---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: samsum
      type: samsum
      split: validation
    metrics:
    - type: rogue1
      value: 46.819522%
    - type: rouge2
      value: 20.898074%
    - type: rougeL
      value: 37.300937%
    - type: rougeLsum
      value: 37.271341%
pipeline_tag: summarization
inference: True
library_name: adapter-transformers
language:
- en
---


# flan-t5-base-cnn-samsum-lora

This model is a fine-tuned version of [braindao/flan-t5-cnn](https://huggingface.co/braindao/flan-t5-cnn) on the [samsum](https://huggingface.co/datasets/samsum) dataset.

The base model [braindao/flan-t5-cnn](https://huggingface.co/braindao/flan-t5-cnn) is a fine-tuned verstion of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the cnn_dailymail 3.0.0 dataset.

## Model API Spaces

Please visit HF Spaces [sooolee/summarize-transcripts-gradio](https://huggingface.co/spaces/sooolee/summarize-transcripts-gradio)


## Model description

* This model further finetuned [braindao/flan-t5-cnn](https://huggingface.co/braindao/flan-t5-cnn) on the more conversational samsum dataset.  
* Huggingface [PEFT Library](https://github.com/huggingface/peft) LoRA (r = 16) and bitsandbytes int-8 was used to speed up training and reduce the model size.
* Only 1.7M parameters were trained (0.71% of original flan-t5-base 250M parameters).
* The model checkpoint is just 7MB.

## Intended uses & limitations

Summarize transcripts such as YouTube transcripts. 

## Training and evaluation data
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

- train_loss: 1.47

### How to use
Note 'max_new_tokens=60' is used in the below example to control the length of the summary. FLAN-T5 model has max generation length = 200  and min generation length = 20 (default).

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

# Load peft config for pre-trained checkpoint etc.
peft_model_id = "sooolee/flan-t5-base-cnn-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') # load_in_8bit=True, 
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. Part of YouTube 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

## Other
Please check out the BART-Large-CNN-Samsum model fine-tuned for the same purpose: [sooolee/bart-large-cnn-finetuned-samsum-lora](https://huggingface.co/sooolee/bart-large-cnn-finetuned-samsum-lora)