---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: ALBERT_trainer_emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.927
---

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# ALBERT_trainer_emotion

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3559
- Accuracy: 0.927

## 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: 20
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1217        | 1.0   | 800  | 0.1936          | 0.93     |
| 0.1054        | 2.0   | 1600 | 0.2105          | 0.9305   |
| 0.0893        | 3.0   | 2400 | 0.2199          | 0.933    |
| 0.0751        | 4.0   | 3200 | 0.2412          | 0.9375   |
| 0.0608        | 5.0   | 4000 | 0.2853          | 0.932    |
| 0.0342        | 6.0   | 4800 | 0.3575          | 0.9315   |
| 0.025         | 7.0   | 5600 | 0.3698          | 0.931    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2