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: results
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9305
      name: Accuracy
---

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# results

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: 0.2314
- Accuracy: 0.9305

## 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: 2.8e-05
- train_batch_size: 4
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4298        | 1.0   | 4000  | 0.4243          | 0.9085   |
| 0.2389        | 2.0   | 8000  | 0.3465          | 0.922    |
| 0.1856        | 3.0   | 12000 | 0.2700          | 0.929    |


### Framework versions

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