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-ia3-finetune-tweetsumm-1724827331
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: Andyrasika/TweetSumm-tuned
          type: Andyrasika/TweetSumm-tuned
        metrics:
          - type: rouge
            value: 0.4407
            name: Rouge1
          - type: f1
            value: 0.8906
            name: F1
          - type: precision
            value: 0.8894
            name: Precision
          - type: recall
            value: 0.8921
            name: Recall

t5-base-ia3-finetune-tweetsumm-1724827331

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: 1.8276
Rouge1: 0.4407
Rouge2: 0.1997
Rougel: 0.3672
Rougelsum: 0.4075
Gen Len: 49.5727
F1: 0.8906
Precision: 0.8894
Recall: 0.8921

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: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len	F1	Precision	Recall
2.2511	1.0	879	1.9364	0.4398	0.1855	0.3668	0.411	49.5182	0.8883	0.8875	0.8892
1.4557	2.0	1758	1.8611	0.4491	0.2031	0.3721	0.4148	49.6091	0.8901	0.8889	0.8915
1.8149	3.0	2637	1.8386	0.4436	0.2001	0.3707	0.4092	49.5636	0.8905	0.889	0.8923
2.7192	4.0	3516	1.8271	0.4366	0.1966	0.3643	0.4041	49.6091	0.8897	0.8878	0.8917
1.7838	5.0	4395	1.8276	0.4407	0.1997	0.3672	0.4075	49.5727	0.8906	0.8894	0.8921

Framework versions

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