---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion-movies-186k
  results: []
---

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# distilbert-base-uncased-finetuned-emotion-movies-186k

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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