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 - training_steps: 10000 | 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 | 18.9890 | | 0.0028 | 4.2 | 4000 | 0.3093 | 18.8023 | | 0.0016 | 5.25 | 5000 | 0.3240 | 18.9653 | | 0.0018 | 6.3 | 6000 | 0.3313 | 18.6451 | | 0.0014 | 7.35 | 7000 | 0.3330 | 18.9446 | | 0.0016 | 8.39 | 8000 | 0.3428 | 18.6422 | | 0.0015 | 9.44 | 9000 | 0.3508 | 18.9564 | | 0.001 | 10.49 | 10000 | 0.3569 | 18.8556 | | 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 | 0.3768 | | 1.4001 | 4.0 | 4 | 2.3837 | 0.3739 | | 1.1937 | 5.0 | 5 | 2.3566 | 0.3855 | | 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 = {103194}, year = {2023}, doi = {10.1016/j.ipm.2022.103194} } ``` | 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 al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. | 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 linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. | 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_pretrained('google/vit-base-patch16-224-in21k') model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_state = outputs.last_hidden_state ``` Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. | 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, 0.5, 0.5) and standard deviation (0.5, 0.5, 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 and Kurt Keutzer and Peter Vajda}, year={2020}, eprint={2006.03677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```bibtex @inproceedings{deng2009imagenet, title={Imagenet: A large-scale hierarchical image database}, author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, booktitle={2009 IEEE conference on computer vision and pattern recognition}, pages={248--255}, year={2009}, organization={Ieee} } ``` | 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 | 0.25 | | 0.6087 | 4.0 | 12 | 0.7325 | 0.25 | | 0.5602 | 5.0 | 15 | 0.7555 | 0.25 | | 0.5034 | 6.0 | 18 | 0.7725 | 0.25 | | 0.4672 | 7.0 | 21 | 0.7983 | 0.25 | | 0.403 | 8.0 | 24 | 0.8314 | 0.25 | | 0.3571 | 9.0 | 27 | 0.8555 | 0.25 | | 0.2792 | 10.0 | 30 | 0.9065 | 0.25 | | 0.2373 | 11.0 | 33 | 0.9286 | 0.25 | | 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](https://object.pouta.csc.fi/OPUS-MT-models/fr-gaa/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-gaa/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-gaa/opus-2020-01-09.eval.txt) | 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-f1: 0.6922 | 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 | 0.3762 | 0.8480 | 0.8942 | 0.8711 | | 0.4287 | 3.0 | 690 | 0.5572 | 0.8578 | 0.9024 | 0.8801 | | 0.4287 | 4.0 | 920 | 0.6087 | 0.8554 | 0.8977 | 0.8766 | | 0.1172 | 5.0 | 1150 | 0.6524 | 0.8456 | 0.8901 | 0.8678 | | 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 12/9/22 Thanks To Liam Brazier for theses art styles. Examples:-      | 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 outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | 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 language model trained on 30M sentences randomly chosen from [AI4Bharat IndicCorp](https://indicnlp.ai4bharat.org/corpora/) dataset : - WER: 0.15322879016421437 - CER: 0.03413696666806267 Note : 5% of a total 10935 samples have been used for evaluation. Evaluation set has 10935 examples which was not part of training training was done on first 95% and eval was done on last 5%. Training was stopped after 180k steps. Output predictions are available under files section. | 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_batch_size="32" - per_device_eval_batch_size="32" - gradient_accumulation_steps="1" - learning_rate="7.5e-5" - warmup_steps="2000" - length_column_name="input_length" - evaluation_strategy="steps" - text_column_name="sentence" - chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – - save_steps="2000" - eval_steps="3000" - logging_steps="100" - layerdrop="0.0" - activation_dropout="0.1" - save_total_limit="3" - freeze_feature_encoder - feat_proj_dropout="0.0" - mask_time_prob="0.75" - mask_time_length="10" - mask_feature_prob="0.25" - mask_feature_length="64" - preprocessing_num_workers 32 | 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 voice or librispeech multilingual datasets, so OpenSLR53 has been used. - Minimum audio duration of 0.5s has been used to filter the training data which excluded may be 10-20 samples. - OpenSLR53 transcripts are *not* part of LM training and LM used to evaluate. | 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_pretrained("tensorspeech/tts-tacotron2-kss-ko") mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-kss-ko") text = "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다." input_ids = processor.text_to_sequence(text) | 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], dtype=tf.int32), ) | 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}, primaryClass={cs.SD} } ``` | 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://github.com/TensorSpeech/TensorFlowTTS}}, } ``` | 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_steps_per_second: 0.572 - epoch: 2.13 - step: 334 | 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 | 3.0 | 573 | 0.4007 | 0.8399 | 0.8384 | | 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 | 18.9997 | | 1.8784 | 0.28 | 10000 | 1.7018 | 24.4009 | 11.6918 | 20.1561 | 23.0073 | 18.9997 | | 1.8628 | 0.42 | 15000 | 1.6934 | 24.385 | 11.683 | 20.1285 | 22.9823 | 18.9997 | | 1.8594 | 0.56 | 20000 | 1.6902 | 24.4407 | 11.6835 | 20.1734 | 23.0369 | 18.9996 | | 1.8537 | 0.7 | 25000 | 1.6864 | 24.3635 | 11.658 | 20.1318 | 22.9782 | 18.9993 | | 1.8505 | 0.84 | 30000 | 1.6856 | 24.4267 | 11.6991 | 20.1629 | 23.0361 | 18.9994 | | 1.8505 | 0.98 | 35000 | 1.6853 | 24.4246 | 11.6944 | 20.1717 | 23.0424 | 18.9996 | | f8a9d5a005efa8bb0e538ace00fcb98f |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Stable Diffusion v2 Model Card This model was generated by Hugging Face using [Apple’s 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, codebase available [here](https://github.com/Stability-AI/stablediffusion). This stable-diffusion-2-1 model is fine-tuned from stable-diffusion-2 (768-v-ema.ckpt) with an additional 55k steps on the same dataset (with punsafe=0.1), and then fine-tuned for another 155k extra steps with punsafe=0.98. These weights here have been converted to Core ML for use on Apple Silicon hardware. There are 4 variants of the Core ML weights: ``` coreml-stable-diffusion-2-base ├── original │ ├── compiled | 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 🧨 [`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 model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @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 Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } | 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 artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. | 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 images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. | 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 propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. | 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 “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). | 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 from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. | 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](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. | 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 (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. | 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 Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* | 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_training: Native AMP | 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 | | 0.0028 | 22.22 | 200 | 0.0028 | 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 sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1') embeddings = model.encode(sentences) print(embeddings) ``` | 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_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` | 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-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ``` | 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 + SentencePiece (spm4k,spm4k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.eval.txt) | 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_constituents: {'nob', 'nno'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-nor/opus-2020-06-17.test.txt - src_alpha3: fin - tgt_alpha3: nor - short_pair: fi-no - chrF2_score: 0.426 - bleu: 23.5 - brevity_penalty: 1.0 - ref_len: 14768.0 - src_name: Finnish - tgt_name: Norwegian - train_date: 2020-06-17 - src_alpha2: fi - tgt_alpha2: no - prefer_old: False - long_pair: fin-nor - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 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 | 0.9 | | 0.3083 | 0.13 | 200 | 0.2023 | 0.9333 | | 0.2922 | 0.16 | 250 | 0.1530 | 0.94 | | 0.2761 | 0.19 | 300 | 0.2035 | 0.9267 | | 0.2145 | 0.22 | 350 | 0.2450 | 0.9 | | 0.258 | 0.26 | 400 | 0.1680 | 0.9267 | | 0.2702 | 0.29 | 450 | 0.1607 | 0.9333 | | 0.2587 | 0.32 | 500 | 0.1496 | 0.9467 | | 0.2822 | 0.35 | 550 | 0.1405 | 0.9333 | | 0.2538 | 0.38 | 600 | 0.1396 | 0.9467 | | 0.2707 | 0.42 | 650 | 0.1626 | 0.9333 | | 0.2408 | 0.45 | 700 | 0.1623 | 0.9067 | | 0.2531 | 0.48 | 750 | 0.1300 | 0.9467 | | 0.2014 | 0.51 | 800 | 0.1529 | 0.9333 | | 0.2454 | 0.54 | 850 | 0.1365 | 0.94 | | 0.2282 | 0.58 | 900 | 0.1447 | 0.9533 | | 0.2554 | 0.61 | 950 | 0.1321 | 0.9467 | | 0.24 | 0.64 | 1000 | 0.1256 | 0.9467 | | 0.2239 | 0.67 | 1050 | 0.1290 | 0.9467 | | 0.2865 | 0.7 | 1100 | 0.1288 | 0.9667 | | 0.2456 | 0.74 | 1150 | 0.1299 | 0.9533 | | 0.2407 | 0.77 | 1200 | 0.1565 | 0.9267 | | 0.2256 | 0.8 | 1250 | 0.1262 | 0.96 | | 0.238 | 0.83 | 1300 | 0.1599 | 0.9333 | | 0.2151 | 0.86 | 1350 | 0.1252 | 0.9333 | | 0.187 | 0.9 | 1400 | 0.1132 | 0.9467 | | 0.2218 | 0.93 | 1450 | 0.1030 | 0.9533 | | 0.2371 | 0.96 | 1500 | 0.1036 | 0.9467 | | 0.2264 | 0.99 | 1550 | 0.1041 | 0.9467 | | 0.2159 | 1.02 | 1600 | 0.1338 | 0.9267 | | 0.1773 | 1.06 | 1650 | 0.1218 | 0.94 | | 0.1381 | 1.09 | 1700 | 0.1593 | 0.94 | | 0.1582 | 1.12 | 1750 | 0.1445 | 0.9533 | | 0.1921 | 1.15 | 1800 | 0.1355 | 0.94 | | 0.206 | 1.18 | 1850 | 0.1511 | 0.9467 | | 0.1679 | 1.22 | 1900 | 0.1394 | 0.94 | | 0.1691 | 1.25 | 1950 | 0.1403 | 0.9333 | | 0.2301 | 1.28 | 2000 | 0.1169 | 0.9467 | | 0.1764 | 1.31 | 2050 | 0.1507 | 0.9333 | | 0.1772 | 1.34 | 2100 | 0.1148 | 0.96 | | 0.1749 | 1.38 | 2150 | 0.1203 | 0.94 | | 0.1912 | 1.41 | 2200 | 0.1037 | 0.94 | | 0.1614 | 1.44 | 2250 | 0.1006 | 0.9533 | | 0.1975 | 1.47 | 2300 | 0.0985 | 0.9533 | | 0.1843 | 1.5 | 2350 | 0.0922 | 0.9533 | | 0.1764 | 1.54 | 2400 | 0.1259 | 0.9467 | | 0.1855 | 1.57 | 2450 | 0.1243 | 0.96 | | 0.1272 | 1.6 | 2500 | 0.2107 | 0.9267 | | 0.241 | 1.63 | 2550 | 0.1142 | 0.9533 | | 0.1584 | 1.66 | 2600 | 0.1194 | 0.9467 | | 0.1568 | 1.7 | 2650 | 0.1196 | 0.9533 | | 0.1896 | 1.73 | 2700 | 0.1311 | 0.9533 | | 0.143 | 1.76 | 2750 | 0.1140 | 0.9533 | | 0.227 | 1.79 | 2800 | 0.1482 | 0.9333 | | 0.1404 | 1.82 | 2850 | 0.1366 | 0.94 | | 0.1865 | 1.86 | 2900 | 0.1174 | 0.94 | | 0.1659 | 1.89 | 2950 | 0.1189 | 0.94 | | 0.1882 | 1.92 | 3000 | 0.1144 | 0.9467 | | 0.1403 | 1.95 | 3050 | 0.1358 | 0.94 | | 0.2193 | 1.98 | 3100 | 0.1092 | 0.9533 | | 0.1392 | 2.02 | 3150 | 0.1278 | 0.9267 | | 0.1292 | 2.05 | 3200 | 0.1186 | 0.96 | | 0.0939 | 2.08 | 3250 | 0.1183 | 0.94 | | 0.1356 | 2.11 | 3300 | 0.1939 | 0.94 | | 0.1175 | 2.14 | 3350 | 0.1499 | 0.94 | | 0.1285 | 2.18 | 3400 | 0.1538 | 0.94 | | 0.1018 | 2.21 | 3450 | 0.1796 | 0.9333 | | 0.1342 | 2.24 | 3500 | 0.1540 | 0.94 | | 0.17 | 2.27 | 3550 | 0.1261 | 0.94 | | 0.1548 | 2.3 | 3600 | 0.1375 | 0.9267 | | 0.1415 | 2.34 | 3650 | 0.1264 | 0.9333 | | 0.1096 | 2.37 | 3700 | 0.1252 | 0.9333 | | 0.1001 | 2.4 | 3750 | 0.1546 | 0.94 | | 0.0934 | 2.43 | 3800 | 0.1534 | 0.94 | | 0.1287 | 2.46 | 3850 | 0.1735 | 0.9333 | | 0.0872 | 2.5 | 3900 | 0.1475 | 0.9467 | | 0.0994 | 2.53 | 3950 | 0.1735 | 0.9467 | | 0.1558 | 2.56 | 4000 | 0.1585 | 0.9467 | | 0.1517 | 2.59 | 4050 | 0.2021 | 0.9333 | | 0.1246 | 2.62 | 4100 | 0.1594 | 0.9267 | | 0.1228 | 2.66 | 4150 | 0.1338 | 0.9533 | | 0.1064 | 2.69 | 4200 | 0.1421 | 0.9467 | | 0.1466 | 2.72 | 4250 | 0.1383 | 0.9467 | | 0.1243 | 2.75 | 4300 | 0.1604 | 0.9533 | | 0.1434 | 2.78 | 4350 | 0.1736 | 0.9333 | | 0.1127 | 2.82 | 4400 | 0.1909 | 0.9267 | | 0.0908 | 2.85 | 4450 | 0.1958 | 0.9333 | | 0.1134 | 2.88 | 4500 | 0.1596 | 0.94 | | 0.1345 | 2.91 | 4550 | 0.1604 | 0.9533 | | 0.1913 | 2.94 | 4600 | 0.1852 | 0.9267 | | 0.1382 | 2.98 | 4650 | 0.1852 | 0.9333 | | 0.1109 | 3.01 | 4700 | 0.1905 | 0.9333 | | 0.1144 | 3.04 | 4750 | 0.1655 | 0.94 | | 0.074 | 3.07 | 4800 | 0.2034 | 0.9333 | | 0.0926 | 3.1 | 4850 | 0.1929 | 0.94 | | 0.0911 | 3.13 | 4900 | 0.1703 | 0.9333 | | 0.0933 | 3.17 | 4950 | 0.1826 | 0.9333 | | 0.1003 | 3.2 | 5000 | 0.1716 | 0.94 | | 0.0889 | 3.23 | 5050 | 0.1843 | 0.9267 | | 0.0841 | 3.26 | 5100 | 0.1670 | 0.94 | | 0.0918 | 3.29 | 5150 | 0.1595 | 0.9467 | | 0.0795 | 3.33 | 5200 | 0.1504 | 0.96 | | 0.0978 | 3.36 | 5250 | 0.1317 | 0.96 | | 0.1202 | 3.39 | 5300 | 0.1641 | 0.9533 | | 0.0935 | 3.42 | 5350 | 0.1473 | 0.96 | | 0.0673 | 3.45 | 5400 | 0.1684 | 0.9533 | | 0.0729 | 3.49 | 5450 | 0.1414 | 0.9533 | | 0.077 | 3.52 | 5500 | 0.1669 | 0.9533 | | 0.1264 | 3.55 | 5550 | 0.1364 | 0.96 | | 0.1282 | 3.58 | 5600 | 0.1575 | 0.9467 | | 0.0553 | 3.61 | 5650 | 0.1440 | 0.9467 | | 0.0953 | 3.65 | 5700 | 0.1526 | 0.9533 | | 0.0886 | 3.68 | 5750 | 0.1633 | 0.94 | | 0.0901 | 3.71 | 5800 | 0.1704 | 0.9467 | | 0.0986 | 3.74 | 5850 | 0.1674 | 0.94 | | 0.0849 | 3.77 | 5900 | 0.1989 | 0.9333 | | 0.0815 | 3.81 | 5950 | 0.1942 | 0.94 | | 0.0973 | 3.84 | 6000 | 0.1611 | 0.94 | | 0.0599 | 3.87 | 6050 | 0.1807 | 0.9267 | | 0.1068 | 3.9 | 6100 | 0.1966 | 0.94 | | 0.0889 | 3.93 | 6150 | 0.1979 | 0.9333 | | 0.0854 | 3.97 | 6200 | 0.2012 | 0.9333 | | 0.1207 | 4.0 | 6250 | 0.1983 | 0.9333 | | 0.0735 | 4.03 | 6300 | 0.1795 | 0.94 | | 0.1148 | 4.06 | 6350 | 0.1966 | 0.94 | | 0.0725 | 4.09 | 6400 | 0.2290 | 0.94 | | 0.0576 | 4.13 | 6450 | 0.1936 | 0.9333 | | 0.0477 | 4.16 | 6500 | 0.2090 | 0.9333 | | 0.0722 | 4.19 | 6550 | 0.1878 | 0.9333 | | 0.0936 | 4.22 | 6600 | 0.2087 | 0.94 | | 0.0715 | 4.25 | 6650 | 0.2040 | 0.94 | | 0.0586 | 4.29 | 6700 | 0.1862 | 0.9333 | | 0.0548 | 4.32 | 6750 | 0.1801 | 0.9267 | | 0.0527 | 4.35 | 6800 | 0.1912 | 0.9333 | | 0.0813 | 4.38 | 6850 | 0.1941 | 0.9333 | | 0.0531 | 4.41 | 6900 | 0.1932 | 0.9267 | | 0.0606 | 4.45 | 6950 | 0.2195 | 0.94 | | 0.1213 | 4.48 | 7000 | 0.1975 | 0.9333 | | 0.0807 | 4.51 | 7050 | 0.1915 | 0.9333 | | 0.076 | 4.54 | 7100 | 0.1987 | 0.9333 | | 0.0595 | 4.57 | 7150 | 0.2052 | 0.9333 | | 0.0832 | 4.61 | 7200 | 0.2039 | 0.9333 | | 0.0657 | 4.64 | 7250 | 0.2186 | 0.94 | | 0.0684 | 4.67 | 7300 | 0.2063 | 0.94 | | 0.0429 | 4.7 | 7350 | 0.2056 | 0.94 | | 0.0531 | 4.73 | 7400 | 0.2139 | 0.94 | | 0.0556 | 4.77 | 7450 | 0.2153 | 0.94 | | 0.0824 | 4.8 | 7500 | 0.2010 | 0.9333 | | 0.039 | 4.83 | 7550 | 0.2079 | 0.94 | | 0.068 | 4.86 | 7600 | 0.2140 | 0.94 | | 0.065 | 4.89 | 7650 | 0.2108 | 0.94 | | 0.0359 | 4.93 | 7700 | 0.2058 | 0.94 | | 0.0592 | 4.96 | 7750 | 0.2029 | 0.94 | | 0.0793 | 4.99 | 7800 | 0.2023 | 0.94 | | 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 [paper](https://arxiv.org/pdf/2210.12579.pdf) and/or [code](https://github.com/iesl/anncur) for more details about the model. | 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 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 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_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|27119|98.6|1.1|0.3|0.2|1.5|18.7| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|27119|98.3|1.3|0.4|0.2|1.9|21.8| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|27120|97.9|1.5|0.5|0.2|2.3|25.2| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|27120|98.4|1.2|0.4|0.1|1.7|19.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|27120|97.2|2.1|0.7|0.3|3.1|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|21409|97.4|2.0|0.6|0.3|2.9|27.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|21409|97.8|1.8|0.4|0.2|2.5|24.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|21409|96.7|2.6|0.7|0.4|3.7|31.6| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|21416|96.6|2.5|1.0|0.3|3.7|32.5| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|21416|97.5|1.9|0.7|0.3|2.9|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|21416|94.6|3.7|1.6|0.5|5.9|37.3| | 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_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|160390|99.6|0.1|0.3|0.2|0.6|18.7| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track|1640|160390|99.4|0.2|0.4|0.2|0.8|21.8| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_2mics|1640|160400|99.2|0.3|0.5|0.2|1.1|25.2| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|160400|99.5|0.2|0.3|0.1|0.7|19.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track|1640|160400|98.8|0.5|0.7|0.3|1.5|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_2mics|1320|126796|98.9|0.4|0.7|0.3|1.4|27.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|126796|99.1|0.4|0.5|0.2|1.1|24.3| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_real_isolated_1ch_track|1320|126796|98.6|0.6|0.8|0.4|1.8|31.7| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_2mics|1320|126812|98.2|0.6|1.1|0.4|2.1|32.5| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|126812|98.8|0.4|0.8|0.3|1.5|28.9| |decode_asr_transformer_normalize_output_wavtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_enh_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track|1320|126812|97.0|1.2|1.9|0.6|3.7|37.3| | 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_wavlm_transformer_init_lr1e-4_accum1_adam_specaug_bypass0_raw_en_char ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 12 patience: 10 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max - - train - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: - ../enh1/exp/enh_train_enh_convtasnet_small_raw/valid.loss.ave_1best.pth:encoder:enh_model.encoder - ../enh1/exp/enh_train_enh_convtasnet_small_raw/valid.loss.ave_1best.pth:separator:enh_model.separator - ../enh1/exp/enh_train_enh_convtasnet_small_raw/valid.loss.ave_1best.pth:decoder:enh_model.decoder - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:frontend:s2t_model.frontend - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:preencoder:s2t_model.preencoder - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:encoder:s2t_model.encoder - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:ctc:s2t_model.ctc - ../asr1/exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_char/valid.acc.ave.pth:decoder:s2t_model.decoder ignore_init_mismatch: false freeze_param: - s2t_model.frontend.upstream num_iters_per_epoch: null batch_size: 12 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/enh_asr_stats_raw_en_char/train/speech_shape - exp/enh_asr_stats_raw_en_char/train/speech_ref1_shape - exp/enh_asr_stats_raw_en_char/train/text_shape.char valid_shape_file: - exp/enh_asr_stats_raw_en_char/valid/speech_shape - exp/enh_asr_stats_raw_en_char/valid/speech_ref1_shape - exp/enh_asr_stats_raw_en_char/valid/text_shape.char batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr05_multi_noisy_si284/wav.scp - speech - sound - - dump/raw/tr05_multi_noisy_si284/spk1.scp - speech_ref1 - sound - - dump/raw/tr05_multi_noisy_si284/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dt05_multi_isolated_1ch_track/wav.scp - speech - sound - - dump/raw/dt05_multi_isolated_1ch_track/spk1.scp - speech_ref1 - sound - - dump/raw/dt05_multi_isolated_1ch_track/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0001 scheduler: null scheduler_conf: {} token_list: data/en_token_list/char/tokens.txt src_token_list: null init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true enh_criterions: - name: si_snr conf: {} wrapper: fixed_order wrapper_conf: {} enh_model_conf: stft_consistency: false loss_type: mask_mse mask_type: null asr_model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false st_model_conf: stft_consistency: false loss_type: mask_mse mask_type: null subtask_series: - enh - asr model_conf: calc_enh_loss: false bypass_enh_prob: 0.0 use_preprocessor: true token_type: char bpemodel: null src_token_type: bpe src_bpemodel: null non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null enh_encoder: conv enh_encoder_conf: channel: 256 kernel_size: 40 stride: 20 enh_separator: tcn enh_separator_conf: num_spk: 1 layer: 4 stack: 2 bottleneck_dim: 256 hidden_dim: 512 kernel: 3 causal: false norm_type: gLN nonlinear: relu enh_decoder: conv enh_decoder_conf: channel: 256 kernel_size: 40 stride: 20 frontend: s3prl frontend_conf: frontend_conf: upstream: wavlm_large download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 100 num_freq_mask: 4 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} asr_preencoder: linear asr_preencoder_conf: input_size: 1024 output_size: 128 asr_encoder: transformer asr_encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d2 normalize_before: true asr_postencoder: null asr_postencoder_conf: {} asr_decoder: transformer asr_decoder_conf: input_layer: embed attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.0 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 st_preencoder: null st_preencoder_conf: {} st_encoder: rnn st_encoder_conf: {} st_postencoder: null st_postencoder_conf: {} st_decoder: rnn st_decoder_conf: {} st_extra_asr_decoder: rnn st_extra_asr_decoder_conf: {} st_extra_mt_decoder: rnn st_extra_mt_decoder_conf: {} required: - output_dir - token_list version: '202204' distributed: false ``` </details> | 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-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, 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}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | 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_energy_squad.py \ --model_type roberta \ --model_name_or_path roberta-base \ --do_train \ --do_eval \ --train_file train-v1.1.json \ --predict_file dev-v1.1.json \ --per_gpu_train_batch_size 12 \ --per_gpu_eval_batch_size 16 \ --learning_rate 3e-5 \ --num_train_epochs 2.0 \ --max_seq_length 320 \ --doc_stride 128 \ --data_dir data \ --output_dir data/roberta-base-squad-v1 2>&1 | tee train-roberta-base-squad-v1.log ``` It took about 2 hours to finish. | 4a75fd2553568b0cba985cff1f540350 |
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