README.md

---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9275
    - name: F1
      type: f1
      value: 0.9273822408882375
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.919
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8882001804445858
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.919
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9194695149914663
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.857858142469294
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.919
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.919
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8684381937860847
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.919
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9182406234430719
      verified: true
    - name: loss
      type: loss
      value: 0.21632428467273712
      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-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: 0.2237
- Accuracy: 0.9275
- F1: 0.9274

## 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: 2e-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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8643        | 1.0   | 250  | 0.3324          | 0.9065   | 0.9025 |
| 0.2589        | 2.0   | 500  | 0.2237          | 0.9275   | 0.9274 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3