Update README.md

README.md

@@ -9,26 +9,27 @@ license: cc-by-nc-sa-4.0
 
 
 ## Dataset Overview
-This dataset features the 7 evaluation tasks presented in the AgroNT (A Foundational Large Language Model for Edible Plant
+This dataset features the 8 evaluation tasks presented in the AgroNT (A Foundational Large Language Model for Edible Plant
 Genomes) paper. The tasks cover single output regression, multi output regression, binary classification, and multi-label classification which
 aim to provide a comprehensive plant genomics benchmark. Additionally, we provide results from in silico saturation mutagenesis analysis of sequences
 from the cassava genome, assessing the impact of >10 million mutations on gene expression levels and enhancer elements. See the ISM section
 below for details regarding the data from this analysis.
 
 
-| Task Name    | # of Datasets(Species) | Task Type    | Sequence Length (base pair) |
+| Name    | # of Datasets(Species) | Task Type    | Sequence Length (base pair) |
 | -------- | ------- | -------- | ------- | 
 | Polyadenylation  | 6    | Binary Classification  | 400    |
 | Splice Site | 2     | Binary Classification | 398     | 
 | LncRNA    | 6    | Binary Classification    | 101-6000  |
 | Promoter Strength    | 2    | Single Variable Regression    | 170 |
+| Terminator Strength    | 2    | Single Variable Regression    | 170 |
 | Chromatin Accessibility    | 7    | Multi-label Classification   | 1000 |
 | Gene Expression    | 6        | Multi-Variable Regression  | 6000 |
 | Enhancer Region    | 1        | Binary Classification    | 1000 |
 
 
 ## Dataset Sizes
-| Config Name    | # Train Samples | # Validation Samples   | # Test Samples |
+| Task Name    | # Train Samples | # Validation Samples   | # Test Samples |
 | -------- | ------- | -------- | ------- | 
 |poly_a.arabidopsis_thaliana|170835|---|30384|
 |poly_a.oryza_sativa_indica_group|98139|---|16776|
@@ -46,6 +47,8 @@ below for details regarding the data from this analysis.
 |lncrna.s_bicolor|8654|---|734|
 |promoter_strength.leaf|58179|6825|7154|
 |promoter_strength.protoplast|61051|7162|7595|
+|terminator_strength.leaf|43294|5309|4806|
+|terminator_strength.protoplast|43289|5309|4811|
 |gene_exp.glycine_max|47136|4803|4803|
 |gene_exp.oryza_sativa|31244|3702|3702|
 |gene_exp.solanum_lycopersicum|27321|3827|3827|
@@ -63,6 +66,16 @@ below for details regarding the data from this analysis.
 *** It is important to note that fine-tuning for lncrna was carried out using all datasets in a single training. The reason for this is that the datasets are small and combining
 them helped to improve learning.
 
+## Example Usage
+```python
+from datasets import load_dataset
+
+task_name='terminator_strength.protoplast' # one of the task names from the above table
+
+dataset = load_dataset("InstaDeepAI/plant-genomic-benchmark",task_name=task_name)
+
+```
+
 ## In Silico Saturation Mutagensis 
 ### File structure for: ISM_Tables/Mesculenta_305_v6_PROseq_ISM_LOG2FC.txt.gz
 Intergenic enhancer regions based on Lozano et al. 2021 (https://pubmed.ncbi.nlm.nih.gov/34499719/) <br>