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: 0.925
    - name: F1
      type: f1
      value: 0.925169929474641
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9185
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8812304360487162
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9185
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9186256759712246
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8685675449036236
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9185
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9185
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8737330835692586
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9185
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9182854700791021
      verified: true
    - name: loss
      type: loss
      value: 0.2216690629720688
      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: 0.2202
- Accuracy: 0.925
- F1: 0.9252

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8419        | 1.0   | 250  | 0.3236          | 0.9025   | 0.8999 |
| 0.258         | 2.0   | 500  | 0.2202          | 0.925    | 0.9252 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1