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---
tags:
- generated_from_trainer
datasets:
- jed351/shikoto_zh_hk
metrics:
- accuracy
model-index:
- name: gpt2-shikoto
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: jed351/shikoto_zh_hk
type: jed351/shikoto_zh_hk
metrics:
- name: Loss
type: loss
value: 3.0956006050109863
license: openrail
---
# gpt2-shikoto
This model was trained on a dataset I obtained from an online novel site.
**Please be aware that the stories (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.
Besides language modeling, another aim of this experiment was to test the accelerate library by offloading certain workloads to CPU as well as finding the optimal training iterations.
The perplexity of this model is 16.12 after 400,000 steps. Comparing to the previous [attempt](https://huggingface.co/jed351/gpt2_tiny_zh-hk-shikoto) 27.02 after 400,000 steps.
It took around the same time duration to train this model but I only used 1 GPU here.
## 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 on 1 NVIDIA Quadro RTX6000 for around 30 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")
model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_base_zh-hk-shikoto")
# 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 |