christinacdl/clickbait_binary_detection

1---2license: mit3tags:4- generated_from_trainer5metrics:6- accuracy7- f18- precision9- recall10model-index:11- name: clickbait_binary_detection12  results: []13datasets:14- christinacdl/clickbait_notclickbait_dataset15language:16- en17pipeline_tag: text-classification18---19 20<!-- This model card has been generated automatically according to the information the Trainer had access to. You21should probably proofread and complete it, then remove this comment. -->22 23# clickbait_binary_detection24 25This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.26It achieves the following results on the evaluation set:27- Loss: 0.463028- Macro F1: 0.915529- Micro F1: 0.921530- Accuracy: 0.921531 32 33Performance on test set:34 35- Accuracy: 0.925799086757990836 37- F1 score: 0.919928243105841338 39- Precision: 0.923379349072488240 41- Recall : 0.916875688364726842 43- Matthews Correlation Coefficient: 0.840229867557690244 45- Precision of each class: [0.931899   0.91485969]46 47- Recall of each class: [0.95152505 0.88222632]48 49- F1 score of each class: [0.94160977 0.89824671]50 51 52## Model description53 54More information needed55 56## Intended uses & limitations57 58More information needed59 60## Training and evaluation data61 62More information needed63 64## Training procedure65 66### Training hyperparameters67 68The following hyperparameters were used during training:69- learning_rate: 2e-0670- train_batch_size: 671- eval_batch_size: 1072- seed: 4273- gradient_accumulation_steps: 274- total_train_batch_size: 1275- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0876- lr_scheduler_type: linear77- num_epochs: 1078- mixed_precision_training: Native AMP79 80### Training results81 82| Training Loss | Epoch | Step  | Validation Loss | Macro F1 | Micro F1 | Accuracy |83|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|84| 0.2296        | 1.0   | 3650  | 0.2236          | 0.9105   | 0.9183   | 0.9183   |85| 0.228         | 2.0   | 7301  | 0.2708          | 0.9115   | 0.9192   | 0.9192   |86| 0.2075        | 3.0   | 10951 | 0.3141          | 0.9164   | 0.9224   | 0.9224   |87| 0.1881        | 4.0   | 14602 | 0.3211          | 0.9143   | 0.9201   | 0.9201   |88| 0.18          | 5.0   | 18252 | 0.3852          | 0.9130   | 0.9188   | 0.9188   |89| 0.1818        | 6.0   | 21903 | 0.3784          | 0.9110   | 0.9174   | 0.9174   |90| 0.1495        | 7.0   | 25553 | 0.4606          | 0.9106   | 0.9156   | 0.9156   |91| 0.1453        | 8.0   | 29204 | 0.4630          | 0.9155   | 0.9215   | 0.9215   |92 93 94### Framework versions95 96- Transformers 4.27.197- Pytorch 2.0.1+cu11898- Datasets 2.9.099- Tokenizers 0.13.3