metadata

metrics:
  - accuracy
model-index:
  - name: gpt2-lihkg
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: lihkg_data
          type: lihkg_data
        metrics:
          - name: Perplexity
            type: Perplexity
            value: 30.93
license: openrail

gpt2-shikoto

Please be aware that the training data might contain inappropriate content. This model is intended for research purposes only.

The base model can be found here, which was obtained by patching a GPT2 Chinese model and its tokenizer with Cantonese characters. Refer to the base model for info on the patching process.

The training data was obtained from scrapping an online forum in Hong Kong called LIHKG. The tool can be found here. Please also check out the Bart model created by her.

Training procedure

Please refer to the script provided by Huggingface.

The model was trained for 400,000 steps with batch size 5 on 2 NVIDIA Quadro RTX6000 for around 40 hours at the Research Computing Services of Imperial College London.

How to use it?

from transformers import AutoTokenizer
from transformers import TextGenerationPipeline, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2_base_zh-hk-lihkg")
model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_base_zh-hk-lihkg")
# try messing around with the parameters
generator = TextGenerationPipeline(model, tokenizer, 
                                   max_new_tokens=200, 
                                   no_repeat_ngram_size=3) #, device=0) #if you have a GPU
input_string = "your input" 
output = generator(input_string)
string = output[0]['generated_text'].replace(' ', '')
print(string)

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1
Datasets 2.8.0
Tokenizers 0.13.2