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pt-sk/
GPT2_NonToxic

Name: GPT2_NonToxic
Rating: 2.5 (9 reviews)

pt-sk/toxic_classification

mit

autotrain_compatible

text-generation-inference

endpoints_compatible

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Aligning the model using Proximal Policy Optimization (PPO). The goal is to train the model to generate non-toxic reviews. The training process utilizes the trl library for reinforcement learning, the transformers library for model handling, and datasets for dataset management. Implementation code is available here: GitHub

python

# Load model and tokenizer directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pt-sk/GPT2_NonToxic")
model = AutoModelForCausalLM.from_pretrained("pt-sk/GPT2_NonToxic")

# Example usage
input_text = "The movie was fantastic"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))