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-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification | 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 Twitter chatter dataset, 590,000 tweets uploaded across Mar 2021 to May 2021 ([github](https://github.com/thepanacealab/covid19_twitter)) | 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 Selenium resulting in over 14,000 fact-checked statements from Jan 2020 to May 2021. - Original labels were divided within following three categories: - `False`: includes false, no evidence, manipulated, fake, not true, unproven and unverified - `Misleading`: includes misleading, exaggerated, out of context and needs context - `True`: includes true and correct | 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://gsds.snu.ac.kr/wp-content/uploads/sites/50/2021/04/GSDS_logo2-e1619068952717.png" width="200" height="80"></a> | 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 | 0.4 | | 0.8765 | 4.0 | 40 | 1.0058 | 0.4 | | 0.6979 | 5.0 | 50 | 0.8488 | 0.7 | | 0.563 | 6.0 | 60 | 0.7221 | 0.7 | | 0.4135 | 7.0 | 70 | 0.6587 | 0.8 | | 0.2509 | 8.0 | 80 | 0.5577 | 0.7 | | 0.0943 | 9.0 | 90 | 0.5840 | 0.7 | | 0.0541 | 10.0 | 100 | 0.6959 | 0.7 | | 0.0362 | 11.0 | 110 | 0.6884 | 0.6 | | 0.0254 | 12.0 | 120 | 0.9263 | 0.6 | | 0.0184 | 13.0 | 130 | 0.7992 | 0.6 | | 0.0172 | 14.0 | 140 | 0.7351 | 0.6 | | 0.0131 | 15.0 | 150 | 0.7664 | 0.6 | | 0.0117 | 16.0 | 160 | 0.8262 | 0.6 | | 0.0101 | 17.0 | 170 | 0.8839 | 0.6 | | 0.0089 | 18.0 | 180 | 0.9018 | 0.6 | | 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 - Aerospacemanufacturer F1: 0.7682 - Aerospacemanufacturer Number: 138 - Anatomicalstructure Precision: 0.6762 - Anatomicalstructure Recall: 0.7269 - Anatomicalstructure F1: 0.7006 - Anatomicalstructure Number: 227 - Artwork Precision: 0.5802 - Artwork Recall: 0.5802 - Artwork F1: 0.5802 - Artwork Number: 131 - Artist Precision: 0.7565 - Artist Recall: 0.7938 - Artist F1: 0.7747 - Artist Number: 1722 - Athlete Precision: 0.7195 - Athlete Recall: 0.7636 - Athlete F1: 0.7409 - Athlete Number: 719 - Carmanufacturer Precision: 0.6806 - Carmanufacturer Recall: 0.8176 - Carmanufacturer F1: 0.7429 - Carmanufacturer Number: 159 - Cleric Precision: 0.6867 - Cleric Recall: 0.5124 - Cleric F1: 0.5869 - Cleric Number: 201 - Clothing Precision: 0.5797 - Clothing Recall: 0.625 - Clothing F1: 0.6015 - Clothing Number: 128 - Disease Precision: 0.6262 - Disease Recall: 0.6768 - Disease F1: 0.6505 - Disease Number: 198 - Drink Precision: 0.7296 - Drink Recall: 0.8112 - Drink F1: 0.7682 - Drink Number: 143 - Facility Precision: 0.6439 - Facility Recall: 0.7203 - Facility F1: 0.6800 - Facility Number: 497 - Food Precision: 0.6786 - Food Recall: 0.5327 - Food F1: 0.5969 - Food Number: 214 - Humansettlement Precision: 0.8594 - Humansettlement Recall: 0.8792 - Humansettlement F1: 0.8692 - Humansettlement Number: 1689 - Medicalprocedure Precision: 0.6545 - Medicalprocedure Recall: 0.7606 - Medicalprocedure F1: 0.7036 - Medicalprocedure Number: 142 - Medication/vaccine Precision: 0.7183 - Medication/vaccine Recall: 0.765 - Medication/vaccine F1: 0.7409 - Medication/vaccine Number: 200 - Musicalgrp Precision: 0.7132 - Musicalgrp Recall: 0.7688 - Musicalgrp F1: 0.7400 - Musicalgrp Number: 372 - Musicalwork Precision: 0.7513 - Musicalwork Recall: 0.7052 - Musicalwork F1: 0.7275 - Musicalwork Number: 407 - Org Precision: 0.6335 - Org Recall: 0.6117 - Org F1: 0.6224 - Org Number: 667 - Otherloc Precision: 0.7514 - Otherloc Recall: 0.6205 - Otherloc F1: 0.6797 - Otherloc Number: 224 - Otherper Precision: 0.4558 - Otherper Recall: 0.5821 - Otherper F1: 0.5112 - Otherper Number: 859 - Otherprod Precision: 0.6076 - Otherprod Recall: 0.5543 - Otherprod F1: 0.5797 - Otherprod Number: 433 - Politician Precision: 0.6228 - Politician Recall: 0.4793 - Politician F1: 0.5417 - Politician Number: 603 - Privatecorp Precision: 0.7159 - Privatecorp Recall: 0.4884 - Privatecorp F1: 0.5806 - Privatecorp Number: 129 - Publiccorp Precision: 0.56 - Publiccorp Recall: 0.6914 - Publiccorp F1: 0.6188 - Publiccorp Number: 243 - Scientist Precision: 0.4545 - Scientist Recall: 0.4497 - Scientist F1: 0.4521 - Scientist Number: 189 - Software Precision: 0.7159 - Software Recall: 0.8046 - Software F1: 0.7577 - Software Number: 307 - Sportsgrp Precision: 0.7845 - Sportsgrp Recall: 0.8701 - Sportsgrp F1: 0.8251 - Sportsgrp Number: 385 - Sportsmanager Precision: 0.6667 - Sportsmanager Recall: 0.5361 - Sportsmanager F1: 0.5943 - Sportsmanager Number: 194 - Station Precision: 0.7406 - Station Recall: 0.8093 - Station F1: 0.7734 - Station Number: 194 - Symptom Precision: 0.6316 - Symptom Recall: 0.5581 - Symptom F1: 0.5926 - Symptom Number: 129 - Vehicle Precision: 0.5514 - Vehicle Recall: 0.6505 - Vehicle F1: 0.5969 - Vehicle Number: 206 - Visualwork Precision: 0.7538 - Visualwork Recall: 0.7951 - Visualwork F1: 0.7739 - Visualwork Number: 693 - Writtenwork Precision: 0.6913 - Writtenwork Recall: 0.6803 - Writtenwork F1: 0.6858 - Writtenwork Number: 563 - Overall Precision: 0.6928 - Overall Recall: 0.7142 - Overall F1: 0.7033 - Overall Accuracy: 0.9355 | 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 Precision | Artwork Recall | Artwork F1 | Artwork Number | Artist Precision | Artist Recall | Artist F1 | Artist Number | Athlete Precision | Athlete Recall | Athlete F1 | Athlete Number | Carmanufacturer Precision | Carmanufacturer Recall | Carmanufacturer F1 | Carmanufacturer Number | Cleric Precision | Cleric Recall | Cleric F1 | Cleric Number | Clothing Precision | Clothing Recall | Clothing F1 | Clothing Number | Disease Precision | Disease Recall | Disease F1 | Disease Number | Drink Precision | Drink Recall | Drink F1 | Drink Number | Facility Precision | Facility Recall | Facility F1 | Facility Number | Food Precision | Food Recall | Food F1 | Food Number | Humansettlement Precision | Humansettlement Recall | Humansettlement F1 | Humansettlement Number | Medicalprocedure Precision | Medicalprocedure Recall | Medicalprocedure F1 | Medicalprocedure Number | Medication/vaccine Precision | Medication/vaccine Recall | Medication/vaccine F1 | Medication/vaccine Number | Musicalgrp Precision | Musicalgrp Recall | Musicalgrp F1 | Musicalgrp Number | Musicalwork Precision | Musicalwork Recall | Musicalwork F1 | Musicalwork Number | Org Precision | Org Recall | Org F1 | Org Number | Otherloc Precision | Otherloc Recall | Otherloc F1 | Otherloc Number | Otherper Precision | Otherper Recall | Otherper F1 | Otherper Number | Otherprod Precision | Otherprod Recall | Otherprod F1 | Otherprod Number | Politician Precision | Politician Recall | Politician F1 | Politician Number | Privatecorp Precision | Privatecorp Recall | Privatecorp F1 | Privatecorp Number | Publiccorp Precision | Publiccorp Recall | Publiccorp F1 | Publiccorp Number | Scientist Precision | Scientist Recall | Scientist F1 | Scientist Number | Software Precision | Software Recall | Software F1 | Software Number | Sportsgrp Precision | Sportsgrp Recall | Sportsgrp F1 | Sportsgrp Number | Sportsmanager Precision | Sportsmanager Recall | Sportsmanager F1 | Sportsmanager Number | Station Precision | Station Recall | Station F1 | Station Number | Symptom Precision | Symptom Recall | Symptom F1 | Symptom Number | Vehicle Precision | Vehicle Recall | Vehicle F1 | Vehicle Number | Visualwork Precision | Visualwork Recall | Visualwork F1 | Visualwork Number | Writtenwork Precision | Writtenwork Recall | Writtenwork F1 | Writtenwork Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | 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| 0.2881 | 1.0 | 21353 | 0.2534 | 0.5191 | 0.6884 | 0.5919 | 138 | 0.5693 | 0.6872 | 0.6228 | 227 | 0.4366 | 0.4733 | 0.4542 | 131 | 0.7109 | 0.8055 | 0.7552 | 1722 | 0.6782 | 0.6801 | 0.6792 | 719 | 0.6552 | 0.7170 | 0.6847 | 159 | 0.4874 | 0.4826 | 0.485 | 201 | 0.4639 | 0.6016 | 0.5238 | 128 | 0.4799 | 0.7222 | 0.5766 | 198 | 0.55 | 0.6923 | 0.6130 | 143 | 0.5305 | 0.6640 | 0.5898 | 497 | 0.4042 | 0.7196 | 0.5176 | 214 | 0.8164 | 0.8792 | 0.8466 | 1689 | 0.5799 | 0.6901 | 0.6302 | 142 | 0.5945 | 0.755 | 0.6652 | 200 | 0.7445 | 0.6425 | 0.6898 | 372 | 0.6667 | 0.6634 | 0.6650 | 407 | 0.5585 | 0.5652 | 0.5618 | 667 | 0.6724 | 0.5223 | 0.5879 | 224 | 0.3835 | 0.4773 | 0.4253 | 859 | 0.5237 | 0.4596 | 0.4895 | 433 | 0.5176 | 0.4643 | 0.4895 | 603 | 0.4688 | 0.1163 | 0.1863 | 129 | 0.4207 | 0.6008 | 0.4949 | 243 | 0.4099 | 0.3492 | 0.3771 | 189 | 0.6686 | 0.7362 | 0.7008 | 307 | 0.7688 | 0.7948 | 0.7816 | 385 | 0.5136 | 0.5825 | 0.5459 | 194 | 0.6667 | 0.8041 | 0.7290 | 194 | 0.5588 | 0.1473 | 0.2331 | 129 | 0.4910 | 0.5291 | 0.5093 | 206 | 0.6662 | 0.7489 | 0.7052 | 693 | 0.6180 | 0.5861 | 0.6016 | 563 | 0.6136 | 0.6640 | 0.6378 | 0.9220 | | 0.2195 | 2.0 | 42706 | 0.2288 | 0.6409 | 0.8406 | 0.7273 | 138 | 0.6908 | 0.6300 | 0.6590 | 227 | 0.5625 | 0.5496 | 0.5560 | 131 | 0.7336 | 0.8124 | 0.7710 | 1722 | 0.6466 | 0.8067 | 0.7178 | 719 | 0.6095 | 0.8050 | 0.6938 | 159 | 0.5848 | 0.4975 | 0.5376 | 201 | 0.5260 | 0.6328 | 0.5745 | 128 | 0.5427 | 0.6414 | 0.5880 | 198 | 0.7042 | 0.6993 | 0.7018 | 143 | 0.6157 | 0.7123 | 0.6604 | 497 | 0.6140 | 0.4907 | 0.5455 | 214 | 0.8454 | 0.8745 | 0.8597 | 1689 | 0.6125 | 0.6901 | 0.6490 | 142 | 0.6898 | 0.745 | 0.7163 | 200 | 0.7299 | 0.7339 | 0.7319 | 372 | 0.6901 | 0.7224 | 0.7059 | 407 | 0.6224 | 0.5907 | 0.6062 | 667 | 0.7312 | 0.6071 | 0.6634 | 224 | 0.4851 | 0.4750 | 0.4800 | 859 | 0.5994 | 0.4804 | 0.5333 | 433 | 0.5675 | 0.5091 | 0.5367 | 603 | 0.6905 | 0.4496 | 0.5446 | 129 | 0.5516 | 0.6379 | 0.5916 | 243 | 0.4570 | 0.3651 | 0.4059 | 189 | 0.7508 | 0.7459 | 0.7484 | 307 | 0.7833 | 0.8260 | 0.8040 | 385 | 0.7071 | 0.5103 | 0.5928 | 194 | 0.6920 | 0.7990 | 0.7416 | 194 | 0.4590 | 0.4341 | 0.4462 | 129 | 0.4717 | 0.7282 | 0.5725 | 206 | 0.7275 | 0.7821 | 0.7538 | 693 | 0.6618 | 0.6430 | 0.6523 | 563 | 0.6711 | 0.6946 | 0.6827 | 0.9317 | | 0.1965 | 3.0 | 64059 | 0.2148 | 0.7073 | 0.8406 | 0.7682 | 138 | 0.6762 | 0.7269 | 0.7006 | 227 | 0.5802 | 0.5802 | 0.5802 | 131 | 0.7565 | 0.7938 | 0.7747 | 1722 | 0.7195 | 0.7636 | 0.7409 | 719 | 0.6806 | 0.8176 | 0.7429 | 159 | 0.6867 | 0.5124 | 0.5869 | 201 | 0.5797 | 0.625 | 0.6015 | 128 | 0.6262 | 0.6768 | 0.6505 | 198 | 0.7296 | 0.8112 | 0.7682 | 143 | 0.6439 | 0.7203 | 0.6800 | 497 | 0.6786 | 0.5327 | 0.5969 | 214 | 0.8594 | 0.8792 | 0.8692 | 1689 | 0.6545 | 0.7606 | 0.7036 | 142 | 0.7183 | 0.765 | 0.7409 | 200 | 0.7132 | 0.7688 | 0.7400 | 372 | 0.7513 | 0.7052 | 0.7275 | 407 | 0.6335 | 0.6117 | 0.6224 | 667 | 0.7514 | 0.6205 | 0.6797 | 224 | 0.4558 | 0.5821 | 0.5112 | 859 | 0.6076 | 0.5543 | 0.5797 | 433 | 0.6228 | 0.4793 | 0.5417 | 603 | 0.7159 | 0.4884 | 0.5806 | 129 | 0.56 | 0.6914 | 0.6188 | 243 | 0.4545 | 0.4497 | 0.4521 | 189 | 0.7159 | 0.8046 | 0.7577 | 307 | 0.7845 | 0.8701 | 0.8251 | 385 | 0.6667 | 0.5361 | 0.5943 | 194 | 0.7406 | 0.8093 | 0.7734 | 194 | 0.6316 | 0.5581 | 0.5926 | 129 | 0.5514 | 0.6505 | 0.5969 | 206 | 0.7538 | 0.7951 | 0.7739 | 693 | 0.6913 | 0.6803 | 0.6858 | 563 | 0.6928 | 0.7142 | 0.7033 | 0.9355 | | 0.1665 | 4.0 | 85412 | 0.2193 | 0.7917 | 0.8261 | 0.8085 | 138 | 0.7069 | 0.7225 | 0.7146 | 227 | 0.5867 | 0.6718 | 0.6263 | 131 | 0.7710 | 0.7938 | 0.7823 | 1722 | 0.6962 | 0.7650 | 0.7290 | 719 | 0.7904 | 0.8302 | 0.8098 | 159 | 0.6221 | 0.5323 | 0.5737 | 201 | 0.5743 | 0.6641 | 0.6159 | 128 | 0.5966 | 0.7172 | 0.6514 | 198 | 0.7914 | 0.7692 | 0.7801 | 143 | 0.6395 | 0.7103 | 0.6730 | 497 | 0.6422 | 0.6121 | 0.6268 | 214 | 0.8338 | 0.9059 | 0.8683 | 1689 | 0.6711 | 0.7183 | 0.6939 | 142 | 0.7635 | 0.775 | 0.7692 | 200 | 0.7669 | 0.7608 | 0.7638 | 372 | 0.6872 | 0.7936 | 0.7366 | 407 | 0.7100 | 0.5982 | 0.6493 | 667 | 0.7181 | 0.7277 | 0.7228 | 224 | 0.4765 | 0.5553 | 0.5129 | 859 | 0.6225 | 0.5866 | 0.6040 | 433 | 0.6078 | 0.5191 | 0.5599 | 603 | 0.7222 | 0.5039 | 0.5936 | 129 | 0.6065 | 0.7737 | 0.6799 | 243 | 0.4783 | 0.5238 | 0.5 | 189 | 0.7313 | 0.7980 | 0.7632 | 307 | 0.8401 | 0.8597 | 0.8498 | 385 | 0.6058 | 0.6495 | 0.6269 | 194 | 0.7512 | 0.7938 | 0.7719 | 194 | 0.6983 | 0.6279 | 0.6612 | 129 | 0.5804 | 0.7184 | 0.6421 | 206 | 0.7571 | 0.8052 | 0.7804 | 693 | 0.6916 | 0.6892 | 0.6904 | 563 | 0.7012 | 0.7309 | 0.7158 | 0.9375 | | 0.1314 | 5.0 | 106765 | 0.2272 | 0.7707 | 0.8768 | 0.8203 | 138 | 0.7137 | 0.7577 | 0.7350 | 227 | 0.6058 | 0.6336 | 0.6194 | 131 | 0.7229 | 0.8513 | 0.7819 | 1722 | 0.7361 | 0.7761 | 0.7556 | 719 | 0.6839 | 0.8302 | 0.7500 | 159 | 0.5845 | 0.6020 | 0.5931 | 201 | 0.6148 | 0.6484 | 0.6312 | 128 | 0.6121 | 0.7172 | 0.6605 | 198 | 0.6970 | 0.8042 | 0.7468 | 143 | 0.6438 | 0.6982 | 0.6699 | 497 | 0.6197 | 0.6776 | 0.6473 | 214 | 0.8390 | 0.8887 | 0.8631 | 1689 | 0.7333 | 0.6972 | 0.7148 | 142 | 0.7443 | 0.815 | 0.7780 | 200 | 0.7217 | 0.7876 | 0.7532 | 372 | 0.7113 | 0.7568 | 0.7333 | 407 | 0.6682 | 0.6522 | 0.6601 | 667 | 0.7136 | 0.7009 | 0.7072 | 224 | 0.5351 | 0.4796 | 0.5058 | 859 | 0.5930 | 0.6259 | 0.6090 | 433 | 0.6112 | 0.5240 | 0.5643 | 603 | 0.7767 | 0.6202 | 0.6897 | 129 | 0.6254 | 0.7284 | 0.6730 | 243 | 0.4815 | 0.4815 | 0.4815 | 189 | 0.7654 | 0.8078 | 0.7861 | 307 | 0.7611 | 0.8935 | 0.8220 | 385 | 0.6667 | 0.6082 | 0.6361 | 194 | 0.7828 | 0.7990 | 0.7908 | 194 | 0.6692 | 0.6899 | 0.6794 | 129 | 0.5983 | 0.6942 | 0.6427 | 206 | 0.7584 | 0.8153 | 0.7858 | 693 | 0.6740 | 0.7052 | 0.6892 | 563 | 0.7006 | 0.7401 | 0.7198 | 0.9378 | | 0.1224 | 6.0 | 128118 | 0.2275 | 0.8286 | 0.8406 | 0.8345 | 138 | 0.6898 | 0.7445 | 0.7161 | 227 | 0.6013 | 0.7252 | 0.6574 | 131 | 0.7574 | 0.8415 | 0.7972 | 1722 | 0.7400 | 0.7483 | 0.7441 | 719 | 0.8084 | 0.8491 | 0.8282 | 159 | 0.7055 | 0.5721 | 0.6319 | 201 | 0.6061 | 0.625 | 0.6154 | 128 | 0.7090 | 0.6768 | 0.6925 | 198 | 0.7868 | 0.7483 | 0.7670 | 143 | 0.6454 | 0.7545 | 0.6957 | 497 | 0.6287 | 0.6963 | 0.6608 | 214 | 0.8548 | 0.8851 | 0.8697 | 1689 | 0.7669 | 0.7183 | 0.7418 | 142 | 0.75 | 0.825 | 0.7857 | 200 | 0.7130 | 0.8280 | 0.7662 | 372 | 0.6848 | 0.8059 | 0.7404 | 407 | 0.7112 | 0.6462 | 0.6771 | 667 | 0.7879 | 0.6964 | 0.7393 | 224 | 0.5378 | 0.5378 | 0.5378 | 859 | 0.6554 | 0.5797 | 0.6152 | 433 | 0.5946 | 0.5887 | 0.5917 | 603 | 0.8131 | 0.6744 | 0.7373 | 129 | 0.6483 | 0.7737 | 0.7054 | 243 | 0.5537 | 0.5185 | 0.5355 | 189 | 0.7704 | 0.7980 | 0.784 | 307 | 0.8415 | 0.8961 | 0.8679 | 385 | 0.6566 | 0.6701 | 0.6633 | 194 | 0.7879 | 0.8041 | 0.7959 | 194 | 0.6159 | 0.7829 | 0.6894 | 129 | 0.5887 | 0.7087 | 0.6432 | 206 | 0.7864 | 0.8023 | 0.7943 | 693 | 0.7388 | 0.6732 | 0.7045 | 563 | 0.7221 | 0.7475 | 0.7346 | 0.9406 | | 0.0964 | 7.0 | 149471 | 0.2456 | 0.7947 | 0.8696 | 0.8304 | 138 | 0.7107 | 0.7577 | 0.7335 | 227 | 0.6522 | 0.6870 | 0.6691 | 131 | 0.7780 | 0.8182 | 0.7976 | 1722 | 0.7546 | 0.7483 | 0.7514 | 719 | 0.7870 | 0.8365 | 0.8110 | 159 | 0.6020 | 0.6020 | 0.6020 | 201 | 0.58 | 0.6797 | 0.6259 | 128 | 0.6129 | 0.7677 | 0.6816 | 198 | 0.7468 | 0.8252 | 0.7841 | 143 | 0.6642 | 0.7284 | 0.6948 | 497 | 0.6840 | 0.6776 | 0.6808 | 214 | 0.8586 | 0.8810 | 0.8697 | 1689 | 0.7836 | 0.7394 | 0.7609 | 142 | 0.7082 | 0.825 | 0.7621 | 200 | 0.7731 | 0.7876 | 0.7803 | 372 | 0.7606 | 0.7494 | 0.7550 | 407 | 0.6726 | 0.6837 | 0.6781 | 667 | 0.7581 | 0.7277 | 0.7426 | 224 | 0.5176 | 0.5634 | 0.5396 | 859 | 0.6599 | 0.6005 | 0.6288 | 433 | 0.5938 | 0.5672 | 0.5802 | 603 | 0.8776 | 0.6667 | 0.7577 | 129 | 0.7198 | 0.7613 | 0.74 | 243 | 0.5078 | 0.5185 | 0.5131 | 189 | 0.7933 | 0.7752 | 0.7842 | 307 | 0.8033 | 0.8909 | 0.8448 | 385 | 0.6071 | 0.7010 | 0.6507 | 194 | 0.7429 | 0.8041 | 0.7723 | 194 | 0.7321 | 0.6357 | 0.6805 | 129 | 0.5775 | 0.7233 | 0.6422 | 206 | 0.7858 | 0.7994 | 0.7926 | 693 | 0.6678 | 0.7282 | 0.6967 | 563 | 0.7199 | 0.7475 | 0.7334 | 0.9403 | | 0.0838 | 8.0 | 170824 | 0.2562 | 0.7722 | 0.8841 | 0.8243 | 138 | 0.6929 | 0.7753 | 0.7318 | 227 | 0.6483 | 0.7176 | 0.6812 | 131 | 0.7859 | 0.8101 | 0.7978 | 1722 | 0.7419 | 0.7316 | 0.7367 | 719 | 0.7389 | 0.8365 | 0.7847 | 159 | 0.5797 | 0.5970 | 0.5882 | 201 | 0.5878 | 0.6797 | 0.6304 | 128 | 0.6574 | 0.7172 | 0.6860 | 198 | 0.7597 | 0.8182 | 0.7879 | 143 | 0.7108 | 0.7123 | 0.7116 | 497 | 0.6511 | 0.7150 | 0.6815 | 214 | 0.8791 | 0.8822 | 0.8806 | 1689 | 0.75 | 0.7606 | 0.7552 | 142 | 0.7594 | 0.805 | 0.7816 | 200 | 0.7842 | 0.8011 | 0.7926 | 372 | 0.7395 | 0.7813 | 0.7599 | 407 | 0.6965 | 0.6777 | 0.6869 | 667 | 0.7179 | 0.75 | 0.7336 | 224 | 0.5081 | 0.5809 | 0.5421 | 859 | 0.6327 | 0.6166 | 0.6246 | 433 | 0.6094 | 0.5821 | 0.5954 | 603 | 0.8776 | 0.6667 | 0.7577 | 129 | 0.7059 | 0.7407 | 0.7229 | 243 | 0.5444 | 0.5185 | 0.5312 | 189 | 0.7722 | 0.7948 | 0.7833 | 307 | 0.8067 | 0.8779 | 0.8408 | 385 | 0.6408 | 0.6804 | 0.6600 | 194 | 0.7546 | 0.8402 | 0.7951 | 194 | 0.6831 | 0.7519 | 0.7159 | 129 | 0.6255 | 0.7136 | 0.6667 | 206 | 0.7392 | 0.8427 | 0.7876 | 693 | 0.7289 | 0.7069 | 0.7178 | 563 | 0.7242 | 0.7514 | 0.7376 | 0.9414 | | 0.0753 | 9.0 | 192177 | 0.2708 | 0.8026 | 0.8841 | 0.8414 | 138 | 0.7054 | 0.8018 | 0.7505 | 227 | 0.6277 | 0.6565 | 0.6418 | 131 | 0.7762 | 0.8380 | 0.8059 | 1722 | 0.7552 | 0.7552 | 0.7552 | 719 | 0.7701 | 0.8428 | 0.8048 | 159 | 0.6610 | 0.5821 | 0.6190 | 201 | 0.5915 | 0.6562 | 0.6222 | 128 | 0.6575 | 0.7273 | 0.6906 | 198 | 0.7887 | 0.7832 | 0.7860 | 143 | 0.7050 | 0.7163 | 0.7106 | 497 | 0.6270 | 0.7383 | 0.6781 | 214 | 0.8441 | 0.8881 | 0.8656 | 1689 | 0.7589 | 0.7535 | 0.7562 | 142 | 0.7125 | 0.855 | 0.7773 | 200 | 0.755 | 0.8118 | 0.7824 | 372 | 0.7512 | 0.8010 | 0.7753 | 407 | 0.6788 | 0.6972 | 0.6879 | 667 | 0.7830 | 0.7411 | 0.7615 | 224 | 0.5155 | 0.5821 | 0.5467 | 859 | 0.6386 | 0.6490 | 0.6438 | 433 | 0.6629 | 0.5804 | 0.6189 | 603 | 0.8598 | 0.7132 | 0.7797 | 129 | 0.6667 | 0.7490 | 0.7054 | 243 | 0.4787 | 0.5344 | 0.505 | 189 | 0.7610 | 0.7883 | 0.7744 | 307 | 0.8285 | 0.8909 | 0.8586 | 385 | 0.7027 | 0.6701 | 0.6860 | 194 | 0.7778 | 0.8299 | 0.8030 | 194 | 0.6923 | 0.7674 | 0.7279 | 129 | 0.6396 | 0.6893 | 0.6636 | 206 | 0.7879 | 0.8095 | 0.7986 | 693 | 0.7110 | 0.7123 | 0.7116 | 563 | 0.7258 | 0.7593 | 0.7422 | 0.9424 | | 0.0574 | 10.0 | 213530 | 0.2862 | 0.8 | 0.8986 | 0.8464 | 138 | 0.7375 | 0.7797 | 0.7580 | 227 | 0.6471 | 0.6718 | 0.6592 | 131 | 0.7831 | 0.8008 | 0.7918 | 1722 | 0.6997 | 0.7747 | 0.7353 | 719 | 0.7714 | 0.8491 | 0.8084 | 159 | 0.6091 | 0.5970 | 0.6030 | 201 | 0.6357 | 0.6406 | 0.6381 | 128 | 0.7 | 0.7071 | 0.7035 | 198 | 0.7436 | 0.8112 | 0.7759 | 143 | 0.6729 | 0.7243 | 0.6977 | 497 | 0.6830 | 0.7150 | 0.6986 | 214 | 0.8627 | 0.8857 | 0.8741 | 1689 | 0.7483 | 0.7535 | 0.7509 | 142 | 0.7611 | 0.86 | 0.8075 | 200 | 0.7846 | 0.8226 | 0.8031 | 372 | 0.7640 | 0.7715 | 0.7677 | 407 | 0.6921 | 0.6942 | 0.6931 | 667 | 0.7478 | 0.7545 | 0.7511 | 224 | 0.5079 | 0.5960 | 0.5485 | 859 | 0.6457 | 0.6397 | 0.6427 | 433 | 0.6223 | 0.5821 | 0.6015 | 603 | 0.8704 | 0.7287 | 0.7932 | 129 | 0.7041 | 0.7737 | 0.7373 | 243 | 0.5073 | 0.5503 | 0.5279 | 189 | 0.7680 | 0.7980 | 0.7827 | 307 | 0.8658 | 0.8883 | 0.8769 | 385 | 0.7111 | 0.6598 | 0.6845 | 194 | 0.7681 | 0.8196 | 0.7930 | 194 | 0.7197 | 0.7364 | 0.7280 | 129 | 0.6192 | 0.7184 | 0.6652 | 206 | 0.7922 | 0.8196 | 0.8057 | 693 | 0.7206 | 0.7194 | 0.72 | 563 | 0.7282 | 0.7572 | 0.7424 | 0.9424 | | 0.0568 | 11.0 | 234883 | 0.2951 | 0.8026 | 0.8841 | 0.8414 | 138 | 0.7458 | 0.7753 | 0.7603 | 227 | 0.6241 | 0.6718 | 0.6471 | 131 | 0.7737 | 0.8240 | 0.7981 | 1722 | 0.7646 | 0.7455 | 0.7549 | 719 | 0.8121 | 0.8428 | 0.8272 | 159 | 0.6685 | 0.5920 | 0.6280 | 201 | 0.6870 | 0.6172 | 0.6502 | 128 | 0.7150 | 0.6970 | 0.7059 | 198 | 0.7872 | 0.7762 | 0.7817 | 143 | 0.6631 | 0.7485 | 0.7032 | 497 | 0.6842 | 0.6682 | 0.6761 | 214 | 0.8594 | 0.8828 | 0.8709 | 1689 | 0.7863 | 0.7254 | 0.7546 | 142 | 0.7824 | 0.845 | 0.8125 | 200 | 0.7628 | 0.8038 | 0.7827 | 372 | 0.7664 | 0.7740 | 0.7702 | 407 | 0.7232 | 0.6777 | 0.6997 | 667 | 0.7820 | 0.7366 | 0.7586 | 224 | 0.5362 | 0.5949 | 0.5640 | 859 | 0.6306 | 0.6467 | 0.6385 | 433 | 0.6472 | 0.5871 | 0.6157 | 603 | 0.8857 | 0.7209 | 0.7949 | 129 | 0.7138 | 0.7901 | 0.7500 | 243 | 0.5075 | 0.5397 | 0.5231 | 189 | 0.7834 | 0.8013 | 0.7923 | 307 | 0.8561 | 0.8961 | 0.8756 | 385 | 0.6809 | 0.6598 | 0.6702 | 194 | 0.7656 | 0.8247 | 0.7940 | 194 | 0.6736 | 0.7519 | 0.7106 | 129 | 0.6262 | 0.6505 | 0.6381 | 206 | 0.7892 | 0.8211 | 0.8048 | 693 | 0.7561 | 0.7105 | 0.7326 | 563 | 0.7390 | 0.7548 | 0.7468 | 0.9431 | | 0.0465 | 12.0 | 256236 | 0.3103 | 0.8194 | 0.9203 | 0.8669 | 138 | 0.7031 | 0.7930 | 0.7453 | 227 | 0.5867 | 0.6718 | 0.6263 | 131 | 0.7829 | 0.8211 | 0.8016 | 1722 | 0.7582 | 0.7413 | 0.7496 | 719 | 0.8059 | 0.8616 | 0.8328 | 159 | 0.6648 | 0.5920 | 0.6263 | 201 | 0.6385 | 0.6484 | 0.6434 | 128 | 0.6827 | 0.7172 | 0.6995 | 198 | 0.7778 | 0.8322 | 0.8041 | 143 | 0.6679 | 0.7324 | 0.6987 | 497 | 0.6864 | 0.7056 | 0.6959 | 214 | 0.8473 | 0.8905 | 0.8684 | 1689 | 0.7552 | 0.7606 | 0.7579 | 142 | 0.7362 | 0.865 | 0.7954 | 200 | 0.7487 | 0.8011 | 0.7740 | 372 | 0.7470 | 0.7764 | 0.7614 | 407 | 0.7042 | 0.7031 | 0.7037 | 667 | 0.7435 | 0.7634 | 0.7533 | 224 | 0.5438 | 0.5856 | 0.5639 | 859 | 0.6261 | 0.6536 | 0.6395 | 433 | 0.6442 | 0.6186 | 0.6311 | 603 | 0.8482 | 0.7364 | 0.7884 | 129 | 0.7283 | 0.7613 | 0.7445 | 243 | 0.5075 | 0.5397 | 0.5231 | 189 | 0.7915 | 0.7915 | 0.7915 | 307 | 0.8564 | 0.8987 | 0.8771 | 385 | 0.6211 | 0.7268 | 0.6698 | 194 | 0.7633 | 0.8144 | 0.7880 | 194 | 0.7313 | 0.7597 | 0.7452 | 129 | 0.6450 | 0.7233 | 0.6819 | 206 | 0.7758 | 0.8139 | 0.7944 | 693 | 0.7189 | 0.7176 | 0.7182 | 563 | 0.7312 | 0.7621 | 0.7464 | 0.9428 | | 0.0459 | 13.0 | 277589 | 0.3141 | 0.8267 | 0.8986 | 0.8611 | 138 | 0.7254 | 0.7797 | 0.7516 | 227 | 0.6099 | 0.6565 | 0.6324 | 131 | 0.7929 | 0.8182 | 0.8054 | 1722 | 0.7562 | 0.7677 | 0.7619 | 719 | 0.8084 | 0.8491 | 0.8282 | 159 | 0.6302 | 0.6020 | 0.6158 | 201 | 0.6412 | 0.6562 | 0.6486 | 128 | 0.6931 | 0.7071 | 0.7000 | 198 | 0.7770 | 0.8042 | 0.7904 | 143 | 0.6834 | 0.7384 | 0.7099 | 497 | 0.6967 | 0.6869 | 0.6918 | 214 | 0.8631 | 0.8845 | 0.8737 | 1689 | 0.7939 | 0.7324 | 0.7619 | 142 | 0.7830 | 0.83 | 0.8058 | 200 | 0.7822 | 0.8011 | 0.7915 | 372 | 0.7482 | 0.7740 | 0.7609 | 407 | 0.6982 | 0.6972 | 0.6977 | 667 | 0.7867 | 0.7411 | 0.7632 | 224 | 0.5323 | 0.5856 | 0.5576 | 859 | 0.6469 | 0.6559 | 0.6514 | 433 | 0.6512 | 0.6036 | 0.6265 | 603 | 0.8611 | 0.7209 | 0.7848 | 129 | 0.7287 | 0.7737 | 0.7505 | 243 | 0.5185 | 0.5185 | 0.5185 | 189 | 0.7910 | 0.8013 | 0.7961 | 307 | 0.8715 | 0.8987 | 0.8849 | 385 | 0.7283 | 0.6907 | 0.7090 | 194 | 0.7512 | 0.7938 | 0.7719 | 194 | 0.7313 | 0.7597 | 0.7452 | 129 | 0.6147 | 0.6893 | 0.6499 | 206 | 0.7947 | 0.8268 | 0.8105 | 693 | 0.7170 | 0.7247 | 0.7208 | 563 | 0.7406 | 0.7588 | 0.7496 | 0.9436 | | 0.0386 | 14.0 | 298942 | 0.3268 | 0.8333 | 0.9058 | 0.8681 | 138 | 0.7092 | 0.7841 | 0.7448 | 227 | 0.6028 | 0.6489 | 0.625 | 131 | 0.7848 | 0.8153 | 0.7998 | 1722 | 0.7701 | 0.7594 | 0.7647 | 719 | 0.8047 | 0.8553 | 0.8293 | 159 | 0.6373 | 0.6119 | 0.6244 | 201 | 0.6204 | 0.6641 | 0.6415 | 128 | 0.6794 | 0.7172 | 0.6978 | 198 | 0.7986 | 0.8042 | 0.8014 | 143 | 0.6691 | 0.7324 | 0.6993 | 497 | 0.7109 | 0.7009 | 0.7059 | 214 | 0.8624 | 0.8834 | 0.8728 | 1689 | 0.7754 | 0.7535 | 0.7643 | 142 | 0.7757 | 0.83 | 0.8019 | 200 | 0.7712 | 0.8065 | 0.7884 | 372 | 0.7621 | 0.7715 | 0.7668 | 407 | 0.6782 | 0.7076 | 0.6926 | 667 | 0.7661 | 0.7455 | 0.7557 | 224 | 0.5417 | 0.5669 | 0.5540 | 859 | 0.6388 | 0.6536 | 0.6461 | 433 | 0.6160 | 0.6119 | 0.6140 | 603 | 0.8889 | 0.7442 | 0.8101 | 129 | 0.7431 | 0.7737 | 0.7581 | 243 | 0.5025 | 0.5397 | 0.5204 | 189 | 0.7915 | 0.7915 | 0.7915 | 307 | 0.8618 | 0.8909 | 0.8761 | 385 | 0.6699 | 0.7113 | 0.6900 | 194 | 0.7621 | 0.8093 | 0.7850 | 194 | 0.7226 | 0.7674 | 0.7444 | 129 | 0.6384 | 0.6942 | 0.6651 | 206 | 0.7872 | 0.8167 | 0.8017 | 693 | 0.7140 | 0.7229 | 0.7184 | 563 | 0.7358 | 0.7585 | 0.7470 | 0.9435 | | 0.037 | 15.0 | 320295 | 0.3308 | 0.8117 | 0.9058 | 0.8562 | 138 | 0.716 | 0.7885 | 0.7505 | 227 | 0.6028 | 0.6489 | 0.625 | 131 | 0.7838 | 0.8252 | 0.8040 | 1722 | 0.7835 | 0.7552 | 0.7691 | 719 | 0.7953 | 0.8553 | 0.8242 | 159 | 0.6630 | 0.5970 | 0.6283 | 201 | 0.6296 | 0.6641 | 0.6464 | 128 | 0.6961 | 0.7172 | 0.7065 | 198 | 0.8 | 0.8112 | 0.8056 | 143 | 0.6849 | 0.7304 | 0.7069 | 497 | 0.7156 | 0.7056 | 0.7106 | 214 | 0.8631 | 0.8845 | 0.8737 | 1689 | 0.7852 | 0.7465 | 0.7653 | 142 | 0.7602 | 0.84 | 0.7981 | 200 | 0.7601 | 0.8091 | 0.7839 | 372 | 0.7506 | 0.7617 | 0.7561 | 407 | 0.6943 | 0.7151 | 0.7046 | 667 | 0.7767 | 0.7455 | 0.7608 | 224 | 0.5351 | 0.5588 | 0.5467 | 859 | 0.6453 | 0.6513 | 0.6483 | 433 | 0.6277 | 0.6153 | 0.6214 | 603 | 0.8981 | 0.7519 | 0.8186 | 129 | 0.7362 | 0.7695 | 0.7525 | 243 | 0.5178 | 0.5397 | 0.5285 | 189 | 0.7806 | 0.7883 | 0.7844 | 307 | 0.8804 | 0.8987 | 0.8895 | 385 | 0.6863 | 0.7216 | 0.7035 | 194 | 0.7696 | 0.8093 | 0.7889 | 194 | 0.7333 | 0.7674 | 0.7500 | 129 | 0.6471 | 0.6942 | 0.6698 | 206 | 0.7917 | 0.8225 | 0.8068 | 693 | 0.7255 | 0.7229 | 0.7242 | 563 | 0.7404 | 0.7600 | 0.7501 | 0.9437 | | 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 | 0.9085 | | 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 | | 6.2758 | 5.45 | 2500 | 6.3507 | | 6.2192 | 6.54 | 3000 | 6.2435 | | 6.1177 | 7.63 | 3500 | 6.2547 | | 6.0904 | 8.71 | 4000 | 6.1996 | | 6.0272 | 9.8 | 4500 | 6.2123 | | 5.9979 | 10.89 | 5000 | 6.1253 | | 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 in [this repository](https://github.com/google-research/maxim). Disclaimer: The team releasing MAXIM did not write a model card for this model so this model card has been written by the Hugging Face team. | 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(requests.get(url, stream=True).raw) image = np.array(image) image = tf.convert_to_tensor(image) image = tf.image.resize(image, (256, 256)) model = from_pretrained_keras("google/maxim-s3-deblurring-reds") predictions = model.predict(tf.expand_dims(image, 0)) ``` For a more elaborate prediction pipeline, refer to [this Colab Notebook](https://colab.research.google.com/github/sayakpaul/maxim-tf/blob/main/notebooks/inference-dynamic-resize.ipynb). | 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 - Accuracy: 0.9434 | 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 | 0.9423 | | 0.0855 | 4.0 | 3588 | 0.2918 | 0.9404 | | 0.0438 | 5.0 | 4485 | 0.3234 | 0.9434 | | 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) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 - training precision: Mixed Precision | 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 | 2.0 | 66592 | 0.3552 | 72.0701 | 65.4937 | 72.0787 | 72.091 | | 0.2309 | 3.0 | 99888 | 0.3525 | 72.5071 | 65.8026 | 72.5132 | 72.512 | | 0.2109 | 4.0 | 133184 | 0.3346 | 74.0842 | 67.4776 | 74.0887 | 74.0968 | | 0.1972 | 5.0 | 166480 | 0.3398 | 74.6051 | 68.6024 | 74.6177 | 74.6365 | | 0.1867 | 6.0 | 199776 | 0.3283 | 74.9022 | 68.2146 | 74.9023 | 74.926 | | 0.1785 | 7.0 | 233072 | 0.3325 | 74.8631 | 68.2468 | 74.8843 | 74.9026 | | 0.1725 | 8.0 | 266368 | 0.3341 | 74.8229 | 68.1808 | 74.8297 | 74.8414 | | 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://object.pouta.csc.fi/OPUS-MT-models/fr-ty/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ty/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ty/opus-2020-01-16.eval.txt) | 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 + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.eval.txt) | 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_constituents: {'ukr'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.test.txt - src_alpha3: nld - tgt_alpha3: ukr - short_pair: nl-uk - chrF2_score: 0.619 - bleu: 40.8 - brevity_penalty: 0.992 - ref_len: 51674.0 - src_name: Dutch - tgt_name: Ukrainian - train_date: 2020-06-17 - src_alpha2: nl - tgt_alpha2: uk - prefer_old: False - long_pair: nld-ukr - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 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 | | 0.09 | 9.95 | 2000 | 0.5106 | 49.2285 | | 0.048 | 12.44 | 2500 | 0.5526 | 49.7806 | | 0.0346 | 14.93 | 3000 | 0.5850 | 50.0319 | | 0.0181 | 17.41 | 3500 | 0.6276 | 50.5592 | | 0.0122 | 19.9 | 4000 | 0.6494 | 50.3327 | | 0.0086 | 22.39 | 4500 | 0.6737 | 50.6688 | | 0.0077 | 24.88 | 5000 | 0.6777 | 50.6724 | | 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 using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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-camera-ready.pdf) and to the model available [here](https://huggingface.co/rbawden/CCASS-pred-titrages-base). If you use this semi-automatic model, please cite our research paper (see [below]( | 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 during the conversion process. Tokenisation is performed using a SentencePiece model, the BPE strategy and a vocab size of 8000. | 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 the beginning of the *titrage*. Model input is the *matière* (matter) concatenated to the *titres* already decided on (separated by <t>), concatenated to the text from the sommaire separated by the token `<t>`. Each example should be on a single line. E.g. `bail <t> résiliation <t> causes <t> La recommendation du tribunal selon l'article...` (fictive example for illustrative purposes, where the matter=bail, the beginning of the *titrage*=résiliation <t> causes. The maximum input length of the model is 1024 input tokens (after tokenisation). ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokeniser = AutoTokenizer.from_pretrained("rbawden/CCASS-semi-auto-titrages-base") model = AutoModelForSeq2SeqLM.from_pretrained("rbawden/CCASS-semi-auto-titrages-base") matiere_and_titrage_prefix = "matter <t> titre" sommaire = "full text from the sommaire on a single line" inputs = tokeniser([matiere_and_titrage_prefix + " <t> " + sommaire], return_tensors='pt') outputs = model.generate(inputs['input_ids']) tokeniser.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenisation_spaces=True) ``` | 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 to avoid the multiplication of different *titres*. | 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 HuggingFace and training continued to smooth out incoherences introduced during the conversion procedure (incompatibilities in the way the SentencePiece and NMT vocabularies are defined, linked to HuggingFace vocabularies being necessarily the same as the tokeniser vocabulary, a constraint that is not imposed in Fairseq). | 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 France’s Court of Cassation Rulings**](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.509.pdf). In Proceedings of the 13th Language Resources and Evaluation Conference, Marseille, France.] ``` @inproceedings{charmet-et-al-2022-complex, tite = {Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings}, author = {Charmet, Thibault and Cherichi, Inès and Allain, Matthieu and Czerwinska, Urszula and Fouret, Amaury, and Sagot, Benoît and Bawden, Rachel}, booktitle = {Proceedings of the 13th Language Resources and Evaluation Conference}, year = {2022}, address = {Marseille, France}, pages = {4754--4766}, url = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.509.pdf} ``` | 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 tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | 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}, 'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'wonderful_engelbart', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 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_scheduler_warmup_ratio: 0.1 - num_epochs: 12 | 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 | 0.9251 | | 0.2406 | 4.0 | 584 | 0.1809 | 0.9198 | | 0.2113 | 5.0 | 730 | 0.1631 | 0.9289 | | 0.1812 | 6.0 | 876 | 0.1561 | 0.9308 | | 0.2082 | 7.0 | 1022 | 0.1507 | 0.9342 | | 0.1922 | 8.0 | 1168 | 0.1611 | 0.9294 | | 0.1715 | 9.0 | 1314 | 0.1536 | 0.9308 | | 0.1675 | 10.0 | 1460 | 0.1609 | 0.9289 | | 0.194 | 11.0 | 1606 | 0.1499 | 0.9337 | | 0.1706 | 12.0 | 1752 | 0.1514 | 0.9323 | | 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 | | 0.0853 | 4.0 | 56 | 0.1600 | 0.6272 | | 0.066 | 5.0 | 70 | 0.1596 | 0.6280 | | 0.0563 | 6.0 | 84 | 0.1146 | 0.7330 | | 0.0777 | 7.0 | 98 | 0.1010 | 0.7646 | | 0.0299 | 8.0 | 112 | 0.0897 | 0.7910 | | 0.0311 | 9.0 | 126 | 0.0832 | 0.8061 | | 0.0274 | 10.0 | 140 | 0.0988 | 0.7697 | | 0.0262 | 11.0 | 154 | 0.1048 | 0.7557 | | 0.0204 | 12.0 | 168 | 0.0615 | 0.8566 | | 0.0254 | 13.0 | 182 | 0.0742 | 0.8270 | | 0.0251 | 14.0 | 196 | 0.0923 | 0.7850 | | 0.0149 | 15.0 | 210 | 0.0663 | 0.8456 | | 0.0141 | 16.0 | 224 | 0.0755 | 0.8241 | | 0.0112 | 17.0 | 238 | 0.0905 | 0.7891 | | 0.0108 | 18.0 | 252 | 0.0834 | 0.8057 | | 0.0096 | 19.0 | 266 | 0.0823 | 0.8082 | | 0.0073 | 20.0 | 280 | 0.0825 | 0.8078 | | 0.0092 | 21.0 | 294 | 0.0869 | 0.7974 | | 0.0075 | 22.0 | 308 | 0.0744 | 0.8266 | | 0.0075 | 23.0 | 322 | 0.0825 | 0.8078 | | 0.0062 | 24.0 | 336 | 0.0797 | 0.8144 | | 0.0065 | 25.0 | 350 | 0.0793 | 0.8152 | | 0.007 | 26.0 | 364 | 0.0840 | 0.8043 | | 0.0067 | 27.0 | 378 | 0.0964 | 0.7753 | | 0.0064 | 28.0 | 392 | 0.0869 | 0.7976 | | 0.0063 | 29.0 | 406 | 0.0766 | 0.8215 | | 0.0057 | 30.0 | 420 | 0.0764 | 0.8219 | | 0.0057 | 31.0 | 434 | 0.0796 | 0.8145 | | 0.0054 | 32.0 | 448 | 0.0853 | 0.8012 | | 0.0044 | 33.0 | 462 | 0.0750 | 0.8253 | | 0.0072 | 34.0 | 476 | 0.0782 | 0.8179 | | 0.006 | 35.0 | 490 | 0.0867 | 0.7979 | | 0.0054 | 36.0 | 504 | 0.0819 | 0.8092 | | 0.0047 | 37.0 | 518 | 0.0839 | 0.8045 | | 0.0043 | 38.0 | 532 | 0.0764 | 0.8221 | | 0.0039 | 39.0 | 546 | 0.0728 | 0.8303 | | 0.0041 | 40.0 | 560 | 0.0755 | 0.8241 | | 0.0038 | 41.0 | 574 | 0.0729 | 0.8301 | | 0.0034 | 42.0 | 588 | 0.0781 | 0.8180 | | 0.0038 | 43.0 | 602 | 0.0762 | 0.8224 | | 0.0032 | 44.0 | 616 | 0.0777 | 0.8189 | | 0.0035 | 45.0 | 630 | 0.0776 | 0.8191 | | 0.0037 | 46.0 | 644 | 0.0765 | 0.8217 | | 0.0036 | 47.0 | 658 | 0.0766 | 0.8216 | | 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 initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_41. | 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: 29.6437 - Gen Len: 141.3408 | 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_epochs: 2 | 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 | 140.5503 | | 3.6965 | 1.98 | 88 | 3.7114 | 32.7887 | 6.5245 | 16.9089 | 29.6437 | 141.3408 | | 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 FinnGPT like I did in my thesis. | 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, top_k=12, temperature=0.9, num_return_sequences=2) [{'generated_text': 'Testilauseella voidaan testata tokenisointia. Tämän jatkaminen on luultavasti vaikeaa, mutta ei mahdotonta. \n Jos et ole kiinnostunut tokenis'}, {'generated_text': 'Testilauseella voidaan testata tokenisointia. Tämän jatkaminen on luultavasti vaikeaa, mutta sen toteuttaminen onnistuu, jos testilaboratorio osaa analysoida'}, {'generated_text': 'Testilauseella voidaan testata tokenisointia. Tämän jatkaminen on luultavasti vaikeaa, mutta sen testaaminen on silti hyödyllistä. Jos testisuorit'}] ``` Example to generate text manually: ```python >>> from transformers import AutoModelForCausalLM,AutoTokenizer >>> model = AutoModelForCausalLM.from_pretrained("hatanp/gpt-fi") >>> tokenizer = AutoTokenizer.from_pretrained("hatanp/gpt-fi") >>> prompt = "Testilauseella voidaan testata tokenisointia. Tämän jatkaminen on luultavasti vaikeaa, mutta" >>> inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") >>> prompt_len = len(tokenizer.decode(inputs[0],skip_special_tokens=True, clean_up_tokenization_spaces=True)) >>> outputs = model.generate(inputs, max_length=len(inputs[0])+20, do_sample=True, top_p=0.9, top_k=12, temperature=0.9) >>> text_out = tokenizer.decode(outputs[0])[prompt_len:] >>> print(text_out) " on olemassa joitain keinoja, joilla voit testata tokenisointia. Tässä artikkelissa käydään läpi testilauseiden" ``` | 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_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | 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 written by the Hugging Face team. | 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 = dataset["test"]["image"][0] feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") inputs = feature_extractor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits | 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_scheduler_warmup_steps: 300 - num_epochs: 15 - mixed_precision_training: Native AMP | 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 | 0.8309 | | 1.0419 | 2.53 | 2000 | 0.3725 | 0.7449 | | 0.9491 | 3.16 | 2500 | 0.3368 | 0.7345 | | 0.9006 | 3.79 | 3000 | 0.3014 | 0.6936 | | 0.8519 | 4.42 | 3500 | 0.2852 | 0.6767 | | 0.8243 | 5.06 | 4000 | 0.2701 | 0.6504 | | 0.7902 | 5.69 | 4500 | 0.2641 | 0.6221 | | 0.7767 | 6.32 | 5000 | 0.2549 | 0.6192 | | 0.7516 | 6.95 | 5500 | 0.2515 | 0.6179 | | 0.737 | 7.59 | 6000 | 0.2408 | 0.5963 | | 0.7217 | 8.22 | 6500 | 0.2429 | 0.6261 | | 0.7101 | 8.85 | 7000 | 0.2366 | 0.5687 | | 0.6922 | 9.48 | 7500 | 0.2277 | 0.5680 | | 0.6866 | 10.11 | 8000 | 0.2242 | 0.5847 | | 0.6703 | 10.75 | 8500 | 0.2222 | 0.5803 | | 0.6649 | 11.38 | 9000 | 0.2247 | 0.5765 | | 0.6513 | 12.01 | 9500 | 0.2182 | 0.5644 | | 0.6369 | 12.64 | 10000 | 0.2128 | 0.5508 | | 0.6425 | 13.27 | 10500 | 0.2132 | 0.5514 | | 0.6399 | 13.91 | 11000 | 0.2116 | 0.5495 | | 0.6208 | 14.54 | 11500 | 0.2105 | 0.5474 | | 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 | 0.9117 | | 0.3304 | 4.0 | 24908 | 0.2793 | 0.9106 | | 0.3066 | 5.0 | 31135 | 0.2659 | 0.9186 | | 0.2881 | 6.0 | 37362 | 0.2668 | 0.9140 | | 0.2754 | 7.0 | 43589 | 0.2643 | 0.9128 | | 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 | 0.5878 | | 0.6793 | 3.46 | 2000 | 0.4912 | 0.5200 | | 0.5234 | 4.33 | 2500 | 0.4529 | 0.4798 | | 0.4368 | 5.19 | 3000 | 0.4239 | 0.4543 | | 0.3839 | 6.06 | 3500 | 0.4326 | 0.4339 | | 0.3315 | 6.92 | 4000 | 0.4265 | 0.4173 | | 0.2878 | 7.79 | 4500 | 0.4304 | 0.4068 | | 0.25 | 8.65 | 5000 | 0.4130 | 0.3940 | | 0.242 | 9.52 | 5500 | 0.4310 | 0.3938 | | 0.2182 | 10.38 | 6000 | 0.4204 | 0.3843 | | 0.2063 | 11.25 | 6500 | 0.4449 | 0.3816 | | 0.2099 | 12.11 | 7000 | 0.4016 | 0.3681 | | 0.1795 | 12.98 | 7500 | 0.4027 | 0.3647 | | 0.1604 | 13.84 | 8000 | 0.4294 | 0.3664 | | 0.1683 | 14.71 | 8500 | 0.4412 | 0.3661 | | 0.1452 | 15.57 | 9000 | 0.4484 | 0.3588 | | 0.1491 | 16.44 | 9500 | 0.4508 | 0.3515 | | 0.1388 | 17.3 | 10000 | 0.4240 | 0.3518 | | 0.1399 | 18.17 | 10500 | 0.4605 | 0.3513 | | 0.1265 | 19.03 | 11000 | 0.4412 | 0.3485 | | 0.1137 | 19.9 | 11500 | 0.4520 | 0.3467 | | 0.106 | 20.76 | 12000 | 0.4873 | 0.3426 | | 0.1243 | 21.63 | 12500 | 0.4456 | 0.3396 | | 0.1055 | 22.49 | 13000 | 0.4819 | 0.3406 | | 0.1124 | 23.36 | 13500 | 0.4613 | 0.3391 | | 0.1064 | 24.22 | 14000 | 0.4842 | 0.3430 | | 0.0875 | 25.09 | 14500 | 0.4661 | 0.3348 | | 0.086 | 25.95 | 15000 | 0.4724 | 0.3371 | | 0.0842 | 26.82 | 15500 | 0.4982 | 0.3381 | | 0.0834 | 27.68 | 16000 | 0.4856 | 0.3337 | | 0.0918 | 28.55 | 16500 | 0.4783 | 0.3344 | | 0.0773 | 29.41 | 17000 | 0.4763 | 0.3322 | | 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 | 0.5576 | | 0.2682 | 4.0 | 66416 | 0.4627 | 0.5647 | | 0.2596 | 5.0 | 83020 | 0.4572 | 0.5768 | | 0.2533 | 6.0 | 99624 | 0.4660 | 0.5753 | | 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 | 1.0 | | 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://github.com/google-research/text-to-text-transfer-transformer) and comes in two sizes: [entailer-11b](https://huggingface.co/allenai/entailer-11b) and [entailer-large](https://huggingface.co/allenai/entailer-large). See https://github.com/allenai/entailment_bank for more details. | 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_epochs: 10.0 | 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 | 0.0989 | | 2.479 | 4.0 | 2656 | 2.4894 | 0.1 | | 2.4179 | 5.0 | 3320 | 2.4404 | 0.0947 | | 2.3881 | 6.0 | 3984 | 2.3931 | 0.102 | | 2.3597 | 7.0 | 4648 | 2.3744 | 0.0967 | | 2.3721 | 8.0 | 5312 | 2.3667 | 0.0935 | | 2.3456 | 9.0 | 5976 | 2.3495 | 0.1036 | | 2.3361 | 10.0 | 6640 | 2.3473 | 0.1025 | | 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 speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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 sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/nli-mpnet-base-v2') embeddings = model.encode(sentences) print(embeddings) ``` | 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_mean_sqrt_len_tokens': False}) ) ``` | 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 2021**), Dominican Republic (virtual). | 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 with average-pooling of BERT subword embeddings is more efficient than pretraining from scratch, and more effective than initializing based on word2vec projections. | 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 pretrain [IndoBERT](https://aclanthology.org/2020.coling-main.66.pdf). Due to Twitter policy, this pretraining data will not be released to public. | 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:** * lower-case all words * converting user mentions and URLs into @USER and HTTPURL, respectively * translating emoticons into text using the [emoji package](https://pypi.org/project/emoji/). | 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</th> <th colspan="2" scope="colgroup">NER</th> <th rowspan="2" scope="colgroup">Average</th> </tr> <tr> <th scope="col">IndoLEM</th> <th scope="col">SmSA</th> <th scope="col">EmoT</th> <th scope="col">HS1</th> <th scope="col">HS2</th> <th scope="col">Formal</th> <th scope="col">Informal</th> </tr> <tr> <td scope="row">mBERT</td> <td>76.6</td> <td>84.7</td> <td>67.5</td> <td>85.1</td> <td>75.1</td> <td>85.2</td> <td>83.2</td> <td>79.6</td> </tr> <tr> <td scope="row">malayBERT</td> <td>82.0</td> <td>84.1</td> <td>74.2</td> <td>85.0</td> <td>81.9</td> <td>81.9</td> <td>81.3</td> <td>81.5</td> </tr> <tr> <td scope="row">IndoBERT (Willie, et al., 2020)</td> <td>84.1</td> <td>88.7</td> <td>73.3</td> <td>86.8</td> <td>80.4</td> <td>86.3</td> <td>84.3</td> <td>83.4</td> </tr> <tr> <td scope="row">IndoBERT (Koto, et al., 2020)</td> <td>84.1</td> <td>87.9</td> <td>71.0</td> <td>86.4</td> <td>79.3</td> <td>88.0</td> <td><b>86.9</b></td> <td>83.4</td> </tr> <tr> <td scope="row">IndoBERTweet (1M steps from scratch)</td> <td>86.2</td> <td>90.4</td> <td>76.0</td> <td><b>88.8</b></td> <td><b>87.5</b></td> <td><b>88.1</b></td> <td>85.4</td> <td>86.1</td> </tr> <tr> <td scope="row">IndoBERT + Voc adaptation + 200k steps</td> <td><b>86.6</b></td> <td><b>92.7</b></td> <td><b>79.0</b></td> <td>88.4</td> <td>84.0</td> <td>87.7</td> <td><b>86.9</b></td> <td><b>86.5</b></td> </tr> </table> | 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 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)}, year={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 merged models can be found bellow in the description of the attached model version. Capabilities NSFW Photography SFW Photography is also possible, see "Trigger Words" bellow. Photorealistic 3D renders Emphasis on human anatomy Limitations Anything not listed above. This is model was created as a baseline to a general purpose model I'm working on. Stylized images and object images are possible, but require a little finesse to generate. Trigger Words This checkpoint does not contain any trigger words. However, placing some tags at the beginning of the prompts can heavily influence the generation. These tags include: "nsfw", "sfw", "erotica", and "nudity", "3d render", "cartoon" Note: For SFW generation, try adding sfw to your prompt and nsfw to your negative prompt. For NSFW generation, try adding either nsfw, erotica, or nudity to your prompt and sfw to your negative prompt. In general, this is more useful for generating sfw images. This concept also applies to 3rd render and cartoon. I recommend leaving 3rd render and cartoon both in your negative prompt for generating photographic images. Basic Prompt Guide This model heavily revolves around UnstablePhotorealv.5. This means that you can the tagging system for PhotoReal, although I would recommend using a combination of the PhotoReal comma system and more natural language prompting. Guide to prompting with PhotoReal - https://docs.google.com/document/d/1-DDIHVbsYfynTp_rsKLu4b2tSQgxtO5F6pNsNla12k0/edit | 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, missing limb, floating limbs, twisted, blurry, fused fingers, long neck, words, logo, text, mutated hands, mutated fingers Modify as needed. For example, adding 3d render, cartoon to your negative prompt will help generate photographic images. The prompts for this model are fairly flexible, experiment to find out what works best for you. | 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 wonderful artists over at the OnePieceCock discord server. To get the best result do a prompt like this 'NAME as cryingchopper, ...' make sure to keep crying chopper with no space because that was how it trained. </p> <img alt="cleanchooper.jpg" src="https://s3.amazonaws.com/moonup/production/uploads/1666321853612-631ba03acf39db4b171a0877.jpeg" title="cleanchooper.jpg"> <a href="https://s3.amazonaws.com/moonup/production/uploads/1666321853612-631ba03acf39db4b171a0877.jpeg" download>Download: Crying-Chopper_model-v1</a> </body> </html> | d5fa323b41aa4003bf966fe3698c5edd |
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