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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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- generated_from_trainer |
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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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--- |
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# distilbert-base-uncased-finetuned-emotion |
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## Model description |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on `emotion` dataset. The dataset consists of 1658616 examples of text and emotion labels. The emotion labels are one of the following: 'joy', 'sadness', 'fear', 'anger', 'surprise'. The model was fine-tuned using the `Trainer` API from the `transformers` library on the `emotion` dataset. |
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## Intended uses & limitations |
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This model is intended to be used for emotion classification tasks. The model is trained on English text data and may not perform well on other languages. The model is trained on the `emotion` dataset and may not perform well on other emotion classification tasks. |
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## Training and evaluation data |
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The model was trained on the `emotion` dataset. The dataset consists of 1658616 examples of text and emotion labels. The emotion labels are one of the following: 'joy', 'sadness', 'fear', 'anger', 'surprise'. |
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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: Use OptimizerNames.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: 2 |
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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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| 0.7763 | 1.0 | 250 | 0.2839 | 0.9145 | 0.9141 | |
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| 0.2336 | 2.0 | 500 | 0.2054 | 0.931 | 0.9309 | |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2054 |
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- Accuracy: 0.931 |
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- F1: 0.9309 |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.5.1+cpu |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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