| --- |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: roberta-base-classification |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # roberta-base-classification |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.8665 |
| - Accuracy: {'accuracy': 0.7342799188640974} |
| - F1: {'f1': 0.7306952447422118} |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:| |
| | No log | 1.0 | 163 | 1.3840 | {'accuracy': 0.6024340770791075} | {'f1': 0.5642145589948825} | |
| | No log | 2.0 | 326 | 1.0832 | {'accuracy': 0.6511156186612576} | {'f1': 0.6334471187444455} | |
| | No log | 3.0 | 489 | 1.0334 | {'accuracy': 0.6977687626774848} | {'f1': 0.6897630671623124} | |
| | 1.0727 | 4.0 | 652 | 1.0970 | {'accuracy': 0.6876267748478702} | {'f1': 0.6871985325785717} | |
| | 1.0727 | 5.0 | 815 | 1.0281 | {'accuracy': 0.7342799188640974} | {'f1': 0.7301024691928815} | |
| | 1.0727 | 6.0 | 978 | 1.1807 | {'accuracy': 0.7018255578093306} | {'f1': 0.7067299604929954} | |
| | 0.2589 | 7.0 | 1141 | 1.2407 | {'accuracy': 0.7342799188640974} | {'f1': 0.7314658348123809} | |
| | 0.2589 | 8.0 | 1304 | 1.3048 | {'accuracy': 0.7403651115618661} | {'f1': 0.731151961567854} | |
| | 0.2589 | 9.0 | 1467 | 1.5180 | {'accuracy': 0.718052738336714} | {'f1': 0.7137872411382804} | |
| | 0.0808 | 10.0 | 1630 | 1.3989 | {'accuracy': 0.7606490872210954} | {'f1': 0.7557677624013166} | |
| | 0.0808 | 11.0 | 1793 | 1.5029 | {'accuracy': 0.7606490872210954} | {'f1': 0.7552919114782913} | |
| | 0.0808 | 12.0 | 1956 | 1.7512 | {'accuracy': 0.7241379310344828} | {'f1': 0.7171770258544846} | |
| | 0.0186 | 13.0 | 2119 | 1.6777 | {'accuracy': 0.7363083164300203} | {'f1': 0.7298768119446929} | |
| | 0.0186 | 14.0 | 2282 | 1.8128 | {'accuracy': 0.7363083164300203} | {'f1': 0.7328169574773649} | |
| | 0.0186 | 15.0 | 2445 | 1.7922 | {'accuracy': 0.7383367139959433} | {'f1': 0.7355194715827496} | |
| | 0.0039 | 16.0 | 2608 | 1.8762 | {'accuracy': 0.7281947261663286} | {'f1': 0.7221386387545444} | |
| | 0.0039 | 17.0 | 2771 | 1.8840 | {'accuracy': 0.7363083164300203} | {'f1': 0.7317008958800432} | |
| | 0.0039 | 18.0 | 2934 | 1.8368 | {'accuracy': 0.7383367139959433} | {'f1': 0.7340167563730315} | |
| | 0.0027 | 19.0 | 3097 | 1.8687 | {'accuracy': 0.7363083164300203} | {'f1': 0.7319705371219094} | |
| | 0.0027 | 20.0 | 3260 | 1.8665 | {'accuracy': 0.7342799188640974} | {'f1': 0.7306952447422118} | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.1 |
|
|
