---
library_name: peft
tags:
- parquet
- text-classification
datasets:
- tweet_eval
metrics:
- accuracy
base_model: aviator-neural/bert-base-uncased-sst2
model-index:
- name: aviator-neural_bert-base-uncased-sst2-finetuned-lora-tweet_eval_irony
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: irony
      split: validation
      args: irony
    metrics:
    - type: accuracy
      value: 0.625130890052356
      name: accuracy
---

<!-- 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. -->

# aviator-neural_bert-base-uncased-sst2-finetuned-lora-tweet_eval_irony

This model is a fine-tuned version of [aviator-neural/bert-base-uncased-sst2](https://huggingface.co/aviator-neural/bert-base-uncased-sst2) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.6251

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| accuracy | train_loss | epoch |
|:--------:|:----------:|:-----:|
| 0.5770   | None       | 0     |
| 0.5728   | 0.7235     | 0     |
| 0.6      | 0.6819     | 1     |
| 0.6178   | 0.6713     | 2     |
| 0.6147   | 0.6542     | 3     |
| 0.6073   | 0.6386     | 4     |
| 0.5958   | 0.6308     | 5     |
| 0.6136   | 0.6202     | 6     |
| 0.6251   | 0.6131     | 7     |


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2