license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
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apache-2.0 | [] | false | Text-to-Image generation with Stable Diffusion First let's install ```bash pip install --upgrade diffusers transformers accelerate ``` We recommend using the model in [half-precision (`fp16`)](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/) as it gives almost a... | 4952be294b015611ca95b214ec70988f |
apache-2.0 | [] | false | Running the model locally You can also simply download the model folder and pass the path to the local folder to the `StableDiffusionPipeline`. ``` git lfs install git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 ``` Assuming the folder is stored locally under `./stable-diffusion-v1-5`, you can run s... | 0fe5dd1092961af5a13712b3109c1df8 |
apache-2.0 | [] | false | disable the following line if you run on CPU pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` | 1705736f78ff32f886afabaabffbfda8 |
apache-2.0 | [] | false | JAX/Flax Diffusers offers a JAX / Flax implementation of Stable Diffusion for very fast inference. JAX shines specially on TPU hardware because each TPU server has 8 accelerators working in parallel, but it runs great on GPUs too. Running the pipeline with the default PNDMScheduler: ```python import jax import nump... | 893c05aff0c358be193871ce2fa28781 |
apache-2.0 | [] | false | shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, jax.device_count()) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])... | 348bd9e9985ebb5d463f0537e85dc479 |
apache-2.0 | [] | false | shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, jax.device_count()) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])... | 9fbcb761a921798850ee437a5449f71a |
apache-2.0 | [] | false | shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, jax.device_count()) prompt_ids = shard(prompt_ids) processed_masked_images = shard(processed_masked_images) processed_masks = shard(processed_masks) images = pipeline(prompt_ids, processed_masks, processed_masked_images, params, p... | e7662f0ae84d57d2f1814733d7778d4b |
apache-2.0 | [] | false | Image-to-Image text-guided generation with Stable Diffusion The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images. ```python import requests import torch from PIL import Image from io import BytesIO from diffusers import StableDiffusionImg2Im... | 29d3f2ef8666207295377d94458838df |
apache-2.0 | [] | false | let's download an initial image url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) init_image = Image.open(BytesIO(response.content)).convert("RGB") init_image = init_image.resize((768, 512)) prompt = "A fantas... | 053526adf925b697e74bfd7f90a76e96 |
apache-2.0 | [] | false | In-painting using Stable Diffusion The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and a text prompt. ```python import PIL import requests import torch from io import BytesIO from diffusers import StableDiffusionInpaintPipeline def download_image(url): response... | a539abbadbfff71d648108626e91a386 |
apache-2.0 | [] | false | Tweak prompts reusing seeds and latents You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. Please have a look at [Reusing seeds for deterministic generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/reusing_seeds). | 1b2d57b1e30640a3d0c48e9c6f77bd07 |
apache-2.0 | [] | false | Fine-Tuning Stable Diffusion Fine-tuning techniques make it possible to adapt Stable Diffusion to your own dataset, or add new subjects to it. These are some of the techniques supported in `diffusers`: Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that c... | be5a390b72f7670dcf324e14c129d82b |
apache-2.0 | [] | false | Stable Diffusion Community Pipelines The release of Stable Diffusion as an open source model has fostered a lot of interesting ideas and experimentation. Our [Community Examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community) contains many ideas worth exploring, like interpolating to c... | ca7431ec1285c07b50343babfc917560 |
apache-2.0 | [] | false | save image image.save("ddpm_generated_image.png") ``` - [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256) - [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024) **Other Image Notebooks**: * [image-to-image generation with Stable Diffusion]... | bb1ff2c22cac90cc21b745f39100533b |
apache-2.0 | [] | false | Web Demos If you just want to play around with some web demos, you can try out the following 🚀 Spaces: | Model | Hugging Face Spaces | |-------------... | be61e7c06b85447ed7fa163a556e857a |
apache-2.0 | [] | false | Definitions **Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image. *Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet <p align="center"> <img src="https://user-images.githubusercontent.com/106... | 51d16a22628ab92e07fd19ed815e14fa |
apache-2.0 | [] | false | Philosophy - Readability and clarity is preferred over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](... | c79927a2b276c545de003197ecf8b13d |
apache-2.0 | [] | false | In the works For the first release, 🤗 Diffusers focuses on text-to-image diffusion techniques. However, diffusers can be used for much more than that! Over the upcoming releases, we'll be focusing on: - Diffusers for audio - Diffusers for reinforcement learning (initial work happening in https://github.com/huggingf... | 6a8d54c1f0f654be81f7e572cbf21fa1 |
apache-2.0 | [] | false | Credits This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished... | 4f892262a04baf160212906883ffcdf0 |
apache-2.0 | [] | false | Citation ```bibtex @misc{von-platen-etal-2022-diffusers, author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf}, title = {Diffusers: State-of-the-art diffusion models}, year = {2022}, publisher = {GitHub}, jou... | cd49c05899fd2948cce5a4febb4f8794 |
apache-2.0 | ['image-classification', 'vision', 'pytorch'] | false | Vision Transformer Fine Tuned on CIFAR10 Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) and **fine-tuned on CIFAR10** at resolution 224x224. Check out the code at my [my Github repo](https://github.com/nateraw/huggingface-vit-finetune). | dd6ba69a308ad69627c92c5e0fb3d14d |
apache-2.0 | ['image-classification', 'vision', 'pytorch'] | false | Usage ```python from transformers import ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests url = 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog10.png' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrained('nateraw/vit-... | 6b24e12df1c4607dd5eb08f242164e86 |
apache-2.0 | ['image-classification', 'vision', 'pytorch'] | false | Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are lin... | fe942de90dfe654701a65dcf1a3e85be |
apache-2.0 | ['generated_from_trainer'] | false | languagemodel This model is a fine-tuned version of [monideep2255/XLRS-torgo](https://huggingface.co/monideep2255/XLRS-torgo) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 1.1173 | 8444063ec134962e0567b7db98b7924b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.3015 | 3.12 | 400 | inf | 1.3984 | | 0.6892 | 6.25 | 800 | inf | 1.1059 | | 0.5069 | 9.37 | 1200 | inf | 1.0300 | |... | 067511375d2e8b0e06af43345d6b4ddd |
apache-2.0 | ['translation'] | false | opus-mt-de-efi * source languages: de * target languages: efi * OPUS readme: [de-efi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-efi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 07102ba37248eef9c45d8ec8a250886f |
apache-2.0 | [] | false | Model Description This **DAMO-YOLO-T** model is a tiny-size object detection model with fast inference speed and high accuracy, trained by **DAMO-YOLO**. DAMO-YOLO is a fast and accurate object detection method, which is developed by TinyML Team from Alibaba DAMO Data Analytics and Intelligence Lab. And it achieves a... | 5203d9b34d36be3e2fb84831b9eb2be1 |
apache-2.0 | [] | false | Chinese Web Demo - We also provide Chinese Web Demo on ModelScope, including [DAMO-YOLO-T](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-t/summary), [DAMO-YOLO-S](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo/summary), [DAMO-YOLO-M](https://www.modelscope.cn/models... | e6a1264eb3896f1060fec616d828b5c7 |
apache-2.0 | [] | false | Model Evaluation |Model |size |mAP<sup>val<br>0.5:0.95 | Latency T4<br>TRT-FP16-BS1| FLOPs<br>(G)| Params<br>(M)| Download | | ------ |:---: | :---: |:---:|:---: | :---: | :---:| |[DAMO-YOLO-T](./configs/damoyolo_tinynasL20_T.py) | 640 | 41.8 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiako... | 8fee8e017d460aa527a6fe175a0dd812 |
apache-2.0 | [] | false | Cite DAMO-YOLO If you use DAMO-YOLO in your research, please cite our work by using the following BibTeX entry: ```latex @article{damoyolo, title={DAMO-YOLO: A Report on Real-Time Object Detection Design}, author={Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang and Xiuyu Sun}, journal={arXiv pr... | 9e357c3ac792cfa113ed097e4bf6b85b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'spoken-language-understanding'] | false | Environments - date: `Thu Nov 10 09:07:40 EST 2022` - python version: `3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `a7bd6522b32ec6472c13f6a2289dcdff4a846c12` - Commit date: `Wed Sep 14 08:34:27 2022 -0400` | 09968be21cd071f3d7f53e97d6647185 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'spoken-language-understanding'] | false | asr_train_asr_hubert_transformer_adam_specaug_meld_raw_en_bpe850 - ASR config: conf/tuning/train_asr_hubert_transformer_adam_specaug_meld.yaml - token_type: bpe - keep_nbest_models: 5 |dataset|Snt|Emotion Classification (%)| |---|---|---| |decoder_asr_asr_model_valid.acc.ave_5best/test|2608|39.22| |decoder_asr_asr_mo... | b4c84c4957ecfd99c4ac7798db541037 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'spoken-language-understanding'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decoder_asr_asr_model_valid.acc.ave_5best/test|2608|24809|55.5|28.0|16.5|8.4|52.9|96.5| |decoder_asr_asr_model_valid.acc.ave_5best/valid|1104|10171|55.3|29.4|15.3|7.0|51.7|96.2| | e93c25df02fa06d78f761e5665a30370 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'spoken-language-understanding'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decoder_asr_asr_model_valid.acc.ave_5best/test|2608|120780|71.1|10.7|18.2|10.6|39.5|96.5| |decoder_asr_asr_model_valid.acc.ave_5best/valid|1104|49323|71.3|11.1|17.6|9.4|38.1|96.2| | 2cf0e04ff90a18cabc2c5645e93fa5f7 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'spoken-language-understanding'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decoder_asr_asr_model_valid.acc.ave_5best/test|2608|35287|57.6|21.8|20.5|7.8|50.2|96.5| |decoder_asr_asr_model_valid.acc.ave_5best/valid|1104|14430|57.4|23.2|19.4|6.1|48.6|96.2| | 1a2770bd57c054eac47da1b6c6621bc8 |
apache-2.0 | ['gpt2', 'turkish'] | false | Model description This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020 Live demo based on this work at : https://www.metayazar.com/ Fine tuned writer on this model: https://huggingface.co/gorkemgoknar/gpt2-turkish-writer Work has been done ... | 2734588d95b77c31ddc1f3287f68f871 |
apache-2.0 | ['gpt2', 'turkish'] | false | Install ```python from transformers import AutoTokenizer, AutoModelWithLMHead import torch tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-small-turkish") model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-small-turkish") | 3740aa69408d3c6819fec0d6b5c119ba |
apache-2.0 | ['gpt2', 'turkish'] | false | model output outputs = model(**inputs, labels=inputs["input_ids"]) loss, logits = outputs[:2] predicted_index = torch.argmax(logits[0, -1, :]).item() predicted_text = tokenizer.decode([predicted_index]) | 4ea57bed5fb4c0cb4f8ed11c4e636136 |
apache-2.0 | ['gpt2', 'turkish'] | false | model output using Top-k sampling text generation method sample_outputs = model.generate(inputs.input_ids, pad_token_id=50256, do_sample=True, max_length=50, | dd3adf66704c68323aeaa9af78757b2e |
apache-2.0 | ['gpt2', 'turkish'] | false | Eval results | epoch\\\\t|train_loss\\\\t|valid_loss\\\\t|accuracy\\\\t|perplexity\\\\t|time | | ----- | -------- |--------- | ---------- | --------- | ----- | |0\\\\t|4.777015\\\\t|4.621834\\\\t|0.292547\\\\t|101.680367\\\\t|2:42:05| |1\\\\t|4.509412\\\\t|4.403999\\\\t|0.305574\\\\t|81.777267\\\\t... | aeeae3bcf4be9b280af94b096e0741e0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-cased_fine_tuned_food_ner This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6129 - Precision: 0.9080 - Recall: 0.9328 - F1: 0.9203 - Accuracy: 0.9095 | 2ba9d5995f924224db631455d2101326 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | 4fd99819e6e9f4d0a1ef4c7334c24c6d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 40 | 1.2541 | 0.7806 | 0.7299 | 0.7544 | 0.6782 | | No log | 2.0 |... | 379d6dc038491f792c7d5ad20f26420c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-demo-F04-2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3203 - Wer: 0.5353 | 1490f30888a976112fcb09786c27e681 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 | b55a96915f85cd153b5b2c0805e9675e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 23.5576 | 0.89 | 500 | 3.3654 | 1.0 | | 3.3953 | 1.79 | 1000 | 3.1729 | 1.0 | | 2.9514 | 2.68 | 1500 | 2.8946 | 1.0 ... | df9b1cc8cfeba2633563da0dd9944a38 |
apache-2.0 | [] | false | Source A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual. The original model is at [https://github.com/PrithivirajDamodaran/Styleformer](https://github.com/PrithivirajDamodaran/Styleformer).  | 1e241a7b69e17137c64edfcf28b14c3f |
apache-2.0 | [] | false | Examples: ``` [Casual] I am quitting my job [Formal] I will be stepping down from my job. ---------------------------------------------------------------------------------------------------- [Casual] Jimmy is on crack and can't trust him [Formal] Jimmy is a crack addict I cannot trust him -------------------------... | 08d792b99b9d1277aeb7fb981bdb3f6c |
apache-2.0 | [] | false | References - [Formality Style Transfer for Noisy Text: Leveraging Out-of-Domain Parallel Data for In-Domain Training via POS Masking](https://www.aclweb.org/anthology/D19-5502.pdf) - [Generative Text Style Transfer for Improved Language Sophistication](http://cs230.stanford.edu/projects_winter_2020/reports/32069807.pd... | 03cade0c03a558f8f3a8101075aa75be |
apache-2.0 | ['generated_from_trainer', 'he', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-lm-hebrew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset with adding ngram models according to [Boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://huggingface.co/blog/wav2vec2-with-ngram) | 862d6f750e1a36291136d2aa1fdecfa3 |
apache-2.0 | ['generated_from_trainer', 'he', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 06b1d33edf23d60418f2234fca09fe0d |
mit | ['MedicalNet', 'medical images', 'medical', '3D', 'Med3D'] | false | MedicalNet This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625). Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset ... | 7f6a0264b49ec9f9064cca23be8ebfc8 |
mit | ['MedicalNet', 'medical images', 'medical', '3D', 'Med3D'] | false | Citing MedicalNet If you use this code or pre-trained models, please cite the following: ``` @article{chen2019med3d, title={Med3D: Transfer Learning for 3D Medical Image Analysis}, author={Chen, Sihong and Ma, Kai and Zheng, Yefeng}, journal={arXiv preprint arXiv:1904.00625}, year={... | 396bd18bcfd3b5704ec445757903890e |
mit | ['MedicalNet', 'medical images', 'medical', '3D', 'Med3D'] | false | Update(2019/07/30) We uploaded 4 pre-trained models based on more datasets (23 datasets). ``` Model name : parameters settings resnet_10_23dataset.pth: --model resnet --model_depth 10 --resnet_shortcut B resnet_18_23dataset.pth: --model resnet --model_depth 18 --resnet_shortcut A resnet_34_23dataset.pth: -... | 3d0540423f3739476f88c615e465c864 |
apache-2.0 | ['generated_from_trainer'] | false | hubert-base-libri-clean-ft100h This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.1324 - Wer: 0.1597 | 7e150373c98053331b4dd5599716dc67 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - 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_sche... | 099824d892cd8adbcd360fb14cca012b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.14 | 250 | 4.1508 | 1.0000 | | 4.4345 | 0.28 | 500 | 3.8766 | 1.0000 | | 4.4345 | 0.42 | 750 | 3.4376 | 1.0000 | |... | 4632a65085cde467ac6caa65eacf67de |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Nepali - Drishti Sharma This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2551 - Wer: 18.8467 | b736fd247571a59f7f0b10477e903921 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 400 - mixed_precision... | 9c07a121a9ab5d8d1056120f6d6612c0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2613 | 0.27 | 400 | 0.2551 | 18.8467 | | 0f876ad0cebd1cd5402f0cdee46f40e6 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr1e05-wd0.01-bs16 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2793 - Rmse: 0.5285 - Mse: 0.2793 - Mae: 0.4342 | da03afac0a7d62535c8a520e67f2a2ef |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 7b57fedcd5bdd67a69435dabce891d4d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2767 | 1.0 | 1245 | 0.2744 | 0.5239 | 0.2744 | 0.4124 | | 0.2739 | 2.0 | 2490 | 0.2757 | 0.5251 | 0.2757 ... | 619482804a9e81450f0247b0e8fc6334 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** | 5bbb3c0bf9af0d41a3854be667f49ef4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/bert-base-nli-max-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 9d4239cfc5625c23b9a8fdf8cf62d315 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 269151a49e34e62c686c8284bddaeed1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTo... | 59138843f5aa7b4300fe590a195b6a95 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() token_embeddings[input_mask_expanded == 0] = -1e9 | 7cb3f691a804752d32fa79508cf77237 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-max-tokens') model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-max-tokens') | acf0b3c3c87e8531c4c0a56f6b646269 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-base-nli-max-tokens) | 8b32d15c43862b17c243250934f2886a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': True, 'pooling_mode_mea... | cf2aefcb7e8aa87be37ff0af2001f7fc |
apache-2.0 | ['image-to-text'] | false | Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provid... | 20fdaba46079d498e5fa3ebd7f7dd2c1 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1363 - F1: 0.8627 | 582abb0fc2be24d0d9d6eb400c5bf95d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 | | 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 | | 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 | ... | 931dd8f7d1de1a05db8a42e949c3fe0e |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for Starcraft:Remastered terrain This is a Stable Diffusion model fine-tuned on Starcraft terrain images on the Space Platform tileset with DreamBooth. It can be used by adding the `instance_prompt`: **isometric scspace terrain** It was trained on 32x32 terrain images from 265 melee maps including o... | add553168c4e851195e9f2d9c71e42ab |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Use CUDA (otherwise it will take 15 minutes) device = "cuda" tilenet = AutoencoderTile.from_pretrained( "wdcqc/starcraft-platform-terrain-32x32", subfolder="tile_vae" ).to(device) pipeline = WaveFunctionDiffusionPipeline.from_pretrained( "wdcqc/starcraft-platform-terrain-32x32", tile_vae = tilenet, ... | f3812d4b93a6a1857430bfe74cc1bba7 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | need to include the dreambooth keyword "isometric scspace terrain" pipeline_output = pipeline( "isometric scspace terrain, corgi", num_inference_steps = 50, wfc_guidance_start_step = 20, wfc_guidance_strength = 5, wfc_guidance_final_steps = 20, wfc_guidance_final_strength = 10, ) image = pipeli... | 4463f31080e29e9bf62a608dc512daa4 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Display generated image as tiles wave = pipeline_output.waves[0] tile_result = wave.argmax(axis=2) from wfd.scmap import demo_map_image display(demo_map_image(tile_result, wfc_data_path = wfc_data_path)) | 9149f52c2662dd65c6c9590b35f2aabc |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Generate map file from wfd.scmap import tiles_to_scx import random, time tiles_to_scx( tile_result, "outputs/generated_{}_{:04d}.scx".format(time.strftime("%Y%m%d_%H%M%S"), random.randint(0, 1e4)), wfc_data_path = wfc_data_path ) | 168ce4e4da0f3ee3ff8fbfbe7386da77 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0627 - Precision: 0.9389 - Recall: 0.9524 - F1: 0.9456 - Accuracy: 0.9866 | 0f5e38c09dc11ffb4b085892bdb72338 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0835 | 1.0 | 1756 | 0.0711 | 0.9200 | 0.9334 | 0.9266 | 0.9825 | | 0.0329 | 2.0 |... | eea520691fd7dce614d3bae462cd83df |
mit | ['roberta-base', 'roberta-base-epoch_69'] | false | RoBERTa, Intermediate Checkpoint - Epoch 69 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | f702e9e1c03220dfd910105d9809089c |
mit | ['roberta-base', 'roberta-base-epoch_69'] | false | Model Description This model was captured during a reproduction of [RoBERTa-base](https://huggingface.co/roberta-base), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM). The intended uses, limitations, training data and training procedure... | ba798f62685b63f142a377daf4b0afbf |
mit | ['roberta-base', 'roberta-base-epoch_69'] | false | How to use Using code from [RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on PyTorch: ``` from transformers import pipeline model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10) model("Hello, I'm the <mask> RoBERTa-base language model") ``` | 9b343f30e01b640f855b2e3bbabb548f |
mit | ['roberta-base', 'roberta-base-epoch_69'] | false | Citation info ```bibtex @article{2207.14251, Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg}, Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictio... | a60c586db9c5b03aa9c474d8f63fcf83 |
cc-by-4.0 | [] | false | Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of English-Catalan datasets, up to 11 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, t... | a97f48876f14f029a69da3c226b35eb9 |
cc-by-4.0 | [] | false | Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-en-ca", revision="main") tokenizer=pyonmttok.Tokenizer(m... | fa210a54afded052e69119eb5b77b526 |
cc-by-4.0 | [] | false | Training data The model was trained on a combination of the following datasets: | Dataset | Sentences | |--------------------|----------------| | Global Voices | 21.342 | | Memories Lluires | 1.173.055 | | Wikimatrix | 1.205.908 | | TED Talks | 50.979 ... | f6f4bafd31b6bdf28322f06d1566a2d9 |
cc-by-4.0 | [] | false | Data preparation All datasets are concatenated and filtered using the [mBERT Gencata parallel filter](https://huggingface.co/projecte-aina/mbert-base-gencata). Before training, the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt... | 2c34fd0c5673b67a6c9587a81f11d54b |
cc-by-4.0 | [] | false | Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparamenters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|---... | e22bc28df85cd5ac62b3d637bd16db30 |
cc-by-4.0 | [] | false | Variable and metrics We use the BLEU score for evaluation on test sets: [Flores-101](https://github.com/facebookresearch/flores), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/), [United Nations](https://zenodo.o... | 9b5c5da86b3f5f59dc16ac6a75467ebb |
cc-by-4.0 | [] | false | .Y33-_tLMIW0), [Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/), [wmt19 biomedical test set](), [wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b9861... | 07eabea6904da4e02b060a3490da8760 |
cc-by-4.0 | [] | false | Evaluation results Below are the evaluation results on the machine translation from English to Catalan compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): | Test set | SoftCatalà | Google Translate | mt-aina-en-ca | |----------------------|--... | 0b560249d8a3f30962d56e627938fd68 |
cc-by-4.0 | [] | false | Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties... | bc1b054267fae4ccf9edc3463b1c248e |
apache-2.0 | ['translation'] | false | opus-mt-sv-mos * source languages: sv * target languages: mos * OPUS readme: [sv-mos](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-mos/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 87d85f5773d78ad1f184b4cd85eeafe1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Vietnamese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 vi dataset. It achieves the following results on the evaluation set: - Loss: 0.9921 - Wer: 34.2172 | 27397ded629c706fba4473cecb206098 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precis... | 8114111d4e28c45523ea75b7cc9fa3de |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 124.0 | 1000 | 0.7998 | 21.7706 | | 0.0001 | 249.0 | 2000 | 0.8833 | 28.9690 | | 0.0 | 374.0 | 3000 | 0.9382 | 30.820... | 88545ec531cf6b70fbff137300975a76 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Whisper Large French This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set: - Loss: 0.00 - Wer: 00.00 | 71ed2f03cfc3b778f57aa9aec4cfb222 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precis... | 1febae21b718cc97a4389395c4cb959d |
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