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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: 0.9235 |
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- name: F1 |
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type: f1 |
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value: 0.923296474937779 |
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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: 0.2195 |
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- Accuracy: 0.9235 |
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- F1: 0.9233 |
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## Model description |
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distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. |
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## Emotion Labels |
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- **label_0:** Sadness |
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- **label_1:** Joy |
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- **label_2:** Love |
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- **label_3:** Anger |
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- **label_4:** Fear |
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- **label_5:** 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: 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: 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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| 0.8537 | 1.0 | 250 | 0.3235 | 0.897 | 0.8958 | |
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| 0.2506 | 2.0 | 500 | 0.2195 | 0.9235 | 0.9233 | |
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### Validation metrics |
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- test_loss : 0.2194512039422989 |
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- test_accuracy : 0.9235 |
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- test_f1 : 0.923296474937779 |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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