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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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