Create README.md

README.md

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+---
+language:
+- en
+tags:
+- medical
+- disease
+- classification
+---
+
+
+# DiLBERT (Disease Language BERT)
+
+The objective of this model was to obtain a specialized disease-related language, trained **from scratch**. <br>
+We created a pre-training corpora starting from **ICD-11** entities, and enriched it with documents from **PubMed** and **Wikipedia** related to the same entities. <br>
+Results of finetuning show that DiLBERT leads to comparable or higher accuracy scores on various classification tasks compared with other general-purpose or in-domain models (e.g., BioClinicalBERT, RoBERTa, XLNet).
+
+Model released with the paper "**DiLBERT: Cheap Embeddings for Disease Related Medical NLP**". <br>
+To summarize the practical implications of our work: we pre-trained and fine-tuned a domain specific BERT model on a small corpora, with comparable or better performance than state-of-the-art models.  
+This approach may also simplify the development of models for languages different from English, due to the minor quantity of data needed for training.
+
+### Composition of the pretraining corpus
+
+
+| Source | Documents | Words |
+|---|---:|---:|
+| ICD-11 descriptions         | 34,676    | 1.0 million   |
+| PubMed Title and Abstracts  | 852,550   | 184.6 million |
+| Wikipedia pages             | 37,074    | 6.1 million   |
+
+### Main repository
+
+For more details check the main repo https://github.com/KevinRoitero/dilbert
+
+# Usage
+
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("beatrice-portelli/DiLBERT")
+model = AutoModelForMaskedLM.from_pretrained("beatrice-portelli/DiLBERT")
+```
+
+