Update README.md

README.md

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 license: cc-by-sa-4.0
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+## Project Description 
+This repository contains the trained model for our paper: **Fine-tuning a Sentence Transformer for DNA & Protein tasks** that is currently under review at BMC Bioinformatics. This model, called **simcse-dna**; is based on the original implementation of **SimCSE [1]**. The original model was adapted for DNA downstream tasks by training it on a small sample size k-mer tokens generated from the human reference genome, and can be used to generate sentence embeddings for DNA tasks.
+
+###  Prerequisites 
+-----------
+Please see the original [SimCSE](https://github.com/princeton-nlp/SimCSE) for installation details. The model will be hosted on Zenodo (DOI: 10.5281/zenodo.11046580). It 
+is also available on 🤗 [huggingface](https://huggingface.co/dsfsi/simcse-dna).
+
+### Usage 
+
+Download the model into a directory then run the following code to get the sentence embeddings:
+
+```python 
+
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+# Import trained model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("/path/to/model/directory/")
+model = AutoModel.from_pretrained("/path/to/model/directory/")
+
+
+#sentences is your list of n DNA tokens of size 6 
+inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
+
+# Get the embeddings
+with torch.no_grad():
+    embeddings = model(**inputs, output_hidden_states=True, return_dict=True).pooler_output
+
+
+```
+The retrieved embeddings can be utilized as input for a machine learning classifier to perform classification.
+
+## Performance on evaluation tasks
+
+Find out more about the datasets and access in the paper **(TBA)**
+
+### Task 1: Detection of colorectal cancer cases (after oversampling)
+
+|  | 5-fold Cross Validation accuracy | Test accuracy |
+| --- | --- | ---|
+| LightGBM | 91 | 63 |
+| Random Forest | **94** | **71** |
+| XGBoost | 93 | 66 |
+| CNN | 42 | 52 |
+
+| | 5-fold Cross Validation F1 | Test F1 |
+| --- | --- | ---|
+| LightGBM |  91 | 66 |
+| Random Forest |  **94** | **72** |
+| XGBoost | 93 | 66 |
+| CNN |  41 | 60 |
+
+### Task 2: Prediction of the Gleason grade group (after oversampling)
+
+|  | 5-fold Cross Validation accuracy | Test accuracy |
+| --- | --- | ---|
+| LightGBM | 97 | 68 |
+| Random Forest | **98** | **78** |
+| XGBoost |97 | 70 |
+| CNN |  35 |  50 |
+
+| | 5-fold Cross Validation F1 | Test F1 |
+| --- | --- | ---|
+| LightGBM |  97 |  70 |
+| Random Forest | **98** | **80** |
+| XGBoost |97 | 70 |
+| CNN |  33 | 59 |
+
+### Task 3: Detection of human TATA sequences (after oversampling)
+
+|  | 5-fold Cross Validation accuracy | Test accuracy |
+| --- | --- | ---|
+| LightGBM | 98  | 93  |
+| Random Forest | **99** | **96** |
+| XGBoost |**99** | 95 |
+| CNN | 38  | 59 |
+
+| | 5-fold Cross Validation F1 | Test F1 |
+| --- | --- | ---|
+| LightGBM | 98 | 92 |
+| Random Forest | **99** | **95** |
+| XGBoost | **99** | 92 |
+| CNN |  58 | 10 |
+
+
+## Authors 
+-----------
+
+* Written by : Mpho Mokoatle, Vukosi Marivate, Darlington Mapiye, Riana Bornman, Vanessa M. Hayes
+* Contact details : [email protected]
+
+## Citation 
+-----------
+Bibtex Reference **TBA**
+
+### References
+
+<a id="1">[1]</a> 
+Gao, Tianyu, Xingcheng Yao, and Danqi Chen. "Simcse: Simple contrastive learning of sentence embeddings." arXiv preprint arXiv:2104.08821 (2021).