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 | [] | false | Pre-trained baseline model - Pre-trained model: [BERTweet](https://github.com/VinAIResearch/BERTweet) - trained based on the RoBERTa pre-training procedure - 850M General English Tweets (Jan 2012 to Aug 2019) - 23M COVID-19 English Tweets - Size of the model: >134M parameters - Further training - Pre-trainin... | 770e0f49d2e86e9431787c3559a6838d |
apache-2.0 | [] | false | 1) Pre-training language model - The model was pre-trained on COVID-19/vaccined related tweets using a masked language modeling (MLM) objective starting from BERTweet. - Following datasets on English tweets were used: - Tweets with trending | a6c2c7639190819546c0e9708a757320 |
apache-2.0 | [] | false | CovidVaccine hashtag, 207,000 tweets uploaded across Aug 2020 to Apr 2021 ([kaggle](https://www.kaggle.com/kaushiksuresh147/covidvaccine-tweets)) - Tweets about all COVID-19 vaccines, 78,000 tweets uploaded across Dec 2020 to May 2021 ([kaggle](https://www.kaggle.com/gpreda/all-covid19-vaccines-tweets)) - COVID-19 ... | a751f8476c0d54d93431b6f7c2fd7ec1 |
apache-2.0 | [] | false | 2) Fine-tuning for fact classification - A fine-tuned model from pre-trained language model (1) for fact-classification task on COVID-19/vaccine. - COVID-19/vaccine-related statements were collected from [Poynter](https://www.poynter.org/ifcn-covid-19-misinformation/) and [Snopes](https://www.snopes.com/) using Seleni... | 91d1a7ec17f48b4235b347baa02b6c5b |
apache-2.0 | [] | false | Contributors - This model is a part of final team project from MLDL for DS class at SNU. - Team BIBI - Vaccinating COVID-NineTweets - Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom - Advisor: Prof. Wen-Syan Li <a href="https://gsds.snu.ac.kr/"><img src="https://... | ebea189fbb2519c23bb4fe9acb866f34 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-16-1 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: 1.0424 - Accuracy: 0.5355 | bc384baee86a9b84b2f326bbd444a046 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0989 | 1.0 | 10 | 1.1049 | 0.1 | | 1.0641 | 2.0 | 20 | 1.0768 | 0.3 | | 0.9742 | 3.0 | 30 | 1.0430 | 0.... | a002217a3888369d10441e14f58ecd9d |
mit | ['generated_from_trainer'] | false | mdeberta_all This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2148 - Aerospacemanufacturer Precision: 0.7073 - Aerospacemanufacturer Recall: 0.8406 - Aerospace... | 2a4d553bc53ef1d17833a36d8a54f2fa |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 15.0 | b8a821a64bfe42e1eef12a14f69dd055 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Aerospacemanufacturer Precision | Aerospacemanufacturer Recall | Aerospacemanufacturer F1 | Aerospacemanufacturer Number | Anatomicalstructure Precision | Anatomicalstructure Recall | Anatomicalstructure F1 | Anatomicalstructure Number | Artwork Pr... | 0f8db13cae613bae2f3a31157343da88 |
mit | ['generated_from_trainer'] | false | xlmRoberta-for-VietnameseQA This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the UIT-Viquad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.8315 | e2930e82edb2262378d17b3f7e1f9343 |
mit | ['generated_from_trainer'] | false | Training and evaluation data Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data. Training data: https://github.com/vietnguyen012/QA_viuit/blob/main/train.json Evaluation data: https://github.com/vietnguyen012/QA_viuit/blob/main/trial/trial.json | c0e702c9cddb177293b9410c12971705 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 55bb74f7a477639d4496fe21d46bed3c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5701 | 1.0 | 2534 | 1.2220 | | 1.2942 | 2.0 | 5068 | 0.9698 | | 1.0693 | 3.0 | 7602 | 0.8315 | | 83a91659ef76bb37a44836baa3343d97 |
mit | ['generated_from_trainer'] | false | deberta_base_fine_tuned_mind This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3914 - Accuracy: 0.9085 | cdee2fab5b573a4eaf76b6e82919c359 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7244 | 1.0 | 3054 | 0.5959 | 0.8013 | | 0.5036 | 2.0 | 6108 | 0.3817 | 0.8805 | | 0.3064 | 3.0 | 9162 | 0.3914 | 0.... | 9005e362b0d84ab3c10edb6c8132fc89 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-from-scratch-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: 6.1253 | 9d82bb1aacc3bf472a301e732079fae5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.7459 | 1.09 | 500 | 6.8361 | | 6.6663 | 2.18 | 1000 | 6.5166 | | 6.4828 | 3.27 | 1500 | 6.4653 | | 6.376 | 4.36 | 2000 | 6.3790 ... | ec0a2937c3f964c0cac8aa12c11dad19 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | MAXIM pre-trained on REDS for image deblurring MAXIM model pre-trained for image deblurring. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released... | 4fd9ac01ab0eb072ae020896bb304554 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | How to use Here is how to use this model: ```python from huggingface_hub import from_pretrained_keras from PIL import Image import tensorflow as tf import numpy as np import requests url = "https://github.com/sayakpaul/maxim-tf/blob/main/images/Deblurring/input/109fromGOPR1096.MP4.png?raw=true" image = Image.open(... | 21c0085498a931cfbd96bc71e31aac53 |
mit | ['generated_from_trainer'] | false | bertimbau-base-finetuned-lener-br-finetuned-peticoes-grupo_competencia This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3234 - Accu... | 926c1ecd8b0129736d26e90415988e65 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.37 | 1.0 | 897 | 0.2100 | 0.9365 | | 0.1662 | 2.0 | 1794 | 0.2009 | 0.9479 | | 0.1205 | 3.0 | 2691 | 0.2489 | 0.... | 77ecc371a431970e6e8d682c92bb793f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Fantasy Scene on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Fantasy Scene concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of fantasy_scene** | 25953daa422ff5ef12fe2b4c0c3782d3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model:  | 4e168e90b26ec02f04c452bef4daf61f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) a... | f62f741365312ab001a4fe7fcfd549e3 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-test-ged-mlsum_max_target_length_10 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mlsum dataset. It achieves the following results on the evaluation set: - Loss: 0.3341 - Rouge1: 74.8229 - Rouge2: 68.1808 - Rougel: 74.8297 - Rougelsum: 74.8414 | ca1692f5f6b4b650d7a7033ac2094599 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 0.5565 | 1.0 | 33296 | 0.3827 | 69.9041 | 62.821 | 69.8709 | 69.8924 | | 0.2636 ... | 41f92f82656d29003ead7b81ba82151b |
apache-2.0 | ['translation'] | false | opus-mt-fr-ty * source languages: fr * target languages: ty * OPUS readme: [fr-ty](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ty/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 18429170a92b4bc7a88e63956d4b2dbf |
apache-2.0 | ['translation'] | false | nld-ukr * source group: Dutch * target group: Ukrainian * OPUS readme: [nld-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-ukr/README.md) * model: transformer-align * source language(s): nld * target language(s): ukr * model: transformer-align * pre-processing: normalization + Sent... | 1ce09b74a71c59144c47f63e4c206143 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: nld-ukr - source_languages: nld - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['nl', 'uk'] - src_constituents: {'nld'} - tgt_const... | c56f9f7b52d9ea6cf045486a877d8b10 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.2982 - F1: 0.9392 | 7ebd511d61801d1171e607a2f0cc0b9e |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | bdff8cd22d4fb297c1e0448e84955be8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1486 | 1.0 | 626 | 0.3336 | 0.9223 | | 0.0934 | 2.0 | 1252 | 0.3148 | 0.9324 | | 0.0314 | 3.0 | 1878 | 0.2982 | 0.9392 | ... | 99ff7a0441eab80e0446aeebd6cbfa92 |
apache-2.0 | ['whisper-event'] | false | Whisper Tiny Tatar - Kirill Milintsevich This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5106 - Wer: 49.2285 | 8546c269e7da14f5ee8c88e1bc5c1387 |
apache-2.0 | ['whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4268 | 2.49 | 500 | 0.6232 | 63.6537 | | 0.2331 | 4.98 | 1000 | 0.5044 | 52.3818 | | 0.1332 | 7.46 | 1500 | 0.4927 | 50.2300... | 5b9d5e2e229660e7e3348e196517f556 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_unispeech-ml_s772 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 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When... | 2115eb16bf05f5c83a44a308f8511248 |
apache-2.0 | ['generated_from_trainer'] | false | eval_masked_v4_rte This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.8360 - Accuracy: 0.6209 | 2d7de098e17b8033a0d3cf419633649b |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3076 - Accuracy: 0.8767 - F1: 0.8771 | 7f88a7e4faec6cd5bd1acd947158f9df |
cc-by-4.0 | [] | false | Cour de Cassation semi-automatic *titrage* prediction model Model for the semi-automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). The models are similar to the automatic models described in [this paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-ca... | 2be6af19ec0616a54922a1aafcb0726f |
cc-by-4.0 | [] | false | Model description The model is a transformer-base model trained on parallel data (sommaires-titrages) provided by the Cour de Cassation. The model was intially trained using the Fairseq toolkit, converted to HuggingFace and then fine-tuned on the original training data to smooth out minor differences that arose durin... | 408f380957bffb73db9f1a31b8304cf5 |
cc-by-4.0 | [] | false | How to use Contrary to the [automatic *titrage* prediction model](https://huggingface.co/rbawden/CCASS-pred-titrages-base) (designed to predict the entire sequence), this model is designed to help in the manual production of *titrages*, by proposing the next *titre* (keyword) in the sequence given a *sommaire* and th... | 627d0dd33e2d26fcbdd88489ed977e0e |
cc-by-4.0 | [] | false | Limitations and bias The models' predictions should not be taken as ground-truth *titrages* and the final decision should be the expert's. The model is not constrained to predict *titres* that have previously been seen, so this should be taken into account in the deployment of this model as a *titrage* tool in order ... | a1e6a2e9a18518eaffedf3193f5e59d9 |
cc-by-4.0 | [] | false | Training data Training data is provided by the Cour de Cassation (the original source being Jurinet data, but with pseudo-anonymisation applied). For training, we use a total of 159,836 parallel examples (each example is a sommaire-titrage pair). Our development data consists of 1,833 held-out examples. | 0f5df177eaf8e2b1cd784d96e515f0d4 |
cc-by-4.0 | [] | false | Preprocessing We use SentencePiece, the BPE strategy and a joint vocabulary of 8000 tokens. This model was converted into the HuggingFace format and integrates a number of normalisation processes (e.g. removing double doubles, apostrophes and quotes, normalisation of different accent formats, lowercasing). | 0095d87d1c2eb245def705058095f4dd |
cc-by-4.0 | [] | false | Training The model was initialised trained using Fairseq until convergence on the development set (according to our customised weighted accuracy measure - please see [the paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf) for more details). The model was then converted to HuggingF... | 1cd791fb855073ff12591ef3d133d406 |
cc-by-4.0 | [] | false | Evaluation results Full results for the initial (automatic) Fairseq models can be found in [the paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf). Results on this semi-automatic model coming soon! | d8c7cf61f301ff6e7695cf499dc474ff |
cc-by-4.0 | [] | false | BibTex entry and citation info <a name="cite"></a> If you use this work, please cite the following article: Thibault Charmet, Inès Cherichi, Matthieu Allain, Urszula Czerwinska, Amaury Fouret, Benoît Sagot and Rachel Bawden, 2022. [**Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of F... | c2707eaee88bbfdb970fe5385fa00a8d |
mit | ['generated_from_trainer'] | false | wonderful_engelbart This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the to... | 6af3316324ce981b40ac35e9f1188040 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, ... | 5b2a9ba373f0d3b69563ac8b2cfd5b5d |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1507 - Accuracy: 0.9342 | 5c5460da8b1e87470158c8d30bf0d6e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 99fa0f3eb9ae44774b7eb92085fe08ba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2891 | 1.0 | 146 | 0.2322 | 0.9068 | | 0.2609 | 2.0 | 292 | 0.1710 | 0.9227 | | 0.2417 | 3.0 | 438 | 0.1830 | 0.... | ed693e447b4e9084b89be205200cb408 |
mit | ['generated_from_trainer'] | false | predict-perception-bertino-cause-object This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo-ai/BERTino) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0766 - R2: 0.8216 | f21e9ea6cffe061949664dc5ca1606e8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | R2 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6807 | 1.0 | 14 | 0.4011 | 0.0652 | | 0.3529 | 2.0 | 28 | 0.2304 | 0.4631 | | 0.1539 | 3.0 | 42 | 0.0596 | 0.8611 | |... | 3312b912ac446effe945315565ba53ef |
apache-2.0 | ['generated_from_keras_callback'] | false | mymodel 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: - Train Loss: 3.4016 - Epoch: 2 | 464ed580599c74d596f78f901268c73b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP | d38169fdfbb457d454d19beb58d08b4a |
mit | ['roberta-base', 'roberta-base-epoch_41'] | false | RoBERTa, Intermediate Checkpoint - Epoch 41 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 4afee0911a401a48d8cafb1cf3ad8908 |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-podimo-data-eval-1-2e This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7114 - Rouge1: 32.7887 - Rouge2: 6.5245 - Rougel: 16.9089 - Rougelsum: ... | 81508f0a5ffbcd47446343ea14964fe3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | 533408d92eff7ccb1f35ef9e5b3df187 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 4.2142 | 0.98 | 44 | 3.8082 | 32.7658 | 6.2506 | 16.7953 | 29.6922 | 140... | 30c20212630533f9c1123707164920ec |
apache-2.0 | ['finnish', 'gpt2'] | false | Model page TODO. Model name in my thesis was FinnGPT but I chose not to pollute the namespace and leave that kind of name for a more serious attempt at Finnish GPT models. You may call this however you want. Example names are Väinö's GPT-FI or by hatanpav/gpt-fi. If you really want you can also refer to this with the ... | bd234353c86c2ebe101b6c0e1af95871 |
apache-2.0 | ['finnish', 'gpt2'] | false | How to use Example with text generation pipeline: ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='hatanp/gpt-fi') >>> generator("Testilauseella voidaan testata tokenisointia. Tämän jatkaminen on luultavasti vaikeaa, mutta", max_length=3,do_sample=True, top_p=0.9, t... | 1aca9529283448148d82dc6a6f1f1b1e |
apache-2.0 | ['generated_from_keras_callback'] | false | devansh71/news-sum-dev-ai5 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: - Train Loss: nan - Validation Loss: nan - Epoch: 3 | ba68eee96de9145618c0c96d9b87450c |
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': 0.05, 'decay_steps': 165000, 'end_learning_r... | 9cf1248c72dd2749a200e3a187b736c2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | nan | nan | 0 | | nan | nan | 1 | | nan | nan | 2 | | nan | nan | 3 | | 164b9bfe2fa12206a0bfd7307774c3db |
apache-2.0 | ['vision', 'image-classification'] | false | ResNet-50 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been writt... | 10d8e3fb2580a92e2146a26f66b3bf0d |
apache-2.0 | ['vision', 'image-classification'] | 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 AutoFeatureExtractor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image =... | 96afd73210b963ba5bd7d0ade24d70f6 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-ab-CV8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2105 - Wer: 0.5474 | baa94447a24a71b3ea281a47541e4208 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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... | 4ce4dd4ad17550281519f4a652c39330 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.7729 | 0.63 | 500 | 3.0624 | 1.0021 | | 2.7348 | 1.26 | 1000 | 1.0460 | 0.9815 | | 1.2756 | 1.9 | 1500 | 0.4618 | 0.830... | 846d108ac2442e2509c5cb8126df6cbe |
apache-2.0 | ['generated_from_trainer'] | false | miny-bert-aug-sst2-distilled This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the augmented_glue_sst2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2643 - Accuracy: 0.9128 | 86faf408989d486e24f6d5a1fa7ecee0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 - mixed_precision_training: Native AMP | b1d6c8be80aa6c6b102f49429a31c959 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.602 | 1.0 | 6227 | 0.3389 | 0.9186 | | 0.4195 | 2.0 | 12454 | 0.2989 | 0.9151 | | 0.3644 | 3.0 | 18681 | 0.2794 ... | f6e9c676ec88171b555faa9ce398a75f |
apache-2.0 | ['generated_from_trainer'] | false | hubert-base-timit-demo-google-colab-ft30ep_v5 This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the timit-asr dataset. It achieves the following results on the evaluation set: - Loss: 0.4763 - Wer: 0.3322 | 111b262e4c6e5f977a24a9f3bbd23e30 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.9596 | 0.87 | 500 | 3.1237 | 1.0 | | 2.5388 | 1.73 | 1000 | 1.1689 | 0.9184 | | 1.0448 | 2.6 | 1500 | 0.6106 | 0.587... | 6e85c33aea89df49c42e6a2807ceaa32 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4386 - Accuracy: 0.5578 | 8a23b20c1cf5e26e4bf18950dcb2c571 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3496 | 1.0 | 16604 | 0.4386 | 0.5578 | | 0.3031 | 2.0 | 33208 | 0.4636 | 0.5607 | | 0.281 | 3.0 | 49812 | 0.4565 ... | af6e8973103745f9c9d1fbb2481ee0f5 |
mit | ['generated_from_trainer'] | false | camembert-base-finetuned-LineCause This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Accuracy: 1.0 - F1: 1.0 - Recall: 1.0 | d55c08ba1ea4bf8bc7050f279a9f74e6 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | e410ebe0652b554b2db5cd3c4ffc741b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:------:| | 0.0428 | 1.0 | 4409 | 0.0002 | 1.0 | 1.0 | 1.0 | | 0.0009 | 2.0 | 8818 | 0.0001 | 1.0 | 1.0 | 1.... | a95572e29e25e0eb8e8dde93e7d36355 |
apache-2.0 | [] | false | Model description Entailer is a text-to-text model trained to create entailment-style explanations for a hypothesis (following the format of [EntailmentBank](https://allenai.org/data/entailmentbank)), as well as verifying both the reasoning and the factuality of the premises. Entailer was built on top of [T5](https... | 76ef2054ae35ffef894034a625d8d930 |
apache-2.0 | ['generated_from_trainer'] | false | reddit-bert-text_10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5198 | c72e1970fba981ff37bd1a1da8ff1831 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9626 | 1.0 | 946 | 2.6163 | | 2.6934 | 2.0 | 1892 | 2.5612 | | 2.5971 | 3.0 | 2838 | 2.5023 | | 73d68c41046d145a6e2626afe3022de1 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | vit-base-cifar10 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 cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 2.3302 - Accuracy: 0.106 | 45816259273efb4de3d274445145dd70 |
apache-2.0 | ['image-classification', 'vision', '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 - num_ep... | 471b570160ae0e1c038492b027285282 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.3324 | 1.0 | 664 | 2.3352 | 0.0967 | | 2.3489 | 2.0 | 1328 | 2.3288 | 0.1049 | | 2.4899 | 3.0 | 1992 | 2.4473 | 0.... | d02dac6bee002520df98f0f37b2cdfc9 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_r-wav2vec2_s863 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | e550238547940e33c9c8ca9f6abfd81e |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/nli-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 744d44eb6bb8afc5fa712e38af240a7f |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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 sen... | 61f829ec3018d84aa7c05eaed4de8a6b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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/nli-mpnet-base-v2) | 856833cf5c38dcb566e985f7e7e1feb8 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mea... | 0760815250eb6b291176885660448675 |
apache-2.0 | ['Twitter'] | false | 1. Paper Fajri Koto, Jey Han Lau, and Timothy Baldwin. [_IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization_](https://arxiv.org/pdf/2109.04607.pdf). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (**EMNLP... | 4524f96b288fdf8c369946a72768b21d |
apache-2.0 | ['Twitter'] | false | 2. About [IndoBERTweet](https://github.com/indolem/IndoBERTweet) is the first large-scale pretrained model for Indonesian Twitter that is trained by extending a monolingually trained Indonesian BERT model with additive domain-specific vocabulary. In this paper, we show that initializing domain-specific vocabulary wi... | 4b24309e32b01e79700a16d17f9b7668 |
apache-2.0 | ['Twitter'] | false | 3. Pretraining Data We crawl Indonesian tweets over a 1-year period using the official Twitter API, from December 2019 to December 2020, with 60 keywords covering 4 main topics: economy, health, education, and government. We obtain in total of **409M word tokens**, two times larger than the training data used to pret... | 42f2a8aa476c0dbdf45d7ce02f56dcad |
apache-2.0 | ['Twitter'] | false | 4. How to use Load model and tokenizer (tested with transformers==3.5.1) ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indolem/indobertweet-base-uncased") model = AutoModel.from_pretrained("indolem/indobertweet-base-uncased") ``` **Preprocessing Steps:** * lowe... | 66c34c408afcc4113594a3c45fccddc6 |
apache-2.0 | ['Twitter'] | false | 5. Results over 7 Indonesian Twitter Datasets <table> <col> <colgroup span="2"></colgroup> <colgroup span="2"></colgroup> <tr> <th rowspan="2">Models</td> <th colspan="2" scope="colgroup">Sentiment</th> <th colspan="1" scope="colgroup">Emotion</th> <th colspan="2" scope="colgroup">Hate Speech<... | d2bd798501a5494f8522197c974c8baf |
apache-2.0 | ['Twitter'] | false | Citation If you use our work, please cite: ```bibtex @inproceedings{koto2021indobertweet, title={IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization}, author={Fajri Koto and Jey Han Lau and Timothy Baldwin}, booktitle={Proceedings of the 2021... | 74d19de3c26719131b9f0f034cbc36c3 |
creativeml-openrail-m | [] | false | VAE NOT REQUIRED BUT RECOMENDED Model requires VAE - https://huggingface.co/stabilityai/sd-vae-ft-mse-original/tree/main File Structure for AUTOMATIC1111-webui : |──sd |----|──stable-diffusion-webui |----|----|──models |----|----|----|──VAE |----|----|----|----|──Put your VAE file here Merged Models A list of ... | a2567d120987f7414d213b08133692b1 |
creativeml-openrail-m | [] | false | heading=h.3znysh7 Example prompt using commas and natural language: Positive A Professional Full Body Photo, of a beautiful young woman, clothed, standing indoors, Caucasian, toned physique, strawberry red hair, neutral expression Negative I recommend something simple like, deformed, bad anatomy, disfigured, missi... | b38986a2c29c3ec6794145b80ad5894e |
other | [] | false | <html> <body> <h1>Welcome to Crying-Chopper Model</h1> <p>This is a Stable Diffusion 1.4 based model that adds the ability to make any character you would like into a Crying Chopper meme as seen in the below picture. This model was trained on about 20 different versions aka characters of this art style, thanks to the ... | d5fa323b41aa4003bf966fe3698c5edd |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.