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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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- emotion |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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model-index: |
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- name: distilbert-base-uncased-fine-tuned-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: 0.9255 |
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- name: F1 |
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type: f1 |
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value: 0.9254141326182981 |
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- name: Recall |
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type: recall |
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value: 0.9255 |
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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-fine-tuned-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: 0.2156 |
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- Accuracy: 0.9255 |
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- F1: 0.9254 |
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- Recall: 0.9255 |
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## Model description |
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This is the resuls of fine-tuning a distilbert-base-uncased trained on a NVIDIA GeForce GTX 1650, using a WSL with 7 gb of ram on windows 11. |
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The fine-tuning was obtained by following the book **Natural Language Processing with Tranformers: Building Languaje Applications with Hugging Fabe, By Lewis Tunstall, Leandro von Werra & Thomas Wolf** |
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Labels are associated to: |
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1. *LABEL_0* is **sadness** |
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2. *LABEL_1* is **joy** |
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3. *LABEL_2* is **love** |
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4. *LABEL_3* is **anger** |
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5. *LABEL_4* is **fear** |
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6. *LABEL_5* is **surprise** |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:| |
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| 0.7838 | 1.0 | 250 | 0.2995 | 0.906 | 0.9039 | 0.906 | |
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| 0.237 | 2.0 | 500 | 0.2156 | 0.9255 | 0.9254 | 0.9255 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.13.2 |
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- Tokenizers 0.12.1 |
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