update file name

README.md

@@ -87,13 +87,13 @@ bash script/scraper.sh <split> <start_idx> <end_idx>
 
 ### 2. Rank the sentences in the knowledge store with BM25
 Then, we rank the scraped sentences for each claim using BM25 (based on the similarity to the claim), keeping the top 100 sentences per claim.
-See [bm25_sentences.py](https://huggingface.co/chenxwh/AVeriTeC/blob/main/src/reranking/bm25_sentences.py) for more argument options. We provide the output file for this step on the dev set [here]().
+See [bm25_sentences.py](https://huggingface.co/chenxwh/AVeriTeC/blob/main/src/reranking/bm25_sentences.py) for more argument options. We provide the output file for this step on the dev set [here](https://huggingface.co/chenxwh/AVeriTeC/blob/main/data_store/dev_top_k_sentences.json).
 ```bash
 python -m src.reranking.bm25_sentences
 ```
 
 ### 3. Generate questions-answer pair for the top sentences
-We use [BLOOM](https://huggingface.co/bigscience/bloom-7b1) to generate QA paris for each of the top 100 sentence, providing 10 closest claim-QA-pairs from the training set as in-context examples. See [question_generation_top_sentences.py](https://huggingface.co/chenxwh/AVeriTeC/blob/main/src/reranking/question_generation_top_sentences.py) for more argument options. We provide the output file for this step on the dev set [here]().
+We use [BLOOM](https://huggingface.co/bigscience/bloom-7b1) to generate QA paris for each of the top 100 sentence, providing 10 closest claim-QA-pairs from the training set as in-context examples. See [question_generation_top_sentences.py](https://huggingface.co/chenxwh/AVeriTeC/blob/main/src/reranking/question_generation_top_sentences.py) for more argument options. We provide the output file for this step on the dev set [here](https://huggingface.co/chenxwh/AVeriTeC/blob/main/data_store/ddev_top_k_qa.json).
 ```bash
 python -m src.reranking.question_generation_top_sentences
 ```

src/reranking/question_generation_top_sentences.py

@@ -58,13 +58,13 @@ if __name__ == "__main__":
     parser.add_argument(
         "-i",
         "--top_k_target_knowledge",
-        default="data_store/dev_top_k.json",
+        default="data_store/dev_top_k_sentences.json",
         help="Directory where the sentences for the scraped data is saved.",
     )
     parser.add_argument(
         "-o",
         "--output_questions",
-        default="data_store/dev_bm25_questions.json",
+        default="data_store/dev_top_k_qa.json",
         help="Directory where the sentences for the scraped data is saved.",
     )
     parser.add_argument(