|
--- |
|
license: gpl |
|
model_name: GPT2 |
|
model_type: GPT2 |
|
language: en |
|
pipeline_tag: text-generation |
|
tags: |
|
- pytorch |
|
- gpt |
|
- gpt2 |
|
--- |
|
|
|
|
|
# Fine-tuning GPT2 with energy plus medical dataset |
|
|
|
Fine tuning pre-trained language models for text generation. |
|
|
|
Pretrained model on Chinese language using a GPT2 for Large Language Head Model objective. |
|
|
|
## Model description |
|
|
|
transferlearning from DavidLanz/uuu_fine_tune_taipower and fine-tuning with medical dataset for the GPT-2 architecture. |
|
|
|
### How to use |
|
|
|
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we |
|
set a seed for reproducibility: |
|
|
|
```python |
|
>>> from transformers import GPT2LMHeadModel, BertTokenizer, TextGenerationPipeline |
|
|
|
>>> model_path = "DavidLanz/DavidLanz/uuu_fine_tune_gpt2" |
|
>>> model = GPT2LMHeadModel.from_pretrained(model_path) |
|
>>> tokenizer = BertTokenizer.from_pretrained(model_path) |
|
|
|
>>> max_length = 200 |
|
>>> prompt = "歐洲能源政策" |
|
>>> text_generator = TextGenerationPipeline(model, tokenizer) |
|
>>> text_generated = text_generator(prompt, max_length=max_length, do_sample=True) |
|
>>> print(text_generated[0]["generated_text"].replace(" ","")) |
|
``` |
|
|
|
```python |
|
>>> from transformers import GPT2LMHeadModel, BertTokenizer, TextGenerationPipeline |
|
|
|
>>> model_path = "DavidLanz/DavidLanz/uuu_fine_tune_gpt2" |
|
>>> model = GPT2LMHeadModel.from_pretrained(model_path) |
|
>>> tokenizer = BertTokenizer.from_pretrained(model_path) |
|
|
|
>>> max_length = 200 |
|
>>> prompt = "蕁麻疹過敏" |
|
>>> text_generator = TextGenerationPipeline(model, tokenizer) |
|
>>> text_generated = text_generator(prompt, max_length=max_length, do_sample=True) |
|
>>> print(text_generated[0]["generated_text"].replace(" ","")) |
|
``` |