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--- |
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library_name: transformers |
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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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- emotion-classification |
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- text-classification |
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- distilbert |
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datasets: |
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- dair-ai/emotion |
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language: |
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- en |
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metrics: |
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- accuracy |
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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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# results |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an Emotion Dataset. |
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## Model description |
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This model fine-tunes DistilBERT for emotion classification. It can detect emotions in language and then classify them into: sadness, joy, love, anger, fear, surprise. |
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## Intended uses & limitations |
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Used to explain the inner emotions of simple sentences. This model may lack contextual reasoning ability and cannot understand connecting words such as transitions. |
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## Training and evaluation data |
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- Training Dataset: dair-ai/emotion (16,000 examples) |
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- Validation set: 2,000 examples |
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- Test set: 2,000 examples |
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- Validation Accuracy: |
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- epoch1:0.9065 |
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- epoch2:0.9345 |
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- epoch3:0.93 |
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- epoch4:0.942 |
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- epoch5:0.94 |
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- Test Accuracy: 0.942 |
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- Training Time: 2:02:44 |
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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: 5e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cpu |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
