license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium Hungarian El Greco This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0,google/fleurs hu,hu_hu dataset. It achieves the following results on the evaluation set: - Loss: 0.3428 - Wer: 18.6422 | 7e3cfd80a7190413a024c376065541c3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - train... | 5f014d63767ffba1cf546d601668ea5d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0621 | 1.05 | 1000 | 0.2690 | 20.5099 | | 0.0174 | 2.1 | 2000 | 0.2705 | 19.2292 | | 0.006 | 3.15 | 3000 | 0.2954 | 1... | 8dc43fabaf983bea3934467a4e5c11fd |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3566 - Accuracy: 0.3855 | 95facf0d9b2f27fd118efdeecfc6cab3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 01b434724aa21382f7bed5ec397c8f55 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7259 | 1.0 | 1 | 2.5917 | 0.2551 | | 2.217 | 2.0 | 2 | 2.5059 | 0.3275 | | 1.7237 | 3.0 | 3 | 2.4355 | 0.... | d54e7b1f4081aa64a88b26880e08632a |
cc-by-sa-4.0 | [] | false | ELECTRA small Japanese discriminator This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language. The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). | a039efbede39339ca4dcd8e9419f4373 |
cc-by-sa-4.0 | [] | false | Model architecture The model architecture is the same as ELECTRA small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 12 layers, 256 dimensions of hidden states, and 4 attention heads. | 752340f86359ce9502bded5cc5eb4e6e |
cc-by-sa-4.0 | [] | false | Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Japanese version of Wikipedia, using Wikipedia dump file as of June 1, 2021. The corpus file is 2.9GB, consisting of approximately 20M sentences. | 7d2c81f0abfd9476c338579c5b13ba59 |
cc-by-sa-4.0 | [] | false | Training The models are trained with the same configuration as ELECTRA small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 128 tokens per instance, 128 instances per batch, and 1M training steps. The size of the generator is 1/4 of the size of the discriminator. | 1464bf7d4f80e4cc6f9cf396e365e029 |
cc-by-sa-4.0 | [] | false | Citation ``` @article{Suzuki-etal-2023-ipm, title = {Constructing and analyzing domain-specific language model for financial text mining} author = {Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi}, journal = {Information Processing & Management}, volume = {60}, number = {2}, pages ... | 7ac81f0a2106ae330fee638c048b41c4 |
apache-2.0 | ['vision'] | false | Vision Transformer (large-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et ... | 408e05ef497f06263cce69faef167b77 |
apache-2.0 | ['vision'] | 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 32x32), which are li... | 807ee83531430187aa4e23c0a0df743a |
apache-2.0 | ['vision'] | false | Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you. | 6b2ad80cb94e24076133093dbce3de81 |
apache-2.0 | ['vision'] | false | How to use Here is how to use this model: ```python from transformers import ViTFeatureExtractor, ViTModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretraine... | 93ce19a2f6225e2f55d6c611d29484f2 |
apache-2.0 | ['vision'] | false | Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, ... | 48294ea37a98b5e6b39fe438c0ad433c |
apache-2.0 | ['vision'] | false | Pretraining The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224. | 685374c19e7506aff28877d1122bc241 |
apache-2.0 | ['vision'] | false | Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance. | 9fb08e17d672a661005044b7a800f7f6 |
apache-2.0 | ['vision'] | false | BibTeX entry and citation info ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez an... | 67c110c712adeef0e7fe5fc8900fe1cb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-8-8 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6925 - Accuracy: 0.5200 | 9e7254816e148a5d31b325e6d36e47dc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP | cd3392652e187d960922516e64fac292 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7061 | 1.0 | 3 | 0.6899 | 0.75 | | 0.6627 | 2.0 | 6 | 0.7026 | 0.25 | | 0.644 | 3.0 | 9 | 0.7158 | 0.... | 59177ea4b7be8e42256e900b5d80d987 |
apache-2.0 | ['translation'] | false | opus-mt-fr-gaa * source languages: fr * target languages: gaa * OPUS readme: [fr-gaa](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-gaa/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | d63b5c34a9ae071267c99ce935d41cb9 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.observational.sa.5-class.seed_43 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.8001 - Accuracy: 0.6987 - Macro-f1: 0.6805 - Weighted-macro-f... | ce768181a2332c869bcb9fa76ddb1b55 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 9f0ba5b56a0c73db4087efe4ccdb0211 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5572 - Accuracy: 0.8578 - F1: 0.9024 - Combined Score: 0.8801 | 91fe2c8f9bc7fca0b82f3b8f5630d501 |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 5.0 | 9dee3514ea53cc1dc11ddd8e1387dd7f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | No log | 1.0 | 230 | 0.4111 | 0.8088 | 0.8704 | 0.8396 | | No log | 2.0 | 460 | 0.37... | 5eb572c04679812aba2140989ac6b910 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3046 - Accuracy: 0.87 - F1: 0.8713 | e60cdc37bbe5296cabfb9c3a4bfc0785 |
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: 2 | e86681931a11787b1f7d6e5b1dcdc69f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 | a588cbbb972c6dc070e5db2375f37fe3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 7b1afa276a14e6ccb9a2e7714ed9e858 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | MODEL BY InternalMegaT How to use: **_brazier_** "your prompt" **_, by Svetoslav Roerich, generative art, aspect ratio 16:9, fortnite art style, stylized layered shapes, warm color scheme art rendition, an ai generated image, by jake parker_** Training on V1 - 3000 steps, 512x512, v1-5 Base, 13 images Uploaded on 12... | 11f75f4fe4a607a5887fa3564b1c1e1d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outp... | 3338cff66d06ce24b3649eace94bca13 |
apache-2.0 | ['automatic-speech-recognition', 'bn', 'hf-asr-leaderboard', 'openslr_SLR53', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset. It achieves the following results on the evaluation set. Without language model : - WER: 0.21726385291857586 - CER: 0.04725010353701041 With 5 gram langua... | 4b722bd134ada456a2c90a6cd65be25e |
apache-2.0 | ['automatic-speech-recognition', 'bn', 'hf-asr-leaderboard', 'openslr_SLR53', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - dataset_name="openslr" - model_name_or_path="facebook/wav2vec2-xls-r-300m" - dataset_config_name="SLR53" - output_dir="./wav2vec2-xls-r-300m-bengali" - overwrite_output_dir - num_train_epochs="50" - per_device_train... | 08e67ff294a25d155a243f0882b46798 |
apache-2.0 | ['automatic-speech-recognition', 'bn', 'hf-asr-leaderboard', 'openslr_SLR53', 'robust-speech-event'] | false | Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0 Notes - Training and eval code modified from : https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event. - Bengali speech data was not available from common v... | ed87ebc3ff648ebac2ac4727766dfc8b |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Multi-band MelGAN trained on KSS (Korean) This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). | d521779aeacc8221a2e09561bab341a9 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Converting your Text to Wav ```python import soundfile as sf import numpy as np import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-kss-ko") tacotron2 = TFAutoModel.from_pret... | 19b9575f77dc84b47d86d79785c54712 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | tacotron2 inference (text-to-mel) decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference( input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32), speaker_ids=tf.convert_to_tensor([0], ... | 32209e177fd9c9b87c6773837e546d00 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Referencing Multi-band MelGAN ``` @misc{yang2020multiband, title={Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech}, author={Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie}, year={2020}, eprint={2005.05106}, archivePrefix={arXiv}, ... | 5d7ee01ed470349fc96e70cc488e33ff |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Referencing TensorFlowTTS ``` @misc{TFTTS, author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata, Trinh Le and Yunchao He}, title = {TensorflowTTS}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\url{https://g... | 816291da324b674aacada5e78680dcc5 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-squad_2 This model is a fine-tuned version of [tomXBE/distilbert-base-uncased-finetuned-squad](https://huggingface.co/tomXBE/distilbert-base-uncased-finetuned-squad) on an unknown dataset. | ed02384da4b1731300dbbf519cf953d9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | b4dbad5d1f56e1e80a3b5281b44da58e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2179 - Accuracy: 0.9245 - F1: 0.9248 | 5e5c86d082f4808e7f7d6a8ebaa796f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 3018bb5ee93869b1ab00dfb9006be8e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8178 | 1.0 | 250 | 0.3219 | 0.9035 | 0.8996 | | 0.2526 | 2.0 | 500 | 0.2179 | 0.9245 | 0.9248 | | aa5650e75f1fdc26f8fd5e8d2c9a4fc7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0122 - eval_runtime: 27.9861 - eval_samples_per_second: 35.732 - eval_step... | 92e41a1cb2aed80c6b155764d0620300 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP | 9fe2497d6bf7c10a0ac0b01584ebcee4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_finetuned_disaster_tweets This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4007 - Accuracy: 0.8399 - F1: 0.8384 | bb11f49a18fe28a3fcfbea349650075c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 17c0c0a29428d55bbdd52ec9926f398f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4594 | 1.0 | 191 | 0.4059 | 0.8163 | 0.8164 | | 0.3399 | 2.0 | 382 | 0.3905 | 0.8346 | 0.8333 | | 0.2859 |... | a4e98aa6b8f0f06b471f08d6c94b6824 |
mit | ['generated_from_keras_callback'] | false | tf-mobilebert-uncased-squad-v2 This model is a fine-tuned version of [csarron/mobilebert-uncased-squad-v2](https://huggingface.co/csarron/mobilebert-uncased-squad-v2) on an unknown dataset. It achieves the following results on the evaluation set: | ea8fdc3d471fc403a8e436f24c9304fa |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-cnndm1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6853 - Rouge1: 24.4246 - Rouge2: 11.6944 - Rougel: 20.1717 - Rougelsum: 23.0424 - Gen Len: 18.9996 | 78fa222319302693eaf7dd2155e1bfe3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | 822afd6a8e1adedb8f2b3ffd63b7d240 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.912 | 0.14 | 5000 | 1.7167 | 24.4232 | 11.7049 | 20.1758 | 23.0345 |... | f8a9d5a005efa8bb0e538ace00fcb98f |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Stable Diffusion v2 Model Card This model was generated by Hugging Face using [Appleโs repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md). This model card focuses on the model associated with the Stable Diffusion v2.1 model, c... | 99955d8dbaaac06298832b67fc365218 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Python inference, "split_einsum" attention ``` Please, refer to https://huggingface.co/blog/diffusers-coreml for details. - Use it with ๐งจ [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base | f876a906fc75cd7430d4d1239311369f |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | examples) - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt). | 9d3d96e517bd1be398c32b8c0e5d038f |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a ... | da46199b3e4827aa6f0c2cdae9e211d1 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other ar... | c3aeb6c839523cb2647c839821821b85 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate... | 483e5bdfa99eaa821f3162f54bc9e86c |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. | c6dc2fdef6f9904bc969e3f766c69e8e |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or ... | 8d576aa9d6fc0c50937b9683976ca473 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to โA red cube on top of a blue sphereโ - Faces and people in general may not be gener... | 800f1fefee06b59acc8db387abe937e5 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images fro... | 6a62298dcbe4fdc977f7756e9a277336 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](http... | fc10668429870cd50b1960d417a2377c |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact | da1308c2acc29be7aba44bfb98e7e164 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted... | 5a57d80e96a4056eb6b4920ca7279907 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | a0671c65f0df6bed9a930f7dec238365 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Es - GoCloud This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the 30seg dataset. It achieves the following results on the evaluation set: - Loss: 0.0028 - Wer: 0.0 | 03bed18060f6bf79276a8e3ef51ba161 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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 - lr_scheduler_warmup_steps: 25 - training_steps: 200 - mixed_precision_... | afdb227f9bf60e123321a7b71c3767e6 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2944 | 5.56 | 50 | 0.1392 | 79.6117 | | 0.08 | 11.11 | 100 | 0.0569 | 46.0472 | | 0.0204 | 16.67 | 150 | 0.0086 | 0.0 ... | d6c8f4ac3ab5b9cf2b161e97b301aa05 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/distiluse-base-multilingual-cased-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 4d3c4c107935112847d93c64c383dce6 |
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... | f92629f5a1341eb12a38f32c7060b358 |
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/distiluse-base-multilingual-cased-v1) | eb4638d757cfc3975c387970079b024e |
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: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mo... | 89ff668e51a619c294c1bf2fbbc6040c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-be... | fea9fd0d05ac1a3ea2eb22b7ae0cbae4 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-large_sst2_negation0.2 This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.6892 | 6227ea2fc0fe5533d4c0bb60fcd3b70e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.431 | 1.0 | 1072 | 3.3426 | | 1.8756 | 2.0 | 2144 | 3.5903 | | 1.6223 | 3.0 | 3216 | 3.6892 | | 82089c4afb40b5477611ae1eed1ab4eb |
apache-2.0 | ['translation'] | false | fin-nor * source group: Finnish * target group: Norwegian * OPUS readme: [fin-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-nor/README.md) * model: transformer-align * source language(s): fin * target language(s): nno nob * model: transformer-align * pre-processing: normalization ... | 8c9ee6dcc8b2ec6f7ec6d0b03e953e3a |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fin-nor - source_languages: fin - target_languages: nor - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-nor/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fi', 'no'] - src_constituents: {'fin'} - tgt_const... | 34dde3a6933d5f6695c9ab4e82c110c0 |
apache-2.0 | ['generated_from_trainer'] | false | IMDB_DistilBERT_5EE This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2023 - Accuracy: 0.94 | b1e0318b28974aa7ec5961dfe2ae04b0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | f7537758903dd736953cea93617004ed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6748 | 0.03 | 50 | 0.5955 | 0.88 | | 0.4404 | 0.06 | 100 | 0.2853 | 0.9 | | 0.3065 | 0.1 | 150 | 0.2208 | 0.... | ac3f0f4375d939be5009e0aa5b4647e2 |
apache-2.0 | [] | false | This repo contains the cross-encoder model which uses \[cls\]-token based pooling to score a query-item pair. This model is used in the experiments for our EMNLP 2022 paper titled "[Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization](https://arxiv.org/pdf/2210.12579.pdf)". See [pap... | c65a9a59b5ddd6e598398fbb081702d3 |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | `espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char` This model was trained by simpleoier using chime4 recipe in [espnet](https://github.com/espnet/espnet/). | 7d048a289513d117ece0eec84d88459d |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 2b663318cd1773fb8685b1e03295b6bc6889c283 pip install -e . cd egs2/chime4/enh_asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char ``` <!-- Genera... | 100dd48ef3b05bdc76cf1e141c084596 |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | Environments - date: `Thu Apr 28 08:15:30 EDT 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.8.1` - Git hash: `` - Commit date: `` | 438ddca01f19f4addfa66b32dff6ae66 |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|27119|98.5|1.2|0.3|0.2|1.7|19.6| |decode_asr_transformer_normalize_outpu... | 0805088aece86ca03ceaa4d900e85c5e |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_2mics|1640|160390|99.5|0.2|0.3|0.2|0.7|19.6| |decode_asr_transformer_normalize_outp... | d00705ea2a95d356876f2cb48b265d1e |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | EnhS2T config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_lr1e-4_accum1_adam_specaug_bypass0.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/enh_asr_train_enh_asr_convtasnet_init_noenhloss_wa... | a15306cb112ffd96c0100486384139e6 |
cc-by-4.0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-En... | 03b273a615f8d42f1cd4eda977a68df7 |
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: 0.1370 - F1: 0.8625 | 769931068f062302053b0bd621bbe00e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 76528abb956a530ae5d9f9221d4661ac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.26 | 1.0 | 525 | 0.1565 | 0.8218 | | 0.1276 | 2.0 | 1050 | 0.1409 | 0.8486 | | 0.0817 | 3.0 | 1575 | 0.1370 | 0.8625 | ... | a8494091e43679995fb21ed6208a95b0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | 7cea77986695276ce563af8296c497d8 |
mit | ['question-answering', 'roberta', 'roberta-base'] | false | RoBERTa-base fine-tuned on SQuAD v1 This model was fine-tuned from the HuggingFace [RoBERTa](https://arxiv.org/abs/1907.11692) base checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer). This model is case-sensitive: it makes a difference between english and English. | d74ed8a6ceea262bf1459d1c066f50ac |
mit | ['question-answering', 'roberta', 'roberta-base'] | false | Fine-tuning - Python: `3.7.5` - Machine specs: `CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz` `Memory: 32 GiB` `GPUs: 2 GeForce GTX 1070, each with 8GiB memory` `GPU driver: 418.87.01, CUDA: 10.1` - script: ```shell | bc557876e2e805a3f944588b28419e80 |
mit | ['question-answering', 'roberta', 'roberta-base'] | false | after install https://github.com/huggingface/transformers cd examples/question-answering mkdir -p data wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json python run_en... | 4a75fd2553568b0cba985cff1f540350 |
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