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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: distilbert-base-uncased-finetuned-emotion |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: emotion |
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type: emotion |
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config: split |
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split: validation |
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args: split |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9455 |
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- name: F1 |
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type: f1 |
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value: 0.9455317055605632 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-uncased-finetuned-emotion |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1789 |
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- Accuracy: 0.9455 |
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- F1: 0.9455 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.799 | 1.0 | 250 | 0.2667 | 0.92 | 0.9206 | |
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| 0.2043 | 2.0 | 500 | 0.1661 | 0.9345 | 0.9341 | |
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| 0.1359 | 3.0 | 750 | 0.1580 | 0.938 | 0.9387 | |
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| 0.1028 | 4.0 | 1000 | 0.1517 | 0.943 | 0.9435 | |
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| 0.0838 | 5.0 | 1250 | 0.1485 | 0.9385 | 0.9384 | |
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| 0.0691 | 6.0 | 1500 | 0.1514 | 0.94 | 0.9402 | |
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| 0.0578 | 7.0 | 1750 | 0.1854 | 0.9345 | 0.9338 | |
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| 0.0488 | 8.0 | 2000 | 0.1707 | 0.9405 | 0.9406 | |
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| 0.0414 | 9.0 | 2250 | 0.1822 | 0.944 | 0.9441 | |
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| 0.0355 | 10.0 | 2500 | 0.1789 | 0.9455 | 0.9455 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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