Update README.md

README.md

@@ -4,52 +4,59 @@ configs:
 - config_name: english-full
   data_files:
   - split: train
-    path: "english/train.jsonl"
+    path: english/train.jsonl
   - split: test
-    path: "english/test.jsonl"
+    path: english/test.jsonl
 - config_name: english-qi
   data_files:
   - split: train
-    path: "english/qi_train.jsonl"
+    path: english/qi_train.jsonl
   - split: test
-    path: "english/qi_test.jsonl"
+    path: english/qi_test.jsonl
 - config_name: english-dp
   data_files:
   - split: train
-    path: "english/dp_train.jsonl"
+    path: english/dp_train.jsonl
   - split: test
-    path: "english/dp_test.jsonl"
+    path: english/dp_test.jsonl
 - config_name: english-custom
   data_files:
   - split: train
-    path: "english/custom_train.jsonl"
+    path: english/custom_train.jsonl
   - split: test
-    path: "english/custom_test.jsonl"
+    path: english/custom_test.jsonl
 - config_name: french-full
   data_files:
   - split: train
-    path: "french/train.jsonl"
+    path: french/train.jsonl
   - split: test
-    path: "french/test.jsonl"
+    path: french/test.jsonl
 - config_name: french-qi
   data_files:
   - split: train
-    path: "french/qi_train.jsonl"
+    path: french/qi_train.jsonl
   - split: test
-    path: "french/qi_test.jsonl"
+    path: french/qi_test.jsonl
 - config_name: french-dp
   data_files:
   - split: train
-    path: "french/dp_train.jsonl"
+    path: french/dp_train.jsonl
   - split: test
-    path: "french/dp_test.jsonl"
+    path: french/dp_test.jsonl
 - config_name: french-custom
   data_files:
   - split: train
-    path: "french/custom_train.jsonl"
+    path: french/custom_train.jsonl
   - split: test
-    path: "french/custom_test.jsonl"
-
+    path: french/custom_test.jsonl
+task_categories:
+- multiple-choice
+language:
+- fr
+- en
+tags:
+- medical question answering
+pretty_name: ecn-qa
 ---
 
 
@@ -61,12 +68,6 @@ Paper: [https://arxiv.org/abs/2405.14654](https://arxiv.org/abs/2405.14654)
 
 ## Dataset Details
 
-In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. 
-Large language models, such as GPT-4, obtain reasonable scores on medical question-answering tasks, but smaller models are far behind. 
-In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. 
-We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model. 
-We show the benefits of our training strategy on a medical answering question dataset.
-The study's findings highlight the potential of small language models in the medical domain when appropriately fine-tuned. 
 
 ### Dataset Description
 
@@ -78,7 +79,7 @@ The questions can be:
 
 There are two versions of this dataset: the **french** and the **english** versions.
 The  **french** split is the original dataset version.
-The english version is an automated translation (using Azure Translation api), hence, it might contain translation errors.
+
 
 ### Use this dataset
 
@@ -107,7 +108,14 @@ ds_english_custom = load_dataset("raidium/ECN-QA", "english-custom")
 - **Developed by:** Raidium
 - **License:** Apache 2.0
 
-### Dataset Sources [optional]
+
+### Warnings
+
+- Some questions require images to be answered. They have not been filtered out in this dataset, to it is impossible to get 100% accuracy on this dataset.
+- The english version is an automated translation (using Azure Translation api), hence, it might contain translation errors.
+
+
+### Dataset Sources
 
 <!-- Provide the basic links for the model. -->