README.md

---
license: apache-2.0
tags:
- generated_from_trainer
- text-classification
- emotion
- pytorch
language:
- en
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-cased-emotion
  results:
  - task:
      type: text-classification
      name: text-classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: validation
    metrics:
    - name: accuracy
      type: accuracy
      value: 0.9235
      verified: true
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9235
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.89608475565062
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9235
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9224273416855945
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8581097243584549
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9235
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9235
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8746813002250796
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9235
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9217456925724525
      verified: true
    - name: loss
      type: loss
      value: 0.32714536786079407
      verified: true
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.938
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9281100797474869
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.938
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9376891512759605
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9029821552608664
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.938
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.938
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.9147207975135915
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.938
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9373403463117288
      verified: true
    - name: loss
      type: loss
      value: 0.23682540655136108
      verified: true
---

<!-- 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. -->

# distilbert-base-cased-emotion

**Training:** The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on [emotion](https://huggingface.co/datasets/emotion) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3272
- Accuracy: 0.9235
- F1: 0.9217
- Precision: 0.9224
- Recall: 0.9235

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2776        | 1.0   | 500  | 0.2954          | 0.9      | 0.8957 | 0.9031    | 0.9    |
| 0.1887        | 2.0   | 1000 | 0.1716          | 0.934    | 0.9344 | 0.9370    | 0.934  |
| 0.119         | 3.0   | 1500 | 0.1614          | 0.9345   | 0.9342 | 0.9377    | 0.9345 |
| 0.1001        | 4.0   | 2000 | 0.2018          | 0.936    | 0.9353 | 0.9359    | 0.936  |
| 0.0704        | 5.0   | 2500 | 0.1925          | 0.935    | 0.9349 | 0.9354    | 0.935  |
| 0.0471        | 6.0   | 3000 | 0.2369          | 0.938    | 0.9373 | 0.9377    | 0.938  |
| 0.0322        | 7.0   | 3500 | 0.2693          | 0.938    | 0.9382 | 0.9392    | 0.938  |
| 0.0137        | 8.0   | 4000 | 0.2926          | 0.937    | 0.9371 | 0.9372    | 0.937  |
| 0.0099        | 9.0   | 4500 | 0.2964          | 0.9365   | 0.9362 | 0.9362    | 0.9365 |
| 0.0114        | 10.0  | 5000 | 0.3044          | 0.935    | 0.9349 | 0.9350    | 0.935  |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6