622.56 | 82800 | 5.4406 | 1.0 | | 0.0311 | 624.06 | 83000 | 5.3966 | 1.0 | | 0.0206 | 625.56 | 83200 | 4.8876 | 1.0013 | | 0.0274 | 627.07 | 83400 | 5.4422 | 1.0 | | 0.0199 | 628.57 | 83600 | 4.9966 | 1.0006 | | 0.0253 | 630.08 | 83800 | 5.1257 | 1.0006 | | 0.027 | 631.58 | 84000 | 5.1748 | 1.0 | | 0.0312 | 633.08 | 84200 | 5.0923 | 1.0006 | | 0.0288 | 634.59 | 84400 | 5.1963 | 1.0 | | 0.0261 | 636.09 | 84600 | 4.8105 | 1.0013 | | 0.0259 | 637.59 | 84800 | 4.9715 | 1.0 | | 0.03 | 639.1 | 85000 | 5.2808 | 1.0 | | 0.0215 | 640.6 | 85200 | 5.3155 | 1.0 | | 0.028 | 642.11 | 85400 | 5.1311 | 1.0 | | 0.0268 | 643.61 | 85600 | 5.1429 | 1.0 | | 0.0238 | 645.11 | 85800 | 5.0456 | 1.0 | | 0.0273 | 646.62 | 86000 | 5.2772 | 1.0 | | 0.0196 | 648.12 | 86200 | 5.2186 | 1.0 | | 0.0212 | 649.62 | 86400 | 4.8386 | 0.9994 | | 0.0243 | 651.13 | 86600 | 5.1898 | 1.0 | | 0.0255 | 652.63 | 86800 | 5.5885 | 1.0 | | 0.0192 | 654.14 | 87000 | 5.7636 | 1.0 | | 0.0272 | 655.64 | 87200 | 5.5897 | 1.0 | | 0.0254 | 657.14 | 87400 | 5.3412 | 1.0 | | 0.0389 | 658.65 | 87600 | 5.2754 | 1.0 | | 0.025 | 660.15 | 87800 | 4.6929 | 1.0 | | 0.0268 | 661.65 | 88000 | 4.7299 | 1.0 | | 0.0303 | 663.16 | 88200 | 5.1038 | 1.0 | | 0.019 | 664.66 | 88400 | 5.4165 | 1.0 | | 0.0197 | 666.17 | 88600 | 5.3144 | 1.0 | | 0.024 | 667.67 | 88800 | 4.7525 | 1.0 | | 0.0272 | 669.17 | 89000 | 5.4154 | 1.0 | | 0.0267 | 670.68 | 89200 | 5.1149 | 0.9994 | | 0.0231 | 672.18 | 89400 | 5.3135 | 1.0 | | 0.034 | 673.68 | 89600 | 4.8940 | 1.0 | | 0.0205 | 675.19 | 89800 | 5.4456 | 1.0 | | 0.0236 | 676.69 | 90000 | 5.1145 | 1.0 | | 0.0276 | 678.2 | 90200 | 5.3905 | 1.0 | | 0.0316 | 679.7 | 90400 | 5.4222 | 1.0 | | 0.0253 | 681.2 | 90600 | 5.1456 | 1.0006 | | 0.0255 | 682.71 | 90800 | 5.3852 | 1.0 | | 0.0185 | 684.21 | 91000 | 4.9756 | 1.0 | | 0.019 | 685.71 | 91200 | 5.9086 | 1.0 | | 0.0232 | 687.22 | 91400 | 5.1278 | 1.0 | | 0.0281 | 688.72 | 91600 | 4.9946 | 1.0 | | 0.0188 | 690.23 | 91800 | 5.0910 | 1.0 | | 0.0189 | 691.73 | 92000 | 4.8426 | 1.0 | | 0.0276 | 693.23 | 92200 | 5.0411 | 1.0 | | 0.021 | 694.74 | 92400 | 4.8377 | 1.0 | | 0.0237 | 696.24 | 92600 | 4.9047 | 1.0 | | 0.0209 | 697.74 | 92800 | 4.3727 | 1.0 | | 0.0212 | 699.25 | 93000 | 4.4921 | 1.0006 | | 0.0228 | 700.75 | 93200 | 4.7233 | 1.0006 | | 0.0187 | 702.26 | 93400 | 4.7441 | 1.0006 | | 0.0217 | 703.76 | 93600 | 4.7464 | 1.0 | | 0.0254 | 705.26 | 93800 | 4.8955 | 0.9994 | | 0.0268 | 706.77 | 94000 | 5.2548 | 1.0 | | 0.0236 | 708.27 | 94200 | 5.2691 | 1.0 | | 0.0174 | 709.77 | 94400 | 4.8898 | 1.0 | | 0.0262 | 711.28 | 94600 | 4.7713 | 1.0 | | 0.0226 | 712.78 | 94800 | 4.8464 | 1.0 | | 0.0193 | 714.29 | 95000 | 5.0019 | 1.0 | | 0.0229 | 715.79 | 95200 | 5.2043 | 1.0 | | 0.014 | 717.29 | 95400 | 4.9124 | 0.9994 | | 0.0274 | 718.8 | 95600 | 4.9160 | 1.0 | | 0.0198 | 720.3 | 95800 | 5.1003 | 1.0 | | 0.0222 | 721.8 | 96000 | 4.9865 | 1.0 | | 0.0133 | 723.31 | 96200 | 5.0203 | 1.0 | | 0.0162 | 724.81 | 96400 | 4.9140 | 1.0 | | 0.024 | 726.32 | 96600 | 4.9667 | 1.0006 | | 0.0197 | 727.82 | 96800 | 5.1605 | 1.0 | | 0.0169 | 729.32 | 97000 | 5.4196 | 1.0 | | 0.0238 | 730.83 | 97200 | 5.3665 | 1.0 | | 0.0182 | 732.33 | 97400 | 5.0020 | 1.0013 | | 0.0212 | 733.83 | 97600 | 5.1674 | 1.0006 | | 0.0165 | 735.34 | 97800 | 4.9132 | 1.0013 | | 0.0242 | 736.84 | 98000 | 4.7047 | 1.0013 | | 0.022 | 738.35 | 98200 | 5.1006 | 1.0 | | 0.022 | 739.85 | 98400 | 4.7582 | 0.9994 | | 0.0178 | 741.35 | 98600 | 5.0911 | 1.0 | | 0.0197 | 742.86 | 98800 | 4.9315 | 1.0 | | 0.022 | 744.36 | 99000 | 4.4003 | 1.0019 | | 0.0114 | 745.86 | 99200 | 4.9068 | 1.0 | | 0.0184 | 747.37 | 99400 | 4.7211 | 1.0 | | 0.0227 | 748.87 | 99600 | 4.8788 | 1.0 | | 0.0127 | 750.38 | 99800 | 5.0337 | 1.0 | | 0.0193 | 751.88 | 100000 | 5.0193 | 1.0 | | 0.0167 | 753.38 | 100200 | 5.1066 | 1.0019 | | 0.0149 | 754.89 | 100400 | 5.2865 | 1.0 | | 0.0219 | 756.39 | 100600 | 5.2765 | 1.0 | | 0.0185 | 757.89 | 100800 | 5.3224 | 1.0 | | 0.0159 | 759.4 | 101000 | 5.1665 | 1.0 | | 0.0178 | 760.9 | 101200 | 5.4050 | 0.9994 | | 0.0119 | 762.41 | 101400 | 5.1887 | 0.9994 | | 0.022 | 763.91 | 101600 | 4.7725 | 1.0 | | 0.0214 | 765.41 | 101800 | 4.9794 | 1.0 | | 0.0151 | 766.92 | 102000 | 4.7806 | 1.0013 | | 0.0181 | 768.42 | 102200 | 4.3024 | 1.0006 | | 0.0166 | 769.92 | 102400 | 4.8876 | 1.0 | | 0.0211 | 771.43 | 102600 | 4.5744 | 1.0 | | 0.0184 | 772.93 | 102800 | 4.8648 | 0.9994 | | 0.02 | 774.44 | 103000 | 4.7831 | 0.9994 | | 0.0188 | 775.94 | 103200 | 4.9471 | 0.9994 | | 0.0201 | 777.44 | 103400 | 4.9495 | 1.0006 | | 0.0169 | 778.95 | 103600 | 5.2604 | 1.0 | | 0.0145 | 780.45 | 103800 | 5.5133 | 1.0 | | 0.0176 | 781.95 | 104000 | 5.1844 | 0.9994 | | 0.0149 | 783.46 | 104200 | 4.6289 | 1.0 | | 0.0181 | 784.96 | 104400 | 5.2480 | 1.0 | | 0.0132 | 786.47 | 104600 | 4.7374 | 0.9994 | | 0.0115 | 787.97 | 104800 | 5.6450 | 1.0 | | 0.014 | 789.47 | 105000 | 5.0672 | 1.0 | | 0.0181 | 790.98 | 105200 | 5.1088 | 1.0 | | 0.0181 | 792.48 | 105400 | 5.0798 | 1.0 | | 0.0146 | 793.98 | 105600 | 5.1835 | 1.0 | | 0.0219 | 795.49 | 105800 | 5.3850 | 1.0 | | 0.0222 | 796.99 | 106000 | 4.9615 | 1.0 | | 0.0176 | 798.5 | 106200 | 4.6814 | 1.0 | | 0.017 | 800.0 | 106400 | 4.9264 | 1.0 | | 0.0134 | 801.5 | 106600 | 5.0058 | 1.0 | | 0.0139 | 803.01 | 106800 | 5.1207 | 1.0 | | 0.0146 | 804.51 | 107000 | 4.8640 | 1.0006 | | 0.018 | 806.02 | 107200 | 4.6173 | 1.0 | | 0.0183 | 807.52 | 107400 | 4.8846 | 1.0 | | 0.0169 | 809.02 | 107600 | 5.2157 | 1.0006 | | 0.0128 | 810.53 | 107800 | 5.1951 | 1.0006 | | 0.0181 | 812.03 | 108000 | 4.9453 | 1.0013 | | 0.0107 | 813.53 | 108200 | 5.0670 | 1.0013 | | 0.0171 | 815.04 | 108400 | 4.8881 | 1.0006 | | 0.0144 | 816.54 | 108600 | 4.8071 | 1.0013 | | 0.0133 | 818.05 | 108800 | 4.9399 | 1.0013 | | 0.0138 | 819.55 | 109000 | 4.5266 | 1.0038 | | 0.0159 | 821.05 | 109200 | 4.9887 | 1.0025 | | 0.0157 | 822.56 | 109400 | 4.8344 | 1.0019 | | 0.0134 | 824.06 | 109600 | 5.1275 | 1.0019 | | 0.0142 | 825.56 | 109800 | 4.7311 | 1.0025 | | 0.0207 | 827.07 | 110000 | 4.5203 | 1.0050 | | 0.0125 | 828.57 | 110200 | 4.5815 | 1.0044 | | 0.0161 | 830.08 | 110400 | 4.7650 | 1.0031 | | 0.0091 | 831.58 | 110600 | 4.9839 | 1.0025 | | 0.0154 | 833.08 | 110800 | 4.9781 | 1.0013 | | 0.012 | 834.59 | 111000 | 5.0515 | 1.0025 | | 0.0182 | 836.09 | 111200 | 5.1645 | 1.0025 | | 0.0135 | 837.59 | 111400 | 5.1050 | 1.0013 | | 0.0171 | 839.1 | 111600 | 4.8125 | 1.0025 | | 0.0152 | 840.6 | 111800 | 5.4213 | 1.0038 | | 0.0103 | 842.11 | 112000 | 5.0478 | 1.0031 | | 0.0112 | 843.61 | 112200 | 4.9343 | 1.0019 | | 0.0156 | 845.11 | 112400 | 5.2666 | 1.0019 | | 0.0122 | 846.62 | 112600 | 5.0991 | 1.0031 | | 0.0178 | 848.12 | 112800 | 5.1477 | 1.0006 | | 0.0174 | 849.62 | 113000 | 5.1716 | 1.0031 | | 0.0136 | 851.13 | 113200 | 5.3554 | 1.0013 | | 0.0123 | 852.63 | 113400 | 4.8209 | 1.0019 | | 0.0095 | 854.14 | 113600 | 4.9493 | 1.0019 | | 0.0183 | 855.64 | 113800 | 4.8060 | 1.0038 | | 0.0111 | 857.14 | 114000 | 4.8071 | 1.0006 | | 0.0121 | 858.65 | 114200 | 4.7184 | 1.0006 | | 0.0079 | 860.15 | 114400 | 4.7966 | 1.0019 | | 0.0129 | 861.65 | 114600 | 4.8338 | 1.0013 | | 0.0103 | 863.16 | 114800 | 4.5879 | 1.0019 | | 0.0117 | 864.66 | 115000 | 4.7938 | 1.0025 | | 0.018 | 866.17 | 115200 | 4.6562 | 1.0 | | 0.0121 | 867.67 | 115400 | 4.6558 | 1.0 | | 0.0184 | 869.17 | 115600 | 4.7076 | 1.0 | | 0.0135 | 870.68 | 115800 | 5.1430 | 1.0 | | 0.0163 | 872.18 | 116000 | 5.2987 | 1.0019 | | 0.014 | 873.68 | 116200 | 5.0680 | 1.0019 | | 0.0114 | 875.19 | 116400 | 4.9600 | 1.0013 | | 0.0145 | 876.69 | 116600 | 5.0292 | 1.0025 | | 0.0125 | 878.2 | 116800 | 4.6149 | 1.0038 | | 0.008 | 879.7 | 117000 | 4.9819 | 1.0019 | | 0.0122 | 881.2 | 117200 | 5.0868 | 1.0019 | | 0.0157 | 882.71 | 117400 | 4.8522 | 1.0031 | | 0.0146 | 884.21 | 117600 | 4.6680 | 1.0044 | | 0.014 | 885.71 | 117800 | 4.4612 | 1.0038 | | 0.0123 | 887.22 | 118000 | 4.4058 | 1.0044 | | 0.0122 | 888.72 | 118200 | 4.6614 | 1.0038 | | 0.012 | 890.23 | 118400 | 4.7230 | 1.0019 | | 0.0159 | 891.73 | 118600 | 5.0094 | 1.0006 | | 0.0087 | 893.23 | 118800 | 4.8522 | 1.0006 | | 0.013 | 894.74 | 119000 | 4.9456 | 1.0013 | | 0.0166 | 896.24 | 119200 | 4.9068 | 1.0019 | | 0.0129 | 897.74 | 119400 | 4.9066 | 1.0019 | | 0.0136 | 899.25 | 119600 | 4.6458 | 1.0019 | | 0.0208 | 900.75 | 119800 | 4.6729 | 1.0013 | | 0.0158 | 902.26 | 120000 | 4.6674 | 1.0019 | | 0.0125 | 903.76 | 120200 | 4.5242 | 1.0025 | | 0.0098 | 905.26 | 120400 | 4.7753 | 1.0025 | | 0.0078 | 906.77 | 120600 | 4.8435 | 1.0013 | | 0.0155 | 908.27 | 120800 | 4.6419 | 1.0006 | | 0.0142 | 909.77 | 121000 | 4.7051 | 1.0013 | | 0.0098 | 911.28 | 121200 | 5.0023 | 1.0 | | 0.0097 | 912.78 | 121400 | 5.2775 | 1.0 | | 0.0103 | 914.29 | 121600 | 5.1593 | 1.0 | | 0.0115 | 915.79 | 121800 | 5.0693 | 1.0 | | 0.0129 | 917.29 | 122000 | 5.4565 | 1.0 | | 0.0123 | 918.8 | 122200 | 4.9698 | 1.0 | | 0.0176 | 920.3 | 122400 | 4.8110 | 1.0019 | | 0.0106 | 921.8 | 122600 | 4.7044 | 1.0031 | | 0.0098 | 923.31 | 122800 | 4.7205 | 1.0031 | | 0.0132 | 924.81 | 123000 | 4.5947 | 1.0013 | | 0.0122 | 926.32 | 123200 | 4.9378 | 1.0 | | 0.0101 | 927.82 | 123400 | 5.0470 | 1.0006 | | 0.0151 | 929.32 | 123600 | 4.7670 | 1.0 | | 0.0089 | 930.83 | 123800 | 4.9127 | 1.0019 | | 0.0107 | 932.33 | 124000 | 4.9719 | 1.0031 | | 0.0084 | 933.83 | 124200 | 5.0971 | 1.0025 | | 0.0111 | 935.34 | 124400 | 4.6700 | 1.0031 | | 0.0121 | 936.84 | 124600 | 4.7236 | 1.0031 | | 0.0104 | 938.35 | 124800 | 4.7380 | 1.0019 | | 0.0101 | 939.85 | 125000 | 4.8754 | 1.0025 | | 0.0145 | 941.35 | 125200 | 5.0708 | 1.0031 | | 0.0096 | 942.86 | 125400 | 4.8973 | 1.0031 | | 0.0158 | 944.36 | 125600 | 5.1670 | 1.0019 | | 0.0106 | 945.86 | 125800 | 5.0989 | 1.0044 | | 0.0157 | 947.37 | 126000 | 5.0135 | 1.0044 | | 0.0092 | 948.87 | 126200 | 4.7069 | 1.0025 | | 0.0121 | 950.38 | 126400 | 4.6865 | 1.0031 | | 0.0117 | 951.88 | 126600 | 4.6496 | 1.0050 | | 0.0107 | 953.38 | 126800 | 5.0900 | 1.0025 | | 0.0107 | 954.89 | 127000 | 4.8722 | 1.0025 | | 0.0137 | 956.39 | 127200 | 4.7971 | 1.0013 | | 0.0106 | 957.89 | 127400 | 5.0268 | 1.0025 | | 0.0138 | 959.4 | 127600 | 5.1737 | 1.0019 | | 0.01 | 960.9 | 127800 | 5.2657 | 1.0025 | | 0.0101 | 962.41 | 128000 | 5.0265 | 1.0019 | | 0.0092 | 963.91 | 128200 | 5.4657 | 1.0019 | | 0.0109 | 965.41 | 128400 | 5.5858 | 1.0019 | | 0.0078 | 966.92 | 128600 | 5.2281 | 1.0031 | | 0.0111 | 968.42 | 128800 | 4.9028 | 1.0013 | | 0.0068 | 969.92 | 129000 | 4.8840 | 1.0019 | | 0.008 | 971.43 | 129200 | 5.3572 | 1.0013 | | 0.0108 | 972.93 | 129400 | 5.0857 | 1.0025 | | 0.0128 | 974.44 | 129600 | 5.1988 | 1.0019 | | 0.0088 | 975.94 | 129800 | 4.9510 | 1.0025 | | 0.013 | 977.44 | 130000 | 5.3014 | 1.0019 | | 0.0108 | 978.95 | 130200 | 4.9356 | 1.0019 | | 0.0112 | 980.45 | 130400 | 4.8090 | 1.0025 | | 0.0115 | 981.95 | 130600 | 5.0231 | 1.0038 | | 0.0166 | 983.46 | 130800 | 4.8263 | 1.0019 | | 0.0061 | 984.96 | 131000 | 4.7797 | 1.0025 | | 0.0097 | 986.47 | 131200 | 4.8421 | 1.0019 | | 0.0115 | 987.97 | 131400 | 4.9492 | 1.0031 | | 0.0058 | 989.47 | 131600 | 4.9278 | 1.0013 | | 0.0089 | 990.98 | 131800 | 4.8451 | 1.0013 | | 0.0133 | 992.48 | 132000 | 4.8694 | 1.0013 | | 0.0074 | 993.98 | 132200 | 5.0419 | 1.0006 | | 0.0088 | 995.49 | 132400 | 5.2701 | 1.0013 | | 0.0116 | 996.99 | 132600 | 5.2893 | 1.0006 | | 0.0068 | 998.5 | 132800 | 4.9237 | 1.0019 | | 0.0096 | 1000.0 | 133000 | 5.0376 | 1.0013 | | 0.0097 | 1001.5 | 133200 | 5.0571 | 1.0025 | | 0.009 | 1003.01 | 133400 | 5.0585 | 1.0019 | | 0.0077 | 1004.51 | 133600 | 4.9175 | 1.0013 | | 0.0099 | 1006.02 | 133800 | 5.0497 | 1.0013 | | 0.0042 | 1007.52 | 134000 | 4.9861 | 1.0013 | | 0.0114 | 1009.02 | 134200 | 5.0604 | 1.0 | | 0.0079 | 1010.53 | 134400 | 4.9738 | 1.0 | | 0.0039 | 1012.03 | 134600 | 5.2350 | 1.0006 | | 0.0081 | 1013.53 | 134800 | 4.8783 | 1.0006 | | 0.0072 | 1015.04 | 135000 | 5.0616 | 1.0019 | | 0.0059 | 1016.54 | 135200 | 4.9421 | 1.0006 | | 0.0052 | 1018.05 | 135400 | 5.2076 | 1.0006 | | 0.0072 | 1019.55 | 135600 | 4.7459 | 1.0019 | | 0.0137 | 1021.05 | 135800 | 4.4156 | 1.0025 | | 0.0086 | 1022.56 | 136000 | 4.7582 | 1.0038 | | 0.006 | 1024.06 | 136200 | 4.6716 | 1.0013 | | 0.0086 | 1025.56 | 136400 | 4.9067 | 1.0006 | | 0.0074 | 1027.07 | 136600 | 5.1224 | 1.0 | | 0.0099 | 1028.57 | 136800 | 4.8046 | 1.0006 | | 0.0129 | 1030.08 | 137000 | 5.0482 | 1.0 | | 0.011 | 1031.58 | 137200 | 4.9666 | 1.0019 | | 0.0071 | 1033.08 | 137400 | 5.0899 | 1.0013 | | 0.0067 | 1034.59 | 137600 | 5.0229 | 1.0006 | | 0.0044 | 1036.09 | 137800 | 5.0371 | 1.0013 | | 0.0101 | 1037.59 | 138000 | 4.9919 | 0.9994 | | 0.006 | 1039.1 | 138200 | 5.1368 | 0.9994 | | 0.0105 | 1040.6 | 138400 | 4.8836 | 0.9987 | | 0.0084 | 1042.11 | 138600 | 4.7600 | 1.0025 | | 0.0089 | 1043.61 | 138800 | 4.9488 | 1.0019 | | 0.0075 | 1045.11 | 139000 | 5.0581 | 1.0006 | | 0.0109 | 1046.62 | 139200 | 5.1216 | 1.0006 | | 0.0028 | 1048.12 | 139400 | 5.0811 | 0.9981 | | 0.0044 | 1049.62 | 139600 | 4.8405 | 1.0013 | | 0.0067 | 1051.13 | 139800 | 5.0062 | 1.0013 | | 0.0057 | 1052.63 | 140000 | 5.0492 | 1.0006 | | 0.0062 | 1054.14 | 140200 | 5.0783 | 1.0 | | 0.0076 | 1055.64 | 140400 | 5.1404 | 1.0 | | 0.0132 | 1057.14 | 140600 | 5.1925 | 1.0006 | | 0.0043 | 1058.65 | 140800 | 5.0225 | 1.0019 | | 0.0032 | 1060.15 | 141000 | 4.9694 | 1.0019 | | 0.004 | 1061.65 | 141200 | 5.1353 | 1.0006 | | 0.0092 | 1063.16 | 141400 | 5.2068 | 1.0013 | | 0.0092 | 1064.66 | 141600 | 5.0825 | 1.0013 | | 0.0057 | 1066.17 | 141800 | 5.0087 | 1.0013 | | 0.0048 | 1067.67 | 142000 | 4.9626 | 1.0025 | | 0.0052 | 1069.17 | 142200 | 5.0154 | 1.0006 | | 0.0094 | 1070.68 | 142400 | 5.0147 | 1.0019 | | 0.008 | 1072.18 | 142600 | 4.9181 | 1.0006 | | 0.0073 | 1073.68 | 142800 | 4.9787 | 1.0019 | | 0.0063 | 1075.19 | 143000 | 4.9458 | 1.0025 | | 0.0069 | 1076.69 | 143200 | 5.0951 | 1.0019 | | 0.005 | 1078.2 | 143400 | 4.8268 | 1.0019 | | 0.008 | 1079.7 | 143600 | 4.8822 | 1.0019 | | 0.0047 | 1081.2 | 143800 | 5.1148 | 1.0019 | | 0.0138 | 1082.71 | 144000 | 4.9464 | 1.0 | | 0.0054 | 1084.21 | 144200 | 4.9410 | 1.0013 | | 0.0061 | 1085.71 | 144400 | 5.1612 | 1.0006 | | 0.0095 | 1087.22 | 144600 | 5.0962 | 1.0006 | | 0.0069 | 1088.72 | 144800 | 5.0092 | 1.0 | | 0.0041 | 1090.23 | 145000 | 5.0815 | 1.0 | | 0.0104 | 1091.73 | 145200 | 5.0631 | 1.0006 | | 0.0025 | 1093.23 | 145400 | 5.1077 | 1.0006 | | 0.0068 | 1094.74 | 145600 | 4.9715 | 1.0019 | | 0.006 | 1096.24 | 145800 | 5.0608 | 1.0013 | | 0.0081 | 1097.74 | 146000 | 5.1091 | 1.0013 | | 0.0068 | 1099.25 | 146200 | 5.1470 | 1.0013 | | 0.0047 | 1100.75 | 146400 | 5.1790 | 1.0006 | | 0.0054 | 1102.26 | 146600 | 5.2646 | 1.0 | | 0.0092 | 1103.76 | 146800 | 5.2853 | 1.0013 | | 0.0066 | 1105.26 | 147000 | 5.0318 | 1.0013 | | 0.008 | 1106.77 | 147200 | 5.0337 | 1.0013 | | 0.0034 | 1108.27 | 147400 | 5.0388 | 1.0013 | | 0.011 | 1109.77 | 147600 | 5.0256 | 1.0019 | | 0.0072 | 1111.28 | 147800 | 5.0236 | 1.0019 | | 0.0129 | 1112.78 | 148000 | 5.0792 | 1.0019 | | 0.0063 | 1114.29 | 148200 | 5.1229 | 1.0 | | 0.0045 | 1115.79 | 148400 | 5.1730 | 1.0013 | | 0.0049 | 1117.29 | 148600 | 5.1491 | 1.0006 | | 0.0039 | 1118.8 | 148800 | 5.1083 | 1.0013 | | 0.008 | 1120.3 | 149000 | 5.0877 | 1.0019 | | 0.0058 | 1121.8 | 149200 | 5.0018 | 1.0019 | | 0.0046 | 1123.31 | 149400 | 5.0775 | 1.0013 | | 0.0057 | 1124.81 | 149600 | 5.0081 | 1.0006 | | 0.0048 | 1126.32 | 149800 | 5.0912 | 1.0013 | | 0.0031 | 1127.82 | 150000 | 5.0987 | 1.0019 | | 0.0059 | 1129.32 | 150200 | 5.0811 | 1.0013 | | 0.0043 | 1130.83 | 150400 | 5.1520 | 1.0019 | | 0.0071 | 1132.33 | 150600 | 5.2432 | 1.0019 | | 0.0115 | 1133.83 | 150800 | 5.1358 | 1.0019 | | 0.0061 | 1135.34 | 151000 | 5.0458 | 1.0019 | | 0.0068 | 1136.84 | 151200 | 5.1654 | 1.0019 | | 0.0107 | 1138.35 | 151400 | 5.0748 | 1.0019 | | 0.0053 | 1139.85 | 151600 | 5.1501 | 1.0019 | | 0.0058 | 1141.35 | 151800 | 5.2146 | 1.0019 | | 0.0053 | 1142.86 | 152000 | 5.2234 | 1.0013 | | 0.0034 | 1144.36 | 152200 | 5.2226 | 1.0019 | | 0.0058 | 1145.86 | 152400 | 5.2033 | 1.0019 | | 0.0039 | 1147.37 | 152600 | 5.2151 | 1.0019 | | 0.0111 | 1148.87 | 152800 | 5.1126 | 1.0019 | | 0.0054 | 1150.38 | 153000 | 5.2183 | 1.0019 | | 0.0068 | 1151.88 | 153200 | 5.2718 | 1.0019 | | 0.0074 | 1153.38 | 153400 | 5.1996 | 1.0019 | | 0.0064 | 1154.89 | 153600 | 5.1910 | 1.0019 | | 0.0058 | 1156.39 | 153800 | 5.1924 | 1.0019 | | 0.004 | 1157.89 | 154000 | 5.2096 | 1.0019 | | 0.0046 | 1159.4 | 154200 | 5.1493 | 1.0019 | | 0.0066 | 1160.9 | 154400 | 5.1710 | 1.0019 | | 0.0055 | 1162.41 | 154600 | 5.1339 | 1.0019 | | 0.0076 | 1163.91 | 154800 | 5.1114 | 1.0019 | | 0.0078 | 1165.41 | 155000 | 5.1069 | 1.0019 | | 0.0039 | 1166.92 | 155200 | 5.1307 | 1.0019 | | 0.0029 | 1168.42 | 155400 | 5.1418 | 1.0025 | | 0.0033 | 1169.92 | 155600 | 5.0614 | 1.0025 | | 0.005 | 1171.43 | 155800 | 5.0568 | 1.0025 | | 0.0048 | 1172.93 | 156000 | 5.0617 | 1.0025 | | 0.005 | 1174.44 | 156200 | 5.0957 | 1.0025 | | 0.0072 | 1175.94 | 156400 | 5.0974 | 1.0025 | | 0.0052 | 1177.44 | 156600 | 5.0740 | 1.0025 | | 0.0042 | 1178.95 | 156800 | 5.0641 | 1.0019 | | 0.0057 | 1180.45 | 157000 | 5.0398 | 1.0013 | | 0.0063 | 1181.95 | 157200 | 4.9869 | 1.0019 | | 0.0046 | 1183.46 | 157400 | 5.0102 | 1.0019 | | 0.005 | 1184.96 | 157600 | 4.9957 | 1.0019 | | 0.0044 | 1186.47 | 157800 | 5.0196 | 1.0019 | | 0.0041 | 1187.97 | 158000 | 5.0451 | 1.0019 | | 0.0053 | 1189.47 | 158200 | 5.0466 | 1.0019 | | 0.0078 | 1190.98 | 158400 | 5.0443 | 1.0019 | | 0.0061 | 1192.48 | 158600 | 5.0306 | 1.0019 | | 0.0069 | 1193.98 | 158800 | 5.0399 | 1.0019 | | 0.0114 | 1195.49 | 159000 | 5.0391 | 1.0019 | | 0.0126 | 1196.99 | 159200 | 5.0403 | 1.0019 | | 0.0067 | 1198.5 | 159400 | 5.0430 | 1.0019 | | 0.0044 | 1200.0 | 159600 | 5.0428 | 1.0019 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.6

markn/adalm-bio-small

22824f560e494e077e6e898a5c22497d3931e521

2022-04-12T21:12:35.000Z

[ "pytorch", "bert", "transformers", "license:mit" ]

null

false

markn

null

markn/adalm-bio-small

0

null

transformers

36,832

--- license: mit ---

huggan/fastgan-few-shot-aurora

8234db31eec47eb4720a814f42455ed30b89b6c0

2022-05-06T22:30:43.000Z

[ "pytorch", "dataset:huggan/few-shot-aurora", "arxiv:2101.04775", "huggan", "gan", "unconditional-image-generation", "license:mit" ]

unconditional-image-generation

false

huggan

null

huggan/fastgan-few-shot-aurora

0

null

36,833

--- tags: - huggan - gan - unconditional-image-generation datasets: - huggan/few-shot-aurora # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # Generate aurora image using FastGAN ## Model description [FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets. This model was trained on a dataset of 272 high-quality images of aurora. #### How to use ```python # Clone this model git clone https://huggingface.co/huggan/fastgan-few-shot-aurora/ def load_generator(model_name_or_path): generator = Generator(in_channels=256, out_channels=3) generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3) _ = generator.eval() return generator def _denormalize(input: torch.Tensor) -> torch.Tensor: return (input * 127.5) + 127.5 # Load generator generator = load_generator("huggan/fastgan-few-shot-aurora") # Generate a random noise image noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0) with torch.no_grad(): gan_images, _ = generator(noise) gan_images = _denormalize(gan_images.detach()) save_image(gan_images, "sample.png", nrow=1, normalize=True) ``` #### Limitations and bias * Converge faster and better with small datasets (less than 1000 samples) ## Training data [few-shot-aurora](https://huggingface.co/datasets/huggan/few-shot-aurora) ## Generated Images ![Example image](example.png) ### BibTeX entry and citation info ```bibtex @article{FastGAN, title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis}, author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal}, journal={ICLR}, year={2021} } ```

mimicheng/codeparrot-ds-sample-1ep-12apr

74d9d6d5c5bed469b64e79b2909849c6d58a4044

2022-04-13T07:16:11.000Z

[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

text-generation

false

mimicheng

null

mimicheng/codeparrot-ds-sample-1ep-12apr

0

null

transformers

36,834

--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-1ep-12apr results: [] ---  # codeparrot-ds-sample-1ep-12apr This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9947 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8723 | 0.37 | 1000 | 2.5340 | | 2.1776 | 0.74 | 2000 | 1.9947 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

huggingtweets/radfemman

8e54c05acd96a0f147b5c263888801474421829f

2022-04-13T06:22:23.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/radfemman

0

null

transformers

36,835

--- language: en thumbnail: http://www.huggingtweets.com/radfemman/1649830938917/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1428572680882688005/rqGxWIRJ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Radfem Ally 🇺🇸</div> <div style="text-align: center; font-size: 14px;">@radfemman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Radfem Ally 🇺🇸. | Data | Radfem Ally 🇺🇸 | | --- | --- | | Tweets downloaded | 227 | | Retweets | 33 | | Short tweets | 14 | | Tweets kept | 180 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29ku9tl5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @radfemman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33qza7xp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33qza7xp/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/radfemman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggan/pix2pix-maps

29b247f1f7559f1667e3c516666c631ebfd785a1

2022-04-13T16:25:52.000Z

[ "pytorch", "dataset:huggan/maps", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]

null

false

huggan

null

huggan/pix2pix-maps

0

null

36,836

--- tags: - huggan - gan datasets: - huggan/maps # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # Pix2Pix trained on the maps dataset ## Model description This model is a [Pix2Pix](https://arxiv.org/abs/1611.07004) model trained on the [huggan/maps](https://huggingface.co/datasets/huggan/maps) dataset. The goal for the model is to turn a satellite map into a geographic map à la Google Maps, and the other way around. The model was trained using the [example script](https://github.com/huggingface/community-events/tree/main/huggan/pytorch/pix2pix) provided by HuggingFace as part of the [HugGAN sprint](https://github.com/huggingface/community-events/tree/main/huggan). ## Intended uses & limitations #### How to use ```python from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet from PIL import Image from torchvision.utils import save_image image = Image.open("...") generator = GeneratorUNet.from_pretrained("huggan/pix2pix-maps") pixel_values = transform(image).unsqueeze(0) output = generator(pixel_values) save_image(output, 'output.png', normalize=True) ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data The data used was huggan/maps. ## Training procedure The following command was used: ```bash accelerate launch train.py --dataset huggan/maps --push_to_hub --model_name pix2pix-maps --checkpoint_interval 1 ``` ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/IsolaZZE16, author = {Phillip Isola and Jun{-}Yan Zhu and Tinghui Zhou and Alexei A. Efros}, title = {Image-to-Image Translation with Conditional Adversarial Networks}, journal = {CoRR}, volume = {abs/1611.07004}, year = {2016}, url = {http://arxiv.org/abs/1611.07004}, eprinttype = {arXiv}, eprint = {1611.07004}, timestamp = {Mon, 13 Aug 2018 16:49:05 +0200}, biburl = {https://dblp.org/rec/journals/corr/IsolaZZE16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

obokkkk/kobert-finetuned-klue-v2

ef5f1945d614548e889146ce106b4d581af5e79e

2022-04-13T11:24:54.000Z

[ "pytorch", "bert", "question-answering", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]

question-answering

false

obokkkk

null

obokkkk/kobert-finetuned-klue-v2

0

null

transformers

36,837

--- tags: - generated_from_trainer model-index: - name: kobert-finetuned-klue-v2 results: [] ---  # kobert-finetuned-klue-v2 This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.6289 | 0.54 | 500 | 5.3024 | | 5.3083 | 1.08 | 1000 | 5.3707 | | 5.3518 | 1.62 | 1500 | 5.3039 | | 5.2912 | 2.16 | 2000 | 5.2800 | | 5.2282 | 2.7 | 2500 | 5.2301 | | 5.1498 | 3.24 | 3000 | 5.2435 | | 5.079 | 3.78 | 3500 | 5.1997 | | 4.8886 | 4.32 | 4000 | 5.1350 | | 4.8166 | 4.86 | 4500 | 5.1441 | | 4.5615 | 5.4 | 5000 | 5.1485 | | 4.4183 | 5.94 | 5500 | 5.0775 | | 4.1282 | 6.48 | 6000 | 5.0402 | | 4.1214 | 7.02 | 6500 | 5.1331 | | 3.7792 | 7.56 | 7000 | 5.1314 | | 3.7455 | 8.1 | 7500 | 5.1936 | | 3.5369 | 8.64 | 8000 | 5.1931 | | 3.4832 | 9.18 | 8500 | 5.2678 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

vabadeh213/autotrain-wikihow-737822494

c22a2d5b572f10ea0fd77e1a1cde9c2bf6bd8cf8

2022-04-13T13:07:34.000Z

[ "pytorch", "mt5", "text2text-generation", "ja", "dataset:vabadeh213/autotrain-data-wikihow", "transformers", "autotrain", "co2_eq_emissions", "autotrain_compatible" ]

text2text-generation

false

vabadeh213

null

vabadeh213/autotrain-wikihow-737822494

0

null

transformers

36,838

--- tags: autotrain language: ja widget: - text: "脅威を感じた蛇は再び襲いかかります。したがって、噛まれた際は速やかに蛇の攻撃範囲から離れましょう。少なくとも6mは間合いを取りましょう。できる限り速やかに医療処置を求めることが大切です。ほとんどの病院は、毒蛇用の抗毒素（血清）を用意しています。病院に到着する前の応急手当だけでは、あまり症状の改善にはつながりません。被害現場からすぐさま救急サービスに通報できれば不幸中の幸いです。救急車を呼べない場合は、何としても助けを求め、みなさんまたは被害者を最寄りの病院へ搬送しなければなりません。みなさんに噛みついた蛇がガラガラヘビかどうかが分からない場合でも、すぐに病院へ直行しましょう。実際に毒が体に回り、症状が出始めたとしても、病院にいれば安心できるでしょう。噛まれた箇所を心臓よりも上に置くと、毒を含んだ血液が猛スピードで心臓に流れ込みます。救助が来るまでの間、できれば被害者の体を静止させましょう。体を動かすと血流が増大し、あっという間に毒が回ります。したがって、毒蛇に噛まれた際は体の動きを最小限に抑えて安静にすることが大切です。もちろん、みなさんの周りに誰もいなければ、じっとしている場合ではありません。すぐに助けを求めましょう。" - text: "噛み傷の周囲は大きく腫れ上がります。傷口の周りの衣類はすべて取り除きましょう。また、患部に付けているアクセサリー類も取り外しましょう。アクセサリーを付けたままにすると、患部が腫れた際に血管を締め付けてしまうため、場合によっては大切なアクセサリーを切断する羽目になります。30秒ほど噛み傷からそのまま出血させましょう。出血とともに、ある程度は毒が傷口から排出されるでしょう。毒を少しでも吸引できればそれに越したことはありません。ただし、必ず毒蛇用の吸引器を使いましょう。吸引ポンプには詳しい取扱説明書が付属しているはずですが、基本的にポンプを直接噛み傷に当てて毒を吸い出します。傷口を水で洗ってはいけません。皮膚を洗い流してしまうと、後で毒の種類を特定しにくくなります。医療者は皮膚に残った毒からガラガラヘビの種類を特定し、みなさんの症状に最適な治療法を決定します。添え木または三角巾で固定すれば、患部の血流を抑えることができます。できるだけ血流を抑えて毒の回りを遅らせましょう。腕を吊るす場合は、衣類を三角形に折り畳むか、または三角形に切りましょう。肘を中心にして腕を三角巾で包みます。みなさんまたは被害者の肘を布の形に合わせて曲げましょう。三角巾の端同士を肩口で結び合わせます。肘から先を三角形の底辺で固定したら、手を外に出します。板切れや丸めた新聞紙を添え木にして腕や脚を支えましょう。それらが手元になければ、衣類を丸めて使いましょう。添え木を腕や脚の側面に当て、傷口の上下の関節を伸ばして固定します。ベルト、テープ、包帯といった手元にあるものを使って添え木をしっかりと縛り付けましょう。ただし、直接傷口の上から縛ってはいけません。傷口の上下いずれかの側で縛りましょう。患部の腫れが酷い場合は、添え木の圧迫を緩める必要があります。" - text: "被害者に話しかけましょう。次々に質問を投げかけ、できる限り被害者の意識を噛み傷から逸らしましょう。不安やパニックは心拍を上昇させ、毒の回りを速めます。みなさん自身が噛まれた場合は、とにかく落ち着きましょう。何度かゆっくりと深く息をして緊張をほぐしましょう。アメリカに滞在中のみなさんは、できれば救急車を待つ間、アメリカ中毒情報センター（1-800-222-1222）に連絡を取りましょう。噛まれた箇所が腫れ上がれば、一目でそれは毒蛇によるものと判断できます。それと同時に皮膚の変色が起こるでしょう。また、一連の細かい刺傷が残る無毒蛇の噛み傷とは違い、毒蛇の噛み傷は1カ所ないしは2カ所に目立った刺傷が残るのが特徴です。これは毒牙の発達に伴い、歯が退化しているためです。さらに、めまい、患部の激しい痛み、視覚障害、他の箇所のチクチクした痛み、そして極度の発汗といった症状も、毒蛇による噛み傷のサインです。皮膚が青ざめるのはショック症状の典型です。他のサインとして、心拍の上昇、過呼吸、吐き気、めまいなどがあります。また、被害者の瞳孔が拡大していないかをチェックしましょう。被害者がショック症状を起こしつつある場合は、仰向けに寝かせて足を30cmほど浮かせましょう。さらに、毛布や上着などで体を包んで温めましょう。呼吸、咳、または体の動きといった生体反応が見られない場合は、直ちに心肺蘇生が必要です。これらの物質は毒の回りを加速させます。したがって、ガラガラヘビに噛まれた直後にアルコールまたはカフェイン飲料で水分補給をするのは禁物です。" datasets: - vabadeh213/autotrain-data-wikihow co2_eq_emissions: 361.800665798794 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 737822494 - CO2 Emissions (in grams): 361.800665798794 ## Validation Metrics - Loss: 2.326287031173706 - Rouge1: 5.2053 - Rouge2: 1.8535 - RougeL: 5.2419 - RougeLsum: 5.228 - Gen Len: 18.3677 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/vabadeh213/autotrain-wikihow-737822494 ```

melnikoff-oleg/bart-end-to-end

55ab6562174cca944643de6a1e86875068b41031

2022-04-13T11:59:46.000Z

[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

melnikoff-oleg

null

melnikoff-oleg/bart-end-to-end

0

null

transformers

36,839

Entry not found

huggingtweets/elonmusk-jeffbezos-sweatystartup

94780eada23add9a26e08095981a5bb9fea2170a

2022-04-13T13:47:00.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/elonmusk-jeffbezos-sweatystartup

0

null

transformers

36,840

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/669103856106668033/UF3cgUk4_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1504792567031992320/7EflpzpQ_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Jeff Bezos & Nick Huber</div> <div style="text-align: center; font-size: 14px;">@elonmusk-jeffbezos-sweatystartup</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Jeff Bezos & Nick Huber. | Data | Elon Musk | Jeff Bezos | Nick Huber | | --- | --- | --- | --- | | Tweets downloaded | 221 | 294 | 3250 | | Retweets | 15 | 22 | 44 | | Short tweets | 67 | 14 | 339 | | Tweets kept | 139 | 258 | 2867 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hd9nbhx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-jeffbezos-sweatystartup's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2f15ydkr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2f15ydkr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-jeffbezos-sweatystartup') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

frozenwalker/SciFive_pubmedqa_question_generation

ea678c0cf37b4426457a003fcf0f1de018f991a6

2022-04-19T11:42:33.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

frozenwalker

null

frozenwalker/SciFive_pubmedqa_question_generation

0

null

transformers

36,841

Entry not found

masakhane/afrimt5_bbj_fr_news

e924d50eb15911e45941520dcf7066ece7782f16

2022-04-13T18:28:32.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afrimt5_bbj_fr_news

0

null

transformers

36,842

--- license: afl-3.0 ---

masakhane/afrimbart_bbj_fr_news

d5ec07e79eeb4b46749070963f446a55fad66aef

2022-04-13T18:28:39.000Z

[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afrimbart_bbj_fr_news

0

null

transformers

36,843

--- license: afl-3.0 ---

masakhane/afribyt5_bbj_fr_news

1ed1883a4e0e479b3e7a7b70b08c802a23f7b4ea

2022-04-13T19:29:55.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afribyt5_bbj_fr_news

0

null

transformers

36,844

--- license: afl-3.0 ---

masakhane/byt5_fr_bbj_news

5ba81fa10eeeb493e04df914bf6434e0858df3a2

2022-04-13T19:29:58.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/byt5_fr_bbj_news

0

null

transformers

36,845

--- license: afl-3.0 ---

masakhane/byt5_bbj_fr_news

dc62009e7fd9f773157e06571fe054f931fda04d

2022-04-13T19:30:01.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/byt5_bbj_fr_news

0

null

transformers

36,846

--- license: afl-3.0 ---

masakhane/mt5_bbj_fr_news

2abaff5c158f0feacdd135f8e747e747a3e5c522

2022-04-13T20:41:14.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/mt5_bbj_fr_news

0

null

transformers

36,847

--- license: afl-3.0 ---

masakhane/mt5_fr_bbj_news

547173268338375fca6258a3200692b87225ee9e

2022-04-13T20:41:07.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/mt5_fr_bbj_news

0

null

transformers

36,848

--- license: afl-3.0 ---

masakhane/mbart50_bbj_fr_news

e07fdf1eb0cc81155de359bc23c376512ebd9cc0

2022-04-13T20:41:18.000Z

[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/mbart50_bbj_fr_news

0

null

transformers

36,849

--- license: afl-3.0 ---

masakhane/m2m100_418M_fr_bbj_news

46570681958c9b8e59ee1b8e0bc7f8842598decf

2022-04-13T21:40:15.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_fr_bbj_news

0

null

transformers

36,850

--- license: afl-3.0 ---

masakhane/m2m100_418M_fr_bbj_rel_news

579c9a135b62a74aad340cf55af9caf01507c151

2022-04-13T21:40:19.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_fr_bbj_rel_news

0

null

transformers

36,851

--- license: afl-3.0 ---

masakhane/m2m100_418M_bbj_fr_rel_news

40949ff2d525d1a3985c783f637e1b836094b526

2022-04-13T21:40:24.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_bbj_fr_rel_news

0

null

transformers

36,852

--- license: afl-3.0 ---

masakhane/m2m100_418M_fr_bbj_rel_news_ft

0b00157684120bdf5d7a5ce07897443df73b0a42

2022-04-14T08:42:45.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_fr_bbj_rel_news_ft

0

null

transformers

36,853

--- license: afl-3.0 ---

masakhane/m2m100_418M_fr_bbj_rel

8635f78e4017ff014116d6f0b031a0b14c62109d

2022-04-14T08:42:48.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_fr_bbj_rel

0

null

transformers

36,854

--- license: afl-3.0 ---

masakhane/m2m100_418M_fr_bbj_rel_ft

144e9b6b42351bb0ce3735abc4b7cdd1b3252b7d

2022-04-14T10:00:19.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_fr_bbj_rel_ft

0

null

transformers

36,855

--- license: afl-3.0 ---

masakhane/m2m100_418M_bbj_fr_rel_ft

1f99875257572a5a807d696773a8e775d5bab245

2022-04-14T10:00:22.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_bbj_fr_rel_ft

0

null

transformers

36,856

--- license: afl-3.0 ---

ntoldalagi/nick_asr_fresh

136346b60c1781725fa77427cc32c17b1ee7d2f7

2022-04-15T01:04:08.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

ntoldalagi

null

ntoldalagi/nick_asr_fresh

0

null

transformers

36,857

Entry not found

lilitket/20220413-210552

bdac66b4f511f417c2380db3409241edf37a6baa

2022-04-14T05:44:32.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

lilitket

null

lilitket/20220413-210552

0

null

transformers

36,858

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: 20220413-210552 results: [] ---  # 20220413-210552 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.0348 - Wer: 1.0006 ## 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-06 - train_batch_size: 1 - 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: 2000 - num_epochs: 1200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:------:|:---------------:|:------:| | 17.1111 | 1.5 | 200 | 16.6792 | 1.0 | | 16.0992 | 3.01 | 400 | 15.3947 | 1.0 | | 10.7668 | 4.51 | 600 | 10.3625 | 1.0 | | 6.2652 | 6.02 | 800 | 7.6849 | 1.0 | | 5.1417 | 7.52 | 1000 | 6.0307 | 1.0 | | 4.6159 | 9.02 | 1200 | 5.0891 | 1.0 | | 4.2444 | 10.53 | 1400 | 4.4120 | 1.0 | | 3.8935 | 12.03 | 1600 | 3.9570 | 1.0 | | 3.6292 | 13.53 | 1800 | 3.6405 | 1.0 | | 3.4535 | 15.04 | 2000 | 3.4523 | 1.0 | | 3.3175 | 16.54 | 2200 | 3.3589 | 1.0 | | 3.2266 | 18.05 | 2400 | 3.2966 | 1.0 | | 3.1825 | 19.55 | 2600 | 3.2658 | 1.0 | | 3.1604 | 21.05 | 2800 | 3.2534 | 1.0 | | 3.1438 | 22.56 | 3000 | 3.2437 | 1.0 | | 3.1176 | 24.06 | 3200 | 3.2169 | 1.0 | | 3.1088 | 25.56 | 3400 | 3.2102 | 1.0 | | 3.0955 | 27.07 | 3600 | 3.1983 | 1.0 | | 3.0763 | 28.57 | 3800 | 3.2092 | 1.0 | | 3.0599 | 30.08 | 4000 | 3.2092 | 1.0 | | 3.0385 | 31.58 | 4200 | 3.2154 | 1.0 | | 2.9996 | 33.08 | 4400 | 3.2120 | 1.0 | | 2.9207 | 34.59 | 4600 | 3.2146 | 1.0 | | 2.8071 | 36.09 | 4800 | 3.2093 | 1.0 | | 2.6412 | 37.59 | 5000 | 3.2282 | 1.0 | | 2.4594 | 39.1 | 5200 | 3.2442 | 1.0 | | 2.2708 | 40.6 | 5400 | 3.2944 | 1.0 | | 2.1279 | 42.11 | 5600 | 3.3260 | 1.0 | | 1.9985 | 43.61 | 5800 | 3.3586 | 1.0 | | 1.8979 | 45.11 | 6000 | 3.3945 | 1.0 | | 1.7838 | 46.62 | 6200 | 3.4761 | 1.0 | | 1.6774 | 48.12 | 6400 | 3.4886 | 1.0 | | 1.5958 | 49.62 | 6600 | 3.6208 | 1.0 | | 1.4957 | 51.13 | 6800 | 3.6501 | 1.0 | | 1.4202 | 52.63 | 7000 | 3.6492 | 1.0 | | 1.3377 | 54.14 | 7200 | 3.7392 | 1.0 | | 1.2872 | 55.64 | 7400 | 3.8624 | 1.0 | | 1.1992 | 57.14 | 7600 | 3.8511 | 1.0 | | 1.1238 | 58.65 | 7800 | 3.9662 | 1.0 | | 1.0775 | 60.15 | 8000 | 3.9267 | 1.0 | | 1.011 | 61.65 | 8200 | 4.0933 | 1.0 | | 0.962 | 63.16 | 8400 | 4.0941 | 1.0 | | 0.9041 | 64.66 | 8600 | 4.1163 | 1.0 | | 0.8552 | 66.17 | 8800 | 4.1937 | 1.0 | | 0.8054 | 67.67 | 9000 | 4.2277 | 1.0 | | 0.7457 | 69.17 | 9200 | 4.3899 | 1.0 | | 0.7292 | 70.68 | 9400 | 4.3621 | 1.0 | | 0.6635 | 72.18 | 9600 | 4.4706 | 1.0 | | 0.6333 | 73.68 | 9800 | 4.4571 | 1.0 | | 0.6109 | 75.19 | 10000 | 4.4594 | 1.0 | | 0.5611 | 76.69 | 10200 | 4.5672 | 1.0 | | 0.5286 | 78.2 | 10400 | 4.4957 | 1.0 | | 0.4894 | 79.7 | 10600 | 4.5278 | 1.0 | | 0.4831 | 81.2 | 10800 | 4.4604 | 1.0 | | 0.4575 | 82.71 | 11000 | 4.7439 | 1.0 | | 0.4418 | 84.21 | 11200 | 4.6511 | 1.0 | | 0.4085 | 85.71 | 11400 | 4.5008 | 1.0 | | 0.4011 | 87.22 | 11600 | 4.7690 | 1.0 | | 0.3791 | 88.72 | 11800 | 4.8675 | 1.0 | | 0.3487 | 90.23 | 12000 | 5.0327 | 1.0 | | 0.3661 | 91.73 | 12200 | 4.8084 | 1.0 | | 0.3306 | 93.23 | 12400 | 4.9095 | 1.0 | | 0.3449 | 94.74 | 12600 | 4.8223 | 1.0 | | 0.2949 | 96.24 | 12800 | 4.8245 | 1.0 | | 0.2987 | 97.74 | 13000 | 5.0803 | 1.0 | | 0.2896 | 99.25 | 13200 | 5.2074 | 1.0 | | 0.2731 | 100.75 | 13400 | 5.1951 | 1.0 | | 0.2749 | 102.26 | 13600 | 5.2071 | 1.0 | | 0.2554 | 103.76 | 13800 | 5.0861 | 1.0 | | 0.2436 | 105.26 | 14000 | 5.0851 | 1.0 | | 0.2494 | 106.77 | 14200 | 4.8623 | 1.0 | | 0.23 | 108.27 | 14400 | 5.0466 | 1.0 | | 0.2345 | 109.77 | 14600 | 5.2474 | 1.0 | | 0.2233 | 111.28 | 14800 | 4.9394 | 1.0 | | 0.2231 | 112.78 | 15000 | 4.9572 | 1.0 | | 0.213 | 114.29 | 15200 | 5.3215 | 1.0 | | 0.2002 | 115.79 | 15400 | 5.3042 | 1.0 | | 0.2023 | 117.29 | 15600 | 5.0279 | 1.0 | | 0.2074 | 118.8 | 15800 | 4.9727 | 1.0 | | 0.2071 | 120.3 | 16000 | 4.6775 | 1.0 | | 0.1915 | 121.8 | 16200 | 4.8386 | 1.0 | | 0.1899 | 123.31 | 16400 | 4.7898 | 1.0 | | 0.1821 | 124.81 | 16600 | 5.3147 | 1.0 | | 0.1908 | 126.32 | 16800 | 5.6218 | 1.0 | | 0.1712 | 127.82 | 17000 | 4.6083 | 1.0 | | 0.1705 | 129.32 | 17200 | 5.2468 | 1.0 | | 0.1664 | 130.83 | 17400 | 5.0412 | 1.0 | | 0.167 | 132.33 | 17600 | 5.0116 | 1.0 | | 0.162 | 133.83 | 17800 | 5.2799 | 1.0 | | 0.1561 | 135.34 | 18000 | 5.2485 | 1.0 | | 0.1501 | 136.84 | 18200 | 5.1109 | 1.0 | | 0.14 | 138.35 | 18400 | 5.2310 | 1.0 | | 0.1576 | 139.85 | 18600 | 5.1631 | 1.0 | | 0.1433 | 141.35 | 18800 | 5.3497 | 1.0 | | 0.148 | 142.86 | 19000 | 4.8892 | 1.0 | | 0.1525 | 144.36 | 19200 | 4.8522 | 1.0 | | 0.1517 | 145.86 | 19400 | 4.7830 | 1.0 | | 0.139 | 147.37 | 19600 | 5.2041 | 1.0 | | 0.1392 | 148.87 | 19800 | 4.7968 | 1.0 | | 0.1351 | 150.38 | 20000 | 5.0326 | 1.0 | | 0.1355 | 151.88 | 20200 | 5.0474 | 1.0 | | 0.138 | 153.38 | 20400 | 4.7491 | 1.0006 | | 0.1332 | 154.89 | 20600 | 5.3905 | 1.0 | | 0.1252 | 156.39 | 20800 | 4.9815 | 1.0 | | 0.1179 | 157.89 | 21000 | 5.3281 | 1.0 | | 0.1228 | 159.4 | 21200 | 5.1108 | 1.0006 | | 0.1311 | 160.9 | 21400 | 4.8016 | 1.0 | | 0.1278 | 162.41 | 21600 | 4.8306 | 1.0 | | 0.1209 | 163.91 | 21800 | 4.6413 | 1.0 | | 0.1199 | 165.41 | 22000 | 4.6375 | 1.0 | | 0.1172 | 166.92 | 22200 | 4.9108 | 1.0 | | 0.1247 | 168.42 | 22400 | 4.6139 | 1.0006 | | 0.1121 | 169.92 | 22600 | 4.4765 | 1.0006 | | 0.125 | 171.43 | 22800 | 4.6819 | 1.0006 | | 0.1259 | 172.93 | 23000 | 4.9577 | 1.0 | | 0.1044 | 174.44 | 23200 | 5.2993 | 1.0006 | | 0.1107 | 175.94 | 23400 | 4.3140 | 1.0 | | 0.1142 | 177.44 | 23600 | 4.5850 | 1.0 | | 0.0971 | 178.95 | 23800 | 4.8177 | 1.0006 | | 0.1186 | 180.45 | 24000 | 4.9972 | 1.0 | | 0.1164 | 181.95 | 24200 | 4.5840 | 1.0 | | 0.1014 | 183.46 | 24400 | 4.9117 | 0.9994 | | 0.1087 | 184.96 | 24600 | 4.5646 | 1.0006 | | 0.1075 | 186.47 | 24800 | 4.6995 | 1.0 | | 0.1111 | 187.97 | 25000 | 4.7877 | 1.0 | | 0.1079 | 189.47 | 25200 | 4.8420 | 1.0 | | 0.1053 | 190.98 | 25400 | 5.1083 | 1.0 | | 0.1048 | 192.48 | 25600 | 4.2876 | 1.0 | | 0.0974 | 193.98 | 25800 | 4.6699 | 1.0006 | | 0.0983 | 195.49 | 26000 | 4.6522 | 1.0 | | 0.0935 | 196.99 | 26200 | 4.9879 | 1.0 | | 0.0948 | 198.5 | 26400 | 4.4146 | 1.0 | | 0.0867 | 200.0 | 26600 | 5.1909 | 1.0 | | 0.0932 | 201.5 | 26800 | 5.2019 | 1.0 | | 0.0951 | 203.01 | 27000 | 3.6893 | 1.0 | | 0.085 | 204.51 | 27200 | 4.3071 | 1.0006 | | 0.0912 | 206.02 | 27400 | 4.4651 | 1.0 | | 0.092 | 207.52 | 27600 | 4.4218 | 1.0 | | 0.0936 | 209.02 | 27800 | 5.1391 | 1.0 | | 0.0989 | 210.53 | 28000 | 4.8787 | 1.0006 | | 0.0898 | 212.03 | 28200 | 4.1418 | 1.0013 | | 0.0943 | 213.53 | 28400 | 4.1857 | 1.0 | | 0.0834 | 215.04 | 28600 | 4.3519 | 1.0 | | 0.0851 | 216.54 | 28800 | 4.3612 | 1.0006 | | 0.0932 | 218.05 | 29000 | 4.2200 | 1.0006 | | 0.0848 | 219.55 | 29200 | 4.2054 | 1.0 | | 0.0873 | 221.05 | 29400 | 4.4815 | 1.0 | | 0.0949 | 222.56 | 29600 | 3.9426 | 1.0 | | 0.0856 | 224.06 | 29800 | 3.7650 | 1.0 | | 0.0768 | 225.56 | 30000 | 3.9774 | 1.0 | | 0.0823 | 227.07 | 30200 | 4.3728 | 1.0 | | 0.0913 | 228.57 | 30400 | 3.7813 | 1.0 | | 0.0951 | 230.08 | 30600 | 4.1581 | 1.0 | | 0.0843 | 231.58 | 30800 | 4.6891 | 1.0 | | 0.0879 | 233.08 | 31000 | 4.2984 | 1.0 | | 0.0807 | 234.59 | 31200 | 3.9511 | 1.0 | | 0.0765 | 236.09 | 31400 | 3.8094 | 1.0 | | 0.0861 | 237.59 | 31600 | 4.3118 | 1.0 | | 0.0596 | 239.1 | 31800 | 4.0774 | 1.0006 | | 0.0752 | 240.6 | 32000 | 3.6005 | 1.0 | | 0.0729 | 242.11 | 32200 | 4.8616 | 1.0 | | 0.0783 | 243.61 | 32400 | 3.9858 | 1.0 | | 0.0759 | 245.11 | 32600 | 4.1231 | 1.0 | | 0.08 | 246.62 | 32800 | 4.5182 | 1.0 | | 0.0782 | 248.12 | 33000 | 3.7721 | 1.0 | | 0.0914 | 249.62 | 33200 | 3.5902 | 1.0 | | 0.0668 | 251.13 | 33400 | 3.9673 | 1.0 | | 0.0798 | 252.63 | 33600 | 3.8693 | 1.0 | | 0.0814 | 254.14 | 33800 | 3.9804 | 1.0006 | | 0.0775 | 255.64 | 34000 | 3.9483 | 1.0 | | 0.0721 | 257.14 | 34200 | 4.6892 | 1.0 | | 0.0722 | 258.65 | 34400 | 4.1972 | 1.0 | | 0.0755 | 260.15 | 34600 | 4.4523 | 1.0 | | 0.0683 | 261.65 | 34800 | 4.1090 | 1.0 | | 0.0698 | 263.16 | 35000 | 4.0634 | 1.0 | | 0.0712 | 264.66 | 35200 | 4.0469 | 1.0006 | | 0.0754 | 266.17 | 35400 | 4.0113 | 1.0006 | | 0.0709 | 267.67 | 35600 | 4.0592 | 1.0 | | 0.0637 | 269.17 | 35800 | 3.7540 | 1.0 | | 0.0688 | 270.68 | 36000 | 3.9645 | 1.0 | | 0.0592 | 272.18 | 36200 | 3.7443 | 1.0 | | 0.0585 | 273.68 | 36400 | 3.8287 | 1.0 | | 0.0734 | 275.19 | 36600 | 3.6780 | 1.0 | | 0.058 | 276.69 | 36800 | 4.0194 | 1.0 | | 0.0707 | 278.2 | 37000 | 3.6663 | 1.0006 | | 0.0728 | 279.7 | 37200 | 3.8640 | 1.0 | | 0.064 | 281.2 | 37400 | 4.5473 | 1.0 | | 0.0583 | 282.71 | 37600 | 4.1813 | 1.0 | | 0.0634 | 284.21 | 37800 | 3.8821 | 1.0 | | 0.0565 | 285.71 | 38000 | 3.9566 | 1.0006 | | 0.0735 | 287.22 | 38200 | 4.5317 | 1.0 | | 0.0797 | 288.72 | 38400 | 3.8040 | 1.0 | | 0.0601 | 290.23 | 38600 | 4.0956 | 1.0 | | 0.0599 | 291.73 | 38800 | 4.0592 | 1.0 | | 0.0517 | 293.23 | 39000 | 3.5204 | 1.0006 | | 0.0622 | 294.74 | 39200 | 4.1739 | 1.0 | | 0.0705 | 296.24 | 39400 | 4.0262 | 1.0 | | 0.0589 | 297.74 | 39600 | 4.2476 | 1.0 | | 0.0606 | 299.25 | 39800 | 3.7931 | 1.0 | | 0.0603 | 300.75 | 40000 | 4.0540 | 0.9994 | | 0.0568 | 302.26 | 40200 | 3.5900 | 1.0 | | 0.0583 | 303.76 | 40400 | 3.8095 | 1.0 | | 0.0513 | 305.26 | 40600 | 3.8949 | 1.0 | | 0.0637 | 306.77 | 40800 | 3.8085 | 1.0 | | 0.0659 | 308.27 | 41000 | 4.2311 | 1.0 | | 0.068 | 309.77 | 41200 | 3.4876 | 1.0006 | | 0.0616 | 311.28 | 41400 | 3.7634 | 1.0 | | 0.0515 | 312.78 | 41600 | 3.8762 | 1.0 | | 0.0584 | 314.29 | 41800 | 4.2070 | 1.0 | | 0.054 | 315.79 | 42000 | 3.9088 | 1.0 | | 0.0571 | 317.29 | 42200 | 3.9679 | 1.0006 | | 0.0497 | 318.8 | 42400 | 3.8443 | 1.0 | | 0.0507 | 320.3 | 42600 | 4.2397 | 1.0 | | 0.0612 | 321.8 | 42800 | 4.2228 | 1.0 | | 0.0467 | 323.31 | 43000 | 3.6684 | 1.0 | | 0.0586 | 324.81 | 43200 | 3.8685 | 1.0013 | | 0.0557 | 326.32 | 43400 | 4.3341 | 1.0006 | | 0.0584 | 327.82 | 43600 | 3.6683 | 1.0 | | 0.0575 | 329.32 | 43800 | 3.9005 | 1.0 | | 0.0571 | 330.83 | 44000 | 3.8594 | 1.0 | | 0.0471 | 332.33 | 44200 | 3.6871 | 1.0 | | 0.055 | 333.83 | 44400 | 4.0402 | 1.0 | | 0.0422 | 335.34 | 44600 | 4.0226 | 1.0 | | 0.0422 | 336.84 | 44800 | 3.5907 | 1.0 | | 0.0513 | 338.35 | 45000 | 3.7380 | 1.0 | | 0.0593 | 339.85 | 45200 | 3.8530 | 1.0 | | 0.0446 | 341.35 | 45400 | 4.0879 | 1.0 | | 0.0492 | 342.86 | 45600 | 3.8984 | 1.0 | | 0.0422 | 344.36 | 45800 | 4.2423 | 1.0 | | 0.0478 | 345.86 | 46000 | 3.6391 | 1.0 | | 0.0425 | 347.37 | 46200 | 4.2352 | 1.0 | | 0.0426 | 348.87 | 46400 | 4.0004 | 1.0 | | 0.0566 | 350.38 | 46600 | 4.0957 | 1.0006 | | 0.0522 | 351.88 | 46800 | 3.9436 | 0.9994 | | 0.0503 | 353.38 | 47000 | 4.3325 | 1.0 | | 0.0513 | 354.89 | 47200 | 3.5738 | 0.9994 | | 0.0428 | 356.39 | 47400 | 3.8233 | 0.9994 | | 0.0402 | 357.89 | 47600 | 3.6210 | 1.0006 | | 0.0575 | 359.4 | 47800 | 3.6991 | 1.0 | | 0.0459 | 360.9 | 48000 | 3.8384 | 1.0 | | 0.0423 | 362.41 | 48200 | 4.2164 | 1.0 | | 0.0401 | 363.91 | 48400 | 4.0001 | 1.0 | | 0.0612 | 365.41 | 48600 | 4.1363 | 0.9994 | | 0.0492 | 366.92 | 48800 | 4.0748 | 0.9994 | | 0.0467 | 368.42 | 49000 | 3.6856 | 0.9994 | | 0.0565 | 369.92 | 49200 | 4.1829 | 1.0 | | 0.0351 | 371.43 | 49400 | 3.9579 | 1.0006 | | 0.0499 | 372.93 | 49600 | 3.8893 | 1.0 | | 0.0477 | 374.44 | 49800 | 3.5199 | 1.0 | | 0.0471 | 375.94 | 50000 | 4.0405 | 1.0013 | | 0.037 | 377.44 | 50200 | 3.8785 | 1.0006 | | 0.0382 | 378.95 | 50400 | 4.2958 | 1.0013 | | 0.0553 | 380.45 | 50600 | 4.3845 | 1.0006 | | 0.0389 | 381.95 | 50800 | 3.7282 | 1.0013 | | 0.0373 | 383.46 | 51000 | 4.0840 | 1.0006 | | 0.0597 | 384.96 | 51200 | 4.0250 | 1.0006 | | 0.0404 | 386.47 | 51400 | 3.6077 | 0.9994 | | 0.0501 | 387.97 | 51600 | 3.5198 | 0.9994 | | 0.0432 | 389.47 | 51800 | 3.7678 | 1.0 | | 0.0462 | 390.98 | 52000 | 3.6467 | 1.0 | | 0.0444 | 392.48 | 52200 | 3.8941 | 1.0006 | | 0.0494 | 393.98 | 52400 | 3.6969 | 1.0 | | 0.0402 | 395.49 | 52600 | 3.5324 | 1.0006 | | 0.0402 | 396.99 | 52800 | 3.8874 | 1.0006 | | 0.0395 | 398.5 | 53000 | 3.3793 | 1.0006 | | 0.0367 | 400.0 | 53200 | 3.6539 | 1.0013 | | 0.0374 | 401.5 | 53400 | 3.4230 | 1.0 | | 0.0407 | 403.01 | 53600 | 3.6068 | 1.0 | | 0.0378 | 404.51 | 53800 | 3.5291 | 1.0013 | | 0.0471 | 406.02 | 54000 | 3.3563 | 1.0006 | | 0.0336 | 407.52 | 54200 | 2.9932 | 1.0044 | | 0.032 | 409.02 | 54400 | 3.3549 | 1.0006 | | 0.0474 | 410.53 | 54600 | 3.1140 | 1.0013 | | 0.0402 | 412.03 | 54800 | 2.8515 | 1.0075 | | 0.0366 | 413.53 | 55000 | 3.1142 | 1.0050 | | 0.041 | 415.04 | 55200 | 3.0917 | 1.0056 | | 0.0358 | 416.54 | 55400 | 3.3401 | 1.0006 | | 0.0299 | 418.05 | 55600 | 3.5304 | 1.0 | | 0.0512 | 419.55 | 55800 | 3.4768 | 1.0 | | 0.0374 | 421.05 | 56000 | 3.2792 | 0.9994 | | 0.037 | 422.56 | 56200 | 3.0088 | 1.0013 | | 0.0395 | 424.06 | 56400 | 3.0185 | 0.9994 | | 0.039 | 425.56 | 56600 | 3.0249 | 1.0 | | 0.0345 | 427.07 | 56800 | 3.3437 | 1.0 | | 0.0438 | 428.57 | 57000 | 3.0905 | 1.0 | | 0.0373 | 430.08 | 57200 | 3.4256 | 1.0019 | | 0.0362 | 431.58 | 57400 | 3.4747 | 1.0006 | | 0.0353 | 433.08 | 57600 | 3.3952 | 1.0019 | | 0.0363 | 434.59 | 57800 | 3.3967 | 1.0006 | | 0.0234 | 436.09 | 58000 | 3.5076 | 0.9994 | | 0.0347 | 437.59 | 58200 | 3.4128 | 0.9987 | | 0.039 | 439.1 | 58400 | 3.5784 | 1.0013 | | 0.041 | 440.6 | 58600 | 3.6292 | 1.0013 | | 0.0348 | 442.11 | 58800 | 3.3197 | 0.9981 | | 0.0285 | 443.61 | 59000 | 3.0545 | 0.9981 | | 0.0293 | 445.11 | 59200 | 3.1784 | 1.0013 | | 0.0394 | 446.62 | 59400 | 2.8197 | 0.9975 | | 0.0282 | 448.12 | 59600 | 3.1450 | 1.0 | | 0.0387 | 449.62 | 59800 | 2.8260 | 0.9987 | | 0.0348 | 451.13 | 60000 | 3.0389 | 0.9987 | | 0.03 | 452.63 | 60200 | 3.1857 | 0.9981 | | 0.029 | 454.14 | 60400 | 3.1893 | 1.0013 | | 0.0333 | 455.64 | 60600 | 3.1428 | 0.9981 | | 0.027 | 457.14 | 60800 | 3.5792 | 0.9994 | | 0.037 | 458.65 | 61000 | 3.4338 | 1.0 | | 0.0327 | 460.15 | 61200 | 2.9592 | 1.0056 | | 0.0283 | 461.65 | 61400 | 3.3657 | 1.0031 | | 0.0337 | 463.16 | 61600 | 3.1392 | 1.0025 | | 0.0275 | 464.66 | 61800 | 3.2706 | 1.0019 | | 0.0304 | 466.17 | 62000 | 3.5289 | 1.0019 | | 0.042 | 467.67 | 62200 | 3.2222 | 1.0019 | | 0.035 | 469.17 | 62400 | 3.3533 | 1.0038 | | 0.038 | 470.68 | 62600 | 3.7162 | 1.0006 | | 0.0297 | 472.18 | 62800 | 3.2382 | 1.0025 | | 0.0314 | 473.68 | 63000 | 3.2555 | 1.0006 | | 0.0288 | 475.19 | 63200 | 3.8791 | 1.0013 | | 0.0336 | 476.69 | 63400 | 3.1988 | 1.0006 | | 0.0371 | 478.2 | 63600 | 3.1025 | 1.0006 | | 0.0288 | 479.7 | 63800 | 3.2712 | 1.0006 | | 0.0374 | 481.2 | 64000 | 3.4452 | 0.9987 | | 0.0329 | 482.71 | 64200 | 3.3956 | 1.0 | | 0.036 | 484.21 | 64400 | 3.4541 | 1.0006 | | 0.03 | 485.71 | 64600 | 3.2153 | 1.0 | | 0.0319 | 487.22 | 64800 | 2.9109 | 0.9962 | | 0.0276 | 488.72 | 65000 | 3.0832 | 0.9975 | | 0.0315 | 490.23 | 65200 | 3.4669 | 1.0 | | 0.0244 | 491.73 | 65400 | 3.4678 | 0.9994 | | 0.0306 | 493.23 | 65600 | 3.6775 | 0.9987 | | 0.0266 | 494.74 | 65800 | 3.3509 | 1.0 | | 0.0247 | 496.24 | 66000 | 3.8882 | 1.0006 | | 0.0322 | 497.74 | 66200 | 3.5801 | 0.9994 | | 0.04 | 499.25 | 66400 | 3.6454 | 1.0006 | | 0.0313 | 500.75 | 66600 | 3.9949 | 1.0013 | | 0.0222 | 502.26 | 66800 | 3.5645 | 0.9987 | | 0.0257 | 503.76 | 67000 | 3.7539 | 0.9994 | | 0.028 | 505.26 | 67200 | 3.6815 | 1.0006 | | 0.0256 | 506.77 | 67400 | 3.3799 | 1.0013 | | 0.0257 | 508.27 | 67600 | 3.6969 | 1.0013 | | 0.0271 | 509.77 | 67800 | 3.7083 | 0.9994 | | 0.0265 | 511.28 | 68000 | 3.3015 | 0.9994 | | 0.0289 | 512.78 | 68200 | 3.7103 | 1.0006 | | 0.0337 | 514.29 | 68400 | 3.1877 | 1.0006 | | 0.0396 | 515.79 | 68600 | 3.7914 | 1.0 | | 0.0316 | 517.29 | 68800 | 3.4757 | 0.9981 | | 0.0303 | 518.8 | 69000 | 3.4129 | 0.9987 | | 0.0298 | 520.3 | 69200 | 3.1453 | 1.0 | | 0.0257 | 521.8 | 69400 | 3.4649 | 1.0 | | 0.0252 | 523.31 | 69600 | 3.2707 | 0.9994 | | 0.0254 | 524.81 | 69800 | 3.0304 | 1.0056 | | 0.0199 | 526.32 | 70000 | 3.4323 | 1.0019 | | 0.0333 | 527.82 | 70200 | 3.4081 | 1.0006 | | 0.0205 | 529.32 | 70400 | 2.9900 | 1.0188 | | 0.0223 | 530.83 | 70600 | 3.3499 | 1.0031 | | 0.0261 | 532.33 | 70800 | 3.2094 | 0.9975 | | 0.0198 | 533.83 | 71000 | 3.2793 | 1.0063 | | 0.021 | 535.34 | 71200 | 3.2266 | 0.9975 | | 0.022 | 536.84 | 71400 | 3.1391 | 0.9994 | | 0.0215 | 538.35 | 71600 | 3.4587 | 0.9994 | | 0.0269 | 539.85 | 71800 | 3.2814 | 0.9994 | | 0.0226 | 541.35 | 72000 | 3.2583 | 0.9981 | | 0.0257 | 542.86 | 72200 | 3.7259 | 0.9987 | | 0.0259 | 544.36 | 72400 | 3.5004 | 0.9994 | | 0.0223 | 545.86 | 72600 | 3.3689 | 0.9975 | | 0.0307 | 547.37 | 72800 | 3.0706 | 0.9950 | | 0.0234 | 548.87 | 73000 | 3.0892 | 0.9969 | | 0.0243 | 550.38 | 73200 | 3.4028 | 0.9987 | | 0.0215 | 551.88 | 73400 | 2.9474 | 0.9981 | | 0.0224 | 553.38 | 73600 | 3.3028 | 0.9981 | | 0.0282 | 554.89 | 73800 | 3.5360 | 0.9994 | | 0.0282 | 556.39 | 74000 | 3.3730 | 0.9981 | | 0.0278 | 557.89 | 74200 | 3.2750 | 1.0 | | 0.0198 | 559.4 | 74400 | 3.0983 | 1.0025 | | 0.0229 | 560.9 | 74600 | 3.2628 | 1.0006 | | 0.0234 | 562.41 | 74800 | 2.9351 | 0.9987 | | 0.0255 | 563.91 | 75000 | 2.9952 | 0.9962 | | 0.0267 | 565.41 | 75200 | 3.1814 | 0.9969 | | 0.0217 | 566.92 | 75400 | 3.1355 | 1.0006 | | 0.0236 | 568.42 | 75600 | 3.5030 | 1.0019 | | 0.0249 | 569.92 | 75800 | 2.8262 | 1.0006 | | 0.0192 | 571.43 | 76000 | 3.0427 | 1.0006 | | 0.0194 | 572.93 | 76200 | 3.0055 | 0.9981 | | 0.0257 | 574.44 | 76400 | 3.2506 | 0.9987 | | 0.0214 | 575.94 | 76600 | 3.2980 | 0.9981 | | 0.0314 | 577.44 | 76800 | 3.4304 | 0.9981 | | 0.0186 | 578.95 | 77000 | 3.2306 | 0.9987 | | 0.0219 | 580.45 | 77200 | 3.4410 | 0.9994 | | 0.0244 | 581.95 | 77400 | 3.3982 | 0.9994 | | 0.0232 | 583.46 | 77600 | 3.0487 | 0.9962 | | 0.026 | 584.96 | 77800 | 2.9061 | 1.0 | | 0.0206 | 586.47 | 78000 | 2.8331 | 1.0006 | | 0.0202 | 587.97 | 78200 | 2.8493 | 0.9956 | | 0.0251 | 589.47 | 78400 | 3.0203 | 0.9956 | | 0.0231 | 590.98 | 78600 | 3.0142 | 0.9969 | | 0.0183 | 592.48 | 78800 | 3.1480 | 0.9969 | | 0.022 | 593.98 | 79000 | 3.0777 | 0.9975 | | 0.0225 | 595.49 | 79200 | 3.3248 | 0.9975 | | 0.0186 | 596.99 | 79400 | 3.3309 | 0.9994 | | 0.0184 | 598.5 | 79600 | 3.4888 | 0.9981 | | 0.0181 | 600.0 | 79800 | 3.2628 | 0.9962 | | 0.0158 | 601.5 | 80000 | 3.4862 | 0.9975 | | 0.0197 | 603.01 | 80200 | 3.2335 | 0.9962 | | 0.0259 | 604.51 | 80400 | 3.1643 | 0.9987 | | 0.0218 | 606.02 | 80600 | 3.6256 | 0.9969 | | 0.0196 | 607.52 | 80800 | 3.3183 | 1.0 | | 0.0127 | 609.02 | 81000 | 3.7223 | 0.9987 | | 0.0182 | 610.53 | 81200 | 3.2768 | 0.9994 | | 0.0234 | 612.03 | 81400 | 3.3749 | 1.0 | | 0.0195 | 613.53 | 81600 | 3.2097 | 0.9987 | | 0.0259 | 615.04 | 81800 | 3.2090 | 0.9987 | | 0.0217 | 616.54 | 82000 | 3.3893 | 0.9981 | | 0.0181 | 618.05 | 82200 | 3.2223 | 0.9994 | | 0.0155 | 619.55 | 82400 | 3.1307 | 0.9969 | | 0.0192 | 621.05 | 82600 | 3.0695 | 0.9975 | | 0.0155 | 622.56 | 82800 | 3.3623 | 0.9975 | | 0.0266 | 624.06 | 83000 | 3.0329 | 0.9937 | | 0.017 | 625.56 | 83200 | 2.8108 | 0.9956 | | 0.0224 | 627.07 | 83400 | 3.2747 | 0.9981 | | 0.0202 | 628.57 | 83600 | 3.1392 | 0.9981 | | 0.0157 | 630.08 | 83800 | 2.9493 | 0.9975 | | 0.0187 | 631.58 | 84000 | 2.9518 | 0.9969 | | 0.0234 | 633.08 | 84200 | 3.3938 | 0.9981 | | 0.0205 | 634.59 | 84400 | 2.9594 | 0.9937 | | 0.0161 | 636.09 | 84600 | 2.7611 | 0.9944 | | 0.0187 | 637.59 | 84800 | 2.8704 | 0.9969 | | 0.0285 | 639.1 | 85000 | 3.1030 | 0.9987 | | 0.0163 | 640.6 | 85200 | 3.2914 | 0.9975 | | 0.0216 | 642.11 | 85400 | 2.9154 | 0.9969 | | 0.0233 | 643.61 | 85600 | 2.7749 | 0.9975 | | 0.0218 | 645.11 | 85800 | 2.8473 | 1.0 | | 0.0196 | 646.62 | 86000 | 2.5928 | 1.0038 | | 0.0158 | 648.12 | 86200 | 2.8972 | 0.9962 | | 0.0156 | 649.62 | 86400 | 2.9890 | 0.9981 | | 0.0131 | 651.13 | 86600 | 2.7676 | 0.9987 | | 0.0206 | 652.63 | 86800 | 2.8754 | 0.9944 | | 0.0197 | 654.14 | 87000 | 3.0768 | 0.9962 | | 0.0139 | 655.64 | 87200 | 2.6567 | 0.9975 | | 0.0139 | 657.14 | 87400 | 3.1492 | 0.9987 | | 0.0226 | 658.65 | 87600 | 2.7989 | 0.9975 | | 0.0182 | 660.15 | 87800 | 3.0076 | 1.0013 | | 0.0164 | 661.65 | 88000 | 2.9625 | 0.9975 | | 0.0221 | 663.16 | 88200 | 2.9768 | 0.9987 | | 0.0144 | 664.66 | 88400 | 3.0221 | 0.9962 | | 0.0178 | 666.17 | 88600 | 2.9881 | 0.9981 | | 0.0214 | 667.67 | 88800 | 3.3127 | 0.9994 | | 0.0193 | 669.17 | 89000 | 3.0776 | 0.9969 | | 0.0211 | 670.68 | 89200 | 2.8097 | 0.9975 | | 0.0223 | 672.18 | 89400 | 3.1911 | 0.9975 | | 0.0222 | 673.68 | 89600 | 3.0041 | 0.9969 | | 0.0209 | 675.19 | 89800 | 2.9853 | 0.9975 | | 0.0116 | 676.69 | 90000 | 2.9112 | 0.9994 | | 0.0159 | 678.2 | 90200 | 2.8274 | 0.9969 | | 0.0185 | 679.7 | 90400 | 2.7524 | 0.9975 | | 0.0123 | 681.2 | 90600 | 2.9673 | 0.9981 | | 0.0152 | 682.71 | 90800 | 3.0797 | 1.0 | | 0.0117 | 684.21 | 91000 | 3.0703 | 0.9994 | | 0.0154 | 685.71 | 91200 | 2.7031 | 0.9987 | | 0.0148 | 687.22 | 91400 | 2.9449 | 0.9994 | | 0.0196 | 688.72 | 91600 | 2.7991 | 0.9981 | | 0.0168 | 690.23 | 91800 | 2.8938 | 0.9969 | | 0.0155 | 691.73 | 92000 | 2.9104 | 0.9975 | | 0.0167 | 693.23 | 92200 | 2.7905 | 0.9975 | | 0.0175 | 694.74 | 92400 | 3.0061 | 0.9962 | | 0.0134 | 696.24 | 92600 | 3.1880 | 0.9975 | | 0.0145 | 697.74 | 92800 | 3.1567 | 0.9975 | | 0.0191 | 699.25 | 93000 | 3.2099 | 0.9981 | | 0.0192 | 700.75 | 93200 | 3.0210 | 0.9962 | | 0.0143 | 702.26 | 93400 | 3.0640 | 0.9962 | | 0.0145 | 703.76 | 93600 | 2.9882 | 0.9956 | | 0.0151 | 705.26 | 93800 | 2.9178 | 0.9969 | | 0.0192 | 706.77 | 94000 | 2.9631 | 0.9962 | | 0.016 | 708.27 | 94200 | 2.8724 | 0.9950 | | 0.0185 | 709.77 | 94400 | 2.8253 | 1.0013 | | 0.0186 | 711.28 | 94600 | 2.8386 | 0.9950 | | 0.015 | 712.78 | 94800 | 2.8134 | 0.9987 | | 0.0137 | 714.29 | 95000 | 3.1884 | 0.9969 | | 0.0168 | 715.79 | 95200 | 2.9308 | 0.9969 | | 0.0092 | 717.29 | 95400 | 3.4363 | 0.9981 | | 0.0167 | 718.8 | 95600 | 3.2090 | 0.9981 | | 0.0095 | 720.3 | 95800 | 3.1477 | 0.9981 | | 0.0166 | 721.8 | 96000 | 2.9676 | 0.9981 | | 0.0162 | 723.31 | 96200 | 3.0809 | 0.9969 | | 0.0214 | 724.81 | 96400 | 3.3432 | 0.9987 | | 0.0153 | 726.32 | 96600 | 3.0991 | 0.9981 | | 0.0126 | 727.82 | 96800 | 3.0080 | 0.9975 | | 0.009 | 729.32 | 97000 | 2.7920 | 0.9981 | | 0.0194 | 730.83 | 97200 | 2.9781 | 0.9950 | | 0.0159 | 732.33 | 97400 | 3.0364 | 0.9969 | | 0.0175 | 733.83 | 97600 | 2.8637 | 0.9975 | | 0.0131 | 735.34 | 97800 | 3.0862 | 0.9956 | | 0.0222 | 736.84 | 98000 | 2.7875 | 0.9987 | | 0.0107 | 738.35 | 98200 | 2.9837 | 0.9962 | | 0.0171 | 739.85 | 98400 | 2.9214 | 0.9981 | | 0.0162 | 741.35 | 98600 | 2.9194 | 0.9956 | | 0.0133 | 742.86 | 98800 | 2.7002 | 1.0069 | | 0.0201 | 744.36 | 99000 | 2.9725 | 0.9962 | | 0.0137 | 745.86 | 99200 | 2.8473 | 0.9975 | | 0.0081 | 747.37 | 99400 | 2.9228 | 0.9981 | | 0.0155 | 748.87 | 99600 | 2.7624 | 0.9981 | | 0.0127 | 750.38 | 99800 | 2.8617 | 1.0019 | | 0.0158 | 751.88 | 100000 | 2.6715 | 1.0044 | | 0.0146 | 753.38 | 100200 | 2.7292 | 1.0019 | | 0.0136 | 754.89 | 100400 | 2.7341 | 0.9987 | | 0.0177 | 756.39 | 100600 | 2.8756 | 1.0 | | 0.0166 | 757.89 | 100800 | 2.7004 | 0.9981 | | 0.0125 | 759.4 | 101000 | 2.6595 | 1.0013 | | 0.012 | 760.9 | 101200 | 2.9655 | 1.0031 | | 0.0075 | 762.41 | 101400 | 2.8493 | 0.9969 | | 0.0161 | 763.91 | 101600 | 3.0451 | 0.9931 | | 0.0159 | 765.41 | 101800 | 2.8029 | 0.9956 | | 0.0179 | 766.92 | 102000 | 2.7643 | 0.9975 | | 0.0123 | 768.42 | 102200 | 2.7163 | 0.9962 | | 0.0131 | 769.92 | 102400 | 2.7235 | 1.0 | | 0.0134 | 771.43 | 102600 | 2.7199 | 0.9944 | | 0.0148 | 772.93 | 102800 | 3.0776 | 0.9962 | | 0.0132 | 774.44 | 103000 | 2.8314 | 0.9987 | | 0.0104 | 775.94 | 103200 | 2.8094 | 0.9969 | | 0.0166 | 777.44 | 103400 | 2.8420 | 0.9956 | | 0.0104 | 778.95 | 103600 | 2.7902 | 0.9944 | | 0.0065 | 780.45 | 103800 | 3.0951 | 0.9962 | | 0.0138 | 781.95 | 104000 | 2.9704 | 0.9956 | | 0.0113 | 783.46 | 104200 | 2.7171 | 0.9994 | | 0.0111 | 784.96 | 104400 | 3.2024 | 0.9975 | | 0.0123 | 786.47 | 104600 | 3.1505 | 0.9956 | | 0.0111 | 787.97 | 104800 | 3.1629 | 0.9969 | | 0.0105 | 789.47 | 105000 | 3.4622 | 0.9975 | | 0.0083 | 790.98 | 105200 | 3.0705 | 0.9987 | | 0.0123 | 792.48 | 105400 | 3.1194 | 1.0 | | 0.0156 | 793.98 | 105600 | 2.9915 | 0.9969 | | 0.0171 | 795.49 | 105800 | 2.9393 | 0.9969 | | 0.0144 | 796.99 | 106000 | 3.1108 | 0.9981 | | 0.0126 | 798.5 | 106200 | 3.1683 | 0.9994 | | 0.0101 | 800.0 | 106400 | 2.8538 | 1.0019 | | 0.0166 | 801.5 | 106600 | 2.8343 | 0.9987 | | 0.014 | 803.01 | 106800 | 3.2088 | 0.9969 | | 0.0121 | 804.51 | 107000 | 2.9219 | 0.9987 | | 0.012 | 806.02 | 107200 | 3.1733 | 0.9994 | | 0.0116 | 807.52 | 107400 | 3.1237 | 1.0 | | 0.0088 | 809.02 | 107600 | 2.9925 | 1.0 | | 0.0119 | 810.53 | 107800 | 2.8498 | 1.0019 | | 0.0128 | 812.03 | 108000 | 2.7667 | 0.9950 | | 0.0099 | 813.53 | 108200 | 2.8768 | 0.9975 | | 0.0135 | 815.04 | 108400 | 3.0824 | 0.9994 | | 0.01 | 816.54 | 108600 | 3.0932 | 1.0006 | | 0.0106 | 818.05 | 108800 | 3.1329 | 0.9975 | | 0.0127 | 819.55 | 109000 | 3.0865 | 0.9962 | | 0.01 | 821.05 | 109200 | 2.8887 | 0.9975 | | 0.0156 | 822.56 | 109400 | 3.1740 | 0.9962 | | 0.0131 | 824.06 | 109600 | 3.2385 | 0.9981 | | 0.0081 | 825.56 | 109800 | 3.3594 | 0.9981 | | 0.0138 | 827.07 | 110000 | 3.4093 | 0.9981 | | 0.0103 | 828.57 | 110200 | 3.2142 | 0.9987 | | 0.009 | 830.08 | 110400 | 3.1570 | 0.9981 | | 0.0065 | 831.58 | 110600 | 3.0934 | 0.9987 | | 0.0101 | 833.08 | 110800 | 2.7369 | 0.9975 | | 0.0084 | 834.59 | 111000 | 2.9523 | 0.9994 | | 0.0123 | 836.09 | 111200 | 2.9521 | 0.9987 | | 0.0061 | 837.59 | 111400 | 2.9804 | 0.9962 | | 0.0098 | 839.1 | 111600 | 3.0332 | 0.9969 | | 0.0086 | 840.6 | 111800 | 2.9643 | 0.9956 | | 0.008 | 842.11 | 112000 | 2.9080 | 0.9956 | | 0.0074 | 843.61 | 112200 | 2.9878 | 0.9962 | | 0.0125 | 845.11 | 112400 | 3.1683 | 0.9956 | | 0.0112 | 846.62 | 112600 | 3.1810 | 1.0019 | | 0.0093 | 848.12 | 112800 | 3.1701 | 0.9994 | | 0.0133 | 849.62 | 113000 | 3.1736 | 1.0019 | | 0.0093 | 851.13 | 113200 | 3.0622 | 0.9950 | | 0.0085 | 852.63 | 113400 | 3.0084 | 0.9994 | | 0.0119 | 854.14 | 113600 | 3.0510 | 1.0038 | | 0.0132 | 855.64 | 113800 | 3.0081 | 0.9975 | | 0.0068 | 857.14 | 114000 | 3.1519 | 0.9975 | | 0.0103 | 858.65 | 114200 | 3.0568 | 0.9950 | | 0.0116 | 860.15 | 114400 | 2.9386 | 0.9969 | | 0.0098 | 861.65 | 114600 | 2.9005 | 0.9975 | | 0.0102 | 863.16 | 114800 | 2.8415 | 0.9981 | | 0.0061 | 864.66 | 115000 | 3.0904 | 0.9969 | | 0.0111 | 866.17 | 115200 | 2.7751 | 1.0 | | 0.011 | 867.67 | 115400 | 2.9156 | 1.0006 | | 0.0159 | 869.17 | 115600 | 2.7727 | 1.0019 | | 0.0093 | 870.68 | 115800 | 2.9028 | 1.0013 | | 0.009 | 872.18 | 116000 | 2.9202 | 1.0 | | 0.012 | 873.68 | 116200 | 3.0764 | 1.0006 | | 0.0126 | 875.19 | 116400 | 3.0276 | 1.0031 | | 0.0091 | 876.69 | 116600 | 3.1787 | 0.9987 | | 0.0105 | 878.2 | 116800 | 3.0780 | 1.0 | | 0.0088 | 879.7 | 117000 | 3.1704 | 1.0056 | | 0.0075 | 881.2 | 117200 | 2.9814 | 1.0 | | 0.0111 | 882.71 | 117400 | 2.9560 | 0.9987 | | 0.0086 | 884.21 | 117600 | 2.9376 | 0.9994 | | 0.0137 | 885.71 | 117800 | 3.1393 | 0.9981 | | 0.0093 | 887.22 | 118000 | 3.0911 | 0.9994 | | 0.0087 | 888.72 | 118200 | 3.1769 | 0.9987 | | 0.0103 | 890.23 | 118400 | 3.2035 | 0.9969 | | 0.0093 | 891.73 | 118600 | 3.0411 | 0.9981 | | 0.0099 | 893.23 | 118800 | 3.1438 | 0.9975 | | 0.0082 | 894.74 | 119000 | 3.0581 | 0.9975 | | 0.0079 | 896.24 | 119200 | 3.2609 | 0.9981 | | 0.0134 | 897.74 | 119400 | 3.0753 | 0.9975 | | 0.012 | 899.25 | 119600 | 3.0611 | 0.9981 | | 0.0127 | 900.75 | 119800 | 3.0978 | 0.9987 | | 0.0092 | 902.26 | 120000 | 3.0483 | 0.9981 | | 0.0107 | 903.76 | 120200 | 3.1183 | 0.9969 | | 0.0104 | 905.26 | 120400 | 3.0339 | 1.0006 | | 0.0066 | 906.77 | 120600 | 3.0664 | 1.0 | | 0.0136 | 908.27 | 120800 | 3.2616 | 0.9987 | | 0.0075 | 909.77 | 121000 | 3.2563 | 0.9994 | | 0.0096 | 911.28 | 121200 | 3.2538 | 1.0019 | | 0.0094 | 912.78 | 121400 | 3.4125 | 0.9994 | | 0.0059 | 914.29 | 121600 | 3.2795 | 0.9981 | | 0.011 | 915.79 | 121800 | 3.0787 | 0.9981 | | 0.01 | 917.29 | 122000 | 3.0978 | 0.9962 | | 0.0105 | 918.8 | 122200 | 3.3389 | 1.0006 | | 0.0116 | 920.3 | 122400 | 3.3616 | 1.0006 | | 0.0098 | 921.8 | 122600 | 3.3360 | 1.0 | | 0.0091 | 923.31 | 122800 | 3.0145 | 1.0006 | | 0.0134 | 924.81 | 123000 | 3.2691 | 0.9987 | | 0.0124 | 926.32 | 123200 | 2.9881 | 1.0038 | | 0.0093 | 927.82 | 123400 | 3.2461 | 0.9981 | | 0.0077 | 929.32 | 123600 | 3.2159 | 0.9975 | | 0.0088 | 930.83 | 123800 | 2.9951 | 1.0 | | 0.0047 | 932.33 | 124000 | 3.1991 | 1.0 | | 0.0078 | 933.83 | 124200 | 3.1098 | 0.9975 | | 0.0066 | 935.34 | 124400 | 3.0344 | 0.9969 | | 0.0108 | 936.84 | 124600 | 3.1163 | 0.9981 | | 0.0086 | 938.35 | 124800 | 3.0668 | 0.9981 | | 0.0071 | 939.85 | 125000 | 2.9860 | 0.9981 | | 0.0096 | 941.35 | 125200 | 3.0639 | 0.9981 | | 0.0066 | 942.86 | 125400 | 3.0273 | 0.9987 | | 0.0071 | 944.36 | 125600 | 2.9351 | 0.9981 | | 0.0084 | 945.86 | 125800 | 3.2054 | 0.9981 | | 0.0099 | 947.37 | 126000 | 3.0305 | 1.0006 | | 0.0084 | 948.87 | 126200 | 3.0412 | 1.0 | | 0.0085 | 950.38 | 126400 | 2.9697 | 0.9994 | | 0.0072 | 951.88 | 126600 | 2.9454 | 0.9956 | | 0.007 | 953.38 | 126800 | 2.9225 | 0.9981 | | 0.0068 | 954.89 | 127000 | 2.9063 | 0.9962 | | 0.0121 | 956.39 | 127200 | 2.7681 | 0.9975 | | 0.0063 | 957.89 | 127400 | 3.0608 | 0.9975 | | 0.0155 | 959.4 | 127600 | 2.9355 | 0.9994 | | 0.0098 | 960.9 | 127800 | 3.0073 | 0.9987 | | 0.0083 | 962.41 | 128000 | 3.0921 | 0.9987 | | 0.0042 | 963.91 | 128200 | 3.2363 | 0.9981 | | 0.0083 | 965.41 | 128400 | 3.0678 | 0.9987 | | 0.0097 | 966.92 | 128600 | 2.8499 | 1.0019 | | 0.0095 | 968.42 | 128800 | 2.9329 | 1.0038 | | 0.0058 | 969.92 | 129000 | 2.7835 | 1.0038 | | 0.0052 | 971.43 | 129200 | 2.9134 | 1.0019 | | 0.0081 | 972.93 | 129400 | 2.9436 | 1.0031 | | 0.0084 | 974.44 | 129600 | 2.8237 | 0.9994 | | 0.0069 | 975.94 | 129800 | 2.9216 | 0.9994 | | 0.0089 | 977.44 | 130000 | 2.8717 | 0.9994 | | 0.0074 | 978.95 | 130200 | 3.0350 | 0.9994 | | 0.0072 | 980.45 | 130400 | 2.8856 | 1.0013 | | 0.0103 | 981.95 | 130600 | 2.9572 | 1.0 | | 0.01 | 983.46 | 130800 | 2.9522 | 1.0031 | | 0.0071 | 984.96 | 131000 | 2.9940 | 1.0031 | | 0.0042 | 986.47 | 131200 | 3.0343 | 1.0019 | | 0.0048 | 987.97 | 131400 | 2.9638 | 1.0069 | | 0.0038 | 989.47 | 131600 | 2.9672 | 1.0063 | | 0.0043 | 990.98 | 131800 | 2.9115 | 1.0063 | | 0.0129 | 992.48 | 132000 | 2.7689 | 1.0006 | | 0.0059 | 993.98 | 132200 | 2.9322 | 1.0038 | | 0.0098 | 995.49 | 132400 | 2.8063 | 1.0013 | | 0.0083 | 996.99 | 132600 | 3.0642 | 0.9981 | | 0.0055 | 998.5 | 132800 | 2.7167 | 1.0056 | | 0.0073 | 1000.0 | 133000 | 2.7715 | 1.0075 | | 0.0048 | 1001.5 | 133200 | 2.9235 | 1.0019 | | 0.0148 | 1003.01 | 133400 | 3.0357 | 1.0031 | | 0.0059 | 1004.51 | 133600 | 2.9261 | 1.0044 | | 0.008 | 1006.02 | 133800 | 2.9927 | 0.9994 | | 0.0093 | 1007.52 | 134000 | 2.9374 | 1.0019 | | 0.0102 | 1009.02 | 134200 | 2.9598 | 0.9987 | | 0.0045 | 1010.53 | 134400 | 3.0381 | 1.0 | | 0.0058 | 1012.03 | 134600 | 3.0556 | 1.0 | | 0.0089 | 1013.53 | 134800 | 3.1323 | 1.0006 | | 0.0058 | 1015.04 | 135000 | 3.0611 | 1.0013 | | 0.0043 | 1016.54 | 135200 | 3.0465 | 0.9981 | | 0.004 | 1018.05 | 135400 | 3.1712 | 0.9962 | | 0.0056 | 1019.55 | 135600 | 2.8690 | 0.9956 | | 0.0098 | 1021.05 | 135800 | 3.0515 | 0.9987 | | 0.0055 | 1022.56 | 136000 | 3.1504 | 0.9987 | | 0.0054 | 1024.06 | 136200 | 3.0969 | 0.9969 | | 0.005 | 1025.56 | 136400 | 3.0669 | 0.9969 | | 0.0073 | 1027.07 | 136600 | 3.0097 | 0.9975 | | 0.005 | 1028.57 | 136800 | 3.0517 | 0.9994 | | 0.0076 | 1030.08 | 137000 | 3.0154 | 1.0 | | 0.0084 | 1031.58 | 137200 | 2.9610 | 1.0025 | | 0.0044 | 1033.08 | 137400 | 2.9468 | 0.9994 | | 0.0063 | 1034.59 | 137600 | 3.0337 | 1.0006 | | 0.0031 | 1036.09 | 137800 | 3.1654 | 1.0006 | | 0.0066 | 1037.59 | 138000 | 3.0546 | 0.9987 | | 0.0053 | 1039.1 | 138200 | 3.0273 | 1.0006 | | 0.0086 | 1040.6 | 138400 | 3.0024 | 0.9962 | | 0.0061 | 1042.11 | 138600 | 2.9791 | 0.9994 | | 0.0057 | 1043.61 | 138800 | 3.1378 | 0.9994 | | 0.0052 | 1045.11 | 139000 | 3.0045 | 0.9987 | | 0.0094 | 1046.62 | 139200 | 2.9606 | 0.9994 | | 0.0027 | 1048.12 | 139400 | 3.1745 | 0.9981 | | 0.0024 | 1049.62 | 139600 | 3.1448 | 0.9981 | | 0.0045 | 1051.13 | 139800 | 3.0968 | 1.0 | | 0.0048 | 1052.63 | 140000 | 3.1278 | 0.9975 | | 0.0079 | 1054.14 | 140200 | 3.2618 | 0.9969 | | 0.0078 | 1055.64 | 140400 | 3.1333 | 0.9969 | | 0.0077 | 1057.14 | 140600 | 3.0945 | 0.9987 | | 0.0029 | 1058.65 | 140800 | 3.1253 | 0.9969 | | 0.0022 | 1060.15 | 141000 | 3.0270 | 0.9969 | | 0.0054 | 1061.65 | 141200 | 3.1937 | 0.9975 | | 0.0097 | 1063.16 | 141400 | 2.9620 | 0.9987 | | 0.006 | 1064.66 | 141600 | 2.9298 | 0.9994 | | 0.0041 | 1066.17 | 141800 | 2.9171 | 1.0013 | | 0.006 | 1067.67 | 142000 | 3.0870 | 0.9994 | | 0.0059 | 1069.17 | 142200 | 2.9893 | 0.9987 | | 0.0054 | 1070.68 | 142400 | 3.0031 | 0.9975 | | 0.0076 | 1072.18 | 142600 | 2.9808 | 0.9969 | | 0.0091 | 1073.68 | 142800 | 3.0710 | 0.9975 | | 0.0081 | 1075.19 | 143000 | 3.2000 | 0.9956 | | 0.0037 | 1076.69 | 143200 | 3.1321 | 0.9956 | | 0.0042 | 1078.2 | 143400 | 3.0672 | 0.9950 | | 0.0031 | 1079.7 | 143600 | 3.0094 | 0.9969 | | 0.003 | 1081.2 | 143800 | 3.0956 | 0.9956 | | 0.0087 | 1082.71 | 144000 | 3.0842 | 0.9969 | | 0.0039 | 1084.21 | 144200 | 2.9722 | 0.9962 | | 0.0059 | 1085.71 | 144400 | 3.0395 | 0.9962 | | 0.0088 | 1087.22 | 144600 | 2.9895 | 0.9950 | | 0.0056 | 1088.72 | 144800 | 2.9265 | 0.9956 | | 0.0031 | 1090.23 | 145000 | 2.9205 | 0.9969 | | 0.0089 | 1091.73 | 145200 | 2.9462 | 0.9969 | | 0.0037 | 1093.23 | 145400 | 2.9021 | 0.9975 | | 0.0047 | 1094.74 | 145600 | 2.9209 | 0.9987 | | 0.0058 | 1096.24 | 145800 | 2.9616 | 0.9975 | | 0.0068 | 1097.74 | 146000 | 2.9260 | 0.9981 | | 0.0051 | 1099.25 | 146200 | 2.9815 | 0.9969 | | 0.003 | 1100.75 | 146400 | 3.0032 | 0.9969 | | 0.0055 | 1102.26 | 146600 | 2.9972 | 0.9975 | | 0.0064 | 1103.76 | 146800 | 3.0282 | 0.9969 | | 0.0045 | 1105.26 | 147000 | 3.0201 | 0.9969 | | 0.0075 | 1106.77 | 147200 | 3.1340 | 0.9956 | | 0.0052 | 1108.27 | 147400 | 3.1120 | 0.9956 | | 0.0075 | 1109.77 | 147600 | 3.1448 | 0.9969 | | 0.0053 | 1111.28 | 147800 | 3.1039 | 0.9981 | | 0.0061 | 1112.78 | 148000 | 3.2061 | 1.0006 | | 0.0105 | 1114.29 | 148200 | 3.1274 | 0.9994 | | 0.0071 | 1115.79 | 148400 | 3.0449 | 1.0013 | | 0.0042 | 1117.29 | 148600 | 3.1285 | 0.9994 | | 0.0017 | 1118.8 | 148800 | 3.0261 | 0.9975 | | 0.0083 | 1120.3 | 149000 | 3.0042 | 0.9944 | | 0.0026 | 1121.8 | 149200 | 2.9851 | 0.9950 | | 0.003 | 1123.31 | 149400 | 2.9439 | 0.9956 | | 0.005 | 1124.81 | 149600 | 2.9231 | 0.9944 | | 0.0039 | 1126.32 | 149800 | 2.9187 | 0.9944 | | 0.0031 | 1127.82 | 150000 | 2.9250 | 0.9950 | | 0.002 | 1129.32 | 150200 | 2.9452 | 0.9969 | | 0.0057 | 1130.83 | 150400 | 2.9566 | 0.9944 | | 0.0046 | 1132.33 | 150600 | 3.0111 | 0.9975 | | 0.0076 | 1133.83 | 150800 | 2.9526 | 0.9962 | | 0.0048 | 1135.34 | 151000 | 2.9313 | 0.9969 | | 0.0052 | 1136.84 | 151200 | 2.9782 | 0.9975 | | 0.0086 | 1138.35 | 151400 | 3.0282 | 0.9975 | | 0.0018 | 1139.85 | 151600 | 3.0765 | 0.9962 | | 0.0046 | 1141.35 | 151800 | 3.0391 | 0.9969 | | 0.0043 | 1142.86 | 152000 | 3.0364 | 0.9962 | | 0.006 | 1144.36 | 152200 | 3.0741 | 0.9975 | | 0.0059 | 1145.86 | 152400 | 3.0201 | 0.9975 | | 0.0038 | 1147.37 | 152600 | 3.0556 | 0.9969 | | 0.007 | 1148.87 | 152800 | 2.9943 | 0.9962 | | 0.0042 | 1150.38 | 153000 | 2.9979 | 0.9981 | | 0.006 | 1151.88 | 153200 | 3.0304 | 0.9981 | | 0.0063 | 1153.38 | 153400 | 2.9844 | 0.9981 | | 0.0067 | 1154.89 | 153600 | 2.9773 | 0.9994 | | 0.0037 | 1156.39 | 153800 | 2.9956 | 0.9994 | | 0.0029 | 1157.89 | 154000 | 2.9977 | 1.0 | | 0.0056 | 1159.4 | 154200 | 2.9639 | 0.9987 | | 0.0063 | 1160.9 | 154400 | 2.9705 | 0.9994 | | 0.0063 | 1162.41 | 154600 | 3.0182 | 0.9994 | | 0.0045 | 1163.91 | 154800 | 3.0294 | 0.9994 | | 0.0107 | 1165.41 | 155000 | 2.9982 | 1.0 | | 0.0047 | 1166.92 | 155200 | 3.0119 | 1.0 | | 0.0043 | 1168.42 | 155400 | 3.0040 | 1.0006 | | 0.0025 | 1169.92 | 155600 | 3.0062 | 1.0 | | 0.0044 | 1171.43 | 155800 | 2.9886 | 1.0006 | | 0.0032 | 1172.93 | 156000 | 3.0112 | 1.0006 | | 0.0056 | 1174.44 | 156200 | 3.0036 | 1.0006 | | 0.0074 | 1175.94 | 156400 | 3.0185 | 1.0 | | 0.0056 | 1177.44 | 156600 | 3.0197 | 1.0 | | 0.0051 | 1178.95 | 156800 | 3.0232 | 0.9994 | | 0.0034 | 1180.45 | 157000 | 3.0373 | 1.0 | | 0.0038 | 1181.95 | 157200 | 3.0297 | 1.0 | | 0.0058 | 1183.46 | 157400 | 3.0453 | 0.9994 | | 0.0052 | 1184.96 | 157600 | 3.0235 | 1.0006 | | 0.0023 | 1186.47 | 157800 | 3.0338 | 1.0006 | | 0.0035 | 1187.97 | 158000 | 3.0402 | 1.0 | | 0.0035 | 1189.47 | 158200 | 3.0333 | 1.0 | | 0.0042 | 1190.98 | 158400 | 3.0572 | 0.9994 | | 0.0049 | 1192.48 | 158600 | 3.0431 | 1.0 | | 0.0017 | 1193.98 | 158800 | 3.0432 | 1.0 | | 0.0089 | 1195.49 | 159000 | 3.0372 | 1.0 | | 0.0089 | 1196.99 | 159200 | 3.0366 | 1.0006 | | 0.0053 | 1198.5 | 159400 | 3.0345 | 1.0006 | | 0.0054 | 1200.0 | 159600 | 3.0348 | 1.0006 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.11.6

huggingtweets/kc_lyricbot

3506e514498e9bea6a8f66e7e959e481f57f8278

2022-04-13T21:14:37.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/kc_lyricbot

0

null

transformers

36,859

--- language: en thumbnail: http://www.huggingtweets.com/kc_lyricbot/1649884470723/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1448393533921112064/q3fCXTyu_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">King Crimson Lyric Bot</div> <div style="text-align: center; font-size: 14px;">@kc_lyricbot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from King Crimson Lyric Bot. | Data | King Crimson Lyric Bot | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 0 | | Short tweets | 231 | | Tweets kept | 3019 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yn81k4o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kc_lyricbot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15ndpk6d) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15ndpk6d/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/kc_lyricbot') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

kangela/Metaphor-FineTuned-BERT

a8be63c2a11e22afc7a455d43fa556bb9835da39

2022-05-19T17:44:32.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

kangela

null

kangela/Metaphor-FineTuned-BERT

0

null

transformers

36,860

Entry not found

huggan/projected_gan_color_field

8d2331fc46c18dcd0b043f5ef20d049f1c8b61af

2022-04-25T11:16:28.000Z

[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]

unconditional-image-generation

false

huggan

null

huggan/projected_gan_color_field

0

null

pytorch

36,861

--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>

huggan/projected_gan_popart

fc975cd4031baccc2d6fb37f8a9fe74438fc5f9d

2022-04-25T11:16:48.000Z

[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]

unconditional-image-generation

false

huggan

null

huggan/projected_gan_popart

0

null

pytorch

36,862

--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>

huggan/projected_gan_abstract_expressionism

477e0e9cac181ed43f328397c60f0e94c40109c8

2022-04-25T11:16:38.000Z

[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]

unconditional-image-generation

false

huggan

null

huggan/projected_gan_abstract_expressionism

0

null

pytorch

36,863

--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>

obokkkk/bert-base-multilingual-cased-finetuned-klue

cd6c21273c9d61dcbbcc5882631a6796394f5358

2022-04-14T12:57:25.000Z

[ "pytorch", "bert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

question-answering

false

obokkkk

null

obokkkk/bert-base-multilingual-cased-finetuned-klue

0

null

transformers

36,864

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-multilingual-cased-finetuned-klue results: [] ---  # bert-base-multilingual-cased-finetuned-klue This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4197 ## 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 - gradient_accumulation_steps: 36 - total_train_batch_size: 288 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.6323 | 5.0 | 500 | 1.6799 | | 1.3765 | 10.0 | 1000 | 1.3027 | | 0.8433 | 15.0 | 1500 | 1.2946 | | 0.5224 | 20.0 | 2000 | 1.4197 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.1

lisdarr/fuck_shanghai_covid19

3dae4b2416f259632eb5d0ce2242b91defc50287

2022-04-14T04:27:57.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

false

lisdarr

null

lisdarr/fuck_shanghai_covid19

0

transformers

36,865

--- license: apache-2.0 ---

pitiwat/argument_wangchanberta

06248b420a1bb15c048dd0a1b1a204d88a678e54

2022-04-14T11:35:08.000Z

[ "pytorch", "camembert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

false

pitiwat

null

pitiwat/argument_wangchanberta

0

null

transformers

36,866

Used for extracting arguments from Thai text.

TalTechNLP/voxlingua107-xls-r-300m-wav2vec

a96a1c36191f8b7a5676fec8228279318b9057a3

2022-04-19T11:08:36.000Z

[ "wav2vec2", "multilingual", "dataset:voxlingua107", "speechbrain", "language-identification", "pytorch", "embeddings", "Language", "Identification", "audio-classification", "wav2vec2.0", "XLS-R-300M", "VoxLingua107", "license:cc-by-4.0" ]

audio-classification

false

TalTechNLP

null

TalTechNLP/voxlingua107-xls-r-300m-wav2vec

0

null

speechbrain

36,867

--- language: multilingual license: cc-by-4.0 tags: - language-identification - speechbrain - wav2vec2 - pytorch - embeddings - Language - Identification - audio-classification - wav2vec2.0 - XLS-R-300M - VoxLingua107 datasets: - voxlingua107 metrics: - Accuracy --- # VoxLingua107 Wav2Vec Spoken Language Identification Model ## Model description This is a spoken language identification model trained on the VoxLingua107 dataset using SpeechBrain. The model is trained using weights of pretrained [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model, Wav2Vec2.0 architecture and negative log likelihood loss. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese). ## Intended uses & limitations The model has two uses: - use 'as is' for spoken language recognition - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data The model is trained on automatically collected YouTube data. For more information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/). #### How to use ```python import torchaudio from speechbrain.pretrained.interfaces import foreign_class language_id = foreign_class(source="TalTechNLP/voxlingua107-xls-r-300m-wav2vec", pymodule_file="encoder_wav2vec_classifier.py", classname="EncoderWav2vecClassifier", hparams_file='inference_wav2vec.yaml', savedir="tmp") # Download Thai language sample from Omniglot and convert to suitable form wav_file = "https://omniglot.com/soundfiles/udhr/udhr_th.mp3" out_prob, score, index, text_lab = language_id.classify_file(wav_file) print("probability:", out_prob) print("label:", text_lab) print("score:", score) print("index:", index) probability: tensor([[[-2.2849e+01, -2.4349e+01, -2.3686e+01, -2.3632e+01, -2.0218e+01, -2.7241e+01, -2.6715e+01, -2.2301e+01, -2.6076e+01, -2.1716e+01, -1.9923e+01, -2.7303e+01, -2.1211e+01, -2.2998e+01, -2.4436e+01, -2.6437e+01, -2.2686e+01, -2.4244e+01, -2.0416e+01, -2.8329e+01, -1.7788e+01, -2.4829e+01, -2.4186e+01, -2.7036e+01, -2.5993e+01, -1.9677e+01, -2.2746e+01, -2.9192e+01, -2.4941e+01, -2.7135e+01, -2.6653e+01, -2.2791e+01, -2.4599e+01, -2.1066e+01, -2.4855e+01, -2.1874e+01, -2.2914e+01, -2.4174e+01, -2.0902e+01, -2.3197e+01, -2.6108e+01, -2.3941e+01, -2.3103e+01, -2.2363e+01, -2.8969e+01, -2.5302e+01, -2.4862e+01, -2.2392e+01, -2.4042e+01, -2.1221e+01, -2.3656e+01, -2.1286e+01, -1.9209e+01, -2.3254e+01, -2.8291e+01, -5.9105e+00, -2.4525e+01, -2.4937e+01, -2.8349e+01, -2.4420e+01, -2.7439e+01, -2.6329e+01, -2.3317e+01, -2.3842e+01, -2.2114e+01, -2.3637e+01, -1.7217e+01, -1.8342e+01, -2.4332e+01, -2.6090e+01, -2.5452e+01, -2.3854e+01, -2.6082e+01, -2.4992e+01, -2.0618e+01, -2.9351e+01, -2.4153e+01, -2.3156e+01, -2.6893e+01, -2.5314e+01, -2.8374e+01, -2.4009e+01, -2.3604e+01, -2.4063e+01, -2.3538e+01, -2.4953e+01, -2.5607e+01, -2.3960e+01, -2.6471e+01, -2.3348e+01, -2.1681e+01, -2.7610e+01, -2.5023e+01, -2.3585e+01, -2.7146e-03, -2.0338e+01, -1.8737e+01, -2.5158e+01, -2.7491e+01, -2.3623e+01, -2.5718e+01, -2.3465e+01, -1.8305e+01, -2.1064e+01, -2.9880e+01, -2.2809e+01, -1.9856e+01]]]) # The identified language ISO code is given in score[0][0] label: [['th']] score: tensor([[-0.0027]]) index: tensor([[94]]) # The scores in the out_prob tensor can be interpreted as log-likelihoods that # the given utterance belongs to the given language (i.e., the larger the better) # The linear-scale likelihood can be retrieved using the following: print(score.exp()) tensor([0.9973]) # Alternatively, use the utterance embedding extractor: signal, fs = torchaudio.load(wav_file) embeddings = language_id.encode_batch(signal) print(embeddings.shape) torch.Size([2, 1, 2048]) ``` #### Limitations and bias Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: - Probably it's accuracy on smaller languages is quite limited - Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) - Based on experiments, it performs satisfactory on accented speech - Probably it doesn't work well on children's speech and on persons with speech disorders ## Training data The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/). VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. ## Training procedure We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model. Training recipe will be published soon. ## Evaluation results | Version | Error Rate (%) | |-----------------------|:------:| | 2022-04-14 | 5.6 | Error rate is calculated on VoxLingua107 development dataset. ### BibTeX entry and citation info ```bibtex @inproceedings{valk2021slt, title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, booktitle={Proc. IEEE SLT Workshop}, year={2021}, } ```

huggingtweets/elonmusk-joebiden

67e9ad350fbad49e9485da977f5b374eb8218c6c

2022-04-14T12:38:39.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/elonmusk-joebiden

0

null

transformers

36,868

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Joe Biden</div> <div style="text-align: center; font-size: 14px;">@elonmusk-joebiden</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Joe Biden. | Data | Elon Musk | Joe Biden | | --- | --- | --- | | Tweets downloaded | 200 | 3249 | | Retweets | 15 | 571 | | Short tweets | 60 | 34 | | Tweets kept | 125 | 2644 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ne2s3c4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-joebiden's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ka86kb6l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ka86kb6l/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-joebiden') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggan/pix2pix-night2day

150e94d5ccdecfb61a98bceee3962c981d175d2e

2022-04-15T04:27:40.000Z

[ "pytorch", "dataset:huggan/night2day", "arxiv:1611.07004", "huggan", "gan", "license:mit" ]

null

false

huggan

null

huggan/pix2pix-night2day

0

null

36,869

--- tags: - huggan - gan datasets: - huggan/night2day # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. ## Intended uses & limitations: Used for reconstruction of images from edges #### How to use ```python from torchvision.transforms import Compose, Resize, ToTensor, Normalize from PIL import Image from torchvision.utils import save_image import cv2 from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet transform = Compose( [ Resize((256, 256), Image.BICUBIC), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) model = GeneratorUNet.from_pretrained('huggan/pix2pix-night2day') def predict_fn(img): inp = transform(img).unsqueeze(0) out = model(inp) save_image(out, 'out.png', normalize=True) return 'out.png' predict_fn(img) ``` #### Limitations and bias * Gives unrealistic colors in the image * Gives Blurry image sometimes ## Training data * [night2day](https://huggingface.co/datasets/huggan/night2day) ## Training procedure ``` # clone the repository git clone https://github.com/huggingface/community-events.git pip install . # change directory cd community-events/huggan/pytorch/pix2pix/ # define config accelerate config # launch training with required parameters accelerate launch train.py --checkpoint_interval 5 --dataset huggan/night2day --push_to_hub --model_name pix2pix-night2day --batch_size 128 --n_epochs 50 ``` ## Generated Images Here, * First Image Row: Input Image * Second Image Row: Generated Image * Third Image Row: Target Image ![image1](7000.png) ![image2](6500.png) ### BibTeX entry and citation info ```bibtex @article{pix2pix2017, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={CVPR}, year={2017} } ```

lilitket/20220414-201056

5eff65cdccdf7e273cd2184df8cc210a9800439d

2022-04-14T21:41:58.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

lilitket

null

lilitket/20220414-201056

0

null

transformers

36,870

Entry not found

lilitket/20220414-210228

5a8fff0ed7110a8cbb35c22d8bfb5bda86df4e08

2022-04-15T10:40:24.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

lilitket

null

lilitket/20220414-210228

0

null

transformers

36,871

Entry not found

huggan/projected_gan_impressionism

87948de0e87971987fa5a0244884d6ae32f03ec4

2022-04-18T21:29:45.000Z

[ "pytorch" ]

null

false

huggan

null

huggan/projected_gan_impressionism

0

null

36,872

dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>

huggan/projected_gan_Hana_Hanak

df86ca72afc7bbd8956769fe14ac105d5dfbb076

2022-04-25T11:17:23.000Z

[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]

unconditional-image-generation

false

huggan

null

huggan/projected_gan_Hana_Hanak

0

null

pytorch

36,873

--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>

huggan/projected_gan_color_field_hana

7f952f515856c2dfd298cceeffd4f908c434aa10

2022-04-25T11:16:59.000Z

[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]

unconditional-image-generation

false

huggan

null

huggan/projected_gan_color_field_hana

0

null

pytorch

36,874

--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>

huggan/projected_gan_abstract_expressionism_hana

e43eb18a4185a4ea0e084e100c97466528b53fb6

2022-04-25T11:17:09.000Z

[ "pytorch", "gan", "dcgan", "projected-gan", "huggan", "unconditional-image-generation" ]

unconditional-image-generation

false

huggan

null

huggan/projected_gan_abstract_expressionism_hana

0

null

pytorch

36,875

--- library_name: pytorch tags: - gan - dcgan - projected-gan - huggan - unconditional-image-generation --- dataset: https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset trained on the official projected gan github code - you can check out the hfspace to see how to use it to generate images fun stuff check out the space demo: https://huggingface.co/spaces/huggan/projected_gan_art Made by:-<br/> [Jeronim Matijević](https://huggingface.co/Cropinky)<br/> [Massimiliano Pappa](https://huggingface.co/maxpappa)<br/>

mT0/mt0_11B_t0_train_ckpt_1012500

91af79ba953686b099e9df3c4de7fc109984dcd8

2022-04-16T03:40:02.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

mT0

null

mT0/mt0_11B_t0_train_ckpt_1012500

0

null

transformers

36,876

Entry not found

ntoldalagi/nick_asr_LID

061feb844ed87dd44535a706e1a809bc480c6c22

2022-04-21T10:47:23.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]

automatic-speech-recognition

false

ntoldalagi

null

ntoldalagi/nick_asr_LID

0

null

transformers

36,877

--- tags: - generated_from_trainer model-index: - name: nick_asr_LID results: [] ---  # nick_asr_LID This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 1.0 - Cer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 50.7955 | 1.0 | 458 | 54.9678 | 1.0 | 1.0 | | 29.3958 | 2.0 | 916 | 37.1618 | 0.9928 | 0.9887 | | 27.1413 | 3.0 | 1374 | 32.5933 | 0.9856 | 0.9854 | | 24.0847 | 4.0 | 1832 | 34.2804 | 0.9784 | 0.9447 | | 492.7757 | 5.0 | 2290 | nan | 0.9736 | 0.9428 | | 0.0 | 6.0 | 2748 | nan | 1.0 | 1.0 | | 0.0 | 7.0 | 3206 | nan | 1.0 | 1.0 | | 0.0 | 8.0 | 3664 | nan | 1.0 | 1.0 | | 0.0 | 9.0 | 4122 | nan | 1.0 | 1.0 | | 0.0 | 10.0 | 4580 | nan | 1.0 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

mimicheng/codeparrot-ds-sample-2ep-14apr

a68a7876fa2c3a8e046a85485293025dff44b9ec

2022-04-15T18:40:03.000Z

[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

text-generation

false

mimicheng

null

mimicheng/codeparrot-ds-sample-2ep-14apr

0

null

transformers

36,878

--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-2ep-14apr results: [] ---  # codeparrot-ds-sample-2ep-14apr This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1367 | 0.37 | 1000 | 2.5592 | | 2.2049 | 0.74 | 2000 | 2.0419 | | 1.8302 | 1.11 | 3000 | 1.8245 | | 1.6214 | 1.49 | 4000 | 1.6913 | | 1.5125 | 1.86 | 5000 | 1.6319 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1

ntoldalagi/nick_asr_COMBO

9af031677926de377eda1c7af586fc88f26b1794

2022-04-24T01:24:22.000Z

[ "pytorch", "tensorboard", "wav2vec2", "transformers", "generated_from_trainer", "model-index" ]

null

false

ntoldalagi

null

ntoldalagi/nick_asr_COMBO

0

null

transformers

36,879

--- tags: - generated_from_trainer model-index: - name: nick_asr_COMBO results: [] ---  # nick_asr_COMBO This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4313 - Wer: 0.6723 - Cer: 0.2408 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 0.0857 | 1.0 | 687 | 1.3883 | 0.7082 | 0.2576 | | 0.0627 | 2.0 | 1374 | 1.4099 | 0.7076 | 0.2561 | | 0.0697 | 3.0 | 2061 | 1.3864 | 0.6906 | 0.2486 | | 0.0575 | 4.0 | 2748 | 1.4356 | 0.6906 | 0.2455 | | 0.0552 | 5.0 | 3435 | 1.4061 | 0.6778 | 0.2440 | | 0.0631 | 6.0 | 4122 | 1.4541 | 0.6839 | 0.2444 | | 0.0418 | 7.0 | 4809 | 1.4258 | 0.6930 | 0.2465 | | 0.0407 | 8.0 | 5496 | 1.4193 | 0.6809 | 0.2451 | | 0.0487 | 9.0 | 6183 | 1.4261 | 0.6778 | 0.2424 | | 0.0371 | 10.0 | 6870 | 1.4313 | 0.6723 | 0.2408 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

Cristianoo/nezha-large-zh

3dd8e95e7311ad9b8cbdb83f4ea04de1f63aa9c2

2022-04-15T07:22:08.000Z

[ "pytorch" ]

null

false

Cristianoo

null

Cristianoo/nezha-large-zh

0

null

36,880

Entry not found

tau/false_large_t5_single_mask_5_1024_0.3_epoch1

9287fecd54a3f903ebe1c89ff33d20a10dde3b13

2022-04-15T07:47:45.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

tau

null

tau/false_large_t5_single_mask_5_1024_0.3_epoch1

0

null

transformers

36,881

Entry not found

tau/false_large_random_paraNone_sentNone_span0_multi_masks_5_1024_0.3_epoch1

48b4581e39f8d6672827e3c54a0a70cbea16b1fa

2022-04-15T08:21:14.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

tau

null

tau/false_large_random_paraNone_sentNone_span0_multi_masks_5_1024_0.3_epoch1

0

null

transformers

36,882

Entry not found

masakhane/afrimt5_fr_ewe_news

cc8f256e3e6ca0aadc5f756993888350e6d9734c

2022-04-15T09:01:20.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afrimt5_fr_ewe_news

0

null

transformers

36,883

--- license: afl-3.0 ---

masakhane/afrimt5_ewe_fr_news

e684c873016b389e702c7eb6649111e74bd7f5a3

2022-04-15T09:01:24.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afrimt5_ewe_fr_news

0

null

transformers

36,884

--- license: afl-3.0 ---

masakhane/afrimbart_fr_ewe_news

bd4f6a98462ff632ffe131c86239ebff041ce6aa

2022-04-15T09:01:37.000Z

[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afrimbart_fr_ewe_news

0

null

transformers

36,885

--- license: afl-3.0 ---

masakhane/afribyt5_ewe_fr_news

b4cb039988fab88d399b322c0c9175001ea4af76

2022-04-15T10:06:29.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afribyt5_ewe_fr_news

0

null

transformers

36,886

--- license: afl-3.0 ---

masakhane/afribyt5_fr_ewe_news

b156c367f02e69be1ecf61df61cca4d3564d622c

2022-04-15T10:06:25.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/afribyt5_fr_ewe_news

0

null

transformers

36,887

--- license: afl-3.0 ---

masakhane/byt5_fr_ewe_news

0fa6efaa879d2a909e5f850a7335ee435877f15d

2022-04-15T10:06:33.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/byt5_fr_ewe_news

0

null

transformers

36,888

--- license: afl-3.0 ---

masakhane/byt5_ewe_fr_news

e9bb988159362cdcab353a392a4177d52a98c611

2022-04-15T10:06:37.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/byt5_ewe_fr_news

0

null

transformers

36,889

--- license: afl-3.0 ---

masakhane/mt5_fr_ewe_news

666d3974bddc414c547a345fbae0f768520fa35c

2022-04-15T11:19:46.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/mt5_fr_ewe_news

0

null

transformers

36,890

--- license: afl-3.0 ---

masakhane/mt5_ewe_fr_news

1996364930ac66520871602c9640fb8ddfe21f8e

2022-04-15T11:19:38.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/mt5_ewe_fr_news

0

null

transformers

36,891

--- license: afl-3.0 ---

masakhane/mbart50_fr_ewe_news

6e559e03c4f762b5189b77c1c99847ec4c91a622

2022-04-15T11:19:26.000Z

[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/mbart50_fr_ewe_news

0

null

transformers

36,892

--- license: afl-3.0 ---

masakhane/mbart50_ewe_fr_news

535139d1b9482217748159aeb1242b58d018e976

2022-04-15T11:19:33.000Z

[ "pytorch", "mbart", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/mbart50_ewe_fr_news

0

null

transformers

36,893

--- license: afl-3.0 ---

masakhane/m2m100_418M_ewe_fr_news

404bb64c3207a0a5377c1a08c484180aa94e10ab

2022-04-15T13:28:00.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_ewe_fr_news

0

null

transformers

36,894

--- license: afl-3.0 ---

masakhane/m2m100_418M_ewe_fr_rel_news

cbaeff3d677296bad068633176cabfba44c61a80

2022-04-15T13:28:09.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_ewe_fr_rel_news

0

null

transformers

36,895

--- license: afl-3.0 ---

masakhane/m2m100_418M_fr_ewe_rel_news_ft

43c7d60c1276258d5184f899d5dd6f5bbb5d251a

2022-04-15T16:28:07.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_fr_ewe_rel_news_ft

0

null

transformers

36,896

--- license: afl-3.0 ---

masakhane/m2m100_418M_ewe_fr_rel_news_ft

25080abbe934676a34fe86a0efd01408ca6c53ec

2022-04-15T16:27:42.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_ewe_fr_rel_news_ft

0

null

transformers

36,897

--- license: afl-3.0 ---

masakhane/m2m100_418M_ewe_fr_rel_ft

a08335a51e2e84fa37eae59410fd80d31a27bbf8

2022-04-15T16:28:00.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_ewe_fr_rel_ft

0

null

transformers

36,898

--- license: afl-3.0 ---

masakhane/m2m100_418M_ewe_fr_rel

36fc61609692fa8fd4a60502a12fdb7d8acfad90

2022-04-15T17:39:03.000Z

[ "pytorch", "m2m_100", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

masakhane

null

masakhane/m2m100_418M_ewe_fr_rel

0

null

transformers

36,899

--- license: afl-3.0 ---