README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - pipeline_tag
base_model: google/flan-t5-large
model-index:
  - name: flan-t5-large-samsum-qlora-merged
    results: []
datasets:
  - samsum
metrics:
  - rouge
pipeline_tag: summarization
library_name: peft

Model description

Parameter-efficient fine-tuning (PEFT) with QLoRA was employed to fine-tune the base google/flan-t5-large model on the samsum dataset containing dialoges. After the fine-tuning, the PEFT model adapter was merged with the base model. The model is intended for generative summarization tasks and achieved the following scores on the test dataset:

• Rogue1: 49.249596%
• Rouge2: 23.513032%
• RougeL: 39.960812%
• RougeLsum: 39.968438%

How to use

Load the model:

from transformers import pipeline
pipeline_model = pipeline("summarization", model="MuntasirHossain/flan-t5-large-samsum-qlora-merged")
summary = pipeline_model(text, max_new_tokens = 50)
print(summary[0]['summary_text'])

Example Inference:

# random sample text from the samsum test dataset
text = """
Emma: Hi, we're going with Peter to Amiens tomorrow.
Daniel: oh! Cool.
Emma: Wanna join?
Daniel: Sure, I'm fed up with Paris.
Emma: We're too. The noise, traffic etc. Would be nice to see some countrysides.
Daniel: I don't think Amiens is exactly countrysides though :P
Emma: Nope. Hahahah. But not a megalopolis either!
Daniel: Right! Let's do it!
Emma: But we should leave early. The days are shorter now.
Daniel: Yes, the stupid winter time.
Emma: Exactly!
Daniel: Where should we meet then?
Emma: Come to my place by 9am.
Daniel: oohhh. It means I have to get up before 7!
Emma: Yup. The early bird gets the worm (in Amiens).
Daniel: You sound like my grandmother.
Emma: HAHAHA. I'll even add: no parties tonight, no drinking dear Daniel
Daniel: I really hope Amiens is worth it!
"""

summary = pipeline_model(text, max_new_tokens = 50)
print(summary[0]['summary_text'])
Emma and Peter are going to Amiens tomorrow. Daniel will join them. They will meet at Emma's place by 9 am. They will not have any parties tonight.

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

Training results

Framework versions

• PEFT 0.8.2
• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2