---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google-bert/bert-large-uncased
metrics:
- accuracy
model-index:
- name: emotion-bert-large-uncased-balanced-lora
  results: []
datasets:
- AdamCodd/emotion-balanced
language:
- en
pipeline_tag: text-classification
---

<!-- 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. -->

# emotion-bert-large-uncased-balanced-lora

This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1721
- Accuracy: 0.942

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 250  | 0.4374          | 0.86     |
| 0.7097        | 2.0   | 500  | 0.2582          | 0.9195   |
| 0.7097        | 3.0   | 750  | 0.2047          | 0.9345   |
| 0.1878        | 4.0   | 1000 | 0.1667          | 0.9385   |
| 0.1878        | 5.0   | 1250 | 0.1861          | 0.935    |
| 0.1306        | 6.0   | 1500 | 0.1871          | 0.9415   |
| 0.1306        | 7.0   | 1750 | 0.1720          | 0.943    |
| 0.1035        | 8.0   | 2000 | 0.1696          | 0.9425   |
| 0.1035        | 9.0   | 2250 | 0.1706          | 0.9415   |
| 0.0851        | 10.0  | 2500 | 0.1721          | 0.942    |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1