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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dir
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
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.
- Loss: 0.1884
- Accuracy: 0.936
## Model description
This model is a simple Pytorch Custom Model that uses BERT to classify the emotions of a given text
## Intended uses & limitations
- It only supports English for now (am willing to add French next)
- 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)
- The emotions it can recognize are limited (the 6 major emotions) so it can't detail to mixed psychological outcomes
- 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
## Training and evaluation data
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
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9469 | 1.0 | 625 | 0.2593 | 0.9202 |
| 0.2403 | 2.0 | 1250 | 0.2080 | 0.9302 |
| 0.1726 | 3.0 | 1875 | 0.1884 | 0.936 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
|