| | --- |
| | license: apache-2.0 |
| | base_model: albert/albert-base-v2 |
| | tags: |
| | - trl |
| | - sft |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: classify-ISIN-STEP6_binary |
| | 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-ISIN-STEP6_binary |
| | |
| | This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0002 |
| | - Accuracy: 1.0 |
| | - F1: 1.0 |
| | - Precision: 1.0 |
| | - Recall: 1.0 |
| | - Accuracy Label Gd622:null: 1.0 |
| | - Accuracy Label Gd622:yes: 1.0 |
| | |
| | ## 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: 200 |
| | - num_epochs: 20 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Gd622:null | Accuracy Label Gd622:yes | |
| | |:-------------:|:-------:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|:-------------------------:|:------------------------:| |
| | | 0.0056 | 2.4691 | 100 | 0.0042 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.001 | 4.9383 | 200 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0005 | 7.4074 | 300 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0003 | 9.8765 | 400 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0002 | 12.3457 | 500 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0002 | 14.8148 | 600 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0002 | 17.2840 | 700 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0002 | 19.7531 | 800 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.43.3 |
| | - Pytorch 2.4.0 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| | |
