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distilbert-base-uncased-finetuned-emotion-movies-186k
This model is a fine-tuned version of distilbert-base-uncased on a 186k movie reviews/emotions self-collected dataset from 1150 movies from TMDB. It achieves the following results on the evaluation set:
- Loss: 0.3572
- Accuracy: 0.8635
- F1: 0.8637
Model description
The model classifies into the following emotions:
- 'LABEL_0': 'sadness'
- 'LABEL_1': 'joy'
- 'LABEL_2': 'love'
- 'LABEL_3': 'anger'
- 'LABEL_4': 'fear'
- 'LABEL_5': 'surprise'
Intended uses & limitations
Academic
Training and evaluation data
The model was trained with a dataset (186k rows) of movies reviews/emotions from 1150 movies from TMDB, taking 20% for testing.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.4956 | 1.0 | 5828 | 0.3770 | 0.8531 | 0.8513 |
| 0.3035 | 2.0 | 11656 | 0.3572 | 0.8635 | 0.8637 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
