---
language:
- en
license: mit
library_name: peft
tags:
- summarization
- text-generation
- toxicity-reduction
- reinforcement-learning
datasets:
- samsum
widget:
- text: 'Summarize the following Conversation: Kate: Good morning. Kai: Hi! How official!
    Kate: I wrote it at 4am Kai: I''ve noticed. Why? Kate: I had to get up early to
    catch the bus to the airport Kai: Where are you flying? Kate: To Antwerp! I''m
    fed up with Cambridge Kai: poor thing. Why? Kate: Just a stupid, elitist place
    without a soul. Or with a soul made of money. Kai: Try to rest a bit in Belgium,
    do not work too much. Kate: I have to work, but at least not in this soulless
    place. Kai: When are you coming back? Kate: I have to see my supervisor on Monday
    <unk> Kai: not too long a break Kate: Still better than nothing. Summary:'
  example_title: Summarization Example 1
- text: 'Summarize the following Conversation: Dean: I feel sick Scott: hungover?
    Dean: no, like I ate something bad Scott: what did you eat yesterday? Dean: breakfast
    at Coffee Lovers'' Scott: this is a rather safe place Dean: and Chinese from TaoTao
    for dinner Scott: now we have a suspect Summary:'
  example_title: Summarization Example 2
pipeline_tag: text2text-generation
inference:
  parameters:
    max_new_tokens: 256
    repetition_penalty: 2.5
    top_p: 0.95
    top_k: 50
    temperature: 0.6
    no_repeat_ngram_size: 2
    num_return_sequences: 1
    do_sample: true
base_model: braindao/flan-t5-cnn
---

# Flan-T5 (base-sized) Dialogue Summarization with reduced toxicity using RLAIF
This model is a **two-fold fine-tuned** [Flan-T5 model](https://huggingface.co/google/flan-t5-base) firstly on the [SAMSUM](https://huggingface.co/datasets/samsum) dataset followed by further fine-tuning using **Reinforcement Learning from AI Feedback(RLAIF)** to detoxify model outputs. <br>
Anthropic's Costitutional AI [paper](https://arxiv.org/abs/2212.08073) from 2022, provides some amazing insights on how RLAIF can be leveraged. Do check out if interested!<br>

More specifically, I've fine-tuned this model on a single downstream task of Dialogue Summarization on the above mentioned dataset with a primary objective of reduced toxicity in generated summaries.

## Model description
This Model has the same architecture and Parameters as its base model. Please refer to this [link](https://arxiv.org/abs/2210.11416) to know more about the model details.

## Intended Use & Limitations
This model is intended to summarize the given dialogue in a way that outputs the less toxic summary even when we pass a dialogue that contains toxic phrases or words.<br>
I've fine-tuned the model with an instruction of `Summarize the following Conversation:` that's prepended at the start of each dialogue followed by `Summary: ` keyword at the end that indicates the start of summary.

Note: 
1. The model is primarily trained with an objective of reduced toxicity in the outputs, we can sometimes expect relatively short outputs that might sometimes(rarely) miss the important message in the dialogue but still being true to its primary goal.
2. Currently, HuggingFace doesn't support PEFT model files for Text2Text-Generation Pipeline directly as Hosted Inference API, so please follow the steps mentioned below in the `Usage` section to load and use the model.

## Usage

You can use this model directly to get the summaries:

```python
import torch

from peft import PeftModel, PeftConfig

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


# Load peft config for pre-trained checkpoint etc.
peft_model_id = "DeathReaper0965/flan-t5-samsum-lora-RLAIF-detoxified"
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') # If required, you can add `load_in_8bit=True` for loading model in 8-bit
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')

input_ids = tokenizer.encode(
              "Summarize the following Conversation: Dean: I feel sick Scott: hungover? Dean: no, like I ate something bad Scott: what did you eat yesterday? Dean: breakfast at Coffee Lovers' Scott: this is a rather safe place Dean: and Chinese from TaoTao for dinner Scott: now we have a suspect Summary:",
              return_tensors="pt"
            ).to("cuda" if torch.cuda.is_available() else "cpu")

summary = model.generate(
            input_ids = input_ids,
            max_new_tokens=256,
            repetition_penalty=2.5,
            top_p=0.95,
            top_k=50, 
            temperature=0.6,
            no_repeat_ngram_size=2,
            num_return_sequences=1,
            do_sample=True)

output = tokenizer.batch_decode(summary, skip_special_tokens=True)

###########OUTPUT###########
# "Dean ate breakfast at Coffee Lovers' yesterday and Chinese from TaoTao for dinner."
```

> Designed and Developed with <span style="color: #e25555;">&hearts;</span> by [Praneet](https://deathreaper0965.github.io/) | [LinkedIn](http://linkedin.com/in/deathreaper0965) | [GitHub](https://github.com/DeathReaper0965/)