Copyright 2023 Anugrah Akbar Praramadhan. All rights reserved.
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Model Description
A GPT-2 (Generative Pretrained Transformer-2) model is a transformer based architecture for Causal Language Modeling, meaning it's required a left token/word as an input prompt for generating the right/next token, developed by Open AI {Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}. See the paper here: https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
Limitation
Since GPT-2 is an unsupervised model and trained using an unlabelled of text sequences without any explicit supervision, the clarity and output of this model often comes with randomness. To overcome this issue we have to create a specific seed for determined output. Supported language for this model is only English (get from GPT-2 pretrained model) and Indonesian (fine tune using Indonesian Wikipedia Dataset).
How To Use
Direct use of using Pytorch:
>>> from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, set_seed
>>> model_name = 'anugrahap/gpt2-indo-textgen'
>>> tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
>>> model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id)
>>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
>>> #set_seed(1)
>>> result = generator("Skripsi merupakan tugas akhir mahasiswa", min_length=10, max_length=30, num_return_sequences=1)
>>> result[0]["generated_text"]
Learn more
| GPT-2 Pretrained Model Medium-345M Parameters
| Indonesian Wikipedia Dataset - 433MB by IndoNLP
| Project Repository
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