transformers pytorch bert text-classification generated_from_trainer dataset:emotion model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased_emotion_ft
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9355
      name: Accuracy
    - type: f1
      value: 0.9356618127644594
      name: F1
    - type: precision
      value: 0.9107946719559769
      name: Precision
---

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# bert-base-uncased_emotion_ft

This model was trained from scratch on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1553
- Accuracy: 0.9355
- F1: 0.9357
- Precision: 0.9108

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.82          | 1.0   | 250  | 0.2697          | 0.9105   | 0.9112 | 0.8759    |
| 0.2002        | 2.0   | 500  | 0.1846          | 0.9325   | 0.9331 | 0.9059    |
| 0.1237        | 3.0   | 750  | 0.1562          | 0.9365   | 0.9368 | 0.9120    |
| 0.097         | 4.0   | 1000 | 0.1553          | 0.9355   | 0.9357 | 0.9108    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
