README.md

---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-finetuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.937
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.93
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8939874310281785
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.93
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9310544672210583
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8930616486578864
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.93
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.93
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8927862771696669
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.93
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.930070287337576
      verified: true
    - name: loss
      type: loss
      value: 0.19910235702991486
      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. -->

# bert-finetuned-emotion

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1582
- Accuracy: 0.937

## 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: 10
- eval_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.553         | 1.0   | 1600 | 0.2631          | 0.9255   |
| 0.161         | 2.0   | 3200 | 0.1582          | 0.937    |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1