Deduplicate causal and bidirectional configs
Browse files- README.md +10 -19
- functional-upstream-gamba-bidi.parquet +2 -2
- functional-upstream-gamba-causal.parquet +2 -2
- functional-upstream-gamba-noncoding-added-bidi.parquet +0 -3
- functional-upstream-gamba-noncoding-added-causal.parquet +0 -3
- functional-upstream-gamba-noncoding-added.parquet +0 -3
- functional-upstream-gamba.parquet +0 -3
README.md
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- hg38
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- parquet
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configs:
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- config_name:
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data_files:
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- split: all
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path: functional-upstream-gamba-causal.parquet
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- config_name:
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data_files:
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- split: all
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path: functional-upstream-gamba-bidi.parquet
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- config_name: noncoding-added-causal
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data_files:
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- split: all
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path: functional-upstream-gamba-noncoding-added-causal.parquet
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- config_name: noncoding-added-bidi
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data_files:
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- split: all
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path: functional-upstream-gamba-noncoding-added-bidi.parquet
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---
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# GAMBA Functional Regions: Feature vs Upstream
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| Config | Train rows | Test rows | Total |
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| `noncoding-added-causal`, `noncoding-added-bidi` | 150,726 | 37,834 | 188,560 |
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## Choosing a context
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```python
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from datasets import load_dataset
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"Taykhoom/functional-upstream-gamba",
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"
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split="all",
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)
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test =
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```
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Here, `split="all"` tells Hugging Face to load the single physical parquet
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- hg38
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- parquet
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configs:
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- config_name: causal
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data_files:
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- split: all
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path: functional-upstream-gamba-causal.parquet
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- config_name: bidi
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data_files:
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- split: all
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path: functional-upstream-gamba-bidi.parquet
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---
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# GAMBA Functional Regions: Feature vs Upstream
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| Config | Train rows | Test rows | Total |
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|---|---:|---:|---:|
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| `causal`, `bidi` | 150,728 | 37,834 | 188,562 |
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Each config includes the `noncoding_regions` extension. Filter
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`category != "noncoding_regions"` to recover the exact 169,674-row,
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ten-category GAMBA paper dataset without storing those rows twice.
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## Choosing a context
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```python
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from datasets import load_dataset
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dataset = load_dataset(
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"Taykhoom/functional-upstream-gamba",
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"bidi",
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split="all",
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paper = dataset.filter(lambda row: row["category"] != "noncoding_regions")
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test = paper.filter(lambda row: row["split"] == "test")
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```
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functional-upstream-gamba-bidi.parquet
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functional-upstream-gamba-causal.parquet
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functional-upstream-gamba-noncoding-added-bidi.parquet
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functional-upstream-gamba.parquet
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