Create README.md

README.md

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+---
+license: mit
+language:
+- tr
+library_name: transformers
+---
+# 🇹🇷 Turkish BERT Model for Software Engineering 
+
+This repository was created within the scope of computer engineering undergraduate graduation project.
+
+This research aims to perform an exploratory case study to determine the functional dimensions of user requirements or use cases for software projects.
+In order to perform this task we created two models, [SE-BERT](https://huggingface.co/burakkececi/bert-software-engineering) and SE-BERTurk.
+
+You can find a detailed description of the project at the [link](https://github.com/burakkececi/software-size-estimation-nlp).
+
+# SE-BERTurk
+
+SE-BERT is a BERT model trained for domain adaptation in a software engineering context.
+
+We applied Masked Language Modeling (MLM), an unsupervised learning technique, for domain adaptation. MLM enhances the model understanding of domain-specific language by masking portions of the input text and training the model to predict the masked words based on the surrounding context.
+
+## Stats
+Created a bilingual [SE corpus](https://drive.google.com/file/d/1IgnJTaR2-pe889TdQZtYF8SKOH92mi1l/view?usp=drive_link) (166Mb) ➡️ [Descriptive stats of the corpus](https://docs.google.com/spreadsheets/d/1Xnn_xfu4tdCtWg-nQ8ce_LHe9F-g0BSmUxzTdi5g1r4/edit?usp=sharing) 
+ * 166K entry = 886K sentence = 10M words
+ * 156K training entry + 10K test entry
+ * Each entry has a maximum length of 512 tokens
+
+The final training corpus has a size of 166MB and 10.554.750 words.
+
+## MLM Training (Domain Adaptation)
+Used ``AdamW`` optimizer and set ``num_epochs = 1``, ``lr = 2e-5``, ``eps = 1e-8``
+  * For T4 GPU ➡️ Set ``batch_size = 6`` (13.5Gb memory) 
+  * For A100 GPU ➡️ Set ``batch_size = 50`` (37Gb memory) and ``fp16 = True``
+
+**Perplexity**
+ * ``3,665`` PPL for SE-BERTurk
+
+### Evaluation Steps:
+1) Calculate ``PPL`` (perplexity) on the test corpus (10K context with a maximum length of 512 tokens)
+2) Calculate ``PPL`` (perplexity) on the requirement datasets
+3) Evaluate performance on downstream tasks:
+  * For size measurement ➡️ ``MAE``, ``MSE``, ``MMRE``, ``PRED(30)``, ``ACC``
+
+## Usage
+
+With Transformers >= 2.11 our SE-BERT uncased model can be loaded like:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("burakkececi/bert-turkish-software-engineering/model")
+model = AutoModel.from_pretrained("burakkececi/bert-turkish-software-engineering/tokenizer")
+```
+
+# Huggingface model hub
+
+All models are available on the [Huggingface model hub](https://huggingface.co/burakkececi).