license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2651 - F1: 0.8355 | be7922389680d525525bcc77a86ed5db |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5954 | 1.0 | 191 | 0.3346 | 0.7975 | | 0.2689 | 2.0 | 382 | 0.2900 | 0.8347 | | 0.1821 | 3.0 | 573 | 0.2651 | 0.8355 | | fb333b01f29ab276c080dad435bbee24 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Fine-tuned XLSR-53 large model for speech recognition in English Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint | 11237fdac48ec3d7da80a618d2c4ed3f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-english, title={Fine-tuned {XLSR}-53 large model for speech recognition in {E}nglish}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english}}, year={2021} } ``` | 85037d71df1a3c32ea59b6062eccc329 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4779 - Wer: 0.3453 | e3caa5e5e9c35442a7547643a81e1e05 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4307 | 4.0 | 500 | 1.4129 | 0.9980 | | 0.626 | 8.0 | 1000 | 0.4605 | 0.4499 | | 0.2199 | 12.0 | 1500 | 0.4457 | 0.3898 | | 0.1303 | 16.0 | 2000 | 0.4418 | 0.3771 | | 0.0851 | 20.0 | 2500 | 0.4647 | 0.3548 | | 0.0604 | 24.0 | 3000 | 0.4603 | 0.3499 | | 0.0461 | 28.0 | 3500 | 0.4779 | 0.3453 | | 2b168032950c52394e2957c83ff2c5a6 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6652 | 5c33011e6a45928c12249483ac6a3c64 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.9109 | 1.0 | 584 | 3.6956 | | 3.7555 | 2.0 | 1168 | 3.6712 | | 3.7002 | 3.0 | 1752 | 3.6652 | | eb7f839c1d3481df3c775f79629c06b7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-finetuned-xglue-ner This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset. It achieves the following results on the evaluation set: - Loss: 0.2489 - Precision: 0.6308 - Recall: 0.6681 - F1: 0.6489 - Accuracy: 0.9274 | 26cb79006343a05cc67b91701b11f8c9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4082 | 1.0 | 1756 | 0.3326 | 0.5600 | 0.5798 | 0.5697 | 0.9118 | | 0.2974 | 2.0 | 3512 | 0.2635 | 0.6143 | 0.6562 | 0.6346 | 0.9248 | | 0.2741 | 3.0 | 5268 | 0.2489 | 0.6308 | 0.6681 | 0.6489 | 0.9274 | | 2d99ad2d61d8355c1b74bf7b8b8deb8f |
mit | [] | false | YB Anime on Stable Diffusion This is the `<anime-character>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:        | fcba532151973baea816b278b9a5ab7b |
cc-by-4.0 | [] | false | Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Spanish datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, technology, biomedical, and news). | 7795a95f7cc24b1ba95632664fd4d3fb |
cc-by-4.0 | [] | false | Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-ca-es", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") tokenized=tokenizer.tokenize("Benvingut al projecte Aina!") translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]]) print(tokenizer.detokenize(translated[0][0]['tokens'])) ``` | e6775b681e1f9d3a8fbbf485b0f25784 |
cc-by-4.0 | [] | false | Training data The was trained on a combination of the following datasets: | Dataset | Sentences | Tokens | |-------------------|----------------|-------------------| | DOCG v2 | 8.472.786 | 188.929.206 | | El Periodico | 6.483.106 | 145.591.906 | | EuroParl | 1.876.669 | 49.212.670 | | WikiMatrix | 1.421.077 | 34.902.039 | | Wikimedia | 335.955 | 8.682.025 | | QED | 71.867 | 1.079.705 | | TED2020 v1 | 52.177 | 836.882 | | CCMatrix v1 | 56.103.820 | 1.064.182.320 | | MultiCCAligned v1 | 2.433.418 | 48.294.144 | | ParaCrawl | 15.327.808 | 334.199.408 | | **Total** | **92.578.683** | **1.875.910.305** | | 8831daa1cb0d7d719e86ba450ebc17b8 |
cc-by-4.0 | [] | false | Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparamenters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|----------------------------------| | Architecture | transformer_vaswani_wmt_en_de_bi | | Embedding size | 1024 | | Feedforward size | 4096 | | Number of heads | 16 | | Encoder layers | 24 | | Decoder layers | 6 | | Normalize before attention | True | | --share-decoder-input-output-embed | True | | --share-all-embeddings | True | | Effective batch size | 96.000 | | Optimizer | adam | | Adam betas | (0.9, 0.980) | | Clip norm | 0.0 | | Learning rate | 1e-3 | | Lr. schedurer | inverse sqrt | | Warmup updates | 4000 | | Dropout | 0.1 | | Label smoothing | 0.1 | The model was trained using shards of 10 million sentences, for a total of 13.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints. | 69352965c1b8edc456f2d596ae01d225 |
cc-by-4.0 | [] | false | Evaluation results Below are the evaluation results on the machine translation from Catalan to Spanish compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): | Test set | SoftCatalà | Google Translate | mt-aina-ca-es | |----------------------|------------|------------------|---------------| | Spanish Constitution | 70,7 | **77,1** | 75,5 | | United Nations | 78,1 | 84,3 | **86,3** | | Flores 101 dev | 23,5 | 24 | **24,1** | | Flores 101 devtest | 24,1 | 24,2 | **24,4** | | Cybersecurity | 67,3 | **76,9** | 75,1 | | wmt 19 biomedical | 60,4 | 62,7 | **63,0** | | wmt 13 news | 22,5 | 23,1 | **23,4** | | aina_aapp_ca-es | 80,9 | 81,4 | **82,8** | | Average | 53,4 | 56,7 | **56,8** | | 5ec0ed911be59fd5025baec2b5d89086 |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | Roberta | 110M | 自然语言推理 NLI | | 9af8381b0cd0eade00c88ad74fcc6e40 |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 模型信息 Model Information 基于[chinese-roberta-wwm-ext-base](https://huggingface.co/hfl/chinese-roberta-wwm-ext)，我们在收集的4个中文领域的NLI（自然语言推理）数据集，总计1014787个样本上微调了一个NLI版本。 Based on [chinese-roberta-wwm-ext-base](https://huggingface.co/hfl/chinese-roberta-wwm-ext), we fine-tuned an NLI version on 4 Chinese Natural Language Inference (NLI) datasets, with totaling 1,014,787 samples. | 299c1c95d5a3cb5a0d8875d7731a573e |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 使用 Usage ``` python from transformers import BertForSequenceClassification from transformers import BertTokenizer import torch tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-NLI') model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-NLI') texta='今天的饭不好吃' textb='今天心情不好' output=model(torch.tensor([tokenizer.encode(texta,textb)])) print(torch.nn.functional.softmax(output.logits,dim=-1)) ``` | 76b571bfc127450c6d8bdfd31926c65d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4170 - Wer: 0.4282 | 570cb998b4e06c27f3a1cba601d4a28f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 - mixed_precision_training: Native AMP | e9386cfb1347c4c4d9b28bdf9fe407c3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.7049 | 0.8 | 200 | 3.0234 | 0.9683 | | 2.9496 | 1.6 | 400 | 2.9348 | 0.9683 | | 2.6582 | 2.4 | 600 | 1.2843 | 0.9818 | | 1.0417 | 3.2 | 800 | 0.6061 | 0.5853 | | 0.7853 | 4.0 | 1000 | 0.5113 | 0.5013 | | 0.681 | 4.8 | 1200 | 0.4723 | 0.4695 | | 0.6074 | 5.6 | 1400 | 0.4528 | 0.4491 | | 0.5539 | 6.4 | 1600 | 0.4818 | 0.4555 | | 0.5257 | 7.2 | 1800 | 0.4439 | 0.4298 | | 0.5038 | 8.0 | 2000 | 0.4495 | 0.4398 | | 0.4868 | 8.8 | 2200 | 0.4467 | 0.4392 | | 0.4727 | 9.6 | 2400 | 0.4076 | 0.4045 | | 0.4493 | 10.4 | 2600 | 0.4559 | 0.4452 | | 0.4452 | 11.2 | 2800 | 0.4174 | 0.4124 | | 0.4407 | 12.0 | 3000 | 0.4188 | 0.4098 | | 0.4385 | 12.8 | 3200 | 0.4123 | 0.4098 | | 0.4192 | 13.6 | 3400 | 0.4090 | 0.4199 | | 0.4061 | 14.4 | 3600 | 0.4170 | 0.4282 | | 6a5de14e0cad48ef13d36ed7944bff42 |
mit | ['generated_from_trainer'] | false | poem-gen-spanish-t5-small-d2 This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9027 | 5a7309fbe6f35c12f61a40de614d8f3b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 5f83028ba19c558a93702a18fbc4d1a0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.223 | 0.73 | 30000 | 3.1479 | | 3.0109 | 1.46 | 60000 | 3.0544 | | 2.8649 | 2.19 | 90000 | 2.9730 | | 2.7603 | 2.93 | 120000 | 2.9301 | | 2.6343 | 3.66 | 150000 | 2.9188 | | 2.5094 | 4.39 | 180000 | 2.9064 | | 2.391 | 5.12 | 210000 | 2.9073 | | 2.3592 | 5.85 | 240000 | 2.9022 | | 56a9fa3609aefce95b13a3d4cec0c8ca |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-hi-wx1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 -HI dataset. It achieves the following results on the evaluation set: - Loss: 0.6552 - Wer: 0.3200 Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA | 437e2fe7a25f2fcb173274f9b9bd3c81 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1800 - num_epochs: 50 - mixed_precision_training: Native AMP | 16ae1813dfc8df68e92b4879886b65b5 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.2663 | 1.36 | 200 | 5.9245 | 1.0 | | 4.1856 | 2.72 | 400 | 3.4968 | 1.0 | | 3.3908 | 4.08 | 600 | 2.9970 | 1.0 | | 1.5444 | 5.44 | 800 | 0.9071 | 0.6139 | | 0.7237 | 6.8 | 1000 | 0.6508 | 0.4862 | | 0.5323 | 8.16 | 1200 | 0.6217 | 0.4647 | | 0.4426 | 9.52 | 1400 | 0.5785 | 0.4288 | | 0.3933 | 10.88 | 1600 | 0.5935 | 0.4217 | | 0.3532 | 12.24 | 1800 | 0.6358 | 0.4465 | | 0.3319 | 13.6 | 2000 | 0.5789 | 0.4118 | | 0.2877 | 14.96 | 2200 | 0.6163 | 0.4056 | | 0.2663 | 16.33 | 2400 | 0.6176 | 0.3893 | | 0.2511 | 17.68 | 2600 | 0.6065 | 0.3999 | | 0.2275 | 19.05 | 2800 | 0.6183 | 0.3842 | | 0.2098 | 20.41 | 3000 | 0.6486 | 0.3864 | | 0.1943 | 21.77 | 3200 | 0.6365 | 0.3885 | | 0.1877 | 23.13 | 3400 | 0.6013 | 0.3677 | | 0.1679 | 24.49 | 3600 | 0.6451 | 0.3795 | | 0.1667 | 25.85 | 3800 | 0.6410 | 0.3635 | | 0.1514 | 27.21 | 4000 | 0.6000 | 0.3577 | | 0.1453 | 28.57 | 4200 | 0.6020 | 0.3518 | | 0.134 | 29.93 | 4400 | 0.6531 | 0.3517 | | 0.1354 | 31.29 | 4600 | 0.6874 | 0.3578 | | 0.1224 | 32.65 | 4800 | 0.6519 | 0.3492 | | 0.1199 | 34.01 | 5000 | 0.6553 | 0.3490 | | 0.1077 | 35.37 | 5200 | 0.6621 | 0.3429 | | 0.0997 | 36.73 | 5400 | 0.6641 | 0.3413 | | 0.0964 | 38.09 | 5600 | 0.6722 | 0.3385 | | 0.0931 | 39.45 | 5800 | 0.6365 | 0.3363 | | 0.0944 | 40.81 | 6000 | 0.6454 | 0.3326 | | 0.0862 | 42.18 | 6200 | 0.6497 | 0.3256 | | 0.0848 | 43.54 | 6400 | 0.6599 | 0.3226 | | 0.0793 | 44.89 | 6600 | 0.6625 | 0.3232 | | 0.076 | 46.26 | 6800 | 0.6463 | 0.3186 | | 0.0749 | 47.62 | 7000 | 0.6559 | 0.3225 | | 0.0663 | 48.98 | 7200 | 0.6552 | 0.3200 | | 398e4a8ddba8a09ac6ca3218e5b6bcb7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-IMDB 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: 0.1905 - Accuracy: 0.9295 | b537ff4f835bc7af3386da5a726afe3b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1928 | 1.0 | 2000 | 0.1905 | 0.9295 | | 25c7f9785785faf50f12a7be6370d657 |
creativeml-openrail-m | ['text-to-image'] | false | It can be used by modifying the `instance_prompt`: **ari** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept:                                                             | 6f20fb9512ee3ad6afccdcf3034a7e5a |
apache-2.0 | ['bert', 'sst2', 'glue', 'torchdistill'] | false | `bert-large-uncased` fine-tuned on SST-2 dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/sst2/ce/bert_large_uncased.yaml). I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**. | 82753cc0b7d81c9934149c53ad64f604 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-analysis-en This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0792 - Accuracy: 0.9803 - F1: 0.9856 - Precision: 0.9875 - Recall: 0.9837 | 89e6a5301a27255a5d0f2d3e9b61cfa6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.426 | 1.0 | 1408 | 0.2718 | 0.8910 | 0.9201 | 0.9251 | 0.9151 | | 0.3247 | 2.0 | 2816 | 0.1552 | 0.9540 | 0.9665 | 0.9656 | 0.9674 | | 0.1582 | 3.0 | 4224 | 0.0792 | 0.9803 | 0.9856 | 0.9875 | 0.9837 | | 41bf9785aacc4f1217f7cec496d29abe |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-torgo-demo-m02-nolm This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0260 - Wer: 0.4968 | 4c111ab712515b12309b3506c3a8498c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4385 | 0.91 | 500 | 4.0160 | 1.0 | | 3.0413 | 1.81 | 1000 | 3.2881 | 1.0 | | 3.0011 | 2.72 | 1500 | 3.2401 | 1.0 | | 2.8653 | 3.62 | 2000 | 3.0338 | 1.0 | | 2.6386 | 4.53 | 2500 | 2.7806 | 1.0492 | | 2.5376 | 5.43 | 3000 | 2.5253 | 1.3647 | | 2.2722 | 6.34 | 3500 | 2.1425 | 1.3252 | | 1.627 | 7.25 | 4000 | 1.4101 | 1.3658 | | 1.2689 | 8.15 | 4500 | 0.9284 | 1.2448 | | 1.0197 | 9.06 | 5000 | 0.6370 | 1.1254 | | 0.8198 | 9.96 | 5500 | 0.4743 | 0.9947 | | 0.7357 | 10.87 | 6000 | 0.3423 | 0.8820 | | 0.5532 | 11.78 | 6500 | 0.2764 | 0.8203 | | 0.5133 | 12.68 | 7000 | 0.2158 | 0.7580 | | 0.4943 | 13.59 | 7500 | 0.1872 | 0.7195 | | 0.3741 | 14.49 | 8000 | 0.1529 | 0.6762 | | 0.3524 | 15.4 | 8500 | 0.1269 | 0.6527 | | 0.3086 | 16.3 | 9000 | 0.1049 | 0.6254 | | 0.3141 | 17.21 | 9500 | 0.0887 | 0.6012 | | 0.2879 | 18.12 | 10000 | 0.0829 | 0.5863 | | 0.3141 | 19.02 | 10500 | 0.0660 | 0.5688 | | 0.2609 | 19.93 | 11000 | 0.0732 | 0.5591 | | 0.2707 | 20.83 | 11500 | 0.0552 | 0.5434 | | 0.2307 | 21.74 | 12000 | 0.0524 | 0.5406 | | 0.1863 | 22.64 | 12500 | 0.0466 | 0.5281 | | 0.2211 | 23.55 | 13000 | 0.0426 | 0.5226 | | 0.1827 | 24.46 | 13500 | 0.0365 | 0.5129 | | 0.1782 | 25.36 | 14000 | 0.0356 | 0.5099 | | 0.1799 | 26.27 | 14500 | 0.0323 | 0.5049 | | 0.1481 | 27.17 | 15000 | 0.0300 | 0.5034 | | 0.1609 | 28.08 | 15500 | 0.0278 | 0.5030 | | 0.1752 | 28.99 | 16000 | 0.0269 | 0.4978 | | 0.1541 | 29.89 | 16500 | 0.0260 | 0.4968 | | d9d5095328e90678a0c3352dc5cf4261 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'DPRNNTasNet', 'audio-to-audio'] | false | Asteroid model `JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k` Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `enh_single` task of the Libri1Mix dataset. Training config: ```yml data: n_src: 1 sample_rate: 16000 segment: 1 task: enh_single train_dir: data/wav16k/min/train-360 valid_dir: data/wav16k/min/dev filterbank: kernel_size: 2 n_filters: 64 stride: 1 masknet: bidirectional: true bn_chan: 128 chunk_size: 250 dropout: 0 hid_size: 128 hop_size: 125 in_chan: 64 mask_act: sigmoid n_repeats: 6 n_src: 1 out_chan: 64 optim: lr: 0.001 optimizer: adam weight_decay: 1.0e-05 training: batch_size: 2 early_stop: true epochs: 200 gradient_clipping: 5 half_lr: true num_workers: 4 ``` Results: On Libri1Mix min test set : ```yml si_sdr: 14.7228101708889 si_sdr_imp: 11.2730288650292 sdr: 15.35661405197161 sdr_imp: 11.853951252758595 sir: Infinity sir_imp: NaN sar: 15.35661405197161 sar_imp: 11.853951252758595 stoi: 0.9300461826351578 stoi_imp: 0.13412635909461715 ``` License notice: This work "DPRNNTasNet_Libri1Mix_enhsignle_16k" is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov, used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); of The WSJ0 Hipster Ambient Mixtures dataset by [Whisper.ai](http://wham.whisper.ai/), used under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Research only). "DPRNNTasNet_Libri1Mix_enhsignle_16k" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Joris Cosentino | 937501e25f9370fe87e05b396a547589 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5657 - Matthews Correlation: 0.5470 | ddf0f6b101175d3df1761cc327874a43 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.521 | 1.0 | 535 | 0.5159 | 0.4152 | | 0.3445 | 2.0 | 1070 | 0.4905 | 0.5022 | | 0.2317 | 3.0 | 1605 | 0.5657 | 0.5470 | | 0.1774 | 4.0 | 2140 | 0.7557 | 0.5282 | | 0.1323 | 5.0 | 2675 | 0.8073 | 0.5455 | | 41b75a9120fc32eb53b256f9ffccf0c5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small hi- HYDDCSEZ This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6357 - Wer: 18.7986 | bd0892f52d0c87aac787addbca9cb60a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0037 | 14.01 | 1000 | 0.4715 | 19.1786 | | 0.0001 | 28.01 | 2000 | 0.5589 | 18.5377 | | 0.0001 | 43.01 | 3000 | 0.6008 | 18.5903 | | 0.0 | 57.01 | 4000 | 0.6234 | 18.7735 | | 0.0 | 72.01 | 5000 | 0.6357 | 18.7986 | | cb3919ad013cbaf822929bfaf1d9086d |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | pythia-6.9b-deduped for general QA <a href="https://colab.research.google.com/gist/pszemraj/351f04fc2afb6346c763885f127284ef/pythia-6-9b-deduped-for-general-qa.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> This model is a fine-tuned version of [EleutherAI/pythia-6.9b-deduped](https://huggingface.co/EleutherAI/pythia-6.9b-deduped) on the pszemraj/HC3-textgen-qa dataset. It achieves the following results on the evaluation set: - Loss: 1.2372 - Accuracy: 0.6769 - perplexity: 3.446 | 05675c92d9cfbc730802d058454a96ea |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Usage Install necessary packages for inference (_unless you have a big boi GPU_) ```bash pip install -U -q transformers bitsandbytes accelerate ``` Basic inference example: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-6.9b-HC3") model = AutoModelForCausalLM.from_pretrained( "pszemraj/pythia-6.9b-HC3", load_in_8bit=True, device_map="auto" ) | 208d676adb8d2da6c72dd6d1bbad5b23 |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | shards are ~4GB each, there are eight total prompt = "I was wondering how much wood a woodchuck could chuck? <answer>" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=300 ) | b6632983815fa20bd793243905cf15d6 |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | default generation config (+ 300 tokens) result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] result = result.split("<end_answer>")[0].strip() import pprint as pp pp.pprint(result) ``` The defautl `GenerationConfig` uses contrastive search with `top_k=4` and `penalty_alpha=0.6`. For more information on inference and parameters to use, see [the transformers docs](https://huggingface.co/docs/transformers/generation_strategies | bb2fa7df9891e490f6893f17ae054d5d |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Intended uses & limitations - **Intended use:** research/exploration into comparing RLHF tuning vs. "guided"/specific tuning on "quality" datasets/responses of _"what the human would want as answer anyway"_ - This is **not** trained/fine-tuned with RLHF and therefore will not be as helpful/generalizable/safe as chatGPT (_outside of the fact that this model is ~30x smaller_) | 7cc2b35a0e83d1e1589362b43201dfd8 |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Training and evaluation data ```yaml model-index: - name: pythia-6.9b-hc3-qa-assistant results: - task: name: Causal Language Modeling type: text-generation dataset: name: pszemraj/HC3-textgen-qa metrics: - name: Accuracy type: accuracy value: 0.6768941789814655 ``` | 991ef5e74ecb7b631db55df82ceb091d |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2598 | 0.99 | 79 | 1.3291 | 0.6496 | | 0.7446 | 1.99 | 158 | 1.2372 | 0.6769 | | 76cfca513f5f902202bc5734cffedc40 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 | 17a15f5da81f4c33be2ed7a86ea41417 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3962 | 1.0 | 18050 | 3.3250 | | 3.2561 | 2.0 | 36100 | 3.2652 | | 3.1727 | 3.0 | 54150 | 3.2572 | | 406cff7225149c526e4284efbfe36669 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-whole-word-word-ids-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: - Loss: 0.6573 | 33404189ea823ba9553c75386797248f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7261 | 1.0 | 157 | 0.6532 | | 0.6766 | 2.0 | 314 | 0.6514 | | 0.6677 | 3.0 | 471 | 0.6555 | | 3813ce00fe2e63d53319cfdf5b08aa18 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | IT5 Base for Informal-to-formal Style Transfer 🧐 This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on Informal-to-formal style transfer on the Italian subset of the XFORMAL dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. | 18c729ec00c8338d1de3365d904a4914 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines i2f = pipeline("text2text-generation", model='it5/it5-base-informal-to-formal') i2f("nn capisco xke tt i ragazzi lo fanno") >>> [{"generated_text": "non comprendo perché tutti i ragazzi agiscono così"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-informal-to-formal") model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-informal-to-formal") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ``` | 8c9540c44530ee3b128a64686825dba1 |
cc-by-sa-4.0 | [] | false | This is a model for correcting spelling and grammar errors in Icelandic text. It is based on the pretrained ByT5 model (https://arxiv.org/abs/2105.13626) and finetuned on Icelandic error correction data along with synthetic error data. The model is trained using the HuggingFace and PyTorch libraries. The model is trained to correct a single sentence at a time, but may work on longer context. The model performs well on correcting a variety of common issues in Icelandic text. This README will be updated soon along with citation reference. | 5a355d9098dadffc3bd1dab5227984cc |
mit | ['generated_from_trainer'] | false | deberta-v3-base-finetuned-imdb This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 3.0016 | 624530b66d491b5bd5a3169fe04a8cb3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.2666 | 1.0 | 6690 | 3.4001 | | 3.3574 | 2.0 | 13380 | 3.1174 | | 3.1715 | 3.0 | 20070 | 3.0034 | | 4e0c3e7ffbfccef476e7366a3cefbc0c |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-wikitext2-test-mlm This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8438 | b99d81469ed6fa3f6fd626e178b5ff23 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - total_eval_batch_size: 5 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - training precision: Mixed Precision | 954116cb72064b6dc4f11195da25a0f2 |
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: 0.3097 - Accuracy: 0.8633 - F1: 0.8647 | dd45ff1ca97e7b6fe6a1163a6716fb43 |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | BLIP-2, OPT-2.7b, fine-tuned on COCO BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. | 7e73350f20221d16ee6f720037a32b7b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | 663380; padding-top:0px;" span title="Protogen x5.3 Raw Output"></center> <center><h1>Protogen x5.3 (Photorealism) Official Release</h1></center> <center><p><em>Research Model by <a href="https://instagram.com/officialvictorespinoza">darkstorm2150</a></em></p></center> </div> | 3a22bb12ffd1a7842e00bf3b9f046ce1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | General info Protogen x5.3 - One Step Closer to Reality by [darkstorm2150](https://instagram.com/officialvictorespinoza) Protogen was warm-started with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and continued fine-tuned from [darkstorm2150/Protogen_x3.4_Official_Release](https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release) Robodiffusion has been removed and 10% Dreamlike-PhotoReal V.2 added, the result is better sampling at 768px to 1024px of humans and surroundings, The results are immediate!!! Also this bad boy comes with a license, so do please read it, thank you! * Model control Now its recommended that you add nude, naked to your negative prompts, its a horny model, well 10% but still....cant be too careful! As for realism, you can use this template modelshoot style, (extremely detailed 8k wallpaper),a medium shot photo of a (what you want here), Intricate, High Detail, dramatic It should also be very "dreambooth-able", being able to generate high fidelity faces with a little amount of steps (see [dreambooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)). | ac58c839834caf615878e67cef232354 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | Granular Adaptive Learning Granular adaptive learning is a machine learning technique that focuses on adjusting the learning process at a fine-grained level, rather than making global adjustments to the model. This approach allows the model to adapt to specific patterns or features in the data, rather than making assumptions based on general trends. Granular adaptive learning can be achieved through techniques such as active learning, which allows the model to select the data it wants to learn from, or through the use of reinforcement learning, where the model receives feedback on its performance and adapts based on that feedback. It can also be achieved through techniques such as online learning where the model adjust itself as it receives more data. Granular adaptive learning is often used in situations where the data is highly diverse or non-stationary and where the model needs to adapt quickly to changing patterns. This is often the case in dynamic environments such as robotics, financial markets, and natural language processing. | c5bc22bff3ab3b16bb37536026cca5ba |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | CKPT [Download ProtoGen x5.3.ckpt (4.27GB)](https://huggingface.co/darkstorm2150/Protogen_v5.3_Official_Release/blob/main/ProtoGen_X5.3.ckpt) [Download ProtoGen x5.3-pruned-fp16.ckpt (1.89GB)](https://huggingface.co/darkstorm2150/Protogen_x5.3_Official_Release/resolve/main/ProtoGen_X5.3-pruned-fp16.ckpt) | 6ba4609ed3a796dadff680ca7c4a08ad |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | Safetensors [Download ProtoGen x5.3.safetensors (4.27GB)](https://huggingface.co/darkstorm2150/Protogen_x5.3_Official_Release/resolve/main/ProtoGen_X5.3.safetensors) [Download ProtoGen x5.3-pruned-fp16.safetensors (1.89GB)](https://huggingface.co/darkstorm2150/Protogen_x5.3_Official_Release/resolve/main/ProtoGen_X5.3-pruned-fp16.safetensors) | e111623c17fd7b7e384c91aa2543340a |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler import torch prompt = ( "modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot body photo of the most beautiful artwork in the world, " "english medieval witch, black silk vale, pale skin, black silk robe, black cat, necromancy magic, medieval era, " "photorealistic painting by Ed Blinkey, Atey Ghailan, Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation, " "trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, photorealistic painting art by midjourney and greg rutkowski" ) model_id = "darkstorm2150/Protogen_v5.3_Official_Release" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") image = pipe(prompt, num_inference_steps=25).images[0] image.save("./result.jpg") ``` | 257e0a1711a10bc023629cdfd4c85566 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | 663380; } </style> <table class="myTable"> <tr> <th>Models</th> <th>Protogen v2.2 (Anime)</th> <th>Protogen x3.4 (Photo)</th> <th>Protogen x5.3 (Photo)</th> <th>Protogen x5.8 (Sci-fi/Anime)</th> <th>Protogen x5.9 (Dragon)</th> <th>Protogen x7.4 (Eclipse)</th> <th>Protogen x8.0 (Nova)</th> <th>Protogen x8.6 (Infinity)</th> </tr> <tr> <td>seek_art_mega v1</td> <td>52.50%</td> <td>42.76%</td> <td>42.63%</td> <td></td> <td></td> <td></td> <td>25.21%</td> <td>14.83%</td> </tr> <tr> <td>modelshoot v1</td> <td>30.00%</td> <td>24.44%</td> <td>24.37%</td> <td>2.56%</td> <td>2.05%</td> <td>3.48%</td> <td>22.91%</td> <td>13.48%</td> </tr> <tr> <td>elldreth v1</td> <td>12.64%</td> <td>10.30%</td> <td>10.23%</td> <td></td> <td></td> <td></td> <td>6.06%</td> <td>3.57%</td> </tr> <tr> <td>photoreal v2</td> <td></td> <td></td> <td>10.00%</td> <td>48.64%</td> <td>38.91%</td> <td>66.33%</td> <td>20.49%</td> <td>12.06%</td> </tr> <tr> <td>analogdiffusion v1</td> <td></td> <td>4.75%</td> <td>4.50%</td> <td></td> <td></td> <td></td> <td>1.75%</td> <td>1.03%</td> </tr> <tr> <td>openjourney v2</td> <td></td> <td>4.51%</td> <td>4.28%</td> <td></td> <td></td> <td>4.75%</td> <td>2.26%</td> <td>1.33%</td> </tr> <tr> <td>hassan1.4</td> <td>2.63%</td> <td>2.14%</td> <td>2.13%</td> <td></td> <td></td> <td></td> <td>1.26%</td> <td>0.74%</td> </tr> <tr> <td>f222</td> <td>2.23%</td> <td>1.82%</td> <td>1.81%</td> <td></td> <td></td> <td></td> <td>1.07%</td> <td>0.63%</td> </tr> <tr> <td>hasdx</td> <td></td> <td></td> <td></td> <td>20.00%</td> <td>16.00%</td> <td>4.07%</td> <td>5.01%</td> <td>2.95%</td> </tr> <tr> <td>moistmix</td> <td></td> <td></td> <td></td> <td>16.00%</td> <td>12.80%</td> <td>3.86%</td> <td>4.08%</td> <td>2.40%</td> </tr> <tr> <td>roboDiffusion v1</td> <td></td> <td>4.29%</td> <td></td> <td>12.80%</td> <td>10.24%</td> <td>3.67%</td> <td>4.41%</td> <td>2.60%</td> </tr> <tr> <td>RPG v3</td> <td></td> <td>5.00%</td> <td></td> <td></td> <td>20.00%</td> <td>4.29%</td> <td>4.29%</td> <td>2.52%</td> </tr> <tr> <td>anything&everything</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>4.51%</td> <td>0.56%</td> <td>0.33%</td> </tr> <tr> <td>dreamlikediff v1</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>5.0%</td> <td>0.63%</td> <td>0.37%</td> </tr> <tr> <td>sci-fidiff v1</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>3.10%</td> </tr> <tr> <td>synthwavepunk v2</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>3.26%</td> </tr> <tr> <td>mashupv2</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>11.51%</td> </tr> <tr> <td>dreamshaper 252</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>4.04%</td> </tr> <tr> <td>comicdiff v2</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>4.25%</td> </tr> <tr> <td>artEros</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>15.00%</td> </tr> </table> | 75580006fc16f5b85dac904b6f11f4f3 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | License By downloading you agree to the terms of these licenses <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license">CreativeML Open RAIL-M</a> <a href="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md">Dreamlike License</a> <a href="https://huggingface.co/coreco/seek.art_MEGA/blob/main/LICENSE.txt">Seek Art Mega License</a> | aebb0bc6198605d4af48a6c02cf1e45a |
mit | ['generated_from_trainer'] | false | roberta-base_mnli_bc This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.2125 - Accuracy: 0.9584 | ce5a261605e1f2bff65df8619f0a790c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2015 | 1.0 | 16363 | 0.1820 | 0.9470 | | 0.1463 | 2.0 | 32726 | 0.1909 | 0.9559 | | 0.0768 | 3.0 | 49089 | 0.2117 | 0.9585 | | 6e7e2350d1b5557bf370d2ff85cbc762 |
apache-2.0 | [] | false | Training Data This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. | caac9c8c4cc66f0fe09a0185e300ceed |
apache-2.0 | [] | false | Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class | 2301e7bcbbdf6b3e0add56b8023846fd |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.1698 - Mae: 0.6090 | 0ecc67868930b5507bfc29faf90998d8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1662 | 1.0 | 333 | 1.2084 | 0.7068 | | 1.0122 | 2.0 | 666 | 1.1698 | 0.6090 | | c67abb113776801446ac628f7cc1e6dc |
mit | [] | false | Toho-pixel on Stable Diffusion This is the `<toho-pixel>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:      | da7dbc6a0bde6df8eedca85e8a241b45 |
apache-2.0 | ['generated_from_trainer'] | false | littledataset This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 | ac7330a477ce730f70a833065f91fe1c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 169 | 0.0001 | | No log | 2.0 | 338 | 0.0000 | | 0.0036 | 3.0 | 507 | 0.0001 | | 0.0036 | 4.0 | 676 | 0.0000 | | 0.0036 | 5.0 | 845 | 0.0000 | | 11027173939671ff1460c3e8cc19a82a |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_gender_male-5_female-5_s896 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | fdd14278da48235b5997eb8029afd296 |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on COCO instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. | 5cd9317e6870479c9c0fa6d9d23f8230 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on COCO instance segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-coco-instance") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-coco-instance") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) | 52b4c27137b231d842b89ec4385b5389 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Persian V2 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 55c54a16d99c48b067c4dc7533f5f3ba |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer !pip install hazm ``` **Prediction** ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset import numpy as np import hazm import re import string import IPython.display as ipd _normalizer = hazm.Normalizer() chars_to_ignore = [ ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", " | a1f67c7db445e3cc9d23b2758d535b61 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | In case of farsi chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits) chars_to_mapping = { 'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی', 'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی", "ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع", "ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", 'ﺍ': "ا", 'ة': "ه", 'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش", 'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ", | 391772096f3e1bbe4024988176f59686 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | "ها": " ها", "ئ": "ی", "a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ", "g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ", "m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ", "s": " اس ", "t": " تی ", "u": " یو ", "v": " وی ", "w": " دبلیو ", "x": " اکس ", "y": " وای ", "z": " زد ", "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", } def multiple_replace(text, chars_to_mapping): pattern = "|".join(map(re.escape, chars_to_mapping.keys())) return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) def remove_special_characters(text, chars_to_ignore_regex): text = re.sub(chars_to_ignore_regex, '', text).lower() + " " return text def normalizer(batch, chars_to_ignore, chars_to_mapping): chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" text = batch["sentence"].lower().strip() text = _normalizer.normalize(text) text = multiple_replace(text, chars_to_mapping) text = remove_special_characters(text, chars_to_ignore_regex) text = re.sub(" +", " ", text) text = text.strip() + " " batch["sentence"] = text return batch def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) batch["speech"] = speech_array return batch def predict(batch): features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids)[0] return batch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-v2") model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-v2").to(device) dataset = load_dataset("common_voice", "fa", split="test[:1%]") dataset = dataset.map( normalizer, fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) ) dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict) max_items = np.random.randint(0, len(result), 20).tolist() for i in max_items: reference, predicted = result["sentence"][i], result["predicted"][i] print("reference:", reference) print("predicted:", predicted) print('---') ``` **Output:** ```text reference: عجم زنده کردم بدین پارسی predicted: عجم زنده کردم بدین پارسی --- reference: لباس هایم کی آماده خواهند شد predicted: لباس خایم کی آماده خواهند شد --- reference: با مهان همنشین شدم predicted: با مهان همنشین شدم --- reference: یکی از بهترین فیلم هایی بود که در این سال ها دیدم predicted: یکی از بهترین فیلمهایی بود که در این سالها دیدم --- reference: اون خیلی بد ماساژ میده predicted: اون خیلی بد ماساژ میده --- reference: هنوزم بزرگترین دستاورد دولت روحانی اینه که رییسی رییسجمهور نشد predicted: هنوزم بزرگترین دستآوردار دولت روانیاینه که ریسی ریسیومرو نشد --- reference: واسه بدنسازی آماده ای predicted: واسه بعدنسافی آماده ای --- reference: خدای من شماها سالمین predicted: خدای من شما ها سالمین --- reference: بهشون ثابت میشه که دروغ نگفتم predicted: بهشون ثابت میشه که دروغ مگفتم --- reference: آیا ممکن است یک پتو برای من بیاورید predicted: سف کمیتخ لظا --- reference: نزدیک جلو predicted: رزیک جلو --- reference: شایعه پراکن دربارهاش دروغ و شایعه می سازد predicted: شایه پراکن دربارهاش دروغ و شایعه می سازد --- reference: وقتی نیاز است که یک چهره دوستانه بیابند predicted: وقتی نیاز است یک چهره دوستانه بیابند --- reference: ممکنه رادیواکتیوی چیزی باشه predicted: ممکنه به آدیوتیوی چیزی باشه --- reference: دهنتون رو ببندید predicted: دهن جن رو ببندید --- reference: پاشیم بریم قند و شکر و روغنمون رو بگیریم تا تموم نشده predicted: پاشین بریم قند و شکر و روغنمون رو بگیریم تا تموم نشده --- reference: اما قبل از تمام کردن بحث تاریخی باید ذکری هم از ناپیکس بکنیم predicted: اما قبل از تمام کردن بحث تاریخی باید ذکری هم از نایپکس بکنیم --- reference: لطفا کپی امضا شده قرارداد را بازگردانید predicted: لطفا کپی امضال شده قرار داد را باز گردانید --- reference: خیلی هم چیز مهمی نیست predicted: خیلی هم چیز مهمی نیست --- reference: شایعه پراکن دربارهاش دروغ و شایعه می سازد predicted: شایه پراکن دربارهاش دروغ و شایعه می سازد --- ``` | 4fbeb63457186c93a67b212df9a94e83 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice. ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import numpy as np import hazm import re import string _normalizer = hazm.Normalizer() chars_to_ignore = [ ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", " | b8fa9293110f0346c4ff298dd22531e1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | "ها": " ها", "ئ": "ی", "a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ", "g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ", "m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ", "s": " اس ", "t": " تی ", "u": " یو ", "v": " وی ", "w": " دبلیو ", "x": " اکس ", "y": " وای ", "z": " زد ", "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", } def multiple_replace(text, chars_to_mapping): pattern = "|".join(map(re.escape, chars_to_mapping.keys())) return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) def remove_special_characters(text, chars_to_ignore_regex): text = re.sub(chars_to_ignore_regex, '', text).lower() + " " return text def normalizer(batch, chars_to_ignore, chars_to_mapping): chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" text = batch["sentence"].lower().strip() text = _normalizer.normalize(text) text = multiple_replace(text, chars_to_mapping) text = remove_special_characters(text, chars_to_ignore_regex) text = re.sub(" +", " ", text) text = text.strip() + " " batch["sentence"] = text return batch def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) batch["speech"] = speech_array return batch def predict(batch): features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids)[0] return batch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-v2") model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-v2").to(device) dataset = load_dataset("common_voice", "fa", split="test") dataset = dataset.map( normalizer, fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) ) dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict) wer = load_metric("wer") print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) ``` **Test Result:** - WER: 31.92% | 3a6b1265945d8a87f5ec3a278a45bb69 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation` datasets were used for training. You can see the training states [here](https://wandb.ai/m3hrdadfi/finetuned_wav2vec_xlsr_persian/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Persian--Vmlldzo1NjY1NjU?accessToken=pspukt0liicopnwe93wo1ipetqk0gzkuv8669g00wc6hcesk1fh0rfkbd0h46unk) The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Persian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) | cbe50885d0f69606e03100791e610c55 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Breton Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Breton Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 7dfe979c8458b8ac51eaf6e0d2fdd53f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "br", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton") chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]' | 57cda3928c87d3f887d00ded9c2715a8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " batch["sentence"] = batch["sentence"].replace("ʼ", "'") batch["sentence"] = batch["sentence"].replace("’", "'") batch["sentence"] = batch["sentence"].replace('‘', "'") speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` The above code leads to the following prediction for the first two samples: ``` Prediction: ["ne' ler ket don a-benn us netra pa vez zer nic'hed evel-si", 'an eil hag egile'] Reference: ['"n\'haller ket dont a-benn eus netra pa vezer nec\'het evel-se." ', 'an eil hag egile. '] ``` | cb83d9a36a53d15f4aaa1bb54526cd36 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Breton test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "br", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton") model.to("cuda") chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]' | 2be03aede43125bf49fcc3465bb211c7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " batch["sentence"] = batch["sentence"].replace("ʼ", "'") batch["sentence"] = batch["sentence"].replace("’", "'") batch["sentence"] = batch["sentence"].replace('‘', "'") speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) | 954d8d0a728302b921a359f8a3b7d528 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 41.71 % | 5521f7ed5ff64ba94099cf6cd47f0cfa |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9575 - Mae: 0.5488 | 2edf6cb8d9b297906ce9229865babefd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1253 | 1.0 | 235 | 0.9960 | 0.5366 | | 0.9708 | 2.0 | 470 | 0.9575 | 0.5488 | | da5f89d79af1d3e74095a7371cd43d1b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/gtr-t5-xl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-xl-1](https://tfhub.dev/google/gtr/gtr-xl/1) to PyTorch. When using this model, have a look at the publication: [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
The model uses only the encoder from a T5-3B model. The weights are stored in FP16.
| c9fcdc7da8d325a18c4594734c71e32a |
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/gtr-t5-xl')
embeddings = model.encode(sentences)
print(embeddings)
```
The model requires sentence-transformers version 2.2.0 or newer.
| e0c57405ab18761664eaf84bd58efb88 |
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/gtr-t5-xl)
| 3044bca41a5b11f03a8b77b9c555189a |
mit | ['roberta-base', 'roberta-base-epoch_71'] | false | RoBERTa, Intermediate Checkpoint - Epoch 71 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_71. | 4d99004b59e2444684c522fc0be95122 |
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