bart-large-cnn-finetuned-samsum-lora

This model is a further fine-tuned version of facebook/bart-large-cnn on the samsum dataset.

The base model bart-large-cnn is a fine-tuned verstion of BART model on the CNN Daily Mail dataset.

Check out sooolee/flan-t5-base-cnn-samsum-lora the model fine-tuned for the same purpose.

Model description

• This model further finetuned 'bart-large-cnn' on the more conversational samsum dataset.
• Huggingface PEFT Library LoRA (r = 8) was used to speed up training and reduce the model size.
• Less than 1.2M parameters were trained (0.23% of original bart-large 510M parameters).
• The model checkpoint is less than 5MB.

Intended uses & limitations

Summarizes transcripts such as YouTube transcripts.

Training and evaluation data

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

• train_loss: 1.28
• rogue1: 43.115465%
• rouge2: 21.563061%
• rougeL: 33.409979%
• rougeLsum: 33.414162%

How to use

Note 'max_new_tokens=60' is used in the example below to control the summary size. BART model has max generation length = 142 (default) and min generation length = 56.

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load peft config for pre-trained checkpoint etc.
peft_model_id = "sooolee/bart-large-cnn-finetuned-samsum-lora"
config = PeftConfig.from_pretrained(peft_model_id)

# load base LLM model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id, device_map='auto')

# Tokenize the text inputs
texts = "<e.g. Transcript>"
inputs = tokenizer(texts, return_tensors="pt", padding=True, ) # truncation=True

# Make inferences
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
with torch.no_grad():    
    output = self.model.generate(input_ids=inputs["input_ids"].to(device), max_new_tokens=60, do_sample=True, top_p=0.9)
    summary = self.tokenizer.batch_decode(output.detach().cpu().numpy(), skip_special_tokens=True)

summary

Framework versions

• Transformers 4.27.2
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.3