README.md

---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- dair-ai/emotion
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9375
      name: Accuracy
    - type: f1
      value: 0.937890467332837
      name: F1
---

# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1448
- Accuracy: 0.9375
- F1: 0.9379

The notebook used to fine-tune this model may be found [HERE](https://www.kaggle.com/marcoloureno/distilbert-base-uncased-finetuned-emotion).

## Model description

DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
with three objectives:

- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
  sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
  model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
  usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
  tokens. It allows the model to learn a bidirectional representation of the sentence.
- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
  model.

This way, the model learns the same inner representation of the English language than its teacher model, while being
faster for inference or downstream tasks.

## Intended uses & limitations
[Emotion](https://huggingface.co/datasets/dair-ai/emotion) is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. This dataset was developed for the paper entitled "CARER: Contextualized Affect Representations for Emotion Recognition" (Saravia et al.) through noisy labels, annotated via distant
supervision as in the paper"Twitter sentiment classification using distant supervision" (Go et al).

The DistilBERT model was fine-tuned to this dataset, allowing for the classification of sentences into one of the six basic emotions (anger, fear, joy, love, sadness, and surprise).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5337        | 1.0   | 250  | 0.1992          | 0.927    | 0.9262 |
| 0.1405        | 2.0   | 500  | 0.1448          | 0.9375   | 0.9379 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3