| --- |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: BertAbstractComp |
| 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. --> |
|
|
| # BertAbstractComp |
|
|
| 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: 0.7130 |
| - Accuracy: 0.8062 |
| - Precision: 0.4972 |
| - Recall: 0.4770 |
| - F1: 0.4772 |
| - Top3: 0.9490 |
| - Top3macro: 0.7051 |
|
|
| ## 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: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 4 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Top3 | Top3macro | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:---------:| |
| | 0.4172 | 1.0 | 1640 | 0.9578 | 0.7640 | 0.4137 | 0.3973 | 0.3969 | 0.9292 | 0.6189 | |
| | 0.4051 | 2.0 | 3280 | 0.7427 | 0.8024 | 0.4759 | 0.4656 | 0.4654 | 0.9430 | 0.6759 | |
| | 0.2359 | 3.0 | 4920 | 0.8947 | 0.8015 | 0.4735 | 0.4777 | 0.4654 | 0.9402 | 0.6772 | |
| | 0.1543 | 4.0 | 6560 | 0.9402 | 0.8097 | 0.4900 | 0.4890 | 0.4839 | 0.9475 | 0.7062 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.2.1 |
| - Tokenizers 0.19.1 |
|
|
