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 | ['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: 0.0598 - Precision: 0.9370 - Recall: 0.9509 - F1: 0.9439 - Accuracy: 0.9869 | 9b0a18aeb1ab33cc31afaea08c5158f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0871 | 1.0 | 1756 | 0.0633 | 0.9197 | 0.9362 | 0.9279 | 0.9833 | | 0.0386 | 2.0 |... | e9682377e2b948df045535b653bc2693 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-product-match This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0014 - F1: 0.9996 - Roc Auc: 0.9996 - Accuracy: 0.9996 | eb840736dcdda9325a6d06da73a192e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.0035 | 1.0 | 3500 | 0.0014 | 0.9996 | 0.9996 | 0.9996 | | 00ffa182f77a8540a68d79ad86737724 |
mit | [] | false | david martinez cyberpunk on Stable Diffusion This is the `<david-martinez-cyberpunk>` 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.i... | 2cef287e81c4f5a65076f31e3427222b |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-ner-finetuned-ner This model is a fine-tuned version of [EffyLi/bert-base-uncased-finetuned-ner](https://huggingface.co/EffyLi/bert-base-uncased-finetuned-ner) on the conll2003 dataset. | 506052b79de31d1c57e1cb40a9c7e2af |
apache-2.0 | ['seq2seq', 'relation-extraction', 't5'] | false | How to use Same code as REBEL-large (https://huggingface.co/Babelscape/rebel-large) ``` text = '''За последние 9 месяцев инвесторы в азиатские долларовые долговые обязательства потеряли 155 миллиардов долларов, пострадав от слабости Китая в дополнение к глобальной распродаже фиксированного дохода, наблюдаемой во всем... | eae2095593017b7210d8cfbd4b89c392 |
apache-2.0 | ['seq2seq', 'relation-extraction', 't5'] | false | We need to use the tokenizer manually since we need special tokens. extracted_text = triplet_extractor.tokenizer.batch_decode([triplet_extractor(text, return_tensors=True, return_text=False, max_length=500)[0]["generated_token_ids"]]) print(extracted_text[0]) | c7e750d7e1740e85bce61c695e3afb79 |
apache-2.0 | ['seq2seq', 'relation-extraction', 't5'] | false | Function to parse the generated text and extract the triplets def extract_triplets(text): triplets = [] relation, subject, relation, object_ = '', '', '', '' text = text.strip() current = 'x' for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): if token == ... | 2b191add96bd7a546777a8a7bdb2aa64 |
mit | [] | false | David Martinez Edgerunners on Stable Diffusion This is the `<david-martinez-edgerunners>` 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_inferen... | bdfb4c2a06068d6bdba973f50ba92599 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Fine-tuned XLSR-53 large model for speech recognition in Japanese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyuby... | 51ac859063fe4158103bab78e39f5881 |
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... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-japanese") audio_paths = ["... | a4ca2938d332c0d0aaf079b86e6cea84 |
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 = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs =... | a13cb3fc8db4e2b0c2694fc7a84e11cb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Japanese test data of Common Voice. ```python import torch import re import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "ja" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japan... | ba6668f33edad96ea5685c6f2401c153 |
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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits pred_ids = torch... | 9d790e3c3ecba6b4e9a928ac331fab14 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-japanese, title={Fine-tuned {XLSR}-53 large model for speech recognition in {J}apanese}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese}}, year... | 3fbbc5c26f8ae5ad95b4ba8c7baf47c2 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | 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 - AB dataset. It achieves the following results on the evaluation set: - Loss: 0.6178 - Wer: 0.5794 | ae85b09b101e3a299240c5ee28329e60 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00025 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 11ba294ff77ebecb4305f23c3ed1e83a |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2793 | 27.27 | 300 | 3.0737 | 1.0 | | 1.5348 | 54.55 | 600 | 0.6312 | 0.6334 | | 196fab636e7f8c05ceed39652b1b4798 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value ... | 72e22232c1b4dfdb58e51c3401da06fb |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;} | 653ebd978897cec1916156a4651a99b9 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-612ecc16-5410-4287-9cca-3bb6bb70aa61 div.sk-container {display: inline-block;position: relative;}</style><div id="sk-612ecc16-5410-4287-9cca-3bb6bb70aa61" class"sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><inpu... | 8c111dc30f2a903434f2a95ce83c792e |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) ``` </details> | 39ba2f71302e4c9f58b1e7d1d71bd4ad |
mit | ['roberta-base', 'roberta-base-epoch_10'] | false | RoBERTa, Intermediate Checkpoint - Epoch 10 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 ... | fc292c672291472d573fbd11dc85f95d |
mit | ['generated_from_keras_callback'] | false | recklessrecursion/Catalan_language-clustered This model is a fine-tuned version of [nandysoham16/13-clustered_aug](https://huggingface.co/nandysoham16/13-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6185 - Train End Logits Accuracy: 0.8160 - Train Star... | 4a0d3250109c7e80c9eb9f4535b51383 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 594e872eedeb216d823581ded41326d2 |
apache-2.0 | ['Super-Resolution', 'computer-vision', 'bsrgan', 'gan'] | false | Model Description [BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .](https://arxiv.org/abs/2103.14006) [BSRGAN-Pip: Packaged version of the BSRGAN repository](https://github.com/kadirnar/bsrgan-pip/) [Paper Repo: Implementation of paper - BSRGAN](https://github.com/cszn/BSRGAN)... | ba1d141bfcb3a8399b0e8f8466f97d9c |
apache-2.0 | ['Super-Resolution', 'computer-vision', 'bsrgan', 'gan'] | false | BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/BSRGANx2', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` | df6a065f02287dfe8a51032d8d7d3bc3 |
apache-2.0 | ['Super-Resolution', 'computer-vision', 'bsrgan', 'gan'] | false | BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={... | 6a3ca5a2a6832db0d5f6cb843b3154c6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-lv60-self-colab This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0563 - Wer: 0.1541 | 8e8982f76b112570e29027817352f2b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 12 - mixed_precision_tr... | 822235885cb04c99190e8858866468cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.3084 | 1.0 | 500 | 3.0693 | 1.0 | | 1.5665 | 2.01 | 1000 | 0.0834 | 0.1749 | | 0.2761 | 3.01 | 1500 | 0.0620 | 0.1593 | |... | cd5591b89fa41cafd0d125005fc3ef35 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | StableDiffusion_finetuning_special_animal_style Dreambooth model trained by jha2ee 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.... | 843512db55d46e636aad3a78ef4e1318 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6340 | 5bd54bc45222863e2a9766a2ea159898 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4965 | 1.0 | 554 | 1.5562 | | 1.2141 | 2.0 | 1108 | 1.5012 | | 0.7883 | 3.0 | 1662 | 1.6340 | | 2e166e7b7fdc0bf93017f76d030d3afc |
mit | ['huggan', 'gan', 'unconditional-image-generation'] | false | Model description [FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model w... | 130ac098cec64b356712ef390c31386a |
mit | ['huggan', 'gan', 'unconditional-image-generation'] | false | Clone this model git clone https://huggingface.co/huggan/fastgan-few-shot-fauvism-still-life/ def load_generator(model_name_or_path): generator = Generator(in_channels=256, out_channels=3) generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3) _ = generator.eval() ... | a4a103bf249ac84d7d82296393aa55d0 |
apache-2.0 | [] | false | FinBertPTBR : Financial Bert PT BR FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. ... | 5bb16b56ac86944ba1b62910a78d3125 |
apache-2.0 | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("turing-usp/FinBertPTBR") model = AutoModel.from_pretrained("turing-usp/FinBertPTBR") ``` | 4fc59941eedecfb55cf1b0bb60049a75 |
apache-2.0 | [] | false | Authors - [Vinicius Carmo](https://www.linkedin.com/in/vinicius-cleves/) - [Julia Pocciotti](https://www.linkedin.com/in/juliapocciotti/) - [Luísa Heise](https://www.linkedin.com/in/lu%C3%ADsa-mendes-heise/) - [Lucas Leme](https://www.linkedin.com/in/lucas-leme-santos/) | 68b85c1ec91323b5cb13433cb461c3c3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium-Assamese This model is a fine-tuned version of [kpriyanshu256/whisper-medium-as-400-32-1e-05-bn](https://huggingface.co/kpriyanshu256/whisper-medium-as-400-32-1e-05-bn) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2780 - Wer: 23.9487 | 3b107cd49abbb0d9f67fd31f1ce12374 |
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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 82402b527a2125042ec957e95b56fc2a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0044 | 6.13 | 200 | 0.2780 | 23.9487 | | d1c244b60db0eac6ec9e6030cb9735a0 |
other | ['vision', 'image-segmentation'] | false | MobileViT + DeepLabV3 (extra small-sized model) MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this... | d71683b557113eab1f0f72a680584b3e |
other | ['vision', 'image-segmentation'] | false | Model description MobileViT is a light-weight, low latency convolutional neural network that combines MobileNetV2-style layers with a new block that replaces local processing in convolutions with global processing using transformers. As with ViT (Vision Transformer), the image data is converted into flattened patches... | d8e5a3d308ffcb364bd3811c371adc98 |
other | ['vision', 'image-segmentation'] | false | Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilevit) to look for fine-tuned versions on a task that interests you. | 54f2c5aad4ae9443e9eea78efa5b2a42 |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model: ```python from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = Mo... | bcaa53e961418c0dd751e88c1b1149c8 |
other | ['vision', 'image-segmentation'] | false | Training data The MobileViT + DeepLabV3 model was pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the [PASCAL VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/) dataset. | d003b33d898ccf6c37cff55ccb0d06b8 |
other | ['vision', 'image-segmentation'] | false | Pretraining The MobileViT networks are trained from scratch for 300 epochs on ImageNet-1k on 8 NVIDIA GPUs with an effective batch size of 1024 and learning rate warmup for 3k steps, followed by cosine annealing. Also used were label smoothing cross-entropy loss and L2 weight decay. Training resolution varies from 16... | 9c2b0bbbc376ff4fd4721824e02b7016 |
other | ['vision', 'image-segmentation'] | false | params | URL | |------------------|-----------------|-----------|-----------------------------------------------------------| | MobileViT-XXS | 73.6 | 1.9 M | https://huggingface.co/apple/deeplabv3-mobilevit-xx-small | | **MobileViT-XS** | **77.1... | 4fe8bafe3dbe16d1444df16700d7c77b |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | 3aeca67b23fa756a2c3e946017f564e1 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-base-subjqa-vanilla-books-qg` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-question-g... | 6006424143e1b3101f3295b0474a8b52 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (books) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generat... | 7413361ec0824046faa867f2be7bddb8 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-base-subjqa-va... | ae7bf57cfbc22352295c6872db483441 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-subjqa-vanilla-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | Score | Type | Dataset ... | 3a3d48d6a2d1aa457975afab48a203ee |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: books - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 16 ... | 6e0a9fda925cf227e6cd88e85af73f3a |
apache-2.0 | ['generated_from_trainer'] | false | test_emotion_trained_test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5866 - F1: 0.7015 | 04d642aad540c417b9f25049ff7c8dc4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.458132814624325e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 0db0aa377f7a3ddd6a538b4c153bc141 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 51 | 0.7877 | 0.5569 | | No log | 2.0 | 102 | 0.6188 | 0.6937 | | No log | 3.0 | 153 | 0.5969 | 0.7068 | |... | d4beee013a78d78fd2a4627d7d2a6125 |
unknown | [] | false | Silvery Trait finetuned style Model Produced from publicly available pictures in landscape, portrait and square format. Using words found in `prompt_words.md` within your prompt will produce better results. Other words can be used also but will tend to produce "weaker" results. Combining the use of the Aesthetic Gra... | 89b373a3d8c95c80ae69f8dbbe76d6a5 |
unknown | [] | false | Example prompts `a sheep, symmetry, by asd artstyle`: * without easthetic_embeddings <img src="https://huggingface.co/cyburn/silvery_trait/resolve/main/1.jpg" alt="Picture." width="500"/> * with easthetic_embeddings <img src="https://huggingface.co/cyburn/silvery_trait/resolve/main/2.jpg" alt="Picture." width="50... | ec513fbc3500c08ff94da1d8b1b329cd |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `asapp/e_branchformer_librispeech` This model was trained by Kwangyoun Kim using librispeech recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Atte... | 64580992b929b5893192dc4fec9d9501 |
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 7a203d55543df02f0369d5608cd6f3033119a135 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train... | 1b604fdf229b6dec309740c7d693fb87 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Jan 2 12:59:49 UTC 2023` - python version: `3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.10.1` - Git hash: `7a203d55543df02f0369d5608cd6f3033119a135` - Commit date: `Fri Dec 23 00:58:49 2022 +0000` | 52d05cc024fa30cdffe58db9fde46cc7 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|26.3| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|95.8|3.8|0.3|0.4|4.6|40.6| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|98.1|... | 1fd8e26ae281aa109bc94e2162f2c70d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|99.6|0.2|0.2|0.2|0.6|26.3| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|98.6|0.9|0.5|0.5|1.9|40.6| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|99... | 9fd3888cbb0761a5ae2b5f415012dd68 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|68010|97.8|1.6|0.6|0.3|2.6|26.3| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|63110|94.9|3.9|1.2|0.8|5.9|40.6| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|65818|97.6|... | dc960e1500c0d6c595c5bff332d3d528 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 8 num_att_plot: 3 dist_backend: nccl dist_init_met... | f245033b24ec659f2287dbcbd4c7ee6f |
mit | ['generated_from_trainer', 'xnli'] | false | XLM-V (base) fine-tuned on XNLI This model is a fine-tuned version of [XLM-V (base)](https://huggingface.co/facebook/xlm-v-base) on the XNLI (XGLUE) dataset. It achieves the following results on the evaluation set: - Loss: 0.6511 - Accuracy: 0.7403 | abbfa8f5ad3db9737aaefa237a001c0f |
mit | ['generated_from_trainer', 'xnli'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | 98479775bf8c6fbfa46039df4f68ef01 |
mit | ['generated_from_trainer', 'xnli'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0994 | 0.08 | 1000 | 1.0966 | 0.3697 | | 1.0221 | 0.16 | 2000 | 1.0765 | 0.4560 | | 0.8437 | 0.24 | 3000 | 0.8472 ... | a43bf1d43805e2d3283b6848eb8898f5 |
apache-2.0 | ['generated_from_keras_callback'] | false | kasrahabib/all-MiniLM-L6-v2-finetunned-fnfreq-clf-promise-finetunned-fnfreq-clf-promise-third This model is a fine-tuned version of [kasrahabib/all-MiniLM-L6-v2-finetunned-fnfreq-clf-promise-finetunned-fnfreq-clf-promise_second](https://huggingface.co/kasrahabib/all-MiniLM-L6-v2-finetunned-fnfreq-clf-promise-finetunn... | cebe89a61ea6fae53473518b3c6e0841 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:-----:| | 0.0903 | 0.0463 | 1.0 | 0.9730 | 0.9863 | 0 | | 0.0549 | 0.0560 | 0.9863 ... | 0e46ba70859a91ac94b57b6f3c3a78a8 |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-finetuned-arxiv This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 2.2912 - Rouge1: 13.6917 - Rouge2: 5.9564 - Rougel: 11.1734 - Rougelsum: 12.6817 - Gen Len:... | e11e7d04e21c6f8d7010f71c9205f673 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP | f473c39218e8e87ff9d40d46b3745d72 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.6027 | 1.0 | 6345 | 2.4504 | 13.3687 | 5.603 | 10.8671 | 12.3297 | 20... | 4ce0a62828df9c0b56d08d4dee03b426 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | carrot_commercial_v1 Dreambooth model Sample pictures of this concept:  was finetuned on a multiclass data with four classes (*check the exact mapping between idx and label in* `model.config`). 1) OK label — the message is OK in context and does not intent to offend or so... | 5b4c1dc80b780be0bc9a7c280106abac |
mit | ['russian', 'pretraining', 'conversational'] | false | Evaluation results Model achieves the following results on the validation datasets (will be posted soon): || OK - F1-score | TOXIC - F1-score | SEVERE TOXIC - F1-score | RISKS - F1-score | |---------|---------------|------------------|-------------------------|------------------| |internet dialogs | 0.896 |... | 7505b9aeef39ea7d433a720cd10f4e4e |
mit | ['russian', 'pretraining', 'conversational'] | false | Use in transformers ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/response-toxicity-classifier-base') model = AutoModelForSequenceClassification.from_pretrained('tinkoff-ai/response-toxicity-classifier-base') inp... | 16402231baee9bb428ed9e05c2dfdf2a |
apache-2.0 | ['summarization', 'AraBERT', 'BERT', 'BERT2BERT', 'MSA', 'Arabic Text Summarization', 'Arabic News Title Generation', 'Arabic Paraphrasing', 'Summarization', 'generated_from_trainer', 'Transformers', 'PyTorch'] | false | Model description The model can be used as follows: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline from arabert.preprocess import ArabertPreprocessor model_name="abdalrahmanshahrour/arabartsummarization" preprocessor = ArabertPreprocessor(model_name="") tokenizer = AutoTokenizer.f... | b748b27775277931bed3cf9a31fee04e |
apache-2.0 | ['summarization', 'AraBERT', 'BERT', 'BERT2BERT', 'MSA', 'Arabic Text Summarization', 'Arabic News Title Generation', 'Arabic Paraphrasing', 'Summarization', 'generated_from_trainer', 'Transformers', 'PyTorch'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.784 | 1.0 | 9380 | 2.3820 | | 2.4954 | 2.0 | 18760 | 2.3418 | | 2.2223 | 3.0 | 28140 | 2.3394 | | fbdc686903472de0e39a83e470887938 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-text-classification-template 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: 0.6637 - F1: 0.5 - Roc Auc: 0.6667 - Accuracy: 0.3333 | 0a18b372c1a392586b3aeaa939c7efec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---:|:-------:|:--------:| | No log | 1.0 | 6 | 0.6637 | 0.5 | 0.6667 | 0.3333 | | 93c50be40da058eb3c9d09060a07b7be |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sente... | false | hkunlp/instructor-base We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, with... | 9f4b6bc48dac787d7de77f6a96d035ab |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sente... | false | Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from InstructorEmbedding import INSTRUCTOR model = INSTRUCTOR('hkunlp/instructor-base') sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction ... | 8ba0d830cc8def53da2f0f5774f164f7 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | datasets) This model transcribes speech in lowercase Spanish alphabet including spaces, and was trained on a composite dataset comprising of 1340 hours of Spanish speech. It is a "large" variant of Conformer-Transducer, with around 120 million parameters. See the [model architecture]( | d75e00052e3f9cb58451a8298959287d |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_es_conformer_transducer_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | 1b17e553150a83b828e9e1e6a12a2ed5 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Datasets All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of 1340 hours of Spanish speech: - Mozilla Common Voice 7.0 (Spanish) - 289 hours after data cleaning - Multilingual LibriSpeech (Spanish) - 801 hours after data cleaning - Voxpopuli transcribed subset (Spanish) - ... | f470eda41e3376977092452a868d19ac |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | MCV 7.0 Dev | MCV 7.0 Test | MLS Dev | MLS Test | Voxpopuli Dev |... | 28a24a02052484c7a731dd70ac75be78 |
cc-by-4.0 | ['generated_from_trainer'] | false | herbert-base-cased-finetuned-squad This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2071 | 06d4a5dbcec42af381a028d2e3700f38 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 233 | 1.2474 | | No log | 2.0 | 466 | 1.1951 | | 1.3459 | 3.0 | 699 | 1.2071 | | c53e8c1c82a8020db912156f03c3ce04 |
apache-2.0 | ['generated_from_trainer'] | false | twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-14_43_21 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1212 -... | b53e409a2f86aaae00f6f794b83251a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 15 | 0.1113 | 0.0 | 0.0 | 0.0 | 0.9752 | | No log | 2.0 | 30 | 0... | 111395d986817ff34d3c509b54bd4003 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-sem_eval-english This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sem_eval_2018_task_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3131 - F1: 0.7114 - Roc Auc: 0.8046 - Accuracy: 0.2810 | cea9d5a20c3f2bba310aa72fff090fe1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.4067 | 1.0 | 855 | 0.3205 | 0.6756 | 0.7766 | 0.2709 | | 0.2828 | 2.0 | 1710 | 0.3062 | 0.7058 ... | b9639bcd8a506400e2cae1bca01dcc86 |
mit | ['generated_from_trainer'] | false | cranky_lichterman 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 tome... | e0352aa65100118a7681e62ce2fb792c |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 6c9ed1228904d5f885b507e4e6be568c |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-humordetection 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: - Loss: 0.3136 - F1: 0.9586 | fac06d8ca7bf49ded17dc64c2d4334ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 375 | 0.1768 | 0.9507 | | 0.2266 | 2.0 | 750 | 0.1910 | 0.9553 | | 0.08 | 3.0 | 1125 | 0.2822 | 0.9529 | |... | ad81920e08b7cc03eb606bfce110ce06 |
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