| | --- |
| | license: apache-2.0 |
| | base_model: distilbert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - rotten_tomatoes |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: my_distilbert_model |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: rotten_tomatoes |
| | type: rotten_tomatoes |
| | config: default |
| | split: test |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8433395872420263 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8432898032121621 |
| | - name: Precision |
| | type: precision |
| | value: 0.843776433767552 |
| | - name: Recall |
| | type: recall |
| | value: 0.8433395872420262 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # my_distilbert_model |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5593 |
| | - Accuracy: 0.8433 |
| | - F1: 0.8433 |
| | - Precision: 0.8438 |
| | - Recall: 0.8433 |
| | |
| | ## 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: 3 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.4222 | 1.0 | 534 | 0.3821 | 0.8424 | 0.8421 | 0.8450 | 0.8424 | |
| | | 0.2558 | 2.0 | 1068 | 0.4620 | 0.8433 | 0.8432 | 0.8445 | 0.8433 | |
| | | 0.1609 | 3.0 | 1602 | 0.5593 | 0.8433 | 0.8433 | 0.8438 | 0.8433 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.34.1 |
| | - Pytorch 2.1.0 |
| | - Datasets 2.14.6 |
| | - Tokenizers 0.14.1 |
| | |
