metadata

license: apache-2.0
tags:
  - summarization
  - generated_from_trainer
datasets:
  - cnn_dailymail
metrics:
  - rouge
model-index:
  - name: t5-small-finetuned-cnn-news
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          args: 3.0.0
        metrics:
          - name: Rouge1
            type: rouge
            value: 24.7231
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: HadiPourmousa/TextSummarization
          type: HadiPourmousa/TextSummarization
          config: HadiPourmousa--TextSummarization
          split: train
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 5.2877
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 1.2836
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 5.0791
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 5.086
            verified: true
          - name: loss
            type: loss
            value: 4.567324161529541
            verified: true
          - name: gen_len
            type: gen_len
            value: 19
            verified: true

t5-small-finetuned-cnn-news

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

Loss: 1.8412
Rouge1: 24.7231
Rouge2: 12.292
Rougel: 20.5347
Rougelsum: 23.4668

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: 0.00056
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

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.0318	1.0	718	1.8028	24.5415	12.0907	20.5343	23.3386
1.8307	2.0	1436	1.8028	24.0965	11.6367	20.2078	22.8138
1.6881	3.0	2154	1.8136	25.0822	12.6509	20.9523	23.8303
1.5778	4.0	2872	1.8269	24.4271	11.8443	20.2281	23.0941
1.501	5.0	3590	1.8412	24.7231	12.292	20.5347	23.4668

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1