Model card: pipeline naming (drop reproduction/reproducible)

README.md

@@ -10,11 +10,11 @@ tags:
 library_name: pytorch
 ---
 
-# EPInformer — reproducible pipeline (pretrained checkpoints)
+# EPInformer — pipeline (pretrained checkpoints)
 
-Pretrained checkpoints for the **[EPInformer reproducible pipeline](https://github.com/pinellolab/EPInformer/tree/reproducible_pipeline)** — a from-raw-ENCODE reproduction of [EPInformer](https://github.com/pinellolab/EPInformer) across 6 cell lines (K562, GM12878, H1, HepG2, HUVEC, NHEK).
+Pretrained checkpoints for the **[EPInformer pipeline](https://github.com/pinellolab/EPInformer/tree/pipeline)** — a from-raw-ENCODE pipeline for [EPInformer](https://github.com/pinellolab/EPInformer) across 6 cell lines (K562, GM12878, H1, HepG2, HUVEC, NHEK).
 
-Two models (defined in `EPInformer/models.py` on the `reproducible_pipeline` branch):
+Two models (defined in `EPInformer/models.py` on the `pipeline` branch):
 
 - **`enhancer_predictor_256bp`** — 256 bp enhancer-activity encoder; predicts `log2(0.1 + sqrt(H3K27ac·DNase))` activity from sequence.
 - **`EPInformer_v2`** — gene-expression model (RNA / CAGE) that reuses the frozen encoder as its sequence backbone.
@@ -42,26 +42,26 @@ CAGE labels exist only for K562/GM12878 (the other four are RNA-only).
 enhancer_encoders/{CELL}/fold_{i}.pt   # CELL ∈ {K562, GM12878, H1, HepG2, HUVEC, NHEK}, i ∈ 1..12
 ```
 
-These are the reproduction's **256 bp enhancer-activity encoders** — the best checkpoint for each of
+These are the pipeline's **256 bp enhancer-activity encoders** — the best checkpoint for each of
 the 12 leave-chromosome-out folds, for all 6 cell lines (72 checkpoints). Any fold works for
 inference; the R values in the table above are pooled across all 12 held-out folds.
 
 > The KLF1 demo in the notebooks uses the original published EPInformer encoder shipped in the repo's
-> `trained_models/pretrained_enhancer_encoder/` — a separate checkpoint from these reproduction folds.
+> `trained_models/pretrained_enhancer_encoder/` — a separate checkpoint from these folds.
 
 ## Usage
 
 ```python
 import torch
 from huggingface_hub import hf_hub_download
-# the model class lives on the reproducible_pipeline branch:
-#   git clone -b reproducible_pipeline https://github.com/pinellolab/EPInformer
+# the model class lives on the pipeline branch:
+#   git clone -b pipeline https://github.com/pinellolab/EPInformer
 from EPInformer.models import enhancer_predictor_256bp
 
-ckpt = hf_hub_download("JiecongLin/EPInformer-reproducible", "enhancer_encoders/K562/fold_8.pt")
+ckpt = hf_hub_download("JiecongLin/EPInformer-pipeline", "enhancer_encoders/K562/fold_8.pt")
 net = enhancer_predictor_256bp()
 net.load_state_dict(torch.load(ckpt, map_location="cpu", weights_only=False)["model_state_dict"])
 net.eval()
 ```
 
-See the **[project wiki](https://github.com/pinellolab/EPInformer/wiki)** for the full training and evaluation guide, and the **[`reproducible_pipeline`](https://github.com/pinellolab/EPInformer/tree/reproducible_pipeline)** branch for code.
+See the **[project wiki](https://github.com/pinellolab/EPInformer/wiki)** for the full training and evaluation guide, and the **[`pipeline`](https://github.com/pinellolab/EPInformer/tree/pipeline)** branch for code.