diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,11 +1,9 @@
 
 # Dense Retrieval for Low Resource Languages - SIGIR 2025
-
-## Overview
 This repository supports the experiments presented in our SIGIR 2025 paper. We explore the effectiveness of dense retrieval methods (ColBERTv2 and SPLADE) on Amharic, a morphologically rich and low-resource language. 
 
 
-## Datasets
+## 1. Datasets
 We used three datasets:
 
 - **[Amharic DR Training Dataset](https://www.irit.fr/AmharicResources/2025-amharic-dense-retrieval-training-dataset/)**: 152 queries along with their relevant documents, curated for training dense retrievers.  
@@ -13,15 +11,15 @@ We used three datasets:
 - **[AfriCLIRMatrix](https://aclanthology.org/2022.emnlp-main.597/)**: A cross-lingual dataset with queries in English, in our case, translated to Amharic using NLLB-200.
 
 
-## Hardware
+## 2. Hardware
 The Hardware used for the BM25 computations is 4 AMD EPYC Rome 7402 cores running at 2.8 GHz.
 The hardware used for the Splade and ColBERT experiments was 4 Intel Xeon 2640 CPU cores running at 2.4 GHz and a GTX1080 CPU on which the models were finetuned and evaluated.
 
 
-## Detailed results and run time
+## 3. Detailed results and run time
 Detailed results and run time are available as [supplementary material](Supplementary%20Material.pdf).
 
-## Citation
+## 4. Citation
 If you use this work, please cite:
 
 > Tilahun Yeshambel, Moncef Garouani, Serge Molina, and Josiane Mothe. 2025.  
@@ -29,11 +27,11 @@ If you use this work, please cite:
 > In Proceedings of SIGIR 2025. ACM. [PDF](XXX)
 
 
-## Getting started
+## 5. Getting started
 
-### Dataset preparation
+### 5.1 Dataset preparation
 
-#### Train dataset
+#### 5.1.a Train dataset
 The train dataset should be downloaded from [https://www.irit.fr/AmharicResources/2025-amharic-dense-retrieval-training-dataset/](https://www.irit.fr/AmharicResources/2025-amharic-dense-retrieval-training-dataset/) and stored in `datasets/train` using the following hierarchy:
 
 - `datasets/train/corpus.tsv`: the corpus containing the documents  
@@ -43,7 +41,7 @@ The train dataset should be downloaded from [https://www.irit.fr/AmharicResource
 - `datasets/train/triples_by_id_num.json`: the list of triples, formatted as JSON Lines where each line is an array with the following elements:  
   `[topic_id, doc_id_positive, doc_id_negative]`
 
-#### Test dataset
+#### 5.1.b Test dataset
 The test dataset should be downloaded from [https://www.irit.fr/AmharicResources/airtc-the-amharic-adhoc-information-retrieval-test-collection/](https://www.irit.fr/AmharicResources/airtc-the-amharic-adhoc-information-retrieval-test-collection/) and stored in `datasets/test` using the following hierarchy:
 
 - `datasets/test/corpus.jsonl`: the corpus containing the documents, formatted as JSON Lines where each line has the following fields:  
@@ -57,7 +55,7 @@ The test dataset should be downloaded from [https://www.irit.fr/AmharicResources
 - `datasets/test/qrels.tsv`: the list of query-document relevancy scores, formatted as tab-separated values with:  
   `topic_id, iteration, doc_id, relevance`
 
-### Environment setup
+### 5.2 Environment setup
 > Tested with Python `3.10.12`
 
 A virtual environment isolated from the user's global environment should be created using their preferred virtual environment tool. In our experiments, the following command was used:
@@ -74,7 +72,7 @@ pip install -r requirements.txt
 ```
 
 
-### Models preparation
+### 5.3 Models preparation
 The experiments can be run with BERT models compatible with the `transformers` library. To use a model, it should be downloaded from Hugging Face to the `models/` folder using:
 
 ```bash
@@ -87,8 +85,12 @@ Example for the Amharic BERT model:
 huggingface-cli download rasyosef/bert-medium-amharic --local-dir models/rasyosef/bert-medium-amharic
 ```
 
-### Running an experiment
-SPLADE and ColBERT experiments use the same configuration file structure, except for the `config['train']['triples']` field, which must be adjusted for the tool being used.
+### 5.4 Running an experiment
+SPLADE and ColBERT experiments use the same configuration file structure, except for the `config['train']['triples']` field, which must be adjusted according to the tool being used.
+
+Example configuration files:
+- `configs/colbert/roberta-amharic-text-embedding-base.2AIRTC.training.json`
+- `configs/splade/roberta-amharic-text-embedding-base.2AIRTC.training.json`
 
 To ensure traceability of results and used parameters, the configuration file is updated after execution with the following fields:
 
@@ -100,13 +102,9 @@ To ensure traceability of results and used parameters, the configuration file is
   - `config['train']['results_path']`: path to the retrieval results  
   - `config['train']['eval_path']`: path to the evaluation results  
 
-> It is recommended to pass a copy of the original configuration file when running experiments.
+> It is recommended to pass a copy of the original configuration file when running experiments as **the passed configuration will be updated and written to disk in-place** with the produced search results and their trec evaluation.
 
-Example configuration files:
-- `configs/colbert/roberta-amharic-text-embedding-base.2AIRTC.training.json`
-- `configs/splade/roberta-amharic-text-embedding-base.2AIRTC.training.json`
-
-#### With ColBERTv2
+#### 5.4.1 With ColBERTv2
 Set the field `config['train']['triples']` to the JSONL triples file:
 
 ```bash
@@ -119,7 +117,8 @@ Then run:
 python -m eval_colbert "$config_path"
 ```
 
-#### With SPLADE
+
+#### 5.4.2 With SPLADE
 Set the field `config['train']['triples']` to the TSV triples file:
 
 ```bash