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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- jed351/shikoto_zh_hk |
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metrics: |
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- accuracy |
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model-index: |
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- name: gpt2-shikoto |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: jed351/shikoto_zh_hk |
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type: jed351/shikoto_zh_hk |
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metrics: |
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- name: Loss |
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type: loss |
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value: 3.0956006050109863 |
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license: openrail |
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--- |
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# gpt2-shikoto |
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This model was trained on a dataset I obtained from an online novel site. |
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**Please be aware that the stories (training data) might contain inappropriate content. This model is intended for research purposes only.** |
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The base model can be found [here](https://huggingface.co/jed351/gpt2-base-zh-hk), which was obtained by |
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patching a [GPT2 Chinese model](https://huggingface.co/ckiplab/gpt2-base-chinese) and its tokenizer with Cantonese characters. |
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Refer to the base model for info on the patching process. |
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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. |
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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. |
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It took around the same time duration to train this model but I only used 1 GPU here. |
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## Training procedure |
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Please refer to the [script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) |
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provided by Huggingface. |
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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. |
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### How to use it? |
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``` |
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from transformers import AutoTokenizer |
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from transformers import TextGenerationPipeline, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2-base-zh-hk") |
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model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_base_zh-hk-shikoto") |
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# try messing around with the parameters |
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generator = TextGenerationPipeline(model, tokenizer, |
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max_new_tokens=200, |
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no_repeat_ngram_size=3) #, device=0) #if you have a GPU |
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input_string = "your input" |
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output = generator(input_string) |
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string = output[0]['generated_text'].replace(' ', '') |
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print(string) |
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``` |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
