Update README.md

Use Markdown headings instead of RST :|

.gitignore

@@ -1,3 +1,7 @@
 *.iml
 .idea
 *.pyc
+
+# Annif directories
+projects
+vocabs

README.md

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-EHRI Multilingual Subject Indexing Test Dataset
-===============================================
+# EHRI Multilingual Subject Indexing Test Dataset
 
-Overview
---------
+## Overview
 
 This dataset consists of texts, derived from descriptions of Holocaust-related archival material, each of which is
-associated with one or more subject terms from the [EHRI Terms](https://portal.ehri-project.eu/vocabularies/ehri_terms) 
-controlled vocabulary. 
+associated with one or more subject terms from the [EHRI Terms](https://portal.ehri-project.eu/vocabularies/ehri_terms)
+controlled vocabulary.
 
 This dataset is described more thoroughly in the following paper, and is published to support reproduction:
 
     Multilingual Automated Subject Indexing: a comparative study of LLMs vs alternative approaches in the context of the EHRI project
 
-Authors
-~~~~~~~
+## Authors
 
 - Maria Dermentzi, Orcid: 0000-0001-8159-7600
 - Mike Bryant, Orcid: 0000-0003-0765-7390
 - Fabio Rovigo, Orcid: 0000-0001-5760-3185
 - Herminio García-González, Orcid: 0000-0001-5590-4857
 
-License
-~~~~~~~
+## License
 
 See accompanying file: LICENSE.txt
 
-Data collection
-~~~~~~~~~~~~~~~
+## Data collection
 
 The data has been exported from the [EHRI Portal](https://portal.ehri-project.eu/)
 on 2023-10-11.
 
-Structure
-~~~~~~~~~
+## Structure
 
 The dataset is split into a training and test portion, comprising 25,732 and 10,860
-descriptions respectively. There is an additional (smaller) test set for qualitative
-evaluation purposes (named `eval`) consisting of 167 items.
+descriptions respectively. There is an additional (smaller) test set for evaluation
+purposes (named `eval`) consisting of 167 items.
 
-Data processing
-~~~~~~~~~~~~~~~
+## Data processing
 
 The processing applied to this dataset and the stratification techniques used are
 described in the referenced paper.
 
-Format
-~~~~~~
+## Format
 
 The data is structured in Annif [Full-text document corpus
-format](https://github.com/NatLibFi/Annif/wiki/Document-corpus-formats), 
-where the basename of each file is the ID of an item on the EHRI portal. 
-For example, the text for a description is containing in `some-id.txt` 
+format](https://github.com/NatLibFi/Annif/wiki/Document-corpus-formats),
+where the basename of each file is the ID of an item on the EHRI portal.
+For example, the text for a description is containing in `some-id.txt`
 and the associated subject terms in `some-id.tsv`.
 
-Reproducibility information
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Python:
-  3.10.12
-Annif:
-  1.1.0.dev0
+## Reproducibility
 
 To reproduce the results of the paper, follow these steps:
 
+    # Install GIT LFS, method here assumes Debian-based Linux 
+    sudo apt install git-lfs
+    git lfs install
+
     # Note: *do this in a virtual environment*
     # This assumes you're using virtualenvwrapper
     mkvirtualenv ehri_masi
     workon ehri_masi
 
     # Checkout and install the modified version of Annif.
-    git clone https://github.com/EHRI/Annif.git EHRI_MASI
-    cd EHRI_MASI
+    git clone https://github.com/EHRI/Annif.git ehri_masi
+    cd ehri_masi
+
+    # Checkout the branch with the modifications for this paper
     git checkout ehri_masi
+
+    # Install python dependencies
+    pip install -r requirements-ehri.txt
     pip install -e .[nn,fasttext,spacy,omikuji]
 
     # Clone the dataset to the data directory
     git clone https://github.com/EHRI/ehri-masi-data.git data
 
+    # Activate the example `projects.cfg`
+    cp projects.cfg.ehri projects.cfg
+
     # Load the test vocabulary with the name used in the project.cfg file
     annif load-vocab ehri_sm data/ehri_sm.ttl
 
-    # Train the models
+    # Clone the model for the EHRI fine-tuned BERT-based model to the `models` directory
+    # NB: this may take some time...
+    git clone https://huggingface.co/mdermentzi/finetuned-bert-base-multilingual-cased-ehri-terms models/finetuned-bert-base-multilingual-cased-ehri-terms
+
+    # Train the Annif models
     for model in tfidf-ehri mllm-ehri fasttext-ehri omikuji-parabel-ehri nn-ehri; do
       echo "Training $model..."
       annif train $model data/train
     done
 
-    # Evaluate the models
+    # Evaluate the Annif models
     for model in tfidf-ehri mllm-ehri fasttext-ehri omikuji-parabel-ehri nn-ehri; do
       echo "Evaluating $model..."
-      annif eval $model data/test
+      annif eval $model data/eval
     done
 
-Version history
-~~~~~~~~~~~~~~~
+    # Evaluate the EHRI fine-tuned model
+    annif eval bertft-ehri data/eval
+
+    # Evaluate the MDeBERTa based zero-shot model. This will be extremely slow
+    # unless you have a good CUDA-supporting graphics card...
+    annif eval mdeberta-ehri data/eval
+
+## Version history
 
-v1.0.0 (2025-01-13): Initial release for paper submission.
+v1.0.0 (2025-01-15): Initial release for paper submission.