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---
library_name: peft
tags:
- parquet
- text-classification
datasets:
- tweet_eval
metrics:
- accuracy
base_model: finiteautomata/betonews-tweetcontext
model-index:
- name: finiteautomata_betonews-tweetcontext-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: 0.6104712041884817
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. -->
# finiteautomata_betonews-tweetcontext-finetuned-lora-tweet_eval_irony
This model is a fine-tuned version of [finiteautomata/betonews-tweetcontext](https://huggingface.co/finiteautomata/betonews-tweetcontext) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.6105
## 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 |
|:--------:|:----------:|:-----:|
| 0.4775 | None | 0 |
| 0.5581 | 0.6867 | 0 |
| 0.5476 | 0.6695 | 1 |
| 0.5717 | 0.6472 | 2 |
| 0.5801 | 0.6296 | 3 |
| 0.5969 | 0.6127 | 4 |
| 0.5927 | 0.6050 | 5 |
| 0.6073 | 0.5979 | 6 |
| 0.6105 | 0.5891 | 7 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2 |