metadata

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_emotion
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: tweet_eval
          type: tweet_eval
          config: emotion
          split: validation
          args: emotion
        metrics:
          - type: accuracy
            value: 0.46524064171123
            name: accuracy

finiteautomata_betonews-tweetcontext-finetuned-lora-tweet_eval_emotion

This model is a fine-tuned version of finiteautomata/betonews-tweetcontext on the tweet_eval dataset. It achieves the following results on the evaluation set:

accuracy: 0.4652

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: 0.0004
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: 4

Training results

accuracy	train_loss	epoch
0.2273	None	0
0.4278	1.2682	0
0.4385	1.2378	1
0.4465	1.2172	2
0.4652	1.2025	3

Framework versions

PEFT 0.8.2
Transformers 4.37.2
Pytorch 2.2.0
Datasets 2.16.1
Tokenizers 0.15.2