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Model: DavidLanz/uuu_fine_tune_gpt2
Source: Original Platform

README.md

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+---
+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(" ",""))
+```