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 | t5-small-finetuned-en-to-es This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8937 - Bleu: 7.4133 - Gen Len: 15.9653 | 4b8f9a9b1dad2ceae39a745c38679577 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 2.27 | 1.0 | 7061 | 1.8937 | 7.4133 | 15.9653 | | 99d551ff6bba6dc1049a99ed33e16c49 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-emotion-KD-BERT 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 emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.4810 - Accuracy: 0.9175 | 4a875b9c73d54c1733d6fd53f0ecd7f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.8247 | 1.0 | 1000 | 2.5170 | 0.7745 | | 1.9864 | 2.0 | 2000 | 1.3436 | 0.874 | | 1.1126 | 3.0 | 3000 | 0.8299 ... | 8d43e6b0dac66d7d1f94a79c79528cf3 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/12-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6856 - Train End Logits Ac... | 9e23a53cfebb516a0e2065173d808677 |
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': 632, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | cb8e32fe76b9344fc18df2f4c27e40cc |
apache-2.0 | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 69a6d6d1724e1b976c149d7d8e13be52 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_wavlm_s115 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) 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 that your speech input is sampled at 1... | 796f8c0086361ccefc49435f4ec1d56e |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased_conll2003-sm-all-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: 0.0489 - Precision: 0.9487 - Recall: 0.9564 - F1: 0.9526 - Accuracy: 0.9916 | 46c3248e7a149c38f4c58e26363a15d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 | daa446c23f0ad4852860cf1daca4e890 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.052 | 1.0 | 3511 | 0.0510 | 0.9374 | 0.9456 | 0.9415 | 0.9898 | | 0.0213 | 2.0 ... | 82fea4fc071318eda3b5a66bece8035c |
mit | [] | false | JRPG Monster art style via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 9703be2eca6b33da475a92b8d1569abb |
mit | [] | false | Model by wooshim This your the Stable Diffusion model fine-tuned the dtv_pkmn_monster_style concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **image** Please use **"feralplmr"** in your prompt to trigger the style. You can also train your own concepts and uplo... | de918e644097e102ead50e62ec585615 |
apache-2.0 | ['sagemaker', 'vit', 'ImageClassification', 'generated_from_trainer'] | false | **A finetuned model for Image classification in Spanish** This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container, The base model is **Vision Transformer (base-sized model)** which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised... | bcfdf7e4fb5f2ca80cb1f0608abd73ab |
apache-2.0 | ['sagemaker', 'vit', 'ImageClassification', 'generated_from_trainer'] | false | BibTeX entry and citation info ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez an... | 677ebab9f927efd8ed1a988d137c7975 |
apache-2.0 | ['sagemaker', 'vit', 'ImageClassification', 'generated_from_trainer'] | false | Dataset [Link to dataset description](http://www.cs.toronto.edu/~kriz/cifar.html) The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, wi... | f0530ad3fcd04c27f5dc5e11b63ad5cd |
apache-2.0 | ['sagemaker', 'vit', 'ImageClassification', 'generated_from_trainer'] | false | Usage for Image Classification ```python from transformers import ViTFeatureExtractor, ViTModel 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 = ViTFeatureExtractor.from_pretrained('google/... | 0ff475ee070951e97b2b80056fde692d |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | This model was trained by ftshijt using thchs30/tts1 recipe in <a href="https://github.com/espnet/espnet/">espnet</a>. <p> </p> <ul> <li><strong>Python API</strong><pre><code class="language-python">See https://github.com/espnet/espnet_model_zoo</code></pre></li> <li><strong>Evaluate in the recipe</strong><pre> ... | bd950c03dc73ef9055697285c9f89709 |
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 fc62b1ce3e50c5ef8a2ac8cedb0d92ac41df54ca pip install -e . cd egs2/americasnlp22/asr1 ./run.sh \ --skip_data_prep false \ ... | 1ba06a6734169c80318ff03fadb9cad9 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Jun 5 12:17:58 CEST 2022` - python version: `3.9.13 (main, May 18 2022, 00:00:00) [GCC 11.3.1 20220421 (Red Hat 11.3.1-2)]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.11.0+cu115` - Git hash: `d55704daa36d3dd2ca24ae3162ac40d81957208c` - Commit date: `Wed Jun 1 02:33:09... | acc4f1942f3a619c6dabbaa947d34e9f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_transformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_raw_gn_bpe100_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// di... | 4c2d5530488d10bea246b4b9be736565 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4884 - Precision: 0.2764 - Recall: 0.1080 - F1: 0.1553 - Accura... | 211ee92a07ea41ec7ba8667f9deeb3b5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 26 | 0.6238 | 0.2 | 0.0089 | 0.0170 | 0.7822 | | No log | 2.0 |... | 4bb00ad9d9036c999c4770f607884e0f |
mit | [] | false | Swedish BERT models for sentiment analysis, Sentiment targets. [Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for target/role assignment in Swedish. The two models are based on the [KB/bert-base-swedish-cased](https://huggingface.co/KB/b... | 5d92def9ec50d8697361a5094d5e9437 |
mit | [] | false | Fear targets The model can be imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear-Targets") classifier_fear_targets= BertForTokenClassification... | 35c84d5e79a2dc3a9b0fa4cf14cb7983 |
mit | [] | false | Verification metrics During training the Fear target model had the following verification metrics when using "any overlap" as the evaluation metric. | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.8361 | 0.7903 | 0.8876 | | d55cdb87e35828819a1a0263a7a3c877 |
mit | [] | false | Swedish-Sentiment-Violence The model be can imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence-Targets") classifier_violence_targets = Bert... | 5e02c02d3daf74be40089689869672b3 |
mit | [] | false | Verification metrics During training the Violence target model had the following verification metrics when using "any overlap" as the evaluation metric. | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.7831| 0.9155| 0.8442 | | 2cf70de8ffd6340b07a53f5995ac3fcb |
afl-3.0 | [] | false | Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | rst-all-11b ... | 1163839bf1cae045478052405a291b63 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 2.1382 - Rouge1: 42.1723 - Rouge2: 1... | dee2a6655e1b9b8363db18e0ec593c70 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.125 | 1.0 | 67679 | 2.1382 | 42.1723 | 15.7664 | 24.5336 | 37.7532 ... | 9682d9206609c48e72d492fa5d0b05eb |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | Wav2Vec2-Base-100h [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Pape... | 5597654cc2f327afdd637fe156ae480f |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import soundfile as sf import torch | 4dfd42e72a5d34963da5f30fbe2c4d7c |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-base-100h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import soundfile as sf import torch from jiwer import wer librispeech_eval = l... | bd2f498faf143c59d2790d7b2247d8fd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 16 - 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_sch... | f5c94dec50cb0d045776d7ac19839135 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, ... | f65e2525aecc4bb19522da22f11d0b6b |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3566 - Accuracy: 0.3855 | 33df2153d94d049c1ed084272aa24d80 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | kim_jung_gi_art_style Dreambooth model trained by apurik-parv with [Shivam shri rao's DreamBooth](https://github.com/ShivamShrirao/diffusers/tree/main/examples/dreambooth/train_dreambooth.py) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-... | 27c6a0d9f852a5679fee5a21887f3ff2 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sur... | 13b61a2d30081c7f97799966e77682ab |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0713 | d75a4c31836a2fbc0c0a0e8eca0482de |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.327 | 1.09 | 500 | 4.6505 | | 4.486 | 2.18 | 1000 | 4.0830 | | 4.0801 | 3.27 | 1500 | 3.9647 | | 3.841 | 4.36 | 2000 | 3.6616 ... | 45c489b8e01914283ede79548fdd9d6f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best` ♻️ Imported from https://zenodo.org/record/5655832 This model was trained by siddhana using fsc_unseen/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | e78bdd9e030cd6d74649665749342b24 |
mit | [] | false | leif jones on Stable Diffusion This is the `<leif-jones>` 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... | 770776866a0239b04098afe39cb3b9a1 |
apache-2.0 | [] | false | ViT model HPU configuration This model only contains the `GaudiConfig` file for running the [ViT](https://huggingface.co/google/vit-base-patch16-224-in21k) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_habana_mixed_precision`:... | 2e5e8c3e72741d2a9dd5edb0b3f29719 |
apache-2.0 | [] | false | Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification exampl... | 125eeb4a68b34418eb7d4497430bde21 |
apache-2.0 | ['vision', 'image-classification'] | false | Vision Transformer (base-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transform... | d448752b1b553f9612779fca1c1fa660 |
apache-2.0 | ['vision', 'image-classification'] | false | Usage instructions Create a `VNCoreMLRequest` that loads the ViT model: ```swift import CoreML import Vision lazy var classificationRequest: VNCoreMLRequest = { do { let config = MLModelConfiguration() config.computeUnits = .all let coreMLModel = try ViT(configuration: config) let visionModel = tr... | f18daa53b875f954af98ce1c1b8c93a0 |
apache-2.0 | ['generated_from_trainer'] | false | billsum_t5_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5045 - Rouge1: 0.1393 - Rouge2: 0.0511 - Rougel: 0.117 - Rougelsum: 0.1171 - Gen Len: 19.0 | 58307e90b050d91d309453fc9691f6a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8011 | 0.1314 | 0.0398 | 0.111 | 0.1107 | 19.0 | |... | a47e8d3dd0ef83b45d0536b8f00b3430 |
mit | ['timelms', 'twitter'] | false | Twitter March 2022 (RoBERTa-base, 128M) This is a RoBERTa-base model trained on 128.06M tweets until the end of March 2022. More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829). Below, we provide some usage examples using the standard Transformers interface. For ... | df0c4277d85e8e0dc27ef987fe21389c |
mit | ['timelms', 'twitter'] | false | Preprocess Text Replace usernames and links for placeholders: "@user" and "http". If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data). ```python def preprocess(text): preprocessed_text... | 21f503ab254f3f18a1ffecd473a50e6a |
mit | ['timelms', 'twitter'] | false | expects whitespace tokenization if len(t) > 1: t = '@user' if t[0] == '@' and t.count('@') == 1 else t t = 'http' if t.startswith('http') else t preprocessed_text.append(t) return ' '.join(preprocessed_text) ``` | 8121856390a457736d9bc5bb635856f1 |
mit | ['timelms', 'twitter'] | false | Example Masked Language Model ```python from transformers import pipeline, AutoTokenizer MODEL = "cardiffnlp/twitter-roberta-base-mar2022" fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL) tokenizer = AutoTokenizer.from_pretrained(MODEL) def pprint(candidates, n): for i in range(n): token... | 14093fb64a5d14cbded8b19e10ac8fbe |
mit | ['timelms', 'twitter'] | false | naive approach for demonstration text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') features = model(**encoded_input) features = features[0].detach().cpu().numpy() return np.mean(features[0], axis=0) MODEL = "cardiffnlp/twitter-roberta-base-mar2022" tokenizer = AutoTokenizer.fro... | 1e8a228aa1ec6ea097e31d0626b608d2 |
mit | ['timelms', 'twitter'] | false | Example Feature Extraction ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel import numpy as np MODEL = "cardiffnlp/twitter-roberta-base-mar2022" tokenizer = AutoTokenizer.from_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) | 823415300841025a2ccfa669a1d7b0a1 |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_vp-sv_s445 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 981552c2513338cd5ec288376893a684 |
mit | ['token-classification', 'sequence-tagger-model', 'pytorch', 'transformers', 'pubmedbert', 'uncased', 'radiology', 'biomedical'] | false | Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings. Associated github repo: https://github.com/MIDRC/Stanford_Penn_Deidentifier | c63a161140cf360c60bbdb0800c031ac |
mit | ['token-classification', 'sequence-tagger-model', 'pytorch', 'transformers', 'pubmedbert', 'uncased', 'radiology', 'biomedical'] | false | Citation ```bibtex @article{10.1093/jamia/ocac219, author = {Chambon, Pierre J and Wu, Christopher and Steinkamp, Jackson M and Adleberg, Jason and Cook, Tessa S and Langlotz, Curtis P}, title = "{Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based method... | 92fcd3403963810307994fab32cdd149 |
apache-2.0 | ['translation'] | false | opus-mt-de-iso * source languages: de * target languages: iso * OPUS readme: [de-iso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-iso/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 0252db144dd3d4151b40e403b6b8bef9 |
apache-2.0 | ['generated_from_trainer'] | false | Bert_Classifier This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 1.1067 - Accuracy: 0.5533 | 6a6b2243870170e9d4ded5e8ef8cfb84 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 188 | 1.0636 | 0.5 | | No log | 2.0 | 376 | 1.0405 | 0.52 | | 0.9962 | 3.0 | 564 | 1.1067 | 0.... | c1e3dc6c3ed1512f83b86bbeec1b4eea |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9291 - Recall: 0.9376 - F1: 0.9333 - Accuracy: 0.9841 | 415a9275e3cf89aadd384e3e1587c719 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2412 | 1.0 | 878 | 0.0688 | 0.9178 | 0.9246 | 0.9212 | 0.9815 | | 0.0514 | 2.0 |... | b2ffe63f7199da150b77b2fc370610ee |
mit | [] | false | model by Kuanchy This your the Stable Diffusion model fine-tuned the Bauti concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a sks person ** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google... | b83104b1cb11fa52bc0c49f4c7295432 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-large-extraction-cnndm_2000-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7621 - Rouge1: 34.9258 - Rouge2: 15.2218 - Rougel: 29.9813 - Rougelsum: 29.9443 - Ge... | 9eaef22fdd8d6b0b7ff32c5bec698ff1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | df882009e1e52a465c2fa3909b5ef988 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1649 | 0.8 | 200 | 1.8161 | 34.9143 | 14.9085 | 29.8629 | 29.811 | 19... | 9c18b96afd33030bac46c3f09821ac37 |
apache-2.0 | [] | false | --fp16 for lower turnaround and resource requirement python run_qa.py \ --model_name_or_path vuiseng9/bert-l-squadv1.1-sl256 \ --dataset_name squad \ --do_eval \ --do_train \ --evaluation_strategy steps \ --eval_steps 250 \ --learning_rate 3e-5 \ --fp16 \ --num_train_epochs 2 \ --per_device_eval_ba... | 57ca27bbeb02bf5e1c5600bcbc58fd90 |
apache-2.0 | [] | false | Evaluation Require ```vuiseng9/transformers (fork)``` , commit: ```ff24569b```, NNCF v2.1+ commit (```8e26365```) ```bash git clone https://huggingface.co/vuiseng9/nncf-qat-kd-bert-l-squadv1.1-sl256 python run_qa.py \ --model_name_or_path ./nncf-qat-kd-bert-l-squadv1.1-sl256 \ --dataset_name squad \ --nncf_confi... | 91042e1eb6a4bd518bc6f03f078a8573 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | charliee Dreambooth model trained by mattyhew 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-dif... | 78150c8487d62df1ec7d38dba29db919 |
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: 0.2201 - Accuracy: 0.9265 - F1: 0.9266 | 99f36747d04be456c3506b0d7fdc85e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8631 | 1.0 | 250 | 0.3221 | 0.904 | 0.9011 | | 0.254 | 2.0 | 500 | 0.2201 | 0.9265 | 0.9266 | | ee021fe5f1f004fb232a8faf1e215230 |
apache-2.0 | ['translation'] | false | opus-mt-zai-es * source languages: zai * target languages: es * OPUS readme: [zai-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zai-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 7d5251e42b287d0e473fa9ad64da522a |
apache-2.0 | ['generated_from_trainer'] | false | sentiment_trained_1234567 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: 1.2854 - F1: 0.7165 | 1ec6f72e4994881e7661a17af9bdf05e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.2140338797769864e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 97d2c476ddc0497fc967a439acaa3c0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.6603 | 1.0 | 11404 | 0.7020 | 0.6992 | | 0.5978 | 2.0 | 22808 | 0.8024 | 0.7151 | | 0.5495 | 3.0 | 34212 | 1.0837 | 0.713... | 080caad0a1d8370e1a68165823b0f3a8 |
cc-by-4.0 | ['generated_from_trainer'] | false | bertin-roberta-base-spanish-finetuned-recores3 This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.0975 - Accuracy: 0.3884 | 873be732fe1819616a8ac1e144d75f73 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - lr_scheduler_warmup_steps: 3000 - num_epochs: 25 | f19933fd8611368498fc846d5ed081e1 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.6095 | 1.0 | 524 | 1.6094 | 0.2342 | | 1.607 | 2.0 | 1048 | 1.5612 | 0.3058 | | 1.4059 | 3.0 | 1572 | 1.6292 ... | 85bf93a17fb82d7d0767eb766a71b348 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Stable Diffusion TrinArt/Trin-sama AI finetune v2: Source(s): [Hugging Face](https://huggingface.co/naclbit/trinart_stable_diffusion_v2) Stable Diffusion TrinArt/Trin-sama AI finetune v2 trinart_stable_diffusion is a SD model finetuned by about 40,000 assorted high resolution manga/anime-style pictures for 8 epochs. ... | 06499c525f3b587056a4da045899af57 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Please Note! This model is NOT the 19.2M images Characters Model on TrinArt, but an improved version of the original Trin-sama Twitter bot model. This model is intended to retain the original SD's aesthetics as much as possible while nudging the model to anime/manga style. | 09d1b2b7a6055e7446aec05db34116ae |
mit | ['generated_from_trainer'] | false | m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu2 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3642 - Bleu: 40.8245 - Meteor: 0.4... | 93f784af39668d484d0544c6e90c1394 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 - mixed_precision_training: Native AMP | 7d18d908054e28dac01755c1a774ccd9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 5.1584 | 0.5 | 100 | 3.2518 | 30.3723 | 0.3633 | 41.5 | | 2.1351 | 1.0 | 200 | 0.9929 | 32.9915 | ... | 993f96d1101b461a0d2af1a852844d96 |
apache-2.0 | ['Poet', 'generated_from_trainer'] | false | mt5-small-ibn-Shaddad-v3 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2668 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 | 2609da0a2ee718cbf461320a9c719a66 |
apache-2.0 | ['Poet', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 1e207c25bb751e4b39cb06147cf49dd0 |
apache-2.0 | ['Poet', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 5.4157 | 1.0 | 935 | 3.2668 | 0.0 | 0.0 | 0.0 | 0.0 | | edbb8ae3bb9cfc522c2748553a64296e |
apache-2.0 | ['translation'] | false | opus-mt-es-tn * source languages: es * target languages: tn * OPUS readme: [es-tn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-tn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 19ca6661a7416b17b89436bfdbc83c3c |
apache-2.0 | ['generated_from_trainer'] | false | koelectra-base-v3-discriminator-finetuned-ner This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Precision: 0.6665 - Recall: 0.7... | 55a19da8e6e865a7c91efb5221774579 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 438 | 0.2588 | 0.5701 | 0.6655 | 0.6141 | 0.9212 | | 0.4333 | 2.0 |... | 686bc97b7c9dc1d4b120517f4b5e7234 |
apache-2.0 | ['translation'] | false | ukr-ita * source group: Ukrainian * target group: Italian * OPUS readme: [ukr-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-ita/README.md) * model: transformer-align * source language(s): ukr * target language(s): ita * model: transformer-align * pre-processing: normalization + Se... | f42bc59c02d5a3f61e74caa6495325e8 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-ita - source_languages: ukr - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'it'] - src_constituents: {'ukr'} - tgt_const... | ac3a29ea16c31b8874058bc6d7775514 |
apache-2.0 | ['generated_from_keras_callback'] | false | ksabeh/bert-base-uncased-attribute-correction-mlm-titles This model is a fine-tuned version of [ksabeh/bert-base-uncased-attribute-correction-mlm](https://huggingface.co/ksabeh/bert-base-uncased-attribute-correction-mlm) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0... | d3f8ed4ef057517f521dde8c78cf0b0b |
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': 23878, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 73c6ef86525fc9c71c349cd45d5eda49 |
apache-2.0 | [] | false | DistilBERT base cased distilled SQuAD This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/distilbert-base-cased), fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-base-cased vers... | fe496ad2f686e3c994a96f2a9c307cdb |
apache-2.0 | ['generated_from_trainer', 'habana'] | false | philschmid/habana-xlm-r-large-amazon-massive This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the AmazonScience/massive dataset. It achieves the following results on the evaluation set: | cfb217979809745cea1d652c0f5c8a17 |
apache-2.0 | ['generated_from_trainer', 'habana'] | false | 8x HPU approx. 41min **train results** ```bash {'loss': 0.2651, 'learning_rate': 2.4e-05, 'epoch': 1.0} {'loss': 0.1079, 'learning_rate': 1.8e-05, 'epoch': 2.0} {'loss': 0.0563, 'learning_rate': 1.2e-05, 'epoch': 3.0} {'loss': 0.0308, 'learning_rate': 6e-06, 'epoch': 4.0} {'loss': 0.0165, 'learning_rate': 0.0, 'epoc... | 52d75102aab71e70427707474242f83e |
apache-2.0 | ['exbert'] | false | ALBERT XXLarge v2 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... | 8b6b50275817d284b6526dc10db9113e |
apache-2.0 | ['exbert'] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate input... | 46fe980102e3287449055d8a2578c9f4 |
apache-2.0 | ['exbert'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v2') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "s... | bc70f9903475178fa1137400d1cb1b80 |
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