Create README.md

README.md

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+---
+language:
+- he
+pipeline_tag: token-classification
+tags:
+- Transformers
+- PyTorch
+---
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+## MenakBERT
+
+A Hebrew BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
+A Hebrew diacritizer based on a BERT-style char-level backbone. Predicts diacritical marks in a seq2seq fashion.
+
+### Model Description
+
+This model is takes tau/tavbert-he and adds a three headed classification head that outputs 3 sequences corresponding to 3 types of Hebrew Niqqud (diacritics).
+It was finetuned on the dataset generously provided by Elazar Gershuni of Nakdimon.
+
+
+- **Developed by:** Jacob Gidron, Ido Cohen and Idan Pinto
+- **Model type:** Bert
+- **Language:** Hebrew
+- **Finetuned from model:** tau/tavbert-he
+
+<!-- ### Model Sources [optional] -->
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/jacobgidron/MenakBert
+<!-- - **Paper [optional]:** [More Information Needed] -->
+<!-- - **Demo [optional]:** [More Information Needed] -->
+
+## Use
+
+The model expects undotted Hebrew text, that may contain numbers and punctuation.
+
+The output is three sequences of diacritical marks, corresponding with:
+1. Dot distinguishing the letters Shin vs Sin.
+2. The dot in the center of a letter that in some case changes pronunciation of certain letters, and in other cases creating a similar affect as an emphasis on the letter, or gemination.
+3. All the rest of the marks, used mostly for vocalization.
+
+The length of each sequence is the same as the input - each mark corresponding with the char at the same possition in the input.
+
+The provided script weaves the sequences together.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+### Training Data
+
+The backbone tau/tavber-he was trained on OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences).
+The fine tuning was done on the Nakdimon dataset, which can be found at https://github.com/elazarg/hebrew_diacritized and contains 274,436 dotted Hebrew tokens across 413 documents.
+For more information see https://arxiv.org/abs/2105.05209
+
+<!-- #### Metrics -->
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+<!-- [More Information Needed] -->
+
+<!-- ### Results -->
+
+<!-- [More Information Needed] -->
+
+
+## Model Card Contact
+
+Ido Cohen - [email protected]