metadata

language:
  - en
license: apache-2.0
datasets:
  - xsum
metrics:
  - rouge
model-index:
  - name: t5-base-finetuned-xsum
    results:
      - task:
          name: Text Summarization
          type: text-summarization
        dataset:
          name: Xsum
          type: xsum
          args: xsum
        metrics:
          - name: rouge
            type: rouge
            value: 0.3414

gpt2-finetuned-xsum

This model is t5-base fine-tuned on Xsum dataset for text summarization.

Model Details

T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.

Training Procedure

To train the T5 model for text-summarization, I have used "summarize" prefix before every sentence and gave the encoding of this sentence as input ids and attention mask. For the labels, I used the encoding of the summaries as the decoder input ids and decoder attention mask.

Usage:

For generating summaries on a example use:

predictions = []
tokenised_dataset = tokenizer(documents, truncation=True, padding='max_length', max_length=1024, return_tensors='pt')
source_ids = tokenised_dataset['input_ids']
source_mask = tokenised_dataset['attention_mask']
output = model.generate(input_ids=source_ids, attention_mask=source_mask, max_length=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Experiments

We report the ROUGE-1, ROUGE-2 and ROUGE-L on the test datasets.

Xsum

ROUGE-1	ROUGE-2	ROUGE-L
0.3414	0.1260	0.2832