metadata

license: apache-2.0
base_model: google-t5/t5-base
tags:
  - generated_from_trainer
datasets:
  - Andyrasika/TweetSumm-tuned
metrics:
  - rouge
  - f1
  - precision
  - recall
model-index:
  - name: t5-base-Full-TweetSumm-1724683206
    results:
      - task:
          name: Summarization
          type: summarization
        dataset:
          name: Andyrasika/TweetSumm-tuned
          type: Andyrasika/TweetSumm-tuned
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.4709
          - name: F1
            type: f1
            value: 0.8952
          - name: Precision
            type: precision
            value: 0.8934
          - name: Recall
            type: recall
            value: 0.8971

t5-base-Full-TweetSumm-1724683206

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: 1.8697
Rouge1: 0.4709
Rouge2: 0.2223
Rougel: 0.3999
Rougelsum: 0.4391
Gen Len: 41.8455
F1: 0.8952
Precision: 0.8934
Recall: 0.8971

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: 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
2.2928	1.0	220	1.8094	0.466	0.2146	0.3912	0.4301	41.9182	0.891	0.8891	0.8931
1.2939	2.0	440	1.7929	0.4605	0.2125	0.3897	0.4259	42.0	0.8928	0.8914	0.8944
0.7227	3.0	660	1.8697	0.4709	0.2223	0.3999	0.4391	41.8455	0.8952	0.8934	0.8971

Framework versions

Transformers 4.44.0
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1