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---
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](https://huggingface.co/jed351/gpt2-base-zh-hk), which was obtained by
patching a [GPT2 Chinese model](https://huggingface.co/ckiplab/gpt2-base-chinese) 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](https://github.com/ayaka14732/lihkg-scraper).
Please also check out the [Bart model](https://huggingface.co/Ayaka/bart-base-cantonese) created by her.
## Training procedure
Please refer to the [script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling)
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 |