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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- AdamCodd/emotion-balanced |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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widget: |
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- text: Your actions were very caring. |
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example_title: Test sentence |
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base_model: distilbert-base-uncased |
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model-index: |
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- name: distilbert-base-uncased-finetuned-emotion-balanced |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: emotion |
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type: emotion |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.9521 |
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name: Accuracy |
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- type: loss |
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value: 0.1216 |
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name: Loss |
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- type: f1 |
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value: 0.9520944952964783 |
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name: F1 |
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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-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 balanced dataset](https://huggingface.co/datasets/AdamCodd/emotion-balanced). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1216 |
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- Accuracy: 0.9521 |
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## Model description |
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This emotion classifier has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split. |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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- seed: 1270 |
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- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 150 |
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- num_epochs: 1 |
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- weight_decay: 0.01 |
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### Training results |
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precision recall f1-score support |
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sadness 0.9882 0.9485 0.9679 1496 |
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joy 0.9956 0.9057 0.9485 1496 |
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love 0.9256 0.9980 0.9604 1496 |
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anger 0.9628 0.9519 0.9573 1496 |
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fear 0.9348 0.9098 0.9221 1496 |
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surprise 0.9160 0.9987 0.9555 1496 |
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accuracy 0.9521 8976 |
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macro avg 0.9538 0.9521 0.9520 8976 |
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weighted avg 0.9538 0.9521 0.9520 8976 |
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test_acc: 0.9520944952964783 |
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test_loss: 0.121663898229599 |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch lightning 2.0.8 |
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- Tokenizers 0.13.3 |
