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README.md

@@ -3,5 +3,6 @@ tags:
 - DNA
 ---
 
-Single-genome version of [InstaDeepAI/plant-multi-species-genomes](https://huggingface.co/datasets/InstaDeepAI/plant-multi-species-genomes) for a demo / debugging 
-training run of [monsoon-nlp/dna-blockdiff](https://huggingface.co/monsoon-nlp/dna-blockdiff)
+Mini mini version of [InstaDeepAI/plant-multi-species-genomes](https://huggingface.co/datasets/InstaDeepAI/plant-multi-species-genomes) with wheat as training set and papaya as validation set.
+
+One model [monsoon-nlp/dna-blockdiff-papaya](https://huggingface.co/monsoon-nlp/dna-blockdiff-papaya) was trained only on the papaya genome as a proof of concept.

plant_genome_file_names.txt

@@ -1 +1,2 @@
-GCF_000150535.2_Papaya1.0_genomic.fna.gz
+GCF_018294505.1_IWGSC_CS_RefSeq_v2.1_genomic.fna.gz
+GCF_000150535.2_Papaya1.0_genomic.fna.gz

wheat-bees.py

@@ -25,7 +25,7 @@ _CITATION = """\
 
 # You can copy an official description
 _DESCRIPTION = """\
-Dataset made of diverse genomes available on NCBI and coming from 48 different species. 
+Dataset made of diverse genomes available on NCBI and coming from 48 different species.
 Test and validation are made of 2 species each. The rest of the genomes are used for training.
 Default configuration "6kbp" yields chunks of 6.2kbp (100bp overlap on each side). The chunks of DNA are cleaned and processed so that
 they can only contain the letters A, T, C, G and N.
@@ -118,12 +118,15 @@ class PlantMultiSpeciesGenomes(datasets.GeneratorBasedBuilder):
         with open(filepaths_txt) as f:
             filepaths = [os.path.join("plant_genomes",filepath.rstrip()) for filepath in f]
 
-        train_paths = filepaths
-
+        train_paths = [filepaths[0]]
         train_downloaded_files = dl_manager.download_and_extract(train_paths)
 
+        validation_paths = [filepaths[1]]
+        validation_downloaded_files = dl_manager.download_and_extract(validation_paths)
+
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": train_downloaded_files, "chunk_length": self.config.chunk_length}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"files": validation_downloaded_files, "chunk_length": self.config.chunk_length}),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`