Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
License:
File size: 1,806 Bytes
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---
license: apache-2.0
tags:
- stripedhyena
- long context
- deep signal processing
- hybrid
- biology
- genomics
task_categories:
- text-generation
language:
- en
pretty_name: open-genome
configs:
- config_name: stage1
data_files:
- split: train
path:
- "stage1/gtdb/gtdb_train_shard_*"
- "stage1/imgpr/imgpr_train.parquet"
- split: validation
path:
- "stage1/gtdb/gtdb_valid_small.parquet"
- "stage1/imgpr/imgpr_valid_small.parquet"
- split: test
path:
- "stage1/gtdb/gtdb_test.parquet"
- "stage1/imgpr/imgpr_test.parquet"
- config_name: stage2
data_files:
- split: train
path: "stage2/train_stage2.parquet"
- split: validation
path: "stage2/valid_stage2.parquet"
- split: test
path: "stage2/test_stage2.parquet"
- config_name: sample
data_files:
- split: validation
path: "stage2/valid_stage2.parquet"
---
### Dataset organization
The OpenGenome dataset is organized in 2 stages, where stage 1 has context length 8k and stage 2 has context length 131k. Each stage has their own datasplits.
```
- stage1
- train
- validation
- test
- stage2
- train
- validation
- test
```
### Instructions to download
You can load a dataset using HF's API, with an example below.
```
from datasets import load_dataset
stage1_data = load_dataset("LongSafari/open-genome", 'stage1')
# access just the train data
stage_1_train_data = stage1_data['train']
```
Note: stage 1 training dataset is sharded into separate files due to it's large size.
We also provide a small dataset sample to test out the pipeline if you prefer.
```
sample_data = load_dataset("LongSafari/open-genome", 'sample')['validation']
```
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