---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- precision
- recall
base_model: bert-base-uncased
model-index:
- name: LoRA-SemEval
  results: []
---

<!-- 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. -->

# LoRA-SemEval

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7185
- Accuracy: 0.6830
- Precision: 0.6857
- Recall: 0.6830
- Micro-avg-recall: 0.6830
- Micro-avg-precision: 0.6830

## 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: 2e-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 | Precision | Recall | Micro-avg-recall | Micro-avg-precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:----------------:|:-------------------:|
| 0.8156        | 1.0   | 2851 | 0.7505          | 0.6628   | 0.6653    | 0.6628 | 0.6628           | 0.6628              |
| 0.6812        | 2.0   | 5702 | 0.7254          | 0.6789   | 0.6819    | 0.6789 | 0.6789           | 0.6789              |
| 0.661         | 3.0   | 8553 | 0.7185          | 0.6830   | 0.6857    | 0.6830 | 0.6830           | 0.6830              |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0