Update README.md

README.md

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 ---
+language: en
+tags:
+- exbert
+
 license: gpl
 ---
+
+
+# TCP 2023 for NTU students
+
+Fine tuning pre-trained language models for text generation.
+
+Pretrained model on Chinese language using a GPT2 for Large Language Head Model objective(GPT2LMHeadModel). 
+
+## Model description
+
+TCP 2023 is a transformers model that has undergone fine-tuning using the GPT-2 architecture.
+It was initially pretrained on an extensive corpus of Chinese data in a self-supervised manner.
+This implies that the pretraining process involved using raw text data without any human annotations, allowing the model to make use of a wide range of publicly available data.
+The model leveraged an automatic process to derive inputs and corresponding labels from these texts.
+To be more specific, the pretraining aimed at predicting the subsequent word in sentences.
+it was trained to guess the next word in sentences.
+
+### 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, AutoTokenizer, pipeline
+
+>>> model_name = "DavidLanz/tcp2023"
+
+>>> model = GPT2LMHeadModel.from_pretrained(model_name)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+>>> text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+>>> generated_text = text_generator(input_text, max_length=max_len, num_return_sequences=1)
+>>> print(generated_text[0]['generated_text'])