metadata

base_model: google-t5/t5-small
datasets:
  - Andyrasika/TweetSumm-tuned
library_name: peft
license: apache-2.0
metrics:
  - rouge
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: ia3-finetune-t5-small-tweetsumm-1724776109
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: Andyrasika/TweetSumm-tuned
          type: Andyrasika/TweetSumm-tuned
        metrics:
          - type: rouge
            value: 0.3032
            name: Rouge1
          - type: f1
            value: 0.8624
            name: F1
          - type: precision
            value: 0.8604
            name: Precision
          - type: recall
            value: 0.8646
            name: Recall

ia3-finetune-t5-small-tweetsumm-1724776109

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

Loss: 2.4772
Rouge1: 0.3032
Rouge2: 0.1016
Rougel: 0.2431
Rougelsum: 0.2761
Gen Len: 49.7364
F1: 0.8624
Precision: 0.8604
Recall: 0.8646

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: 0.001
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
3.074	1.0	110	2.7180	0.2578	0.07	0.2025	0.2348	49.7455	0.8554	0.8499	0.8613
2.8218	2.0	220	2.5242	0.2895	0.0902	0.2315	0.2639	49.7	0.8603	0.8578	0.863
2.5886	3.0	330	2.4772	0.3032	0.1016	0.2431	0.2761	49.7364	0.8624	0.8604	0.8646

Framework versions

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