Update README.md

README.md

@@ -1,3 +1,56 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- legal
+- news
+library_name: transformers
 ---
+# GPT-Neo-1.3B SimCTG for Conditional News Generation
+[SimCTG](https://github.com/yxuansu/SimCTG) model (released by Su et.al. in this [paper](https://arxiv.org/abs/2202.06417)), leveraging [GPT-Neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) (a large language model).
+
+## Data Details
+It was trained on a large news corpus containing news content from 19 different publishers. Detailed dataset configuration is as follow: 
+
+|    Publisher     | Data Number |
+| :--------------: | :---------: |
+|     Guardian     |   250,000   |
+|       BBC        |   240,872   |
+| WashingtonPost  |   167,401   |
+|    USAToday     |   234,648   |
+|     Reuters      |   822,110   |
+|  NYT (New York Times)  |   245,150   |
+|       CNBC       |   231,060   |
+|       Hill       |   205,410   |
+|      People      |   132,630   |
+|       CNN        |   121,760   |
+|       Vice       |   97,750    |
+|     Mashable     |   91,100    |
+|     Refinery     |   84,100    |
+| BI (Business Insider) |   53,014    |
+|    TechCrunch    |   49,040    |
+|      Verge       |   48,327    |
+|       TMZ        |   46,490    |
+|      Axios       |   44,280    |
+|       Vox        |    44120    |
+
+## Training Details
+We use the prompt template `Publisher: {vox} article: ` for training. We trained the model about 3 epochs on 3 NVIDIA A40 GPU.
+
+## How to use
+```python
+>>> from transformers import GPTNeoForCausalLM, AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen")
+>>> model = GPTNeoForCausalLM.from_pretrained("PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen")
+
+>>> publisher = "Reuters"
+>>> assert publisher in ["Reuters", "NYT", "CNBC", "Hill", "People", "CNN", "Vice", "Mashable", "Refinery", "BI", "TechCrunch", "Verge", "TMZ", "Axios", "Vox", "Guardian", "BBCNews", "WashingtonPost", "USAToday"]
+>>> prompt = f"Publisher: {publisher.lower()} article: "
+
+>>> inputs = tokenizer(prompt, return_tensors="pt")
+>>> out = model.generate(**inputs, penalty_alpha=0.6)
+>>> print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
+```