metadata

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

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 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