Update README.md

README.md

@@ -28,7 +28,7 @@ We explore **continued pre-training on domain-specific corpora** for large langu
 ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
 
 **************************** **Updates** ****************************
-* 2024/4/2: Released the training and testing splits of all the evaluation datasets: [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt)
+* 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
 * 2024/1/16: 🎉 Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!🎉
 * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
 * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
@@ -49,52 +49,42 @@ Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is si
 ## Domain-Specific LLaMA-2-Chat
 Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
 
-For example, to chat with the finance-chat model:
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+## Domain-Specific Tasks
 
-model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
-tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat")
+### Pre-templatized/Formatted Testing Splits
+To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
 
-# Put your input here:
-user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered
-Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange
-MMM Chicago Stock Exchange, Inc.
-1.500% Notes due 2026 MMM26 New York Stock Exchange
-1.750% Notes due 2030 MMM30 New York Stock Exchange
-1.500% Notes due 2031 MMM31 New York Stock Exchange
+**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
 
-Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''
+### Raw Datasets
+We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: 
+- [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt)
+- [RCT](https://huggingface.co/datasets/AdaptLLM/RCT)
+- [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA)
+- [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA)
+- [Headline](https://huggingface.co/datasets/AdaptLLM/Headline)
+- [NER](https://huggingface.co/datasets/AdaptLLM/NER)
+
+The other datasets used in our paper have already been available in huggingface, so you can directly load them with the following code
+```python
+from datasets import load_dataset
 
-# Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!)
-our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this
-prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]"
+# MQP:
+dataset = load_dataset('medical_questions_pairs')
 
-# # NOTE:
-# # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this:
-# your_system_prompt = "Please, check if the answer can be inferred from the pieces of context provided."
-# prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]"
+# PubmedQA:
+dataset = load_dataset('bigbio/pubmed_qa')
 
-inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
-outputs = model.generate(input_ids=inputs, max_length=4096)[0]
+# SCOTUS
+dataset = load_dataset("lex_glue", 'scotus')
 
-answer_start = int(inputs.shape[-1])
-pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
+# CaseHOLD
+dataset = load_dataset("lex_glue", 'case_hold')
 
-print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
+# UNFAIR-ToS
+dataset = load_dataset("lex_glue", 'unfair_tos')
 ```
 
-## Domain-Specific Tasks
-
-### Pre-templatized/Formatted Datasets
-To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
-
-**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
-
-### Raw Datasets
-We have also uploaded all the raw datasets, for fine-tuning or other usage: [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt)
-
-
 ## Citation
 If you find our work helpful, please cite us:
 ```bibtex
@@ -108,7 +98,7 @@ url={https://openreview.net/forum?id=y886UXPEZ0}
 }
 ```
 
-And the original dataset:
+and the original dataset:
 ```bibtex
 @article{ChemProt,
   author    = {Jens Kringelum and