textrl 0.1.6

TextRL - use reinforcement learning to adjust text generation results.

TextRL

Text generation with reinforcement learning using huggingface's transformer.
Implementation of ChatGPT for human interaction to improve generation model with reinforcement learning.

Introduction

This project is trying to use reinforcement learning to adjust text generation results. It is based on any text-generation model on huggingaface's transformer with PFRL and OpenAI GYM.

Example

Controllable generation via RL to let Elon Musk speak ill of DOGE

before: i think dogecoin is a great idea.
after: i think dogecoin is a great idea, but I think it is a little overused.

Installation

pip install

pip install pfrl@git+https://github.com/voidful/pfrl.git
pip install textrl

Build from source

git clone and cd into this project.

pip install -e .

Usage

init agent and environment

from textrl import TextRLEnv, TextRLActor

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("any models")
model = AutoModelWithLMHead.from_pretrained("any models")
model.eval()

setup reward function for environment

• predicted(list[str]): will be the list of predicted token
• finish(bool): it met the end of sentence or not

class MyRLEnv(TextRLEnv):
    def get_reward(self, input_item, predicted_list, finish):  # predicted will be the list of predicted token
        if "[UNK]" in predicted_list:
            reward = -1
        else:
            reward = 1
        return reward

prepare for training

• observation_input should be a list of all possible input string for model training

env = MyRLEnv(model, tokenizer, observation_input=observaton_list)
actor = TextRLActor(env, model, tokenizer)
agent = actor.agent_ppo(update_interval=10, minibatch_size=2000, epochs=20)

Train

n_episodes = 1000
max_episode_len = 200  # max sentence length

for i in range(1, n_episodes + 1):
    obs = env.reset()
    R = 0
    t = 0
    while True:
        action = agent.act(obs)
        obs, reward, done, pred = env.step(action)
        R += reward
        t += 1
        reset = t == max_episode_len
        agent.observe(obs, reward, done, reset)
        if done or reset:
            break
    if i % 10 == 0:
        print('episode:', i, 'R:', R)
    if i % 50 == 0:
        print('statistics:', agent.get_statistics())
print('Finished.')

another way to train

import logging
import sys

logging.basicConfig(level=logging.INFO, stream=sys.stdout, format='')

pfrl.experiments.train_agent_with_evaluation(
    agent,
    env,
    steps=1000,
    eval_n_steps=None,
    eval_n_episodes=1500,
    train_max_episode_len=50,
    eval_interval=10000,
    outdir='somewhere',
)

prediction

agent.load("somewhere/best")  # loading the best model
actor.predict("input text")

dump trained model to huggingface's model

textrl-dump --model ./model_path_before_rl --rl ./rl_path --dump ./output_dir