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--- |
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license: apache-2.0 |
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base_model: bert-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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model-index: |
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- name: dir |
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results: [] |
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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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# bert-emotion |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [Emotions](https://www.kaggle.com/datasets/nelgiriyewithana/emotions) dataset from Kaggle, with the best results on that last it can also provide a verbose understanding of the general emotion themes of English text. |
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- Loss: 0.1884 |
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- Accuracy: 0.936 |
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## Model description |
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This model is a simple Pytorch Custom Model that uses BERT to classify the emotions of a given text |
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## Intended uses & limitations |
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- It only supports English for now (am willing to add French next) |
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- The input text has a limit in size, it can suit a mid-size paragraph easily but can't handle large documents (you can bypass this by dividing the document to paragraphs and make a weights summation) |
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- The emotions it can recognize are limited (the 6 major emotions) so it can't detail to mixed psychological outcomes |
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- Fine Tuning time : well we all know how BERT can be slow sometimes so i suggest for anyone who wants to develop over the idea to use DistelBERT for faster results |
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## Training and evaluation data |
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This dataset contains two key columns: 'text' and 'label'. The 'label' column represents six different emotion classes: sadness (0), joy (1), love (2), anger (3), fear (4), and surprise (5). Get ready to dive deep into the world of human emotions |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.9469 | 1.0 | 625 | 0.2593 | 0.9202 | |
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| 0.2403 | 2.0 | 1250 | 0.2080 | 0.9302 | |
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| 0.1726 | 3.0 | 1875 | 0.1884 | 0.936 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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