Update readme file

README.md

@@ -1,11 +1,119 @@
 ---
-license: apache-2.0
-datasets:
-- MiMe-MeMo/Corpus-v1.1
 language:
-- da
-- 'no'
-base_model:
-- vesteinn/DanskBERT
-pipeline_tag: fill-mask
----
+  - da
+  - no
+license: cc-by-4.0
+datasets:
+  - MiMe-MeMo/Corpus-v1.1
+  - MiMe-MeMo/Sentiment-v1
+  - MiMe-MeMo/WSD-Skaebne
+metrics:
+  - f1
+tags:
+  - historical-texts
+  - digital-humanities
+  - sentiment-analysis
+  - word-sense-disambiguation
+  - danish
+  - norwegian
+model-index:
+  - name: MeMo-BERT-03
+    results:
+      - task:
+          type: text-classification
+          name: Sentiment Analysis
+        dataset:
+          name: MiMe-MeMo/Sentiment-v1
+          type: text
+        metrics:
+          - name: f1
+            type: f1
+            value: 0.77
+      - task:
+          type: text-classification
+          name: Word Sense Disambiguation
+        dataset:
+          name: MiMe-MeMo/WSD-Skaebne
+          type: text
+        metrics:
+          - name: f1
+            type: f1
+            value: 0.61
+---
+
+# MeMo-BERT-03
+
+**MeMo-BERT-03** is a pre-trained language model for **historical Danish and Norwegian literary texts** (1870–1900).  
+It was introduced in [Al-Laith et al. (2024)](https://aclanthology.org/2024.lrec-main.431/) as part of the first dedicated PLMs for historical Danish and Norwegian.
+
+## Model Description
+
+- **Architecture:** XLM-RoBERTa-base (24 layers, 1024 hidden size, 16 heads, vocab size 250k)  
+- **Pre-training strategy:** Continued pre-training of [DanskBERT](https://huggingface.co/vesteinn/DanskBERT) on historical data  
+- **Training objective:** Masked Language Modeling (MLM, 15% masking)  
+- **Training data:** MeMo Corpus v1.1 (839 novels, ~53M words, 1870–1900)  
+- **Hardware:** 2 × A100 GPUs  
+- **Training time:** ~32 hours  
+
+## Intended Use
+
+- **Primary tasks:**  
+  - Sentiment Analysis (positive, neutral, negative)  
+  - Word Sense Disambiguation (historical vs. modern senses of *skæbne*, "fate")  
+
+- **Intended users:**  
+  - Researchers in Digital Humanities, Computational Linguistics, and Scandinavian Studies.  
+  - Historians of literature studying 19th-century Scandinavian novels.  
+
+- **Not intended for:**  
+  - Contemporary Danish/Norwegian NLP tasks (performance may degrade).  
+  - High-stakes applications (e.g., legal, medical, political decision-making).  
+
+## Training Data
+
+- **Corpus:** [MeMo Corpus v1.1](https://huggingface.co/datasets/MiMe-MeMo/Corpus-v1.1) (Bjerring-Hansen et al. 2022)  
+- **Time period:** 1870–1900  
+- **Size:** 839 novels, 690 MB, 3.2M sentences, 52.7M words  
+- **Preprocessing:** OCR-corrected, normalized to modern Danish spelling, tokenized, lemmatized, annotated  
+
+## Evaluation
+
+### Benchmarks
+
+| Task | Dataset | Test F1 | Notes |
+|------|---------|---------|-------|
+| Sentiment Analysis | MiMe-MeMo/Sentiment-v1 | **0.77** | 3-class (pos/neg/neu) |
+| Word Sense Disambiguation | MiMe-MeMo/WSD-Skaebne | **0.61** | 4-class (pre-modern, modern, figure of speech, ambiguous) |
+
+### Comparison
+
+MeMo-BERT-03 outperforms MeMo-BERT-1, MeMo-BERT-2, and contemporary baselines (DanskBERT, ScandiBERT, DanBERT, BotXO) across both tasks.
+
+## Limitations
+
+- Domain-specific: trained only on **novels from 1870–1900**.  
+- May not generalize to other genres (newspapers, folk tales, poetry).  
+- Evaluation datasets are relatively small.  
+- OCR/normalization errors remain in some texts.  
+
+## Ethical Considerations
+
+- All texts are **public domain** (authors deceased).  
+- Datasets released under **CC BY 4.0**.  
+- Word sense annotations created by literary scholars, no sensitive personal data.  
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@inproceedings{al-laith-etal-2024-development,
+    title = "Development and Evaluation of Pre-trained Language Models for Historical {D}anish and {N}orwegian Literary Texts",
+    author = "Al-Laith, Ali and Conroy, Alexander and Bjerring-Hansen, Jens and Hershcovich, Daniel",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    pages = "4811--4819",
+    url = "https://aclanthology.org/2024.lrec-main.431/"
+}