transformers tensorboard safetensors albert text-classification generated_from_trainer dataset:emotion base_model:albert/albert-base-v2 base_model:finetune:albert/albert-base-v2 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: albert-base-v2
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: AlBert-finetuned-emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9325
      name: Accuracy
---

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# AlBert-finetuned-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: 0.2185
- Accuracy: 0.9325

## 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: 3.069458879876956e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 22
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4279        | 1.0   | 1000 | 0.3697          | 0.888    |
| 0.2448        | 2.0   | 2000 | 0.2732          | 0.914    |
| 0.1731        | 3.0   | 3000 | 0.2270          | 0.923    |
| 0.1305        | 4.0   | 4000 | 0.2193          | 0.9285   |
| 0.1053        | 5.0   | 5000 | 0.2185          | 0.9325   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
