Update README.md

README.md

@@ -28,25 +28,25 @@ Prehaps we have a DNA sequence for which the highest genus probability is very l
 ## BarcodeBERT DNA Embeddings.
 The model we used to train the DNA embeddings is [BarcodeBERT](https://github.com/Kari-Genomics-Lab/BarcodeBERT). We trained the model on the 'nucraw' column of DNA sequences from the latest release of the [BOLD Database](http://www.boldsystems.org/index.php/datapackages/Latest). We followed the preprocessing steps outlined by the [BarcodeBERT paper](https://arxiv.org/pdf/2311.02401).
 
-## Classification Model Performance and Baseline Comparison of Ecological Layer Data Inclusion
+## Classification Model Performance
+<!-- Baseline Comparison of Ecological Layer Data Inclusion -->
 We trained a fine-tuned single fully connected linear layer on our DNA embeddings with ecological layer data and without ecological layer data to predict genuses. We found that the model achieved a test accuracy of 82%.
 <!-- | Model       | Test Accuracy |
 | ----------- | ------------- |
 | DNA Only    |        %  |
 | DNA & Env   |        %  |
  -->
-Our results can be validated with the
-[BarcodeBERT-Finetuned-Amazon-DNAOnly](https://huggingface.co/LofiAmazon/BarcodeBERT-Finetuned-Amazon-DNAOnly) and [BarcodeBERT-Finetuned-Amazon](https://huggingface.co/LofiAmazon/BarcodeBERT-Finetuned-Amazon) models, testing splits of the [BOLD-Embeddings-Amazon](https://huggingface.co/datasets/LofiAmazon/BOLD-Embeddings-Amazon), [BOLD-Embeddings-Ecolayers-Amazon](https://huggingface.co/datasets/LofiAmazon/BOLD-Embeddings-Ecolayers-Amazon) datasets, and the fine tuning script on our [repo](https://github.com/vshulev/amazon-lofi-beats/blob/master/fine_tune.py).
+Our results can be validated with the [BarcodeBERT-Finetuned-Amazon](https://huggingface.co/LofiAmazon/BarcodeBERT-Finetuned-Amazon) models, testing splits of the [BOLD-Embeddings-Amazon](https://huggingface.co/datasets/LofiAmazon/BOLD-Embeddings-Amazon), [BOLD-Embeddings-Ecolayers-Amazon](https://huggingface.co/datasets/LofiAmazon/BOLD-Embeddings-Ecolayers-Amazon) datasets, and the fine tuning script on our [repo](https://github.com/vshulev/amazon-lofi-beats/blob/master/fine_tune.py).
 
 ## Future Work and Downstream Tasks
 We describe interesting avenues for future work that were not in scope due to time constraints.
 
 Future Tasks for the DNA Identifier Tool:
-- The tool was intended to have two t-SNE plots: one showing the embedding space of the top k most common species in the area surrounding the given coordinate, and the other showing how the user's sequence embedding is positioned in the space and show it's nearest species clusters
-- Add legends with images of each genus
+- The tool was intended to have two t-SNE plots: one showing the embedding space of the top k most common species in the area surrounding the given coordinate, and the other showing how the user's sequence embedding is positioned in the space and show its nearest species clusters
 - Include a CSV upload task to process many DNA sequences at once
 - Include more ecological layers such as layers peratining to soil properties
-- Compare different models for the genus classification task
+- Add legends with images of each genus
+- Compare more models for the genus classification task, like the [BarcodeBERT-Finetuned-Amazon-DNAOnly](https://huggingface.co/LofiAmazon/BarcodeBERT-Finetuned-Amazon-DNAOnly) model
 - BOLD has very interesting other columns such as depth, elevation, habitat (text description), and others, but they are extremely sparse (>90%). We could add these as optional features to our Tool and update our model to handle these input
 - Add a Retrieval Augmented Generation LLM tool that scrapes traditional/ecological knowledge about species to help predict genus