metadata

base_model: google-t5/t5-base
datasets:
  - Andyrasika/TweetSumm-tuned
library_name: peft
license: apache-2.0
metrics:
  - rouge
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: t5-base-LoRA-TweetSumm-1724689228
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: Andyrasika/TweetSumm-tuned
          type: Andyrasika/TweetSumm-tuned
        metrics:
          - type: rouge
            value: 0.4651
            name: Rouge1
          - type: f1
            value: 0.8924
            name: F1
          - type: precision
            value: 0.8906
            name: Precision
          - type: recall
            value: 0.8943
            name: Recall

t5-base-LoRA-TweetSumm-1724689228

This model is a fine-tuned version of google-t5/t5-base on the Andyrasika/TweetSumm-tuned dataset. It achieves the following results on the evaluation set:

Loss: 1.7954
Rouge1: 0.4651
Rouge2: 0.218
Rougel: 0.3904
Rougelsum: 0.4291
Gen Len: 41.8818
F1: 0.8924
Precision: 0.8906
Recall: 0.8943

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len	F1	Precision	Recall
2.3566	1.0	440	1.8523	0.4801	0.2302	0.4078	0.4472	41.6727	0.8942	0.8938	0.8947
1.2968	2.0	880	1.7823	0.447	0.2102	0.3795	0.4136	41.9091	0.8929	0.8925	0.8935
1.7438	3.0	1320	1.7954	0.4651	0.218	0.3904	0.4291	41.8818	0.8924	0.8906	0.8943

Framework versions

PEFT 0.12.1.dev0
Transformers 4.44.0
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1