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 | ['vision', 'maxim', 'image-to-image'] | false | Intended uses & limitations You can use the raw model for image retouching tasks. The model is [officially released in JAX](https://github.com/google-research/maxim). It was ported to TensorFlow in [this repository](https://github.com/sayakpaul/maxim-tf). | 0ed6c06ab7d9042abe8e5771d1781606 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | How to use Here is how to use this model: ```python from huggingface_hub import from_pretrained_keras from PIL import Image import tensorflow as tf import numpy as np import requests url = "https://github.com/sayakpaul/maxim-tf/raw/main/images/Enhancement/input/748.png" image = Image.open(requests.get(url, stream=... | 5d1e10a779253003b9af0dbdb009d0d5 |
mit | ['generated_from_trainer'] | false | covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5162 - Accu... | 3522e656ad3fb87afdfbf6948f75f4a5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.4275469935864394e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | cfeba49463b0885c06b467fbeb6c835a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8058 | 1.0 | 632 | 0.5946 | 0.1411 | | 0.5512 | 2.0 | 1264 | 0.5162 | 0.0862 | | 0.4049 | 3.0 | 1896 | 0.6612 | 0.... | 1a5024d4739ee3ce20bb83d48b7d9788 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech to text'] | false | Wav2Vec-OSR Finetuned facebook's wav2vec2 model for speech to text module of [The Sound Of AI open source research group](https://thesoundofaiosr.github.io/). The original base model is pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech... | b34d13e92cc46725782ba63c3fedf276 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech to text'] | false | Abstract We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task define... | 78d4a3d73c4eed0212a4d32142ad1ef2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech to text'] | false | load tokenizer, data_processor and model tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("iamtarun/wav2vec-osr") processor = Wav2Vec2Processor.from_pretrained("iamtarun/wav2vec-osr") model = Wav2Vec2ForCTC.from_pretrained("iamtarun/wav2vec-osr") model = model.eval() device = "cuda" if torch.cuda.is_available() else... | ec8d2c7320c205ee28918f0d3874ba07 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech to text'] | false | speech data is passed to data processor whose output is then fed to model input_values = processor(ds["speech"][:2], sampling_rate=rate, padding="longest", return_tensors="pt").input_values.to(device) | 54655c5af169b17ffd4a77b404677025 |
apache-2.0 | ['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation'] | false | GENIUS: generating text using sketches! - **Paper: [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://arxiv.org/abs/2211.10330)** - **GitHub: [GENIUS, Pre-training/Data Augmentation Tutorial](https://github.com/beyondguo/genius)** **GE... | 8b24d81792fffa0c359dc8a794162d38 |
apache-2.0 | ['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation'] | false | genius-chinese from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline checkpoint = 'beyond/genius-base-chinese' tokenizer = BertTokenizer.from_pretrained(checkpoint) genius_model = BartForConditionalGeneration.from_pretrained(checkpoint) genius_generator = Text2TextGeneration... | c54e44d70f5a74b0ed2e24c8e858ef06 |
apache-2.0 | ['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation'] | false | params | Language | comment| |------------------------|--------------------------------|-------|---------| | [`genius-large`](https://huggingface.co/beyond/genius-large) | 406M | English | The version used in paper | | [`genius-large-k2t`](https://huggingface.co/beyond/genius-large-k2t) | 406M | English | keyword... | ecfb531d736bd1a28b135f5f259b4f3a |
apache-2.0 | ['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation'] | false | Comparison / 效果对比 The following comes the comparison between [BART-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) and our proposed [GENIUS-base-chinese](https://huggingface.co/beyond/genius-base-chinese).\ 下面对比了[BART-base-chinese](https://huggingface.co/fnlp/bart-base-chinese)和我们提出的**GENIUS-base-chinese*... | 9c32fb70e9873aeaf2c70294b691b41a |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class'] | false | DreamBooth model for the Dilbert concept trained by CSAle on the CSAle/DilbertDiffusionDataset dataset. This is a Stable Diffusion model fine-tuned on the Dilbert concept. It can be used by modifying the `instance_prompt`: **dilbert** | 84486d995a5bed9261a4ccee35b75d8c |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Flair NER model trained on GermEval14 dataset This model was trained on the official [GermEval14](https://sites.google.com/site/germeval2014ner/data) dataset using the [Flair](https://github.com/flairNLP/flair) framework. It uses a fine-tuned German DistilBERT model from [here](https://huggingface.co/distilbert-base... | a490e5cfdb8dde852158a22c2edeea61 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Results | Dataset \ Run | Run 1 | Run 2 | Run 3† | Run 4 | Run 5 | Avg. | ------------- | ----- | ----- | --------- | ----- | ----- | ---- | Development | 87.05 | 86.52 | **87.34** | 86.85 | 86.46 | 86.84 | Test | 85.43 | 85.88 | 85.72 | 85.47 | 85.62 | 85.62 † denotes that this model is selected f... | bd65da23c013b61e5f6a6058b1a502b7 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | dataset, model and embedding imports from flair.datasets import GERMEVAL_14 from flair.embeddings import TransformerWordEmbeddings from flair.models import SequenceTagger from flair.trainers import ModelTrainer if __name__ == "__main__": | a1434aedcaedd856d98c48b646fbc713 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | initialize embeddings embeddings = TransformerWordEmbeddings( model=hf_model, layers="-1", subtoken_pooling="first", fine_tune=True, use_context=False, respect_document_boundaries=False, ) | 4cc132c16fa3261852050df2e7b382d7 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | init bare-bones sequence tagger (no reprojection, LSTM or CRF) tagger: SequenceTagger = SequenceTagger( hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type='ner', use_crf=False, use_rnn=False, reproj... | d26ff4682786595bcbbc5ac07b29b558 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | train with XLM parameters (AdamW, 20 epochs, small LR) from torch.optim.lr_scheduler import OneCycleLR trainer.train( output_folder, learning_rate=5.0e-5, mini_batch_size=16, mini_batch_chunk_size=1, max_epochs=10, scheduler=OneCy... | f42a1351b8f2385d492b2525c1d20edf |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the sichuan concept trained by loveunk on the loveunk/sichuan_cuisine dataset. This is a Stable Diffusion model fine-tuned on the sichuan concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of sichuan cuisine** This model was created as part of the DreamBooth Ha... | c4e9291c0411522cf2817e59fad25651 |
apache-2.0 | ['translation'] | false | opus-mt-lus-es * source languages: lus * target languages: es * OPUS readme: [lus-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lus-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 8948a06190a2cd4f62b1ce167d95cae9 |
apache-2.0 | ['generated_from_trainer'] | false | bart-paraphrase-v1-e1 This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1525 - Rouge1: 71.1522 - Rouge2: 65.1426 - Rougel: 68.9323 - Rougelsum: 69.2231 - Gen Le... | 9022601061bac26a3b3d40289020ea0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.1102 | 1.0 | 3547 | 0.1525 | 71.1522 | 65.1426 | 68.9323 | 69.2231 | 19... | df5f79451e6a4dc60c5a324b9b9738ae |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Stable Diffusion TrinArt Derrida model (Characters v2) Derrida (formerly TrinArt Characters v2) is a stable diffusion v1-based model that was further improved on the previous characters v1 model. While this is still a versatility and compositional variation anime/manga model like other TrinArt models, when compared t... | 422da53c54c76482a7d7f0889f688c65 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Custom autoencoder Note: The autoencoder uploaded here is the same checkpoint as v1. We also provide a separate checkpoint for the custom KL autoencoder. As suggested by the Latent Diffusion paper, we found that training the autoencoder and the latent diffusion model separately improves the result. Since the official... | 0da69e9b3a8a8987a2d8f8df8951c9ab |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Safety Consideration The dataset has been filtered to avoid extremely NSFW materials, but slightly less strict than v1. As with any other image generation model, we don't recommend deploying this model publicly without safety considerations and measures. Depends on prompting, one may still be able to extract highly qu... | 9093e8927bf307d9c2cc7e9e1c115336 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Examples Below images are directly generated by the native TrinArt service with its idiosyncratic upscaler, parser and processes. Your mileage may vary.    @ Bit192, Inc. Twitter https://twitter.com/naclbbr (Japanese) https://twitter.com/naclbbre (English) - Stable Diffusion - Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bjorn | 1c582caf065204e8aa2c766f164ceb1b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab7 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: 0.6917 - Wer: 0.5426 | 635d7973078e734e618f58cd0d93505d |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1400 - num_epochs: 50 - mixed_precision_t... | cb5074a291639ccdfe3cbec001df3cb6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1854 | 13.89 | 500 | 3.1687 | 1.0 | | 1.7033 | 27.78 | 1000 | 0.7289 | 0.5659 | | 0.4208 | 41.67 | 1500 | 0.6917 | 0.5426 | ... | c28f6d95e13d15ce150a99bc39e90f1c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | e9b8d097c603743ac32dcfa59e68863b |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-v2.3 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset. It achieves the following results on the evaluation set: - Loss: 0.2296 - Precision: 0.8456 - Recall: 0.8456 - F1: 0.8456 - Accuracy: 0.9585 | ccfd28f0511914398973cb5af73f14b1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3219 | 1.0 | 3228 | 0.2632 | 0.7960 | 0.8054 | 0.8007 | 0.9383 | | 0.2259 | 2.0 |... | 6cd9ec93b6249b33de69cf5a14e055a1 |
apache-2.0 | ['image-classification'] | false | VAN-Small
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
| 3d3de57de59b90fae7e69bb64c65a4bc |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | **Min-Illust-Background-Diffusion** This fine-tuned Stable Diffusion v1.5 model was trained for 2250 iterations with a batch size of 4, on a selection of artistic works by Sin Jong Hun. Training was performed using [ShivamShrirao/diffusers](https://github.com/ShivamShrirao/diffusers) with full precision, prior-preser... | 2c330595a06e4b4a7b649ac1da2a39fa |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2144 - Accuracy: 0.924 - F1: 0.9242 | b70acddf12ed01d89b5ce1e21aefe0a6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8028 | 1.0 | 250 | 0.3015 | 0.91 | 0.9089 | | 0.2382 | 2.0 | 500 | 0.2144 | 0.924 | 0.9242 | | 8991be5def1a61bb4bb22d1c34eba551 |
mit | ['generated_from_trainer'] | false | roberta-large-with-labeled-data-and-unlabeled-gab-reddit-semeval2023-task10-13300-labeled-sample This model is a fine-tuned version of [HPL/roberta-large-unlabeled-gab-reddit-semeval2023-task10-57000sample](https://huggingface.co/HPL/roberta-large-unlabeled-gab-reddit-semeval2023-task10-57000sample) on the None datas... | af218f06354e9cff86bbc43818bd3765 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9921 | 1.0 | 832 | 1.9311 | | 1.9284 | 2.0 | 1664 | 1.8428 | | 1.8741 | 3.0 | 2496 | 1.8364 | | 1.816 | 4.0 | 3328 | 1.7889 ... | 9ba1d2d72a502627e02abda69ca38f18 |
mit | [] | false | German ELECTRA base generator Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906... | 33319de27a1997de517d1e38eece0de9 |
mit | [] | false | Overview **Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf) **Architecture:** ELECTRA base (generator) **Language:** German See also: deepset/gbert-base deepset/gbert-large deepset/gelectra-base deepset/gelectra-large deepset/gelectra-base-generator deepset/gelectra-large-generator | 529e80b91026da79d01829c37c2e61ba |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2155 - Accuracy: 0.9265 - F1: 0.9266 | b923fe37a7d564c0474f2716f779e9c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3133 | 0.9075 | 0.9054 | | No log | 2.0 | 500 | 0.2155 | 0.9265 | 0.9266 | | 9cb9bfbf9aabde46b7dc6e084ca5089a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Danish (Common Voice Corpus 11.0) This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 da dataset. It achieves the following results on the evaluation set: - Loss: 0.5081 - Wer: 16.1336 | 0b1c7ffddd9762005012c212fcaa8198 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0229 | 7.01 | 1000 | 0.4464 | 16.5124 | | 0.0072 | 15.0 | 2000 | 0.5081 | 16.1336 | | 0.0048 | 22.01 | 3000 | 0.5193 | 1... | f8ab848c61289b514e65b1aa271c793b |
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: 0.3300 - Accuracy: 0.86 - F1: 0.8627 | d3034e0daf4995a5db4942e6daf028ed |
apache-2.0 | ['translation'] | false | afr-rus * source group: Afrikaans * target group: Russian * OPUS readme: [afr-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md) * model: transformer-align * source language(s): afr * target language(s): rus * model: transformer-align * pre-processing: normalization + Se... | c42e28d9540dd0baecf9d7185aa7967c |
apache-2.0 | ['translation'] | false | System Info: - hf_name: afr-rus - source_languages: afr - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['af', 'ru'] - src_constituents: {'afr'} - tgt_const... | f46410c2a0edd5bb58c459829ad556eb |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-300m-pl-cv8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset. It achieves the following results on the evaluation set while training: - Loss: 0.1716 - Wer: 0.1697 - Cer: 0.0385 The `eval.py` script... | 439e915b82dde5de61fb6f8f3cef0572 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Model description Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly... | c1ade01c93e4f7ff3e06a712498b8f15 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) 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"],... | ef0d74a30409fc2dc59a5b12917789cd |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Evaluation The model can be evaluated using the attached `eval.py` script: ``` python eval.py --model_id comodoro/wav2vec2-xls-r-300m-pl-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config pl ``` | 1bde98feed33f06eddaa49c2d00cb29b |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used: - learning_rate: 1e-4 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 1 - total_train_batch_size: 640 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_st... | 43b4acb2b7c183501286f0c3e96143ba |
apache-2.0 | ['translation'] | false | opus-mt-sv-sg * source languages: sv * target languages: sg * OPUS readme: [sv-sg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-sg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://... | 1820993cf5113dd381e591b26de1d596 |
apache-2.0 | [] | false | Enformer Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enf... | ff77fce038a5bc046e595b472729d2c5 |
apache-2.0 | [] | false | Citation info ``` Avsec, Ž., Agarwal, V., Visentin, D. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18, 1196–1203 (2021). https://doi.org/10.1038/s41592-021-01252-x ``` | 6cb1f0b1d74cc16be61d8ea8ae14e2fc |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mrpc-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mrpc](https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7869 - Matthews Correlation: 0.1551 | f682e41a476af1a113625b9135a6343f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6097 | 1.87 | 500 | 0.6213 | 0.0 | | 0.6008 | 3.73 | 1000 | 0.6170 | 0.0 | | 0.5... | de3c0cb833bb171efe6da85e8387a0ee |
apache-2.0 | ['translation'] | false | opus-mt-en-is * source languages: en * target languages: is * OPUS readme: [en-is](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-is/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | 3f86c5684f4b70a47a655d1392917d7e |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | ulyana Dreambooth model trained by qweewqewqqwe with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-d... | 54caa6393b62271fc916653782adebda |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_vp-fr_s179 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | efe7ce2e933df43713273f6c6d0cc0e3 |
apache-2.0 | ['generated_from_trainer'] | false | swin-large-patch4-window7-224-fv-finetuned-memes This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6502 - Accuracy: 0.8601 - Prec... | 855c92e8815e89456fd2283e735cb044 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.2077 | 0.99 | 20 | 0.9499 | 0.6461 | 0.6764 | 0.6461 | 0.5863 | | 0.5687 | 1.99 |... | 39571287b0b87171eeaeeb0f009db698 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | neonHorror This is a Stable Diffusion model about horror illustrations with a little bit of neon lights. Some recomendations: the magic word for your prompts is neonHorror .In some times, you would put some prompts like: request, in neonHorror style or an illustration of request, in neonHorror style or neonHorror... | 7dae8e6bf2acb04bbef457be27457b43 |
mit | [] | false | kira-sensei on Stable Diffusion This is the `<kira-sensei>` 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... | de8e4d4b5852b3b89fc61b7ddadbbffd |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-target-glue-sst2 This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4627 - Accuracy: 0.8142 | 1572d7e1c0fda1bb6c6d930066ea3891 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5912 | 0.24 | 500 | 0.4989 | 0.7718 | | 0.445 | 0.48 | 1000 | 0.4712 | 0.7844 | | 0.3945 | 0.71 | 1500 | 0.4460 | 0.... | 06450aa3d281f74d8bf78bd16be1a422 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.0659 | 0.34 | 5000 | 3.9176 | | 1.8404 | 0.67 | 10000 | 3.7958 | | f1e74313aaa0b5b2fd24f60f3b2b68e8 |
gpl-3.0 | [] | false | Keras Implementation of Convolutional autoencoder for image denoising This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise. Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder Keras Example Link:- https://keras.io/examples/v... | e19959a771e863643e79b7b6a0a77ed4 |
gpl-3.0 | [] | false | Intended uses & limitations - The trained model can be used to remove noise from any grayscale image. - Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28. | 9dfa50d2df8f1f0aacfea29a5d04765e |
gpl-3.0 | [] | false | Training and evaluation data - Original mnist train & test dataset were loaded from tensorflow datasets. - Then Some noise was added to train & test images. - Noisy images were used as input images and original clean images were used as output images for training. | b6d85e8a366339699695b926e918765d |
gpl-3.0 | [] | false | Training hyperparameter The following hyperparameters were used during training: - optimizer: 'adam' - loss: 'binary_crossentropy' - epochs: 100 - batch_size: 128 - ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function. | 2d305132fe6ce7d505d63a391a4efb22 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Greek This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 1.5908 - Wer: 118.8406 | 569b83105bc8e43e105b50a4a1eb32dc |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.5 | 2 | 1.5908 | 118.8406 | | bb93abd18e9b5228d8ae795110030c33 |
openrail++ | ['stable-diffusion', 'text-to-image', 'image-to-image', 'hdri', '360 VR'] | false | A HDRI-themed model to automatically generate HDRI and 360VR of any Indoor, Outdoor and Urban locations. Textual Inversion embedding on 768x768 images of various HDRI. Download both hdrimaker.ckpt and hdrimaker.yaml to your Stable-diffusion folder. >>> link to Examples: https://panoraven.com/en/slider/qWY5x96nsK <<<... | 235afd3d56874e9a697931f424c47ed4 |
openrail++ | ['stable-diffusion', 'text-to-image', 'image-to-image', 'hdri', '360 VR'] | false | Random prompts: Here are some examples using modifiers after the subject, but feel free to use try writing your prompts without them at first. - Example prompt A:  close up of a street in a beautiful fairytale world with ca... | ddb6bb0990a21a80a9f116ebe76156c4 |
openrail++ | ['stable-diffusion', 'text-to-image', 'image-to-image', 'hdri', '360 VR'] | false | HDRI preview: - Panoraven is a brilliant website which allows you to quickly view and share your panorama (Registration isn't needed) https://panoraven.com/en/share-360-photo - 360 Panorama viewer is another simple viewer to preview your images https://renderstuff.com/tools/360-panorama-web-viewer/ | d8276217722f193bedad628a146fda7a |
apache-2.0 | [] | false | The [BERTić](https://huggingface.co/classla/bcms-bertic)* [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS) * The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić ... | e1755eca3725489d4f32fafa72c2b90f |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'italian-speech-corpus', 'english-speech-corpus', 'arabic-speech-corpus', 'spontaneous', 'speech', 'PyTorch'] | false | Wav2vec 2.0 XLS-R For Spontaneous Speech Emotion Recognition This is the model that got first place in the SER track of the Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022) Workshop. The following datasets were used in the training: - [CORAA SER... | ec3562e692c60209375dd113a360dec3 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'italian-speech-corpus', 'english-speech-corpus', 'arabic-speech-corpus', 'spontaneous', 'speech', 'PyTorch'] | false | .YO6yI-gzaUk): a dataset that provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: angry, calm, disgust, fearful, happy, neutral, sad and surprised. - [BAVED](https://github.com/40uf411/Basic-Arabic-Vocal-Emotions-Dataset): a collection of audio recordings of Arabic... | 93e8b26c0de23b931e2e839716abb134 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'italian-speech-corpus', 'english-speech-corpus', 'arabic-speech-corpus', 'spontaneous', 'speech', 'PyTorch'] | false | Datasets Details The following image shows the overall distribution of the datasets:  The following image shows the number of instances ... | 945e7b14d495c7e5ee6824b922d40c4f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-sw-3 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: - eval_loss: 0.3003 - eval_wer: 0.4063 - eval_runtime: 773.9058 - eval_samples_per_s... | c5e55369bf2291b94d5f9a60210a4a7b |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr5e05-wd0.08-bs64 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: 0.2772 - Rmse: 0.5265 - Mse: 0.2772 - Mae: 0.4320 | 21f43ed77abf17198b519a5178c50b51 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 48b9bf9387a2478d7b359ab6f6d1513a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2781 | 1.0 | 623 | 0.2744 | 0.5238 | 0.2744 | 0.4159 | | 0.2744 | 2.0 | 1246 | 0.2774 | 0.5267 | 0.2774 ... | da7c1474fad45b68686841db660b82d1 |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | HindSBERT This is a HindBERT model (l3cube-pune/hindi-bert-v2) trained on the NLI dataset. <br> Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP <br> A better sentence similarity model (fine-tuned version of this model) is shared here : https://huggingface.co/l3cube-pune/hindi-sentenc... | c4f9ac7c24bbf02854593135fc36ebf2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | chispita Dreambooth model trained by vhanla with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffu... | 02a8185acc5348aa4e3db81019dbde84 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-ner_cv 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: 0.8548 - Precision: 0.3327 - Recall: 0.2358 - F1: 0.2760 - Accuracy: 0.7815 | f086d2e244511e44ae67ca8c58e5c21e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 30 | e9c3ceca68f72c39cbcb1c328eb8e168 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 5.0 | 30 | 1.0790 | 0.0 | 0.0 | 0.0 | 0.7537 | | No log | 10.0 |... | edda1e15dc6927dbb3449bb7e8d02e75 |
cc-by-sa-4.0 | [] | false | yacis-electra-small-cyberbullying This is an [ELECTRA](https://github.com/google-research/electra) Small model for the Japanese language finetuned for automatic cyberbullying detection. The original foundation model was originally pretrained on 5.6 billion words [YACIS](https://github.com/ptaszynski/yacis-corpus) b... | 84f7126c1aa14b9e567afbcb30e8446e |
cc-by-sa-4.0 | [] | false | Model architecture The original model was pretrained using ELECTRA Small model settings and can be found here: [https://huggingface.co/ptaszynski/yacis-electra-small-japanese](https://huggingface.co/ptaszynski/yacis-electra-small-japanese) | 3a48cb2d596d241183f6ac2adf5e80c0 |
cc-by-sa-4.0 | [] | false | Citations Please, cite this model using the following citation. ``` @inproceedings{shibata2022yacis-electra, title={日本語大規模ブログコーパスYACISに基づいたELECTRA事前学習済み言語モデルの作成及び性能評価}, % title={Development and performance evaluation of ELECTRA pretrained language model based on YACIS large-scale Japanese blog corpus [in Japanes... | 5cf23fb7753165b090a6629d04d030de |
cc-by-4.0 | ['roberta'] | false | hate-roberta-hasoc-hindi hate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021. The label mappings are 0 -> None, 1 -> Offensive, 2 -> Hate, 3 -> Profane. More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/... | 3e779aaf5c1b2d1fb02dd87ce9007b7a |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2559 - F1: 0.8441 | 60d4b80c3cb8a289e32035e993be937e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.655 | 1.0 | 140 | 0.2818 | 0.7803 | | 0.2404 | 2.0 | 280 | 0.2618 | 0.8207 | | 0.149 | 3.0 | 420 | 0.2559 | 0.8441 | ... | 1d458bba88fd280f74155eed1546ad19 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | e92f6fef8a78d4b8f89cbe52066d8c71 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2978 | 1.0 | 6621 | 1.2262 | | 1.0378 | 2.0 | 13242 | 1.0048 | | 0.9537 | 3.0 | 19863 | 0.9414 | | 427a7fa9bd4d83a3abec73fbe256a011 |
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