---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: ALBERT_Emotions_tuned
  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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ALBERT_Emotions_tuned

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: 0.1846
- 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.1   | 100  | 1.2655          | 0.5575   |
| No log        | 0.2   | 200  | 1.0801          | 0.6415   |
| No log        | 0.3   | 300  | 1.0138          | 0.661    |
| No log        | 0.4   | 400  | 1.0651          | 0.605    |
| 1.1328        | 0.5   | 500  | 0.7816          | 0.758    |
| 1.1328        | 0.6   | 600  | 0.6307          | 0.7885   |
| 1.1328        | 0.7   | 700  | 0.5072          | 0.848    |
| 1.1328        | 0.8   | 800  | 0.5057          | 0.8305   |
| 1.1328        | 0.9   | 900  | 0.3853          | 0.889    |
| 0.5503        | 1.0   | 1000 | 0.3769          | 0.8915   |
| 0.5503        | 1.1   | 1100 | 0.3778          | 0.8995   |
| 0.5503        | 1.2   | 1200 | 0.3899          | 0.9005   |
| 0.5503        | 1.3   | 1300 | 0.3330          | 0.9085   |
| 0.5503        | 1.4   | 1400 | 0.3339          | 0.9085   |
| 0.3049        | 1.5   | 1500 | 0.2662          | 0.915    |
| 0.3049        | 1.6   | 1600 | 0.3209          | 0.9045   |
| 0.3049        | 1.7   | 1700 | 0.3110          | 0.898    |
| 0.3049        | 1.8   | 1800 | 0.3185          | 0.9075   |
| 0.3049        | 1.9   | 1900 | 0.2439          | 0.922    |
| 0.2485        | 2.0   | 2000 | 0.2190          | 0.925    |
| 0.2485        | 2.1   | 2100 | 0.2372          | 0.9235   |
| 0.2485        | 2.2   | 2200 | 0.2497          | 0.9265   |
| 0.2485        | 2.3   | 2300 | 0.2811          | 0.9195   |
| 0.2485        | 2.4   | 2400 | 0.2350          | 0.9195   |
| 0.1587        | 2.5   | 2500 | 0.2303          | 0.9245   |
| 0.1587        | 2.6   | 2600 | 0.2242          | 0.9285   |
| 0.1587        | 2.7   | 2700 | 0.2141          | 0.9325   |
| 0.1587        | 2.8   | 2800 | 0.2185          | 0.9315   |
| 0.1587        | 2.9   | 2900 | 0.2047          | 0.9315   |
| 0.1398        | 3.0   | 3000 | 0.2036          | 0.9335   |


### Framework versions

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