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 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_no-pretraining_s764 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (it)](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 bee... | 43206d469a0c9f35e9b51629182be24e |
mit | ['mt5', 'question-generation'] | false | mt5-base for Indonesian Unanswerable Question Generation (cased) [mT5-base](https://huggingface.co/google/mt5-base) model fine-tuned on machine-translated SQuAD 2.0 dataset for generating unanswerable questions in Indonesian. Please refer to [this paper](https://arxiv.org/abs/2210.13778) for more details on the model... | 51591fddaa9b9b2c33c1b418a03a1ef0 |
mit | ['mt5', 'question-generation'] | false | Citation Info ```bibtex @misc{putri2022idk, doi = {10.48550/ARXIV.2210.13778}, url = {https://arxiv.org/abs/2210.13778}, author = {Putri, Rifki Afina and Oh, Alice}, title = {IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension}, publisher = {arXiv}, year = {2022}, } ``` | 43425e3066b4fca8ebc66a875f284536 |
apache-2.0 | ['generated_from_keras_callback'] | false | cptanalatriste/request-for-help This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1342 - Train Sparse Categorical Accuracy: 1.0 - Validation Loss: 0.1514 - Validation Spars... | 4dc03ac8d99b1b33cf78f0229084b89b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.8291 | 0.375 | 0.7483 ... | 641a574bf7dc6c7c0bc54af4ca7ca20f |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr1e05-wd0.01-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2756 - Rmse: 0.5250 - Mse: 0.2756 - Mae: 0.4181 | 5a96c6152a3bb5d2bed7126b414c2f17 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2769 | 1.0 | 623 | 0.2768 | 0.5261 | 0.2768 | 0.4282 | | 0.2743 | 2.0 | 1246 | 0.2739 | 0.5234 | 0.2739 ... | 509854fb01452386d15eeb47448fe07a |
apache-2.0 | ['translation'] | false | opus-mt-ro-fr * source languages: ro * target languages: fr * OPUS readme: [ro-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ro-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 12550426a46566f9cd914d37326b193b |
mit | [] | false | Rikiboy Art on Stable Diffusion This is the `<Rikiboy-Art>` 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 al... | 9fec7d0a23628e940d9f1c9d163cda71 |
mit | [] | false | hseBERT **hseBert-it-cased** is a BERT model obtained by MLM adaptive-tuning [**bert-base-italian-xxl-cased**](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on texts of Italian regulation (Testo unico sulla sicurezza sul lavoro - D.lgs. 9 aprile 2008, n. 81, Codice dell'Ambiente - D.lgs. 3 aprile 2006, n.... | 9be19e90f6eea876fdf39fd48d4de8d7 |
mit | [] | false | Usage ```python from transformers import AutoModel, AutoTokenizer model_name = "bullmount/hseBert-it-cased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` | a475f06e09675f91b59dde35de329b46 |
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: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 35.0 | 7b0c37d98ca54d21a160a0703590e0b3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2408 - F1: 0.8592 | f119ea142c5e85b878907a371faab22a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 3 - mixed_precision_training: Native AMP | 6d767ea7f49772dc1841e1cbd1d32117 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3357 | 1.0 | 4290 | 0.2775 | 0.8017 | | 0.2102 | 2.0 | 8580 | 0.2218 | 0.8382 | | 0.126 | 3.0 | 12870 | 0.2408 | 0.859... | 4dc8030fd9055301e55e8ce79ea970d9 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-EL32 (Deep-Narrow version) T5-Efficient-SMALL-EL32 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | 63629419a4d58406f83525e09b0c1dad |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-el32** - is of model type **Small** with the following variations: - **el** is **32** It has **142.36** million parameters and thus requires *ca.* **569.44 MB** of memory in full precision (*fp32*) or **284.72 MB** of memory in half precision ... | 939599b30cc8627880449a8c837a70b7 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'lora'] | false | Usage To use this LoRA you have to download the file, as well as drop it into the "\stable-diffusion-webui\models\Lora" folder To use it in a prompt, please refer to the extra networks panel in your Automatic1111 webui. I highly recommend using it at around 0.8 strength for the best results. If you'd like to support... | 5c59dc2c12006af70efbcf54a6e90312 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'lora'] | false | Example Pictures <table> <tr> <td><img src=https://i.imgur.com/TEyaCSQ.png width=50% height=100%/></td> </tr> <tr> <td><img src=https://i.imgur.com/PAlVGfg.png width=50% height=100%/></td> </tr> <tr> <td><img src=https://i.imgur.com/LB2uVVo.png width=50% height=100%/></td> </tr> </table> | 458c0867808783c8e803683ee06e2c4b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'lora'] | false | License This embedding 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 embedding to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on ... | def5eda3f92950eb0950427ec7df2ec3 |
apache-2.0 | ['generated_from_trainer'] | false | test-distilbert-to-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: 0.5410 - Matthews Correlation: 0.5444 | a47b037a82faaaa824af77c2be057077 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5244 | 1.0 | 535 | 0.5352 | 0.4122 | | 0.348 | 2.0 | 1070 | 0.4897 | 0.5169 | | 0.2... | ab82eda0d362be3c3934ec2b48313da7 |
mit | ['generated_from_keras_callback'] | false | W4nkel/microsoftTurkishTrain This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9404 - Validation Loss: 0.8487 - Train Accuracy: 0.... | 1df26c4b50a35c0b02ec3d9f978809b0 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.3932 - F1: 0.7032 | c6cb1a94bd7bb81a9ed5ceaa498cad78 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1504 | 1.0 | 50 | 0.5992 | 0.4786 | | 0.5147 | 2.0 | 100 | 0.4307 | 0.6468 | | 0.3717 | 3.0 | 150 | 0.3932 | 0.7032 | ... | 3ccd56846afea21819ebf353d674ca64 |
mit | ['generated_from_trainer'] | false | mbart-large-50-English_German_Translation This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2342 - Bleu: 35.5931 - Rouge: {'rouge1': 0.5803386608353808, 'rouge2': 0.393... | dbdcdfffc72398c86b89f57288475086 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Meteor | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------------------... | 16c4536f8a491b87d0f7ba6fdbe21284 |
apache-2.0 | ['generated_from_keras_callback'] | false | jmparejaz/QA-finetuned-distilbert-TFv3 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7657 - Train End Logits Accuracy: 0.7881 - Train Start Logits Accuracy: 0.7... | d7bf1e9d899962f8b5a73e0ff02341c5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0002, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_ste... | ebbf90f42f3a95f8aa37cd119642668a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:-----:| | 2.1678 | 0.4575 | 0.4238 | 0 | | 1.2064 | 0.6709 | 0.6336 ... | d66fa37ce60863569c15bbcbc5f9c05d |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0650 - Precision: 0.9357 - Recall: 0.9522 - F1: 0.9439 - Accuracy: 0.9864 | b23affa1b72d5355a4a52a6d2d76458f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0874 | 1.0 | 1756 | 0.0702 | 0.9245 | 0.9340 | 0.9293 | 0.9822 | | 0.0334 | 2.0 |... | faf03bb0b724f1620d0f5da57fd7e640 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_400k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 400k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | f41e82c0bd292f8dd0e2e932a170291b |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_400k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_400k') model = TFBertModel.from_pretrained("google/multibe... | 0e807fb55da6e342bf441a95f8c7f75a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) and [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnis... | 79a75af3cd6fe91530c19503c64a505e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Finnish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Finnish using the [Common Voice](https://huggingface.co/datasets/common_voice), [CSS10 Finnish](https://www.kaggle.com/bryanpark/finnish-single-speaker-speech-dataset) and [Finnish pa... | d942896a8917d49cdc9898d33cc4eaeb |
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 librosa import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "fi", split="test[:2%]") processor = Wav2Vec2P... | f9610b1ff13b76548e89f89afacbc972 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(sampling_rate, speech_array).squeeze() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_datase... | ed25b1379a63f1feff1b68d1214144f5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Finnish test data of Common Voice. ```python import librosa import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "fi", ... | d1ab3dae0e0d99becb1cd124c1b542e8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(sampling_rate, speech_array).squeeze() return batch test_datas... | 371077bc1ebd59f175c2fbd8285d6c92 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio 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(lo... | b26268cb8ca38d2dafb505db10d0095d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation` and `other` datasets were used for training as well as `CSS10 Finnish` and `Finnish parliament session 2` datasets. The script used for training can be found from [Google Colab](https://colab.research.google.com/drive/1vnEGC9BnNRmVyIHj-0UsVulh_cUYSGWA?usp=sharing) | 231699de206d7fd6c4da92c9fb3f9578 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 1.0099 - Accuracy: 0.7917 | 6b8daa48065aa4429c5a7e19cad20ed3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7577 | 1.0 | 4597 | 0.6133 | 0.7624 | | 0.3729 | 2.0 | 9194 | 0.6351 | 0.7841 | | 0.1405 | 3.0 | 13791 | 1.0099 ... | 81cf51c593ef4b25623bc23712dc9bea |
apache-2.0 | ['translation'] | false | opus-mt-en-ig * source languages: en * target languages: ig * OPUS readme: [en-ig](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ig/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 8a8eaacd8da8cec5a6048d13b3e62079 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1500k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1500k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | ae465ad542db46942460e86af895f867 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1500k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1500k') model = TFBertModel.from_pretrained("google/multib... | c49ba169f4eac231fdfd61a93b88a0b2 |
mit | ['generated_from_trainer'] | false | nostalgic_jones This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekk... | 455d14ac290851bf0022ce4e87f1d81b |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, ... | f249ccbe41c90aa9b1bf6936703a9804 |
apache-2.0 | ['generated_from_keras_callback'] | false | Manirathinam21/DistilBert_SMSSpam_classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an SMSSpam Detection dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0114 - Train Accuracy: 0.9962 - Epoch: 2 | ba9dd91a190992064d21078ff750ffd6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Model description Tokenizer used is DistilBertTokenizerFast with return_tensors='tf' parameter in tokenizer because building model in a tensorflow framework Model: TFDistilBertForSequenceClassification Optimizer: Adam with learning rate=5e-5 Loss: SparseCategoricalCrossentropy | 4cc1c48f4aa5e7fa7775da0755b2f480 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training procedure After Tokenized, Encoded datasets are converted to Dataset Objects by using tf.data.Dataset.from_tensor_slices((dict(train_encoding), train_y)) This step is done to inject a dataset into TFModel in a specific TF format | cf3868bdb51fff39d0a5352037a4fafb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Epoch | |:----------:|:--------------:|:-----:| | 0.0754 | 0.9803 | 0 | | 0.0252 | 0.9935 | 1 | | 0.0114 | 0.9962 | 2 | | bdcac2a0bc3af2aa0f294641e9c5e0bb |
other | ['art'] | false | モデル説明 (model explanation) - [MoeDiffusionPlusPlus](https://huggingface.co/ThePioneer/MoeDiffusionPlusPlus/blob/main/MoeDiffusion%2B%2B_V2.ckpt) 0.7 : [DreamShaper 3.3 (full)](https://civitai.com/models/4384/dreamshaper) 0.3。 - [WaifuDiffusionのvae](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8... | 3a20e80f0e3942e31c082497008bba07 |
other | ['art'] | false | サンプル画像 (sample images) ``art by yaguru magiku``プロンプトを適切な強さで追加することで、YaguruMagikuスタイルの顔を出力できる。逆に外すことで、DreamShaper風の写実タッチに近づけることもできる。 Add the propmpt ``art by yaguru magiku`` with a proper strength to get the face in the style of YaguruMagiku. To generate a more photorealistic image close to DreamShaper, you can just re... | 0530a1841565b8e01e28ea3adf027171 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the hasbulla concept trained by carlosabadia on the carlosabadia/hasbulla dataset. This is a Stable Diffusion model fine-tuned on the hasbulla concept with DreamBooth. It can be used by modifying the `instance_prompt`: **hasbulla person** This model was created as part of the DreamBooth Hackatho... | e1c7ff6b0376cc8f2383834ab64b526a |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on `Hasbulla` images for the wildcard theme. It was also featured in Hasbulla's Twitter account! <blockquote class="twitter-tweet"><p lang="sv" dir="ltr">Hasbulla Van Gogh <a href="https://t.co/5f0uPKhi6U">pic.twitter.com/5f0uPKhi6U</a></p>— Hasbulla �... | 3d69b92b1acb288507e9650777f6bf4a |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Gradio & Colab Model supported in a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2546302539462541342539372532304875676769... | 389143ad518f5db7511544b1d4912fb2 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Usage ```python import torch from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler pipe = StableDiffusionPipeline.from_pretrained( "carlosabadia/hasbulla", scheduler = DPMSolverMultistepScheduler.from_pretrained("carlosabadia/hasbulla", subfolder="scheduler"), torch_dtype=torch.floa... | e3367254148ecb79b02c91c49e233731 |
mit | [] | false | SlovakT5-small This model was trained on slightly adapted code from [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling). If you want to know about training details or evaluation results, see [SlovakT5_report.pdf](https://huggingface.co/ApoTro/slovak-t5-small/res... | e81f27885e39456bcfed0397af9b96bb |
mit | [] | false | How to use SlovakT5-small can be fine-tuned for a lot of different downstream tasks. For example, NER: ```python from transformers import AutoTokenizer, T5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("ApoTro/slovak-t5-small") model = T5ForConditionalGeneration.from_pretrained("ApoTro/slovak-t5-... | c6c387a5c3280d09345763ca0133b14f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Ta - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2269 - Wer: 21.7243 | ee2488a17b32239f9e5a870bfa3fe89e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 10000 - mixed_prec... | 7d327634dd228fb3441b0deafe7c9020 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.5559 | 0.1 | 1000 | 0.3963 | 35.3308 | | 0.3891 | 0.2 | 2000 | 0.3146 | 29.1511 | | 0.3425 | 0.3 | 3000 | 0.2834 | 2... | eeb9fe883e0a278f51097442ca57457a |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](ht... | 1fe22af8f6fe712a3b02aa0ed6c82b90 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Model description EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions... | f25f8ecc1bc9c33bc1bb5169792182d0 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import EdsrModel, ImageLoader from PIL import Image import requests url = 'https://pa... | bae5d6070207c812f676597961026f09 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, EdsrModel, EdsrConfig training_args = TrainingArguments( output_dir='./results', | 3f56681524b750cc64cf1c76849569d0 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al... | 940189dfb85961ed362986a62de5f663 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | BibTeX entry and citation info ```bibtex @InProceedings{Lim_2017_CVPR_Workshops, author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu}, title = {Enhanced Deep Residual Networks for Single Image Super-Resolution}, booktitle = {The IEEE Conference on Computer Vision and Pattern... | 084a9be9f1856f6505937010ad17d4c4 |
apache-2.0 | ['generated_from_trainer'] | false | facility-classifier 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: 0.4422 - Accuracy: 0.7872 - F1: 0.7854 | 8b975be4ec129d60646049e99ee7fe43 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.671 | 1.0 | 12 | 0.6529 | 0.6596 | 0.6441 | | 0.5845 | 2.0 | 24 | 0.5722 | 0.7447 | 0.7461 | | 0.4902 |... | 7c2d3113b236d3b84a429e11aa4b919a |
apache-2.0 | ['bert', 'stsb', 'glue', 'kd', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on STS-B dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb... | 9e47aa32777a30e0929211f193e9a214 |
creativeml-openrail-m | [] | false | This repository hosts the TFLite models for the [KerasCV Stable Diffusion model](https://github.com/keras-team/keras-cv/blob/master/keras_cv/models/stable_diffusion). The model can be broken into three parts: * Text encoder * Image decoder * Denoiser For each model, there is an equivalent TFLite model in this reposi... | 18f915126d3765df2d04d9b43cb0440a |
mit | ['audio', 'automatic-speech-recognition'] | false | S2T-MEDIUM-LIBRISPEECH-ASR `s2t-medium-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/spe... | c25d080dd34715486c0ecfb3ad0bcb14 |
mit | ['audio', 'automatic-speech-recognition'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure... | 558ba0ed1bce0827041905cab28f1465 |
mit | ['audio', 'automatic-speech-recognition'] | false | Evaluation on LibriSpeech Test The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset. ```python from datasets import load_dataset from evaluate import load from transformers import Speech2TextForConditionalGene... | c6d835f7a0f78fb2fa5106102718e99d |
mit | ['audio', 'automatic-speech-recognition'] | false | change to "other" for other test dataset wer = load("wer") model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-librispeech-asr").to("cuda") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-librispeech-asr", do_upper_case=True) def map_to_pred(batch): features = ... | 6a14539ee131306725f4554a4d0a9e28 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | 665ff3832a8a7c2ccb3c8b0d5d74d305 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Odia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Odia using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 1d6bd5c3ba76c809548d8668e0bcf63c |
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 test_dataset = load_dataset("common_voice", "or", split="test[:2%]") processor = Wav2Vec2Processor.from_pret... | 817735b1aee489017b502a8476122a4b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Odia 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", "or", split="test") wer = load... | 1d400157adc724144ac0fd77a7c8272d |
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.a... | 4b0332b62dbb4e965256fc2d1f0339be |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train` and `validation` datasets were used for training.The colab notebook used can be found [here](https://colab.research.google.com/drive/1s8DrwgB5y4Z7xXIrPXo1rQA5_1OZ8WD5?usp=sharing). | 3e9bb64491ebc55715aebadf19fb5c3c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout c8f11ef7f5c571fbcc34d53da449353bd75037ce pip install -e . cd egs2/an4/asr1 ./run.sh --skip_data_prep false --skip_train true --... | d8427c89d73101ad265974c5ea205fce |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri Aug 19 17:38:46 KST 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.10.1` - Git hash: `c8f11ef7f5c571fbcc34d53da449353bd75037ce` - Commit date: `Fri Aug 19 17:20:13 2022 +0900` | 2b3069bc64c69723f1603971d72e65fa |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.ave/test|130|773|92.0|5.8|2.2|0.4|8.4|33.1| |decode_asr_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.ave/train_dev|100|591|89.5|7.3|3.2|0.5|11.0|4... | 44e4f07d71723dc861eb0500f5ceb6d6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.ave/test|130|2565|96.3|1.1|2.6|0.6|4.3|33.1| |decode_asr_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.ave/train_dev|100|1915|94.1|1.9|4.0|0.4|6.3|... | 35fa240c0a7c54ca47d15928256a348b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.ave/test|130|2695|96.4|1.1|2.5|0.6|4.1|33.1| |decode_asr_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.ave/train_dev|100|2015|94.4|1.8|3.8|0.3|6.0|... | 86f46b60d69b59adcd4921980ef36cf3 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_transformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_raw_en_bpe30_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dis... | 64be320a6de82ddb8ba52d36ee678cca |
mit | ['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: 0.0001 | fd7f57baa3fee7cec4d5878effc7efdf |
apache-2.0 | [] | false | Deprem NER Training Results ``` precision recall f1-score support 0 0.85 0.91 0.88 734 1 0.77 0.84 0.80 207 2 0.71 0.88 0.79 130 3 0.68 0.76 0.72 94 4 ... | 8960b7b289bb0cdc5d2552575a01b66d |
apache-2.0 | [] | false | Preprocessing Funcs ``` tr_stopwords = stopwords.words('turkish') tr_stopwords.append("hic") tr_stopwords.append("dm") tr_stopwords.append("vs") tr_stopwords.append("ya") def remove_punct(tok): tok = re.sub(r'[^\w\s]', '', tok) return tok def normalize(tok): if tok.isdigit(): tok = "digit" return tok de... | 2c54e6498cb9af65c0673399847c233f |
apache-2.0 | [] | false | Other HyperParams ``` training_args = TrainingArguments( output_dir="./output", evaluation_strategy="epoch", per_device_train_batch_size=32, per_device_eval_batch_size=32, weight_decay=0.01, report_to=None, num_train_epochs=4 ) ``` ``` class_weights[0] = 1.0 class_weights[1] = 1.5167249178... | 2933eae63d6d2907be76d59a393d794e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Turkish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | a0f64341c514504c1634224193f2ec70 |
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 from unicode_tr import unicode_tr test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") pr... | 17781875d640421bc87b1c6bd038a3bd |
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): \tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \tbatch["speech"] = resampler(speech_array).squeeze().numpy() \treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:... | 9ea013b2176ff295424f53f78fd6f765 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Turkish 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", "tr", split="test") w... | 96cc532bea21d1d1e332e949813ebe56 |
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): \tbatch["sentence"] = str(unicode_tr(re.sub(chars_to_ignore_regex, "", batch["sentence"])).lower()) \tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \tbatch["speech"] = resampler(speech_array).squeeze().numpy() \treturn batch ... | fb8b060dc0e804e50209d93eda751b55 |
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): \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \twith torch.no_grad(): \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits \tpred_ids = torch.argmax(lo... | 24f927f3dafef52a3440cbf38e9bf20e |
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