Improve model card: Add pipeline tag and library name

README.md

@@ -1,15 +1,17 @@
 ---
-license: apache-2.0
+base_model:
+- google/flan-t5-base
 datasets:
 - yale-nlp/MDCure-72k
 language:
 - en
-base_model:
-- google/flan-t5-base
+license: apache-2.0
 tags:
 - multi-document
 - long-context
 - Long Context
+pipeline_tag: summarization
+library_name: transformers
 ---
 
 # MDCure-FlanT5-Base
@@ -41,7 +43,9 @@ We recommend using the latest version of HF Transformers, or any `transformers>4
 
 ## Quickstart
 
-Below we provide a code snippet demonstrating how to load the tokenizer and model and generate content in response to an input context concerning multiple source documents and a related question or instruction. We strongly recommend to separate the texts and/or instruction using `\n\n` or `<doc-sep>` to maintain consistency with the format of the data used during training.
+Below we provide a code snippet demonstrating how to load the tokenizer and model and generate content in response to an input context concerning multiple source documents and a related question or instruction. We strongly recommend to separate the texts and/or instruction using `
+
+` or `<doc-sep>` to maintain consistency with the format of the data used during training.
 
 ```python
 model = AutoModelForSeq2SeqLM.from_pretrained("yale-nlp/MDCure-FlanT5-Base", device_map='auto',torch_dtype="auto",) 
@@ -50,7 +54,13 @@ tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-FlanT5-Base")
 source_text_1 = ...
 source_text_2 = ...
 source_text_3 = ...
-input_text = f"{source_text_1}\n\n{source_text_2}\n\n{source_text_3}\n\nWhat happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences."
+input_text = f"{source_text_1}
+
+{source_text_2}
+
+{source_text_3}
+
+What happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences."
 
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device)
 outputs = model.generate(input_ids)
@@ -69,7 +79,7 @@ We open-source our custom multi-document instruction scoring model, MDCureRM, as
 | **MDCure-Qwen2-1.5B-Instruct**    | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-1.5B-Instruct) | **Qwen2-1.5B-Instruct** fine-tuned with MDCure-72k  |
 | **MDCure-Qwen2-7B-Instruct**      | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-7B-Instruct) | **Qwen2-7B-Instruct** fine-tuned with MDCure-72k    |
 | **MDCure-LLAMA3.1-8B-Instruct**   | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-8B-Instruct) | **LLAMA3.1-8B-Instruct** fine-tuned with MDCure-72k  |
-| **MDCure-LLAMA3.1-70B-Instruct**  | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-70B-Instruct) | **LLAMA3.1-70B-Instruct** fine-tuned with MDCure-72 |
+| **MDCure-LLAMA3.1-70B-Instruct**  | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-70B-Instruct) | **LLAMA3.1-70B-Instruct** fine-tuned with MDCure-72k |
 
 ## Citation