license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | multiplenegativesrankingloss) using Mean-pooling, cosine-similarity as similarity function, and a scale of 20. | Dataset | Number of training tuples | |--------------------------------------------------------|:--------------------------:| | [WikiAnswers](https://github.com/afader/oqa | 47e6bbe3a916d6c18a31a986fbc61c36 |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml)... | 74993f1be29f1aad8056ae1710442a53 |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Technical Details In the following some technical details how this model must be used: | Setting | Value | | --- | :---: | | Dimensions | 768 | | Produces normalized embeddings | Yes | | Pooling-Method | Mean pooling | | Suitable score functions | dot-product, cosine-similarity, or euclidean distance | Note: This mo... | 4d19c00d73b2811421f32d3837705a7b |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Usage and Performance The trained model can be used like this: ```python from sentence_transformers import SentenceTransformer, util question = "That is a happy person" contexts = [ "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] | 3a149139bb9419cb44def4d93d13fa49 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1385 - F1: 0.8607 | 046d0f899637806beacf01acd7c86ed0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2565 | 1.0 | 525 | 0.1725 | 0.8200 | | 0.128 | 2.0 | 1050 | 0.1388 | 0.8514 | | 0.0804 | 3.0 | 1575 | 0.1385 | 0.8607 | ... | 8da133f0602443c4827a2c29a430abe1 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-XXL-NL4 (Deep-Narrow version) T5-Efficient-XXL-NL4 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 and wa... | 1e149068699746ea5c8359a3292a9e43 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-xxl-nl4** - is of model type **Xxl** with the following variations: - **nl** is **4** It has **1207.34** million parameters and thus requires *ca.* **4829.35 MB** of memory in full precision (*fp32*) or **2414.68 MB** of memory in half precision (*f... | 03b5cb05f4708c397c6663497b1e0117 |
apache-2.0 | ['question-answering', 'question-generation', 'multitask-model'] | false | mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System [Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Turkish Question Answering dataset](https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering... | 369b98fb8dd7654da6c8d98b6e9081ce |
apache-2.0 | ['question-answering', 'question-generation', 'multitask-model'] | false | Requirements ❗❗❗ ``` !pip install transformers==4.4.2 !pip install sentencepiece==0.1.95 !git clone https://github.com/ozcangundes/multitask-question-generation.git %cd multitask-question-generation/ ``` | b9b4cf46088923d961fc4550631ac010 |
apache-2.0 | ['question-answering', 'question-generation', 'multitask-model'] | false | Usage 🚀🚀 ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ozcangundes/mt5-multitask-qa-qg-turkish") model = AutoModelForSeq2SeqLM.from_pretrained("ozcangundes/mt5-multitask-qa-qg-turkish") from pipelines import pipeline | 283abf93491f9f0efd5249ab912accf2 |
apache-2.0 | ['question-answering', 'question-generation', 'multitask-model'] | false | sample text text="Özcan Gündeş, 1993 yılı Tarsus doğumludur. Orta Doğu Teknik Üniversitesi \\\\ Endüstri Mühendisliği bölümünde 2011 2016 yılları arasında lisans eğitimi görmüştür. \\\\ Yüksek lisansını ise 2020 Aralık ayında, 4.00 genel not ortalaması ile \\\\ Boğaziçi Üniversitesi, Yönetim Bilişim Sistemleri bölümünd... | a123974ac9c7df6c841692a5dee5ade4 |
apache-2.0 | ['question-answering', 'question-generation', 'multitask-model'] | false | output => [{'answer': 'Tarsus', 'question': 'Özcan Gündeş nerede doğmuştur?'}, {'answer': '1993', 'question': 'Özcan Gündeş kaç yılında doğmuştur?'}, {'answer': '2011 2016', 'question': 'Özcan Gündeş lisans eğitimini hangi yıllar arasında tamamlamıştır?'}, {'answer': 'Boğaziçi Üniversitesi, Yönetim Bilişim Sisteml... | 7e73cc19628836d99424695e51edd4f7 |
apache-2.0 | ['question-answering', 'question-generation', 'multitask-model'] | false | ACKNOWLEDGEMENT This work is inspired from [Suraj Patil's great repo](https://github.com/patil-suraj/question_generation). I would like to thank him for the clean codes and also,[Okan Çiftçi](https://github.com/okanvk) for the Turkish dataset 🙏 | c973a47ae6b16933cb6981e993532e3b |
apache-2.0 | ['translation'] | false | opus-mt-pap-de * source languages: pap * target languages: de * OPUS readme: [pap-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pap-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](http... | 09f07fa983144e6e8f5bf9e9ea03ea72 |
mit | [] | false | This model is the **passage** encoder of ANCE-Tele trained on TriviaQA, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE... | 189a3aab123591fc600851497a6057d2 |
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... | 4f354c7593d4962c3d7605bbbd25c787 |
mit | ['huggan', 'gan', 'unconditional-image-generation'] | false | Clone this model git clone https://huggingface.co/huggan/fastgan-few-shot-shells/ 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() return gene... | 778478ecae7fd846d251880905cdc1fb |
mit | ['huggan', 'gan', 'unconditional-image-generation'] | false | Generate a random noise image noise = torch.zeros(1, 256, 1, 1, device=device).normal_(0.0, 1.0) with torch.no_grad(): gan_images, _ = generator(noise) gan_images = _denormalize(gan_images.detach()) save_image(gan_images, "sample.png", nrow=1, normalize=True) ``` | a2b82afe237c420b3cd33f6854e626c2 |
mit | ['huggan', 'gan', 'unconditional-image-generation'] | false | BibTeX entry and citation info ```bibtex @article{FastGAN, title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis}, author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal}, journal={ICLR}, year={2021} } ``` | 50d5ade5fd55e05075a579d645ec5eee |
apache-2.0 | ['generated_from_keras_callback'] | false | opus-mt-ar-en-finetunedTanzil-v7-ar-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1919 - Validation Loss: 0.5047 - Train Rouge1: 49.6877 - Train... | 5bc2c4dc27592d5075db0ed079b7c76f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 0.1959 | 0.5105 | 48.2182 | 23.4978 ... | 1e4f5bc800f7b9e0daf82d8af02d1913 |
apache-2.0 | ['wav2vec2'] | false | LeBenchmark: wav2vec2 large model trained on 1K hours of French speech LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. For more information on the different benchmarks that can be used to evaluate the wav2vec2 models... | ea5d51b6add73c6f4b1d1de962f0ba8e |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - https://huggingface.co/patrickvonplaten/dummy These are LoRA adaption weights for https://huggingface.co/patrickvonplaten/dummy. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.  and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20.0 | d5de2990a4cb44aab2ec60243637a2a5 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-common_voice-es-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - ES dataset. It achieves the following results on the evaluation set: - Loss: 0.1788 - Wer: 1.0239 | 417c7ba2152e45c20a37733ece88a813 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.02 | 100 | 6.6465 | 1.0 | | No log | 0.04 | 200 | 3.0150 | 1.0 | | No log | 0.05 | 300 | 2.8622 | 1.000... | f1fc65dfbe3fe3436a67378861132d09 |
apache-2.0 | ['generated_from_trainer'] | false | M4_MLM_cross This model is a fine-tuned version of [S2312dal/M4_MLM](https://huggingface.co/S2312dal/M4_MLM) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0222 - Pearson: 0.9472 - Spearmanr: 0.8983 | fe30d117835f016d53e1336a639f1adc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 0.0353 | 1.0 | 131 | 0.0590 | 0.8326 | 0.8225 | | 0.0478 | 2.0 | 262 | 0.0368 | 0.9234 | 0.8894 | | 0.0256 ... | 34bc301e49f718d70e7272b6f6b52b86 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_general-roberta-large-v1-5-95 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://... | a3d7e9192b2bec1fb61bbb85875b8c47 |
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: 0.0630 - Precision: 0.9313 - Recall: 0.9483 - F1: 0.9397 - Accuracy: 0.9856 | a607d240487968a0cfc9a1d1a533afb0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.084 | 1.0 | 1756 | 0.0652 | 0.9203 | 0.9387 | 0.9294 | 0.9842 | | 0.0387 | 2.0 |... | ab5e1bbdce054274e2bfb85a8ccedd5d |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_no-pretraining_s512 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (nl)](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... | e110a8c03be8eddcdb0a87d1c9265593 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [Open SLR64](http://openslr.org/64/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices but t... | d7bd1c2e620b08dcc26061898d0fa027 |
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, given that your dataset has Marathi `actual_text` and `path_in_folder` columns: ```python import torch, torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 775477b185c51f2ec715bd82befa4947 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Since marathi is not present on Common Voice, script for reading the below dataset can be picked up from the eval script below mr_test_dataset = all_data['test'] processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marat... | 939d255c6caf107f7647ef78ad7ca590 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Preprocessing the datasets. We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch mr_test_dataset = mr_test_dataset.map(speech_file_to_array_fn) ... | e31bcfd2d0c999527a86d1e219e08c04 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation Evaluated on 10% of the Marathi data on Open SLR-64. ```python import os, re, torch, torchaudio from datasets import Dataset, load_metric import pandas as pd from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | ed3ebf098090e22c596ba9b66344729c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO : include the path of the dataset extracted from http://openslr.org/64/ audio_df = pd.read_csv(os.path.join(dataset_path,'line_index.tsv'),sep='\t',header=None) audio_df.columns = ['path_in_folder','actual_text'] audio_df['path_in_folder'] = audio_df['path_in_folder'].apply(lambda x: dataset_path + x + '.wav') aud... | 358c4fa00427aa0ea93082e451752d70 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | seed number is important for reproducibility of WER score mr_test_dataset = all_data['test'] wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") model.to("cuda") chars_to_ignore_rege... | 949e9d2b5ee58d0ab550fbc54c2618fe |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Preprocessing the datasets. We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["actual_text"] = re.sub(chars_to_ignore_regex, '', batch["actual_text"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"]) batch["speech"] = resampler(speech_array).squeeze... | 87642f420ba1002d9adf64e60470ffc8 |
mit | [] | false | Ralph McQuarrie on Stable Diffusion This is the `<ralph-mcquarrie>` 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. Yo... | bf9760bc5b376dbae45d13b13fbd96a4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7528 - Accuracy: 0.9181 | e137f866704808850d6db30f3f075b09 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.3044 | 0.7623 | | 3.7959 | 2.0 | 636 | 1.8674 | 0.8597 | | 3.7959 | 3.0 | 954 | 1.1377 | 0.... | 2d7a49439d5b547d9b315219384c2d7b |
apache-2.0 | ['generated_from_trainer'] | false | convnext_flyswot This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - F1: 0.9592 | 7d83451297184610efcaf14377159f27 |
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: 666 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP | 3bba0313b04dea2041d38e3aa1b606d1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 52 | 0.6833 | 0.7484 | | No log | 2.0 | 104 | 0.3666 | 0.8750 | | No log | 3.0 | 156 | 0.2090 | 0.9321 | |... | 3554e6f5743401ecc37ec9bf2d8ec5ae |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-sigir-support-refute-no-label-40 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8371 | babb4c7b2fc98eccbebf32660813dfe8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.4511 | 1.0 | 252 | 2.0790 | | 2.0373 | 2.0 | 504 | 1.8538 | | 1.8052 | 3.0 | 756 | 1.6633 | | 1.6663 | 4.0 | 1008 | 1.5591 ... | cb5236e1650d5e452555b90d4746713b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 | a7c44776010b2f481ac0ab7be9d549a8 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-finetuned-dapt_tapt-lm-ai 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: | 1ed77eadf362540c9088b8a1391e3566 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | f83915d26142b453e5ee08bc67efbe4d |
apache-2.0 | ['vision', 'image-classification'] | false | Convolutional Vision Transformer (CvT) CvT-13 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://gith... | 7045f6699ab1c59cb44b5bed40a45932 |
apache-2.0 | ['vision', 'image-classification'] | false | Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, CvtForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Im... | 2909a9cfa92ff739fe3ddcc75a678fc4 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_age_teens-10_sixties-0_s109 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 23901f1673e29fedf88cc9996c7303a8 |
apache-2.0 | ['translation'] | false | gem-gem * source group: Germanic languages * target group: Germanic languages * OPUS readme: [gem-gem](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gem-gem/README.md) * model: transformer * source language(s): afr ang_Latn dan deu eng enm_Latn fao frr fry gos got_Goth gsw isl ksh ltz nds ... | a3c187a16f55f3238ab1a9efc7f2adb2 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-deueng.deu.eng | 24.5 | 0.519 | | newssyscomb2009-engdeu.eng.deu | 18.7 | 0.495 | | news-test2008-deueng.deu.eng | 22.8 | 0.509 | | news-test2008-engdeu.eng.deu | 18.6 | 0.485 | | newstest2009-deue... | 279a4e1306990e3d779b47da8936f07e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: gem-gem - source_languages: gem - target_languages: gem - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gem-gem/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['da', 'sv', 'af', 'nn', 'fy', 'fo', 'de', 'nb', 'nl',... | 7718c11dee0bf18cbafe65bf9213bebe |
apache-2.0 | ['generated_from_keras_callback'] | false | Imene/vit-base-patch16-384-wi4 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1742 - Train Accuracy: 0.9982 - Train Top-3-accuracy: 0.9997 - Validati... | dd952da460fc8b5f3fff132323ecaeb0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1800, 'end_learning_ra... | d60346df56c19482011ea8ab7271db3f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 3.7777 | 0.0845 | 0.1855 ... | baa2715b39feabd0ee720e24009508ef |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | 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", "pt", split="test[:2%]") processor = Wav2Vec2Processor.from_... | 253b1456c5aa6a8c78fc87240f30cea4 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | false | Evaluation The model can be evaluated as follows on the Portuguese 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", "pt", split="test") ... | 1346d95a3f58308c7b7bd9b4b9a3b597 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \tbatch["sentence"] = 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 test_dataset = te... | b40a91e7215db6e462c7291b4d2da890 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | 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 pred_ids = torch.argmax(lo... | d06bb1ca141a46e32f1624ad714f29c9 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | aperturescience Dreambooth model trained by Wusul 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... | 51f86d945331edbb8c1b5d14688d577a |
mit | ['generated_from_trainer'] | false | TExAS-SQuAD-es This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-es dataset. It achieves the following results on the evaluation set: - Exact match: xx.xx% - F1-score: xx.xx% | f77e089246475fd1cf92e99cc9985f17 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.0645 | 0.24 | 1000 | 1.7915 | | 1.8458 | 0.47 | 2000 | 1.7873 | | 1.8208 | 0.71 | 3000 | 1.6628 | | 1.7743 | 0.95 | 4000 | 1.5684 ... | 33f254f64c920fe8f37ef299ecca142c |
mit | [] | false | On Kawara on Stable Diffusion This is the `<on-kawara>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also t... | 5ea5d9e8ba82c22e768135a09084dbc2 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_transport-roberta-large-v1-2-3 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with con... | 9e441e77baa83ae65a5a083c95235743 |
other | ['generated_from_trainer'] | false | segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 2.9348 - Mean Iou: 0.0598 - Mean Accuracy: 0.1188 - Overall Accuracy: 0.3515 | b4b1762c1a358c02c81fb08d78262151 |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-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: 50 | 16c7f2acadaf5684d0494727d9575a2a |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | 8487b765ae7e51f267a683086df376ec |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_wavlm_s677 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sample... | 3946d0c8988055f8b581ccb20c9290e9 |
apache-2.0 | [] | false | Whisper Medium TR This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.211673 - Wer: 18.51 | 2a4316f52a664c087363ec9cf36bdb5a |
apache-2.0 | [] | false | Training and evaluation data Data used for training is the initial 10% of train and validation of [Turkish Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/tr/train) 11.0 from Mozilla Foundation. Weight decay showed to have slightly better result also on the evaluation datase... | afcfff7652fe8be8fd12f98428a028a2 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precisi... | c8c94995ce5da83e3fcf3bfbaf6cd35e |
cc-by-sa-4.0 | ['deberta', 'deberta-v2', 'fill-mask'] | false | How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese') model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese') sentence = ... | a965b542caf6a25b79047753bf029952 |
cc-by-sa-4.0 | ['deberta', 'deberta-v2', 'fill-mask'] | false | Training procedure We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp). Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https:... | 95ed6b8db98f9c94e308675483620bcb |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.9068 - Wer: 0.7900 | e246e8d88daa8405433a3ce8f9985eca |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 64613cb5435f2df00b6e85e49465999e |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.6926 | 19.05 | 400 | 2.7538 | 1.0 | | 0.7031 | 38.1 | 800 | 0.9068 | 0.7900 | | 929441e512cb55e814c6423a11121a0f |
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: 0.1637 - F1: 0.8621 | 12fbcc839288c7e716770ac285b3ec71 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 715 | 0.2046 | 0.8109 | | 0.2163 | 2.0 | 1430 | 0.1678 | 0.8467 | | 0.2163 | 3.0 | 2145 | 0.1637 | 0.8621 | ... | 5a6a7a19d5841bdacbef8a35eb3d7343 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'dreambooth-hackathon', 'landscape'] | false | Dreambooth Model for Landscapes trained on images from Genshin Impact. This is a Stable Diffusion model fine-tuned on the landscape concept with DreamBooth. It can be used by modifying the `instance_prompt`: **ggenshin landscape** This model was created as part of the DreamBooth Hackathon 🔥. | 65453d0504ba6b66f733f03198a4a274 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'dreambooth-hackathon', 'landscape'] | false | Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('Apocalypse-19/Genshin-Landscape-Diffusion') image = pipeline().images[0] image ``` | dc88f8362409034796df3c6b0eea3ef8 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'dreambooth-hackathon', 'landscape'] | false | Examples Some examples of images generated by the model are shown below, with their prompts.  A picture of the woods, ggenshin landscape, eer... | 4cd457aeda4a68932ad4b72ff804eba4 |
mit | ['legal'] | false | Training Data For building the pre-training corpus of Indian legal text, we collected a large corpus of case documents from the Indian Supreme Court and many High Courts of India. The court cases in our dataset range from 1950 to 2019, and belong to all legal domains, such as Civil, Criminal, Constitutional, and so on... | faaf2c490212c4fad8faf1d3c6fb91a8 |
mit | ['legal'] | false | Training Setup This model is initialized with the [Legal-BERT model](https://huggingface.co/zlucia/legalbert) from the paper [When does pretraining help?: assessing self-supervised learning for law and the CaseHOLD dataset of 53,000+ legal holdings](https://dl.acm.org/doi/abs/10.1145/3462757.3466088). In our work, we ... | df27f72e7cc94b2ebc87ddcfcf261b7b |
mit | ['legal'] | false | Model Overview This model uses the same tokenizer as [CaseLawBERT](https://huggingface.co/zlucia/legalbert). This model has the same configuration as the [bert-base-uncased model](https://huggingface.co/bert-base-uncased): 12 hidden layers, 768 hidden dimensionality, 12 attention heads, ~110M parameters. | d5a577c4e940e2f551a5fc4b5239c465 |
mit | ['legal'] | false | Usage Using the model to get embeddings/representations for a piece of text ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("law-ai/InCaseLawBERT") text = "Replace this string with yours" encoded_input = tokenizer(text, return_tensors="pt") model = AutoModel.from_p... | c0ed5905d50217849ac4b273f11c8054 |
mit | ['legal'] | false | Fine-tuning Results We have fine-tuned all pre-trained models on 3 legal tasks with Indian datasets: * Legal Statute Identification ([ILSI Dataset](https://arxiv.org/abs/2112.14731))[Multi-label Text Classification]: Identifying relevant statutes (law articles) based on the facts of a court case * Semantic Segmentatio... | 6dd2b3d2c2955edfce9fede00fd834a3 |
mit | ['legal'] | false | Citation ``` @article{paul-2022-pretraining, doi = {10.48550/ARXIV.2209.06049}, url = {https://arxiv.org/abs/2209.06049}, author = {Paul, Shounak and Mandal, Arpan and Goyal, Pawan and Ghosh, Saptarshi}, title = {Pre-training Transformers on Indian Legal Text}, publisher = {arXiv}, year = {2022}, copyri... | 0a7d7378d85baeb4adb333d699932167 |
mit | ['legal'] | false | About Us We are a group of researchers from the Department of Computer Science and Technology, Indian Insitute of Technology, Kharagpur. Our research interests are primarily ML and NLP applications for the legal domain, with a special focus on the challenges and oppurtunites for the Indian legal scenario. We have, a... | 019bbe3d6b48161534d56b9b55b33fd4 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | arisha_scetch Dreambooth model trained by igorshmel 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-stab... | a673d00865e6ad777e37c9a1ba58051b |
mit | ['generated_from_keras_callback'] | false | topic_classification_03 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0459 - Train Sparse Categorical Accuracy: 0.6535 - Valid... | 36a268d25c4046b215f25ac956137f68 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 1.2710 | 0.5838 | 1.1683 ... | b4da06d91ee3a952ca8b7757e33efd79 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-bert-sst2-1_mobilebert-only-distillation This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 2.2808 - Accuracy: 0.8291 | 740fd3acd40696100f524a68893a39fe |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | cff470b9306362514f4fed96e032d1c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4252 | 1.0 | 4210 | 2.6253 | 0.8142 | | 0.519 | 2.0 | 8420 | 2.4860 | 0.8245 | | 0.4986 | 3.0 | 12630 | 2.2808 ... | 70a0f4c7a8cb92b1d5e5596c4fb62e8d |
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