| --- |
| license: apache-2.0 |
| base_model: albert-base-v2 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - emotion |
| metrics: |
| - accuracy |
| model-index: |
| - name: ALBERT_Emotions_tuned |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: emotion |
| type: emotion |
| config: split |
| split: validation |
| args: split |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.927 |
| --- |
| |
| <!-- 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. --> |
|
|
| # ALBERT_Emotions_tuned |
|
|
| This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the emotion dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1846 |
| - Accuracy: 0.927 |
|
|
| ## 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: 5e-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 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | No log | 0.1 | 100 | 1.2655 | 0.5575 | |
| | No log | 0.2 | 200 | 1.0801 | 0.6415 | |
| | No log | 0.3 | 300 | 1.0138 | 0.661 | |
| | No log | 0.4 | 400 | 1.0651 | 0.605 | |
| | 1.1328 | 0.5 | 500 | 0.7816 | 0.758 | |
| | 1.1328 | 0.6 | 600 | 0.6307 | 0.7885 | |
| | 1.1328 | 0.7 | 700 | 0.5072 | 0.848 | |
| | 1.1328 | 0.8 | 800 | 0.5057 | 0.8305 | |
| | 1.1328 | 0.9 | 900 | 0.3853 | 0.889 | |
| | 0.5503 | 1.0 | 1000 | 0.3769 | 0.8915 | |
| | 0.5503 | 1.1 | 1100 | 0.3778 | 0.8995 | |
| | 0.5503 | 1.2 | 1200 | 0.3899 | 0.9005 | |
| | 0.5503 | 1.3 | 1300 | 0.3330 | 0.9085 | |
| | 0.5503 | 1.4 | 1400 | 0.3339 | 0.9085 | |
| | 0.3049 | 1.5 | 1500 | 0.2662 | 0.915 | |
| | 0.3049 | 1.6 | 1600 | 0.3209 | 0.9045 | |
| | 0.3049 | 1.7 | 1700 | 0.3110 | 0.898 | |
| | 0.3049 | 1.8 | 1800 | 0.3185 | 0.9075 | |
| | 0.3049 | 1.9 | 1900 | 0.2439 | 0.922 | |
| | 0.2485 | 2.0 | 2000 | 0.2190 | 0.925 | |
| | 0.2485 | 2.1 | 2100 | 0.2372 | 0.9235 | |
| | 0.2485 | 2.2 | 2200 | 0.2497 | 0.9265 | |
| | 0.2485 | 2.3 | 2300 | 0.2811 | 0.9195 | |
| | 0.2485 | 2.4 | 2400 | 0.2350 | 0.9195 | |
| | 0.1587 | 2.5 | 2500 | 0.2303 | 0.9245 | |
| | 0.1587 | 2.6 | 2600 | 0.2242 | 0.9285 | |
| | 0.1587 | 2.7 | 2700 | 0.2141 | 0.9325 | |
| | 0.1587 | 2.8 | 2800 | 0.2185 | 0.9315 | |
| | 0.1587 | 2.9 | 2900 | 0.2047 | 0.9315 | |
| | 0.1398 | 3.0 | 3000 | 0.2036 | 0.9335 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.38.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
