| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: albert/albert-base-v2 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: classify-articles |
| | 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. --> |
| |
|
| | # classify-articles |
| |
|
| | This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3819 |
| | - Accuracy: 0.9070 |
| | - F1: 0.9061 |
| | - Precision: 0.9126 |
| | - Recall: 0.9070 |
| | - Accuracy Label Economy: 0.9429 |
| | - Accuracy Label Politics: 0.9574 |
| | - Accuracy Label Science: 0.9362 |
| | - Accuracy Label Sports: 0.96 |
| | - Accuracy Label Technology: 0.6944 |
| |
|
| | ## 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 |
| | - 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: 500 |
| | - num_epochs: 3 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Economy | Accuracy Label Politics | Accuracy Label Science | Accuracy Label Sports | Accuracy Label Technology | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------:|:-----------------------:|:----------------------:|:---------------------:|:-------------------------:| |
| | | 1.3703 | 1.3072 | 100 | 1.3775 | 0.4930 | 0.4238 | 0.6100 | 0.4930 | 0.8 | 0.0213 | 0.7021 | 0.72 | 0.2222 | |
| | | 0.4329 | 2.6144 | 200 | 0.4495 | 0.8977 | 0.9004 | 0.9134 | 0.8977 | 0.9429 | 0.8936 | 0.9149 | 0.96 | 0.75 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.44.2 |
| | - Pytorch 2.4.1 |
| | - Datasets 2.21.0 |
| | - Tokenizers 0.19.1 |
| |
|
