license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure... | 67a92c7e4fe390430433b4e54c3dfbe8 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Training data The s2t-small-covost2-en-et-st is trained on English-Estonian subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset | 0aac1c66bf7ab3d8f916f81e8830f605 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-amazon-shoe-reviews 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: 1.3445 - Accuracy: 0.48 - F1: [0. 0. 0. 0. 0.64864865] - ... | e87f4cba5c90e70c0a3960d785e8dc34 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:-----------... | 3a0230aba3d888e136bfbfef212d8e2e |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-roundup-3-2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2234 - Rouge1: 50.9324 - Rouge2: 30.5257 - Rougel: 32.2166 - Rougelsum: 47.9849... | 38e5f43786178fe218dc9848c1f10a5e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 258 | 1.2775 | 50.0638 | 30.3036 | 32.9555 | 47.3277 | ... | 799cedc266f660cf343c8584fe6ace84 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft750_reg4 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: 0.7393 - Mse: 0.7393 - Mae: 0.6578 - R2: 0.3041 - Accuracy: 0.4733 | 346d3e95d171a250e229540276707bf0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 1.6703 | 1.0 | 188 | 0.7393 | 0.7393 | 0.6578 | 0.3041 | 0.4733 | | 91ec14d260ab4146c3cd55fe2ae70f31 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-es_s803 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 5cf31f15926beb4c168a90f51b260702 |
apache-2.0 | ['text-classification'] | false | How to use Colab: [link](https://colab.research.google.com/drive/1veKO9hke7myxKigZtZho_F-UM2fD9kp8) ```python from transformers import pipeline model_name = "IlyaGusev/rubertconv_toxic_clf" pipe = pipeline("text-classification", model=model_name, tokenizer=model_name, framework="pt") text = "Ты придурок из интерн... | dc1e25bbaeae601a3f1acb3fc303cc65 |
apache-2.0 | ['text-classification'] | false | Training data Datasets: - [2ch]( https://www.kaggle.com/blackmoon/russian-language-toxic-comments) - [Odnoklassniki](https://www.kaggle.com/alexandersemiletov/toxic-russian-comments) - [Toloka Persona Chat Rus](https://toloka.ai/ru/datasets) - [Koziev's Conversations](https://github.com/Koziev/NLP_Datasets/blob/maste... | 8121262809200f670d641356b0959bdc |
mit | ['generated_from_trainer'] | false | tluo_xml_roberta_base_amazon_review_sentiment This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9552 - Accuracy: 0.6003 | 58cb0a7b393b017393f9219d57dfd7e0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5664 | 0.33 | 5000 | 1.3816 | 0.5688 | | 0.9494 | 0.67 | 10000 | 0.9702 | 0.5852 | | 0.9613 | 1.0 | 15000 | 0.9545 ... | 641a7f7643a0a7857adc26586e01fb6b |
apache-2.0 | ['translation'] | false | zho-swe * source group: Chinese * target group: Swedish * OPUS readme: [zho-swe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-swe/README.md) * model: transformer-align * source language(s): cmn cmn_Bopo cmn_Hani cmn_Latn * target language(s): swe * model: transformer-align * pre-proce... | a952a03b154984547ea506b265b1fa82 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zho-swe - source_languages: zho - target_languages: swe - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-swe/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['zh', 'sv'] - src_constituents: {'cmn_Hans', 'nan', ... | 77e679726eae6eaa3e818c4298165d17 |
mit | ['bengali-ner', 'bengali', 'bangla', 'NER'] | false | Multi-lingual BERT Bengali Name Entity Recognition `mBERT-Bengali-NER` is a transformer-based Bengali NER model build with [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) model and [Wikiann](https://huggingface.co/datasets/wikiann) Datasets. | 28df377e07ed7d9bf0a81491fe498a43 |
mit | ['bengali-ner', 'bengali', 'bangla', 'NER'] | false | How to Use ```py from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("sagorsarker/mbert-bengali-ner") model = AutoModelForTokenClassification.from_pretrained("sagorsarker/mbert-bengali-ner") nlp = pipeline("ner", model=m... | f8214385078741b28a1a7ae01c4a7489 |
mit | ['bengali-ner', 'bengali', 'bangla', 'NER'] | false | Training Details - mBERT-Bengali-NER trained with [Wikiann](https://huggingface.co/datasets/wikiann) datasets - mBERT-Bengali-NER trained with [transformers-token-classification](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb) script - mBERT-Bengali-NER t... | 6347ed3d046f534216b059a24e2895c6 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2886 - Bleu: 28.1507 - Gen Len: 34.1136 | 1bb5169a824d8d5b8a3af7067813ed38 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.7437 | 1.0 | 38145 | 1.2886 | 28.1507 | 34.1136 | | 57c0064fa4549096dcdb2f2c4dc39727 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Romanian - Kevin Jiang This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ro dataset. It achieves the following results on the evaluation set: - Loss: 0.3094 - Wer: 17.7659 | f056cd23dd1b79d1823385aca524af91 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.006 | 8.01 | 1000 | 0.2752 | 17.9924 | | 0.0007 | 17.01 | 2000 | 0.3094 | 17.7659 | | 0.0004 | 25.02 | 3000 | 0.3281 | 17.907... | d9987e92c2955237e8bebd6306a9b1f5 |
apache-2.0 | ['generated_from_trainer'] | false | dataset_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4921 - eval_accuracy: 0.8647 - eval_runtime: 12.5977 - eval_samples_per_... | 90874f97d425cd71b93bdfd17f6d7bf9 |
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: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 5fc0ef23b842b8aae54f843b2db5fe99 |
mit | [] | false | ki on Stable Diffusion This is the `<ki-mars>` (Ki from the Disney Mars Needs Mom) 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.ipyn... | d70c3fb77656edd78f7ddf72cb709943 |
apache-2.0 | ['image-classification', 'vision'] | false | Data2Vec-Vision (large-sized model, fine-tuned on ImageNet-1k) BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and L... | 138e5cf3c131008f44886e458eb0a668 |
apache-2.0 | ['image-classification', 'vision'] | false | Abstract *While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning... | 794280670b5a6cc3df4180702c6e38e3 |
apache-2.0 | ['image-classification', 'vision'] | false | Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=data2vec-vision) to look for fine-tuned versions on a task that interests you. | 0eab237440cf9a1340fb9f6c6f54dfef |
apache-2.0 | ['image-classification', 'vision'] | false | How to use 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 BeitFeatureExtractor, Data2VecVisionForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.... | 64f1e66479b480a4cfc23dcd724fed1e |
apache-2.0 | ['image-classification', 'vision'] | false | Pretraining For all pre-training related hyperparameters, we refer to the [original paper](https://arxiv.org/abs/2106.08254) and the [original codebase](https://github.com/facebookresearch/data2vec_vision/tree/main/beit) | 43ea99e573d755017fdbe0310b32d359 |
apache-2.0 | ['image-classification', 'vision'] | false | Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 1 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance. We evaluated the model on `Image... | a34b5fbd29194a7b2c974b9ba369c5a6 |
apache-2.0 | ['image-classification', 'vision'] | false | BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2202.03555, doi = {10.48550/ARXIV.2202.03555}, url = {https://arxiv.org/abs/2202.03555}, author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael}, keywords = {Machine Learning (cs... | 1cf662e88b23f4484e93268554c4f9d1 |
cc-by-sa-4.0 | [] | false | Training data [Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset as of June 20, 2021 which is released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) is used for training. The dataset is splitted into three subsets - train, valid an... | f83c4770f091024be28a893b3436241e |
cc-by-sa-4.0 | [] | false | Model description The model architecture is the same as BERT base model (hidden_size: 768, num_hidden_layers: 12, num_attention_heads: 12, max_position_embeddings: 512) except for a vocabulary size. The vocabulary size is set to 32,000 instead of an original size of 30,522. For the model, `transformers.BertForPreTra... | d558a0a63f575bc1ada7c287a73c3495 |
cc-by-sa-4.0 | [] | false | Tokenizer description [SentencePiece](https://github.com/google/sentencepiece) tokenizer is used as a tokenizer for this model. While training, the tokenizer model was trained with 1,000,000 samples which were extracted from the train split. The vocabulary size is set to 32,000. A `add_dummy_prefix` option is set to... | 1296385fde2371e1e427889eb6bb954b |
cc-by-sa-4.0 | [] | false | Training Training details are as follows. * gradient update is every 256 samples (batch size: 8, accumulate_grad_batches: 32) * gradient clip norm is 1.0 * Learning rate starts from 0 and linearly increased to 0.0001 in the first 10,000 steps * The training set contains around 20M samples. Because 80k * 256 ~ 20M, 1... | 2ded1a839af5a699c8cb9fa4ff7b67ba |
cc-by-sa-4.0 | [] | false | Usage First, install dependecies. ```sh $ pip install torch==1.8.0 transformers==4.8.2 sentencepiece==0.1.95 ``` Then use `transformers.pipeline` to try mask fill task. ```sh >>> import transformers >>> pipeline = transformers.pipeline("fill-mask", "colorfulscoop/bert-base-ja", revision="v1.0") >>> pipeline("専門として... | d63c6e1dd293ab5a12b591c75c499e44 |
cc-by-sa-4.0 | [] | false | License Copyright (c) 2021 Colorful Scoop All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). **Disclaimer:** The model potentially has possibility that it generates similar texts in the training data, texts no... | d1cfa6569e82ce2721edfee68eb365d1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 43fdb4ef5440995ed3cc683ae53fcde1 |
apache-2.0 | ['generated_from_keras_callback'] | false | kasrahabib/all-MiniLM-L6-v2-trained-on-types-of-nf-req This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4135 - Validation Loss: 0... | c59326c33a354892912d4fd4c1ee3b67 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1430, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 99a12bbf6a30eb28a27b0f917824c19e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:-----:| | 0.4139 | 0.3683 | 0.9135 | 0.9135 | 0.9135 | 0 | | 0.4217 | 0.3686 | 0.9126 ... | f3140857eae55d2e20256f8dd606b5b5 |
apache-2.0 | [] | false | Pretrained model for our paper (https://arxiv.org/abs/2210.07468) ```bibtex @inproceedings{wu-etal-2022-continued, title = "Transparency Helps Reveal When Language Models Learn Meaning", author = "Zhaofeng Wu and William Merrill and Hao Peng and Iz Beltagy and Noah A. Smith", url = {https://arxiv.org/abs/... | 06f0d8b833910cb6f204efcfa19b0037 |
apache-2.0 | ['automatic-speech-recognition'] | false | whisper-small-tonga_5hrs This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9145 - Wer: 52.2928 | 2edbf0594ece8d564acbff7b88598021 |
apache-2.0 | ['automatic-speech-recognition'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | 930bf8afd46eb3c6bb8b4550953ebafa |
apache-2.0 | ['automatic-speech-recognition'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.3353 | 1.45 | 200 | 1.9984 | 113.0627 | | 1.7712 | 2.9 | 400 | 1.2576 | 72.0656 | | 1.1476 | 4.35 | 600 | 1.0129 | 59... | 83964707e7f055cde84e65cd68f154da |
mit | ['summarization', 't5-large-summarization', 'pipeline:summarization'] | false | Finetuning Corpus `t5-large-finetuned-xsum` model is based on `t5-large model` by [huggingface](https://huggingface.co/t5-large), finetuned using [XSUM](https://huggingface.co/datasets/xsum) datasets. | 5032576775857d94f0a10109df59e7af |
mit | ['summarization', 't5-large-summarization', 'pipeline:summarization'] | false | Load Finetuned Model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer tokenizer = AutoTokenizer.from_pretrained("sysresearch101/t5-large-finetuned-xsum") model = model = AutoModelForSeq2SeqLM.from_pretrained("sysresearch101/t5-l... | d1197f7a66e8c0e53c289325de4e18c9 |
mit | ['summarization', 't5-large-summarization', 'pipeline:summarization'] | false | generate summary input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt') summary_ids = model.generate(input_ids, min_length=20, max_length=80, num_beams=10, repetition_penalty=2.5, length_penalty=1.0, early_stopping=True, ... | a583eaed2f787859c71a51e65caa43f6 |
mit | ['summarization', 't5-large-summarization', 'pipeline:summarization'] | false | How to use via a pipeline Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html): ```python from transformers import pipeline summarizer = pipeline("summarization", model="sysresearch101/t5-large-finetuned-xsum") ARTICLE = """ New York (CNN)When Lian... | ce29e652965afecd17b4a1fff28ad339 |
apache-2.0 | ['generated_from_trainer'] | false | byt5-base-top_v2 This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) on the top_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0154 - Exact Match: 0.8533 | 7c47a617b5fa355a5be35c76703a34f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 0.3866 | 0.82 | 200 | 0.0434 | 0.0278 | | 0.0335 | 1.65 | 400 | 0.0224 | 0.0396 | | 0.0206 | 2.47 | 600 | 0.0184 ... | 56b08341d12af9649093a826c76f3f4a |
creativeml-openrail-m | ['text-to-image'] | false | It can be used by modifying the `instance_prompt`: **merylstryfetrigun** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `d... | e6826a26b1e82269b24ad49969a5b973 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0469 - Accuracy: 0.9856 | 0b958d80f08a45dbf8a18871dd53b7b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1491 | 1.0 | 190 | 0.0890 | 0.9715 | | 0.1021 | 2.0 | 380 | 0.0578 | 0.9811 | | 0.0694 | 3.0 | 570 | 0.0469 | 0.... | 366cd5cd5d4de8811e9ed83f71902dbf |
apache-2.0 | ['generated_from_trainer'] | false | prova_Classi 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: 1.5530 - Accuracy: 0.716 | 8444775b1cd29424c370451d5f98b9ae |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00013441028267541125 - train_batch_size: 32 - eval_batch_size: 16 - seed: 17 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | d176aff89bd89c5510a0d86dafbe74db |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7022 | 1.0 | 1426 | 0.6581 | 0.7105 | | 0.5199 | 2.0 | 2852 | 0.6835 | 0.706 | | 0.2923 | 3.0 | 4278 | 0.7941 | 0.... | 4fbe2269fbd4b00db127df856f0f11af |
mit | ['generated_from_trainer'] | false | Facebook_Mit_HPS This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3681 - Accuracy: 0.9281 | 038dc74639561aa8e70b485e7e4d8588 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.906763521176542e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 30 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 5b2e6c5768abc02ec1d4ec5944bc2845 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 292 | 0.2394 | 0.9238 | | 0.2248 | 2.0 | 584 | 0.3112 | 0.9178 | | 0.2248 | 3.0 | 876 | 0.3681 | 0.... | 5d9bb2437326161302bce23979dcb17f |
apache-2.0 | ['translation'] | false | spa-ara * source group: Spanish * target group: Arabic * OPUS readme: [spa-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-ara/README.md) * model: transformer * source language(s): spa * target language(s): apc apc_Latn ara arq * model: transformer * pre-processing: normalization + ... | 8671999a13615fea6438450d9a8b8360 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-ara - source_languages: spa - target_languages: ara - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-ara/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'ar'] - src_constituents: {'spa'} - tgt_const... | 65ee242d0fe21da7e8e0b3f48c543369 |
apache-2.0 | ['generated_from_trainer'] | false | Vin1-P3 This model is a fine-tuned version of [HuyenNguyen/FPT-P3-23000](https://huggingface.co/HuyenNguyen/FPT-P3-23000) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3024 - Wer: 15.8504 | 9d6de6c02daee7cb258d41cdc3362bc5 |
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: 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: 900 - mixed_precisio... | afbac6df61ed93ab396933c5dc672097 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4313 | 0.77 | 300 | 0.3838 | 20.6388 | | 0.2984 | 1.54 | 600 | 0.3246 | 17.2285 | | 0.1865 | 2.31 | 900 | 0.3024 | 15.850... | b694002126f09021639c47849b274771 |
apache-2.0 | [] | false | Introduction A led-base-16384 model to summarize ArXiv papers. Inputs are the abstracts of papers and full documents, and outputs are the summaries of the papers. [Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer | 25732a51be92755801ff714dc3ef70a0 |
apache-2.0 | [] | false | longformer). As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from [*bart-base*](https://huggingface.co/facebook/bart-base) since both models share the exact same architecture. To be able to... | 9e088915cd98499624df3efccdf3c86d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9411 - Accuracy: 0.7821 | 47b76a44fcc14a1bfc1500e3b80107ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3582 | 1.0 | 264 | 1.0124 | 0.7546 | | 0.8162 | 2.0 | 528 | 1.0190 | 0.7844 | | 0.5596 | 3.0 | 792 | 0.9411 | 0.... | 6579a618d97232f4c0fe5b419f1f3037 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | hubert-base-common-language This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the common_language dataset. It achieves the following results on the evaluation set: - Loss: 1.3477 - Accuracy: 0.7317 | 494460b1237cf7eb06feef8ce7ded425 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 4 - seed: 0 - distributed_type: IPU - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) an... | ebe48fbc513d0ceb7f8a73666d31ee97 |
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: 0.1373 - Accuracy: 0.9385 - F1: 0.9387 | 4b32602cbcde1e72a425d8deb42857f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 44a142da913ccaa8ed8b9423a1d204c1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5331 | 1.0 | 250 | 0.1900 | 0.93 | 0.9290 | | 0.1412 | 2.0 | 500 | 0.1373 | 0.9385 | 0.9387 | | 43454e5a5609f863c8f016ed32f00f7b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-sentiment-reddit-crypto This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the crypto-related reddit comments dataset. It achieves the following results on the evaluation set: - Loss: 0.3070 - Accuracy: 0.8915 Accuracy on the fin... | d19888af450ee131ba56a8f195d385f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training and evaluation data Training and validation data collected from 2 sources: 1. [Kaggle reddit cryptocurrency posts and comments](https://www.kaggle.com/datasets/gpreda/reddit-cryptocurrency) 2. [Kaggle reddit cryptocurrency related posts from various subreddits](https://www.kaggle.com/datasets/leukipp/reddit... | 4c0f6ae147c3588ba80bfbd8ec050ce2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2823 | 1.0 | 5109 | 0.2658 | 0.8840 | | 0.1905 | 2.0 | 10218 | 0.3070 | 0.8915 | | b0d2fd1dfb8b9539373474e801a03f2d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Training config See full config in [`config.yaml`](./config.yaml) ```yaml config: null print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_raw_bpe ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_ran... | 0bd57b4121a42f8863f0f22ff94a0c1b |
cc0-1.0 | ['audio', 'automatic-speech-recognition', 'voxrex'] | false | Wav2vec 2.0 large VoxRex (C) **Disclaimer:** This is a work in progress.<br> **Update 2022-01-08:** Updated to VoxRex-C version, use git to get the older (B) version.<br> **Update 2022-05-16:** Paper is is [here](https://arxiv.org/abs/2205.03026). This model has been pretrained for 400,000 updates on the P4-10k corp... | 5c764a437380976bec6db6efc9ae5079 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-eng 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: 0.4391 - Wer: 0.3836 | 0b57b15a1d1bef498f3805079a313a6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.8306 | 1.0 | 500 | 2.9588 | 1.0 | | 2.1928 | 2.01 | 1000 | 1.2215 | 0.9355 | | 1.1547 | 3.01 | 1500 | 0.9228 | 0.713... | dc5ca4f92b0db2e13691baaf331646bc |
mit | ['generated_from_trainer'] | false | xtremedistil-l6-h384-uncased-changed This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.5931 | 07561d2847242a4da7806ed87f599e8c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6372 | 1.0 | 8235 | 1.6719 | | 1.4847 | 2.0 | 16470 | 1.5866 | | 1.4038 | 3.0 | 24705 | 1.5931 | | 6b13b9b5a316a0c5b1850e7bbe55a57e |
apache-2.0 | ['translation'] | false | por-cat * source group: Portuguese * target group: Catalan * OPUS readme: [por-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/por-cat/README.md) * model: transformer-align * source language(s): por * target language(s): cat * model: transformer-align * pre-processing: normalization + S... | 44939f1be04c51f7b18e78ebdf1f24f6 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: por-cat - source_languages: por - target_languages: cat - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/por-cat/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['pt', 'ca'] - src_constituents: {'por'} - tgt_const... | 45866b5618469a315881a820032c92e8 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_unispeech-ml_s325 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | c76e5959d1daf7c83593fb8efaad2350 |
apache-2.0 | ['multiberts', 'multiberts-seed_6'] | false | MultiBERTs - Seed 6 MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variation... | 4bb209340c2fe27bf39ad66a45b4512c |
apache-2.0 | ['multiberts', 'multiberts-seed_6'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_6') model = TFBertModel.from_pretrained("google/multiberts-seed_6... | ca3a5d173801b4740e6b38dc187be7fa |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | all-MiniLM-L6-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. | a2c741d4f15f223a07371403ea5a519c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sente... | 0624a89a3a70fcf4a03c8e0c1ca03e67 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v1) ------ | 44d7d7f35d12a15c1f2edd4b359b4829 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Multi-scale Residual Network for Image Super-Resolution (MSRN) MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Multi-scale Residual Network for Image Super-Resolution](https://openaccess.the... | 951f362ca4b78ea9d7e870ee449216a6 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Model description The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image". | 439c9d131eabd3018c9d00c966e5699b |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import MsrnModel, ImageLoader from PIL import Image import requests url = 'https://pa... | 53cb110199d76bd4d76fc810d6f4e561 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, MsrnModel, MsrnConfig training_args = TrainingArguments( output_dir='./results', | e0b941898483b00af743c157173ae022 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al... | 0e54bb57f45a16a70db37e80b91373ed |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | BibTeX entry and citation info ```bibtex @InProceedings{Agustsson_2017_CVPR_Workshops, author = {Agustsson, Eirikur and Timofte, Radu}, title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Work... | c46ee577e0b9249bc78cbf01fc31498e |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-sst2-target-glue-qqp This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3294 - Accuracy: 0.8517 - F1: 0.8131 | 58983c4252139907d7fbf3036bdc2740 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4741 | 0.04 | 500 | 0.4233 | 0.7913 | 0.7483 | | 0.4192 | 0.09 | 1000 | 0.3860 | 0.8136 | 0.7685 | | 0.401 |... | ff7ffcb0be881cc9bfd19467fd2c12a7 |
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